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@article{Gloyn2003a,
abstract = {The genes ABCC8 and KCNJ11, which encode the subunits sulfonylurea receptor 1 (SUR1) and inwardly rectifying potassium channel (Kir6.2) of the $\beta$-cell ATP-sensitive potassium (KATP) channel, control insulin secretion. Common polymorphisms in these genes (ABCC8 exon 16–3t/c, exon 18 T/C, KCNJ11 E23K) have been variably associated with type 2 diabetes, but no large (∼2,000 subjects) case-control studies have been performed. We evaluated the role of these three variants by studying 2,486 U.K. subjects: 854 with type 2 diabetes, 1,182 population control subjects, and 150 parent-offspring type 2 diabetic trios. The E23K allele was associated with diabetes in the case-control study (odds ratio [OR] 1.18 [95{\%} CI 1.04–1.34], P = 0.01) but did not show familial association with diabetes. Neither the exon 16 nor the exon 18 ABCC8 variants were associated with diabetes (1.04 [0.91–1.18], P = 0.57; 0.93 [0.71–1.23], P = 0.63, respectively). Meta-analysis of all case-control data showed that the E23K allele was associated with type 2 diabetes (K allele OR 1.23 [1.12–1.36], P = 0.000015; KK genotype 1.65 [1.34–2.02], P = 0.000002); but the ABCC8 variants were not associated. Our results confirm that E23K increases risk of type 2 diabetes and show that large-scale association studies are important for the identification of diabetes susceptibility alleles. },
annote = {10.2337/diabetes.52.2.568},
author = {Gloyn, Anna L and Weedon, Michael N and Owen, Katharine R and Turner, Martina J and Knight, Bridget A and Hitman, Graham and Walker, Mark and Levy, Jonathan C and Sampson, Mike and Halford, Stephanie and McCarthy, Mark I and Hattersley, Andrew T and Frayling, Timothy M},
doi = {10.2337/diabetes.52.2.568 },
journal = {Diabetes },
month = {feb},
number = {2 },
pages = {568--572},
title = {{Large-Scale Association Studies of Variants in Genes Encoding the Pancreatic $\beta$-Cell KATP Channel Subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) Confirm That the KCNJ11 E23K Variant Is Associated With Type 2 Diabetes }},
url = {http://diabetes.diabetesjournals.org/content/52/2/568.abstract},
volume = {52 },
year = {2003}
}
@article{Marchini2007a,
abstract = {Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.},
author = {Marchini, Jonathan and Howie, Bryan and Myers, Simon and McVean, Gil and Donnelly, Peter},
doi = {10.1038/ng2088},
isbn = {1061-4036 (Print)$\backslash$r1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nature genetics},
number = {7},
pages = {906--13},
pmid = {17572673},
title = {{A new multipoint method for genome-wide association studies by imputation of genotypes.}},
volume = {39},
year = {2007}
}
@article{Savic2011,
abstract = {Genome-wide association studies (GWAS) have consistently implicated noncoding variation within the TCF7L2 locus with type 2 diabetes (T2D) risk. While this locus represents the strongest genetic determinant for T2D risk in humans, it remains unclear how these noncoding variants affect disease etiology. To test the hypothesis that the T2D-associated interval harbors cis-regulatory elements controlling TCF7L2 expression, we conducted in vivo transgenic reporter assays to characterize the TCF7L2 regulatory landscape. We found that the 92-kb genomic interval associated with T2D harbors long-range enhancers regulating various aspects of the spatial–temporal expression patterns of TCF7L2, including expression in tissues involved in the control of glucose homeostasis. By selectively deleting this interval, we establish a critical role for these enhancers in robust TCF7L2 expression. To further determine whether variation in Tcf7l2 expression may lead to diabetes, we developed a Tcf7l2 copy-number allelic series in mice. We show that a null Tcf7l2 allele leads, in a dose-dependent manner, to lower glycemic profiles. Tcf7l2 null mice also display enhanced glucose tolerance coupled to significantly lowered insulin levels, suggesting that these mice are protected against T2D. Confirming these observations, transgenic mice harboring multiple Tcf7l2 copies and overexpressing this gene display reciprocal phenotypes, including glucose intolerance. These results directly demonstrate that Tcf7l2 plays a role in regulating glucose tolerance, suggesting that overexpression of this gene is associated with increased risk of T2D. These data highlight the role of enhancer elements as mediators of T2D risk in humans, strengthening the evidence that variation in cis-regulatory elements may be a paradigm for genetic predispositions to common disease.},
author = {Savic, Daniel and Ye, Honggang and Aneas, Ivy and Park, Soo Young and Bell, Graeme I. and Nobrega, Marcelo A.},
doi = {10.1101/gr.123745.111},
isbn = {1549-5469 (Electronic)$\backslash$r1088-9051 (Linking)},
issn = {10889051},
journal = {Genome Research},
number = {9},
pages = {1417--1425},
pmid = {21673050},
title = {{Alterations in TCF7L2 expression define its role as a key regulator of glucose metabolism}},
volume = {21},
year = {2011}
}
@book{RDevelopmentCoreTeam2011a,
abstract = {R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.},
author = {{R Development Core Team}, R},
booktitle = {R Foundation for Statistical Computing},
doi = {10.1007/978-3-540-74686-7},
isbn = {3900051070},
issn = {16000706},
number = {2.11.1},
pages = {409},
title = {{R: A Language and Environment for Statistical Computing}},
url = {http://www.r-project.org},
volume = {1},
year = {2011}
}
@article{Gusev2014,
abstract = {Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg2 ) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16{\%} of imputed SNPs (and 24{\%} of genotyped SNPs) but explained an average of 79{\%} (SE = 8{\%}) of hg2 from imputed SNPs (5.1?? enrichment; p = 3.7 ?? 10 -17) and 38{\%} (SE = 4{\%}) of hg2 from genotyped SNPs (1.6?? enrichment, p = 1.0 ?? 10 -4). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1{\%} of the genome, explained <10{\%} of hg2despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.},
author = {Gusev, Alexander and Lee, S. Hong and Trynka, Gosia and Finucane, Hilary and Vilhj??lmsson, Bjarni J. and Xu, Han and Zang, Chongzhi and Ripke, Stephan and Bulik-Sullivan, Brendan and Stahl, Eli and K??hler, Anna K. and Hultman, Christina M. and Purcell, Shaun M. and McCarroll, Steven A. and Daly, Mark and Pasaniuc, Bogdan and Sullivan, Patrick F. and Neale, Benjamin M. and Wray, Naomi R. and Raychaudhuri, Soumya and Price, Alkes L.},
doi = {10.1016/j.ajhg.2014.10.004},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Gusev{\_}partitionH2{\_}2014.pdf:pdf},
isbn = {1537-6605 (Electronic)$\backslash$r0002-9297 (Linking)},
issn = {15376605},
journal = {American Journal of Human Genetics},
number = {5},
pages = {535--552},
pmid = {25439723},
title = {{Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases}},
volume = {95},
year = {2014}
}
@article{Battle2014,
abstract = {Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation—by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.},
author = {Battle, Alexis and Mostafavi, Sara and Zhu, Xiaowei and Potash, James B and Weissman, Myrna M and McCormick, Courtney and Haudenschild, Christian D and Beckman, Kenneth B and Shi, Jianxin and Mei, Rui and Urban, Alexander E and Montgomery, Stephen B and Levinson, Douglas F and Koller, Daphne},
doi = {10.1101/gr.155192.113},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Battle{\_}DGN{\_}2014.pdf:pdf},
issn = {1088-9051},
journal = {Genome Research},
month = {jan},
number = {1},
pages = {14--24},
publisher = {Cold Spring Harbor Laboratory Press},
title = {{Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals}},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875855/},
volume = {24},
year = {2014}
}
@article{DeFronzo2009a,
abstract = {Insulin resistance in muscle and liver and $\beta$-cell failure represent the core pathophysiologic defects in type 2 diabetes. It now is recognized that the $\beta$-cell failure occurs much earlier and is more severe than previously thought. Subjects in the upper tertile of impaired glucose tolerance (IGT) are maximally/near-maximally insulin resistant and have lost over 80{\%} of their $\beta$-cell function. In addition to the muscle, liver, and $\beta$-cell (triumvirate), the fat cell (accelerated lipolysis), gastrointestinal tract (incretin deficiency/resistance), $\alpha$-cell (hyperglucagonemia), kidney (increased glucose reabsorption), and brain (insulin resistance) all play important roles in the development of glucose intolerance in type 2 diabetic individuals. Collectively, these eight players comprise the ominous octet and dictate that: 1) multiple drugs used in combination will be required to correct the multiple pathophysiological defects, 2) treatment should be based upon reversal of known pathogenic abnormalities and not simply on reducing the A1C, and 3) therapy must be started early to prevent/slow the progressive $\beta$-cell failure that already is well established in IGT subjects. A treatment paradigm shift is recommended in which combination therapy is initiated with diet/exercise, metformin (which improves insulin sensitivity and has antiatherogenic effects), a thiazolidinedione (TZD) (which improves insulin sensitivity, preserves $\beta$-cell function, and exerts antiatherogenic effects), and exenatide (which preserves $\beta$-cell function and promotes weight loss). Sulfonylureas are not recommended because, after an initial improvement in glycemic control, they are associated with a progressive rise in A1C and progressive loss of $\beta$-cell function.},
author = {DeFronzo, Ralph A.},
doi = {doi: 10.2337/db09-9028},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Diabetes-2009-DeFronzo-773-95.pdf:pdf},
isbn = {1939-327X (Electronic)$\backslash$n0012-1797 (Linking)},
issn = {1939-327X},
journal = {Diabetes Care},
number = {4},
pages = {773--795},
pmid = {19336687},
title = {{From the Triumvirate to the Ominous Octet: A New Paradigm for the Treatment of Type 2 Diabetes Mellitus}},
volume = {58},
year = {2009}
}
@article{Hoffmann2011,
abstract = {The success of genome-wide association studies has paralleled the development of efficient genotyping technologies. We describe the development of a next-generation microarray based on the new highly-efficient Affymetrix Axiom genotyping technology that we are using to genotype individuals of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH). The array contains 674,517 SNPs, and provides excellent genome-wide as well as gene-based and candidate-SNP coverage. Coverage was calculated using an approach based on imputation and cross validation. Preliminary results for the first 80,301 saliva-derived DNA samples from the RPGEH demonstrate very high quality genotypes, with sample success rates above 94{\%} and over 98{\%} of successful samples having SNP call rates exceeding 98{\%}. At steady state, we have produced 462 million genotypes per week for each Axiom system. The new array provides a valuable addition to the repertoire of tools for large scale genome-wide association studies. },
author = {Hoffmann, Thomas J and Kvale, Mark N and Hesselson, Stephanie E and Zhan, Yiping and Aquino, Christine and Cao, Yang and Cawley, Simon and Chung, Elaine and Connell, Sheryl and Eshragh, Jasmin and Ewing, Marcia and Gollub, Jeremy and Henderson, Mary and Hubbell, Earl and Iribarren, Carlos and Kaufman, Jay and Lao, Richard Z and Lu, Yontao and Ludwig, Dana and Mathauda, Gurpreet K and McGuire, William and Mei, Gangwu and Miles, Sunita and Purdy, Matthew M and Quesenberry, Charles and Ranatunga, Dilrini and Rowell, Sarah and Sadler, Marianne and Shapero, Michael H and Shen, Ling and Shenoy, Tanushree R and Smethurst, David and {Van den Eeden}, Stephen K and Walter, Larry and Wan, Eunice and Wearley, Reid and Webster, Teresa and Wen, Christopher C and Weng, Li and Whitmer, Rachel A and Williams, Alan and Wong, Simon C and Zau, Chia and Finn, Andrea and Schaefer, Catherine and Kwok, Pui-Yan and Risch, Neil},
doi = {10.1016/j.ygeno.2011.04.005},
issn = {0888-7543},
journal = {Genomics},
month = {aug},
number = {2},
pages = {79--89},
title = {{Next generation genome-wide association tool: Design and coverage of a high-throughput European-optimized SNP array}},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146553/},
volume = {98},
year = {2011}
}
@article{Gloyn2003,
author = {et al. Gloyn, A.L.},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Diabetes-2003-Gloyn-568-72.pdf:pdf},
journal = {Diabetes},
pages = {568--572},
title = {{Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes.}},
volume = {52},
year = {2003}
}
@article{TheGTExConsortium2015,
abstract = {Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine.We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project.We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.},
author = {{The GTEx Consortium}},
doi = {10.1126/science.1262110},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/gtex{\_}pilo{\_}2015.pdf:pdf},
isbn = {0036-8075},
issn = {1095-9203},
journal = {Science},
pages = {648--660},
pmid = {25954001},
title = {{The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25954001},
volume = {348},
year = {2015}
}
@article{Howie2009,
annote = {10.1371/journal.pgen.1000529},
author = {Howie, B N and Donnelly, P and Marchini, J},
journal = {PLoS Genet.},
pages = {e1000529},
title = {{A flexible and accurate genotype imputation method for the next generation of genome-wide association studies}},
volume = {5},
year = {2009}
}
@article{Stegle2012,
abstract = {We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.},
author = {Stegle, Oliver and Parts, Leopold and Piipari, Matias and Winn, John and Durbin, Richard},
doi = {10.1038/nprot.2011.457},
isbn = {1750-2799 (Electronic)$\backslash$r1750-2799 (Linking)},
issn = {1750-2799},
journal = {Nature protocols},
keywords = {Algorithms,Factor Analysis, Statistical,Gene Expression Profiling,Gene Expression Profiling: methods,Gene Expression Profiling: statistics {\&} numerical,Genetic Association Studies,Genetic Association Studies: methods,Models, Statistical,Sensitivity and Specificity,Software},
number = {3},
pages = {500--7},
pmid = {22343431},
title = {{Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3398141{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2012}
}
@article{Albert2015,
abstract = {We are in a phase of unprecedented progress in identifying genetic loci that cause variation in traits ranging from growth and fitness in simple organisms to disease in humans. However, a mechanistic understanding of how these loci influence traits is lacking for the majority of loci. Studies of the genetics of gene expression have emerged as a key tool for linking DNA sequence variation to phenotypes. Here, we review recent insights into the molecular nature of regulatory variants and describe their influence on the transcriptome and the proteome. We discuss conceptual advances from studies in model organisms and present examples of complete chains of causality that link individual polymorphisms to changes in gene expression, which in turn result in physiological changes and, ultimately, disease risk.},
author = {Albert, Frank W and Kruglyak, Leonid},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Albert{\_}eQTLs-Disease-Review{\_}2015.pdf:pdf},
issn = {1471-0056},
journal = {Nat Rev Genet},
month = {apr},
number = {4},
pages = {197--212},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{The role of regulatory variation in complex traits and disease}},
url = {http://dx.doi.org/10.1038/nrg3891 10.1038/nrg3891},
volume = {16},
year = {2015}
}
@article{Buenrostro2013,
abstract = {We describe an assay for transposase-accessible chromatin using sequencing (ATAC-seq), based on direct in vitro transposition of sequencing adaptors into native chromatin, as a rapid and sensitive method for integrative epigenomic analysis. ATAC-seq captures open chromatin sites using a simple two-step protocol with 500-50,000 cells and reveals the interplay between genomic locations of open chromatin, DNA-binding proteins, individual nucleosomes and chromatin compaction at nucleotide resolution. We discovered classes of DNA-binding factors that strictly avoided, could tolerate or tended to overlap with nucleosomes. Using ATAC-seq maps of human CD4(+) T cells from a proband obtained on consecutive days, we demonstrated the feasibility of analyzing an individual's epigenome on a timescale compatible with clinical decision-making.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Buenrostro, Jason D and Giresi, Paul G and Zaba, Lisa C and Chang, Howard Y and Greenleaf, William J},
doi = {10.1038/nmeth.2688},
eprint = {NIHMS150003},
file = {:Users/jtorres/Library/Application Support/Mendeley Desktop/Downloaded/Buenrostro et al. - 2013 - Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding.pdf:pdf},
isbn = {5200401194},
issn = {1548-7105},
journal = {Nature methods},
keywords = {CD4-Positive T-Lymphocytes,CD4-Positive T-Lymphocytes: cytology,Cell Separation,Chromatin,Chromatin: chemistry,Computational Biology,Computational Biology: methods,DNA-Binding Proteins,DNA-Binding Proteins: chemistry,Dimerization,Enhancer Elements, Genetic,Epigenomics,Flow Cytometry,Flow Cytometry: methods,Humans,Interleukin-2,Interleukin-2: genetics,Nucleosomes,Nucleosomes: chemistry,Polymerase Chain Reaction,Polymerase Chain Reaction: methods,Transcription Factors,Transcription Factors: chemistry},
number = {12},
pages = {1213--8},
pmid = {24097267},
title = {{Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.}},
url = {http://dx.doi.org/10.1038/nmeth.2688},
volume = {10},
year = {2013}
}
@article{Tibshirani1994,
abstract = {Document: Details (1994) Robert Tibshirani CiteSeer.IST - Copyright Penn State and NEC},
archivePrefix = {arXiv},
arxivId = {1369–7412/11/73273},
author = {Tibshirani, Robert},
doi = {10.2307/2346178},
eprint = {11/73273},
isbn = {0849320240},
issn = {00359246},
journal = {Journal of the Royal Statistical Society B},
number = {1},
pages = {267--288},
pmid = {16272381},
primaryClass = {1369–7412},
title = {{Regression Selection and Shrinkage via the Lasso}},
url = {http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.7574},
volume = {58},
year = {1994}
}
@article{The1000GenomesProjectConsortium2012,
abstract = {By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98{\%} of accessible single nucleotide polymorphisms at a frequency of 1{\%} in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.},
archivePrefix = {arXiv},
arxivId = {Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., {\&} Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic acids research, 29(1), 308-11. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fc},
author = {{The 1000 Genomes Project Consortium}},
doi = {10.1038/nature11632},
eprint = {/www.pubmedcentral.nih.gov/articlerender.fc},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/1K{\_}Genomes{\_}2012.pdf:pdf},
isbn = {1476-4687 (Electronic)$\backslash$r0028-0836 (Linking)},
issn = {00280836},
journal = {Nature},
keywords = {Alleles,Binding Sites,Binding Sites: genetics,Conserved Sequence,Conserved Sequence: genetics,Continental Population Groups,Continental Population Groups: genetics,Evolution,Genetic Variation,Genetic Variation: genetics,Genetics,Genome,Genome-Wide Association Study,Genomics,Haplotypes,Haplotypes: genetics,Human,Human: genetics,Humans,Medical,Molecular,Nucleotide Motifs,Polymorphism,Population,Sequence Deletion,Sequence Deletion: genetics,Single Nucleotide,Single Nucleotide: genetics,Transcription Factors,Transcription Factors: metabolism},
number = {7422},
pages = {56--65},
pmid = {23128226},
primaryClass = {Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., {\&} Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic acids research, 29(1), 308-11. Retrieved from http:},
title = {{An integrated map of genetic variation from 1,092 human genomes.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3498066{\&}tool=pmcentrez{\&}rendertype=abstract$\backslash$nhttp://www.nature.com/nature/journal/v491/n7422/full/nature11632.html$\backslash$nhttp://www.nature.com/doifinder/10.1038/nature11632},
volume = {491},
year = {2012}
}
@article{Sancar2014,
abstract = {Circadian clocks orchestrate behavioral and physiological processes in a time-of-day dependent manner. The network of clock-controlled genes is intimately interconnected with metabolic regulatory circuits. Circadian clocks rhythmically regulate the expression and activity of key metabolic players, which in turn feed back on the circadian machinery on the transcriptional and post-transcriptional level. Mutations of clock genes are often associated with metabolic defects, especially in lipid and glucose metabolism. Accumulating data suggest that the reciprocal coordination of circadian and metabolic pathways is crucial for cellular homeostasis and the health of the organism.},
author = {Sancar, Gencer and Brunner, Michael},
doi = {10.1007/s00018-014-1574-7},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/circ{\_}diabetes{\_}2015.pdf:pdf},
isbn = {1439-4413 (Electronic)$\backslash$r0012-0472 (Linking)},
issn = {1420-9071},
journal = {Cellular and molecular life sciences : CMLS},
keywords = {circadian clock,energy metabolism},
number = {14},
pages = {2667--80},
pmid = {24515123},
title = {{Circadian clocks and energy metabolism.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24515123},
volume = {71},
year = {2014}
}
@article{Broussard2012,
abstract = {BACKGROUND: Insufficient sleep increases the risk for insulin resistance, type 2 diabetes, and obesity, suggesting that sleep restriction may impair peripheral metabolic pathways. Yet, a direct link between sleep restriction and alterations in molecular metabolic pathways in any peripheral human tissue has not been shown. OBJECTIVE: To determine whether sleep restriction results in reduced insulin sensitivity in subcutaneous fat, a peripheral tissue that plays a pivotal role in energy metabolism and balance. DESIGN: Randomized, 2-period, 2-condition, crossover clinical study. SETTING: University of Chicago Clinical Resource Center. PARTICIPANTS: Seven healthy adults (1 woman, 6 men) with a mean age of 23.7 years (SD, 3.8) and mean body mass index of 22.8 kg/m(2) (SD, 1.6). INTERVENTION: Four days of 4.5 hours in bed or 8.5 hours in bed under controlled conditions of caloric intake and physical activity. MEASUREMENTS: Adipocytes collected from subcutaneous fat biopsy samples after normal and restricted sleep conditions were exposed to incremental insulin concentrations. The ability of insulin to increase levels of phosphorylated Akt (pAkt), a crucial step in the insulin-signaling pathway, was assessed. Total Akt (tAkt) served as a loading control. The insulin concentration for the half-maximal stimulation of the pAkt-tAkt ratio was used as a measure of cellular insulin sensitivity. Total body insulin sensitivity was assessed using a frequently sampled intravenous glucose tolerance test. RESULTS: The insulin concentration for the half-maximal pAkt-tAkt response was nearly 3-fold higher (mean, 0.71 nM [SD, 0.27] vs. 0.24 nM [SD, 0.24]; P = 0.01; mean difference, 0.47 nM [SD, 0.33]; P = 0.01), and the total area under the receiver-operating characteristic curve of the pAkt-tAkt response was 30{\%} lower (P = 0.01) during sleep restriction than during normal sleep. A reduction in total body insulin sensitivity (P = 0.02) paralleled this impaired cellular insulin sensitivity. LIMITATION: This was a single-center study with a small sample size. CONCLUSION: Sleep restriction results in an insulin-resistant state in human adipocytes. Sleep may be an important regulator of energy metabolism in peripheral tissues. PRIMARY FUNDING SOURCE: National Institutes of Health.},
author = {Broussard, Josiane L. and Ehrmann, David A. and {Van Cauter}, Eve and Tasali, Esra and Brady, Matthew J.},
doi = {10.7326/0003-4819-157-8-201210160-00005},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Brossard{\_}brady{\_}sleep{\_}2012.pdf:pdf},
isbn = {0003-4819},
issn = {00034819},
journal = {Annals of Internal Medicine},
number = {8},
pages = {549--557},
pmid = {23070488},
title = {{Impaired insulin signaling in human adipocytes after experimental sleep restriction: A randomized, crossover study}},
volume = {157},
year = {2012}
}
@article{Doyle2011,
abstract = {The genetic and physiological similarities between mice and humans have focused considerable attention on rodents as potential models of human health and disease. Together with the wealth of resources, knowledge, and technologies surrounding the mouse as a model system, these similarities have propelled this species to the forefront of biomedical research. The advent of genomic manipulation has quickly led to the creation and use of genetically engineered mice as powerful tools for cutting edge studies of human disease research including the discovery, refinement, and utility of many currently available therapeutic regimes. In particular, the creation of genetically modified mice as models of human disease has remarkably changed our ability to understand the molecular mechanisms and cellular pathways underlying disease states. Moreover, the mouse models resulting from gene transfer technologies have been important components correlating an individual’s gene expression profile to the development of disease pathologies. The objective of this review is to provide physician-scientists with an expansive historical and logistical overview of the creation of mouse models of human disease through gene transfer technologies. Our expectation is that this will facilitate on-going disease research studies and may initiate new areas of translational research leading to enhanced patient care.},
author = {Doyle, Alfred and McGarry, Michael P and Lee, Nancy A and Lee, James J},
doi = {10.1007/s11248-011-9537-3},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Doyle{\_}transgenic{\_}2012.pdf:pdf},
issn = {1573-9368},
journal = {Transgenic Research},
number = {2},
pages = {327--349},
title = {{The construction of transgenic and gene knockout/knockin mouse models of human disease}},
url = {http://dx.doi.org/10.1007/s11248-011-9537-3},
volume = {21},
year = {2011}
}
@article{Altshuler2000a,
abstract = {Genetic association studies are viewed as problematic and plagued by irreproducibility. Many associations have been reported for type 2 diabetes, but none have been confirmed in multiple samples and with comprehensive controls. We evaluated 16 published genetic associations to type 2 diabetes and related sub-phenotypes using a family-based design to control for population stratification, and replication samples to increase power. We were able to confirm only one association, that of the common Pro12Ala polymorphism in peroxisome proliferator-activated receptor-gamma(PPARgamma) with type 2 diabetes. By analysing over 3,000 individuals, we found a modest (1.25-fold) but significant (P=0.002) increase in diabetes risk associated with the more common proline allele (85{\%} frequency). Moreover, our results resolve a controversy about common variation in PPARgamma. An initial study found a threefold effect, but four of five subsequent publications failed to confirm the association. All six studies are consistent with the odds ratio we describe. The data implicate inherited variation in PPARgamma in the pathogenesis of type 2 diabetes. Because the risk allele occurs at such high frequency, its modest effect translates into a large population attributable risk-influencing as much as 25{\%} of type 2 diabetes in the general population.},
author = {Altshuler, D and Hirschhorn, J N and Klannemark, M and Lindgren, C M and Vohl, M C and Nemesh, J and Lane, C R and Schaffner, S F and Bolk, S and Brewer, C and Tuomi, T and Gaudet, D and Hudson, T J and Daly, M and Groop, L and Lander, E S},
doi = {10.1038/79216},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Altshuler{\_}pparg{\_}2000.pdf:pdf},
isbn = {1061-4036 (Print)$\backslash$r1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nat Genet},
keywords = {Adult Age of Onset Aged Alanine Alleles Blood Gluc},
number = {1},
pages = {76--80},
pmid = {10973253},
title = {{The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10973253},
volume = {26},
year = {2000}
}
@article{Lonsdale2013,
author = {Lonsdale, John and Thomas, Jeffrey and Salvatore, Mike and Phillips, Rebecca and Lo, Edmund and Shad, Saboor and Hasz, Richard and Walters, Gary and Garcia, Fernando and Young, Nancy and Foster, Barbara and Moser, Mike and Karasik, Ellen and Gillard, Bryan and Ramsey, Kimberley and Sullivan, Susan and Bridge, Jason and Magazine, Harold and Syron, John and Fleming, Johnelle and Siminoff, Laura and Traino, Heather and Mosavel, Maghboeba and Barker, Laura and Jewell, Scott and Rohrer, Dan and Maxim, Dan and Filkins, Dana and Harbach, Philip and Cortadillo, Eddie and Berghuis, Bree and Turner, Lisa and Hudson, Eric and Feenstra, Kristin and Sobin, Leslie and Robb, James and Branton, Phillip and Korzeniewski, Greg and Shive, Charles and Tabor, David and Qi, Liqun and Groch, Kevin and Nampally, Sreenath and Buia, Steve and Zimmerman, Angela and Smith, Anna and Burges, Robin and Robinson, Karna and Valentino, Kim and Bradbury, Deborah and Cosentino, Mark and Diaz-Mayoral, Norma and Kennedy, Mary and Engel, Theresa and Williams, Penelope and Erickson, Kenyon and Ardlie, Kristin and Winckler, Wendy and Getz, Gad and DeLuca, David and MacArthur, Daniel and Kellis, Manolis and Thomson, Alexander and Young, Taylor and Gelfand, Ellen and Donovan, Molly and Meng, Yan and Grant, George and Mash, Deborah and Marcus, Yvonne and Basile, Margaret and Liu, Jun and Zhu, Jun and Tu, Zhidong and Cox, Nancy J and Nicolae, Dan L and Gamazon, Eric R and Im, Hae Kyung and Konkashbaev, Anuar and Pritchard, Jonathan and Stevens, Matthew and Flutre, Timothee and Wen, Xiaoquan and Dermitzakis, Emmanouil T and Lappalainen, Tuuli and Guigo, Roderic and Monlong, Jean and Sammeth, Michael and Koller, Daphne and Battle, Alexis and Mostafavi, Sara and McCarthy, Mark and Rivas, Manual and Maller, Julian and Rusyn, Ivan and Nobel, Andrew and Wright, Fred and Shabalin, Andrey and Feolo, Mike and Sharopova, Nataliya and Sturcke, Anne and Paschal, Justin and Anderson, James M and Wilder, Elizabeth L and Derr, Leslie K and Green, Eric D and Struewing, Jeffery P and Temple, Gary and Volpi, Simona and Boyer, Joy T and Thomson, Elizabeth J and Guyer, Mark S and Ng, Cathy and Abdallah, Assya and Colantuoni, Deborah and Insel, Thomas R and Koester, Susan E and Little, A Roger and Bender, Patrick K and Lehner, Thomas and Yao, Yin and Compton, Carolyn C and Vaught, Jimmie B and Sawyer, Sherilyn and Lockhart, Nicole C and Demchok, Joanne and Moore, Helen F},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/longsdale{\_}GTEx{\_}2013.pdf:pdf},
issn = {1061-4036},
journal = {Nat Genet},
month = {jun},
number = {6},
pages = {580--585},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{The Genotype-Tissue Expression (GTEx) project}},
url = {http://dx.doi.org/10.1038/ng.2653 10.1038/ng.2653 http://www.nature.com/ng/journal/v45/n6/abs/ng.2653.html{\#}supplementary-information},
volume = {45},
year = {2013}
}
@article{Harrow2012,
abstract = {The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35{\%} of transcriptional start sites are supported by CAGE clusters and 62{\%} of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.},
author = {Harrow, Jennifer and Frankish, Adam and Gonzalez, Jose M. and Tapanari, Electra and Diekhans, Mark and Kokocinski, Felix and Aken, Bronwen L. and Barrell, Daniel and Zadissa, Amonida and Searle, Stephen and Barnes, If and Bignell, Alexandra and Boychenko, Veronika and Hunt, Toby and Kay, Mike and Mukherjee, Gaurab and Rajan, Jeena and Despacio-Reyes, Gloria and Saunders, Gary and Steward, Charles and Harte, Rachel and Lin, Michael and Howald, C??dric and Tanzer, Andrea and Derrien, Thomas and Chrast, Jacqueline and Walters, Nathalie and Balasubramanian, Suganthi and Pei, Baikang and Tress, Michael and Rodriguez, Jose Manuel and Ezkurdia, Iakes and {Van Baren}, Jeltje and Brent, Michael and Haussler, David and Kellis, Manolis and Valencia, Alfonso and Reymond, Alexandre and Gerstein, Mark and Guig??, Roderic and Hubbard, Tim J.},
doi = {10.1101/gr.135350.111},
isbn = {1549-5469 (Electronic)$\backslash$n1088-9051 (Linking)},
issn = {10889051},
journal = {Genome Research},
number = {9},
pages = {1760--1774},
pmid = {22955987},
title = {{GENCODE: The reference human genome annotation for the ENCODE project}},
volume = {22},
year = {2012}
}
@article{Maurano2012,
abstract = {Genome-wide association studies have identified many noncoding variants associated with common diseases and traits. We show that these variants are concentrated in regulatory DNA marked by deoxyribonuclease I (DNase I) hypersensitive sites (DHSs). Eighty-eight percent of such DHSs are active during fetal development and are enriched in variants associated with gestational exposure-related phenotypes. We identified distant gene targets for hundreds of variant-containing DHSs that may explain phenotype associations. Disease-associated variants systematically perturb transcription factor recognition sequences, frequently alter allelic chromatin states, and form regulatory networks. We also demonstrated tissue-selective enrichment of more weakly disease-associated variants within DHSs and the de novo identification of pathogenic cell types for Crohn's disease, multiple sclerosis, and an electrocardiogram trait, without prior knowledge of physiological mechanisms. Our results suggest pervasive involvement of regulatory DNA variation in common human disease and provide pathogenic insights into diverse disorders.},
author = {Maurano, Matthew T and Humbert, Richard and Rynes, Eric and Thurman, Robert E and Haugen, Eric and Wang, Hao and Reynolds, Alex P and Sandstrom, Richard and Qu, Hongzhu and Brody, Jennifer and Shafer, Anthony and Neri, Fidencio and Lee, Kristen and Kutyavin, Tanya and Stehling-Sun, Sandra and Johnson, Audra K and Canfield, Theresa K and Giste, Erika and Diegel, Morgan and Bates, Daniel and Hansen, R Scott and Neph, Shane and Sabo, Peter J and Heimfeld, Shelly and Raubitschek, Antony and Ziegler, Steven and Cotsapas, Chris and Sotoodehnia, Nona and Glass, Ian and Sunyaev, Shamil R and Kaul, Rajinder and Stamatoyannopoulos, John A},
doi = {10.1126/science.1222794},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Maurano{\_}eQTLs-GWAS{\_}2012.pdf:pdf},
isbn = {1095-9203 (Electronic)$\backslash$r0036-8075 (Linking)},
issn = {1095-9203},
journal = {Science},
keywords = {Alleles,Chromatin,Chromatin: metabolism,Chromatin: ultrastructure,Crohn Disease,Crohn Disease: genetics,DNA,DNA: genetics,Deoxyribonuclease I,Deoxyribonuclease I: metabolism,Disease,Disease: genetics,Electrocardiography,Fetal Development,Fetus,Fetus: metabolism,Gene Regulatory Networks,Genetic,Genetic Variation,Genome,Genome-Wide Association Study,Human,Humans,Multiple Sclerosis,Multiple Sclerosis: genetics,Nucleic Acid,Phenotype,Polymorphism,Promoter Regions,Regulatory Elements,Regulatory Sequences,Single Nucleotide,Transcription Factors,Transcription Factors: chemistry,Transcription Factors: genetics,Transcription Factors: metabolism,Transcriptional},
number = {6099},
pages = {1190--1195},
pmid = {22955828},
title = {{Systematic Localization of Common Disease-Associated Variation in Regulatory DNA}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3771521{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {337},
year = {2012}
}
@article{Smemo2014a,
abstract = {Genome-wide association studies (GWAS) have reproducibly associated variants within introns of FTO with increased risk for obesity and type 2 diabetes (T2D). Although the molecular mechanisms linking these noncoding variants with obesity are not immediately obvious, subsequent studies in mice demonstrated that FTO expression levels influence body mass and composition phenotypes. However, no direct connection between the obesity-associated variants and FTO expression or function has been made. Here we show that the obesity-associated noncoding sequences within FTO are functionally connected, at megabase distances, with the homeobox gene IRX3. The obesity-associated FTO region directly interacts with the promoters of IRX3 as well as FTO in the human, mouse and zebrafish genomes. Furthermore, long-range enhancers within this region recapitulate aspects of IRX3 expression, suggesting that the obesity-associated interval belongs to the regulatory landscape of IRX3. Consistent with this, obesity-associated single nucleotide polymorphisms are associated with expression of IRX3, but not FTO, in human brains. A direct link between IRX3 expression and regulation of body mass and composition is demonstrated by a reduction in body weight of 25 to 30{\%} in Irx3-deficient mice, primarily through the loss of fat mass and increase in basal metabolic rate with browning of white adipose tissue. Finally, hypothalamic expression of a dominant-negative form of Irx3 reproduces the metabolic phenotypes of Irx3-deficient mice. Our data suggest that IRX3 is a functional long-range target of obesity-associated variants within FTO and represents a novel determinant of body mass and composition.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Smemo, Scott and Tena, Juan J and Kim, Kyoung-Han and Gamazon, Eric R and Sakabe, Noboru J and G{\'{o}}mez-Mar{\'{\i}}n, Carlos and Aneas, Ivy and Credidio, Flavia L and Sobreira, D{\'{e}}bora R and Wasserman, Nora F and Lee, Ju Hee and Puviindran, Vijitha and Tam, Davis and Shen, Michael and Son, Joe Eun and Vakili, Niki Alizadeh and Sung, Hoon-Ki and Naranjo, Silvia and Acemel, Rafael D and Manzanares, Miguel and Nagy, Andras and Cox, Nancy J and Hui, Chi-Chung and Gomez-Skarmeta, Jose Luis and N{\'{o}}brega, Marcelo A},
doi = {10.1038/nature13138},
eprint = {NIHMS150003},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Smemo{\_}FTO-IRX3{\_}2014.pdf:pdf},
isbn = {1476-4687 (Electronic)$\backslash$r0028-0836 (Linking)},
issn = {1476-4687},
journal = {Nature},
keywords = {Adipose Tissue,Adipose Tissue: metabolism,Animals,Basal Metabolism,Basal Metabolism: genetics,Body Mass Index,Body Weight,Body Weight: genetics,Brain,Brain: metabolism,Diabetes Mellitus,Diet,Dominant,Dominant: genetics,Genes,Genetic,Genetic: genetics,Homeodomain Proteins,Homeodomain Proteins: genetics,Homeodomain Proteins: metabolism,Humans,Hypothalamus,Hypothalamus: metabolism,Introns,Introns: genetics,Male,Mice,Mixed Function Oxygenases,Mixed Function Oxygenases: genetics,Obesity,Obesity: genetics,Oxo-Acid-Lyases,Oxo-Acid-Lyases: genetics,Phenotype,Polymorphism,Promoter Regions,Proteins,Proteins: genetics,Single Nucleotide,Single Nucleotide: genetics,Thinness,Thinness: genetics,Transcription Factors,Transcription Factors: deficiency,Transcription Factors: genetics,Transcription Factors: metabolism,Type 2,Type 2: genetics,Zebrafish,Zebrafish: embryology,Zebrafish: genetics},
number = {7492},
pages = {371--5},
pmid = {24646999},
title = {{Obesity-associated variants within FTO form long-range functional connections with IRX3.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4113484{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {507},
year = {2014}
}
@article{Wheeler2016,
abstract = {For most complex traits, gene regulation is known to play a crucial mechanistic role as demonstrated by the consistent enrichment of expression quantitative trait loci (eQTLs) among trait-associated variants. Thus, understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. However, a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body has not been reported. Here we fill this gap through analyses of the RNA-seq data from a comprehensive set of tissue samples generated by the GTEx Project and the DGN whole blood cohort. We find that local h2 can be relatively well characterized with 50{\%} of expressed genes showing significant h2 in DGN and 8-19{\%} in GTEx. However, the current sample sizes (n < 362 in GTEx) only allow us to compute distal h2 for a handful of genes (3{\%} in DGN and <1{\%} in GTEx). Thus, we focus on local regulation. Bayesian Sparse Linear Mixed Model (BSLMM) analysis and the sparsity of optimal performing predictors provide compelling evidence that local architecture of gene expression traits is sparse rather than polygenic across DGN and all 40 GTEx tissues examined. To further delve into the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).},
author = {Wheeler, Heather E and Shah, Kaanan P and Brenner, Jonathon and Garcia, Tzintzuni and Aquino-Michaels, Keston and Cox, Nancy J and Nicolae, Dan L and Im, Hae Kyung},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Wheeler{\_}sparsity{\_}2016.pdf:pdf},
journal = {bioRxiv},
month = {mar},
title = {{Survey of the Heritability and Sparsity of Gene Expression Traits Across Human Tissues}},
url = {http://biorxiv.org/content/early/2016/03/15/043653.1.abstract},
year = {2016}
}
@article{Morris2012b,
abstract = {To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.},
author = {Morris, A P and Voight, B F and Teslovich, T M and Ferreira, T and Segre, A V and Steinthorsdottir, V and Strawbridge, R J and Khan, H and Grallert, H and Mahajan, A and Prokopenko, I and Kang, H M and Dina, C and Esko, T and Fraser, R M and Kanoni, S and Kumar, A and Lagou, V and Langenberg, C and Luan, J and Lindgren, C M and Muller-Nurasyid, M and Pechlivanis, S and Rayner, N W and Scott, L J and Wiltshire, S and Yengo, L and Kinnunen, L and Rossin, E J and Raychaudhuri, S and Johnson, A D and Dimas, A S and Loos, R J and Vedantam, S and Chen, H and Florez, J C and Fox, C and Liu, C T and Rybin, D and Couper, D J and Kao, W H and Li, M and Cornelis, M C and Kraft, P and Sun, Q and van Dam, R M and Stringham, H M and Chines, P S and Fischer, K and Fontanillas, P and Holmen, O L and Hunt, S E and Jackson, A U and Kong, A and Lawrence, R and Meyer, J and Perry, J R and Platou, C G and Potter, S and Rehnberg, E and Robertson, N and Sivapalaratnam, S and Stancakova, A and Stirrups, K and Thorleifsson, G and Tikkanen, E and Wood, A R and Almgren, P and Atalay, M and Benediktsson, R and Bonnycastle, L L and Burtt, N and Carey, J and Charpentier, G and Crenshaw, A T and Doney, A S and Dorkhan, M and Edkins, S and Emilsson, V and Eury, E and Forsen, T and Gertow, K and Gigante, B and Grant, G B and Groves, C J and Guiducci, C and Herder, C and Hreidarsson, A B and Hui, J and James, A and Jonsson, A and Rathmann, W and Klopp, N and Kravic, J and Krjutskov, K and Langford, C and Leander, K and Lindholm, E and Lobbens, S and Mannisto, S and Mirza, G and Muhleisen, T W and Musk, B and Parkin, M and Rallidis, L and Saramies, J and Sennblad, B and Shah, S and Sigurethsson, G and Silveira, A and Steinbach, G and Thorand, B and Trakalo, J and Veglia, F and Wennauer, R and Winckler, W and Zabaneh, D and Campbell, H and van Duijn, C and Uitterlinden, A G and Hofman, A and Sijbrands, E and Abecasis, G R and Owen, K R and Zeggini, E and Trip, M D and Forouhi, N G and Syvanen, A C and Eriksson, J G and Peltonen, L and Nothen, M M and Balkau, B and Palmer, C N and Lyssenko, V and Tuomi, T and Isomaa, B and Hunter, D J and Qi, L and {Wellcome Trust Case Control}, Consortium and Meta-Analyses of, Glucose and {Insulin-related traits Consortium}, Investigators and {Genetic Investigation of}, ANthropometric Traits Consortium and {Asian Genetic Epidemiology Network-Type 2 Diabetes}, Consortium and {South Asian Type 2 Diabetes}, Consortium and Shuldiner, A R and Roden, M and Barroso, I and Wilsgaard, T and Beilby, J and Hovingh, K and Price, J F and Wilson, J F and Rauramaa, R and Lakka, T A and Lind, L and Dedoussis, G and Njolstad, I and Pedersen, N L and Khaw, K T and Wareham, N J and Keinanen-Kiukaanniemi, S M and Saaristo, T E and Korpi-Hyovalti, E and Saltevo, J and Laakso, M and Kuusisto, J and Metspalu, A and Collins, F S and Mohlke, K L and Bergman, R N and Tuomilehto, J and Boehm, B O and Gieger, C and Hveem, K and Cauchi, S and Froguel, P and Baldassarre, D and Tremoli, E and Humphries, S E and Saleheen, D and Danesh, J and Ingelsson, E and Ripatti, S and Salomaa, V and Erbel, R and Jockel, K H and Moebus, S and Peters, A and Illig, T and de Faire, U and Hamsten, A and Morris, A D and Donnelly, P J and Frayling, T M and Hattersley, A T and Boerwinkle, E and Melander, O and Kathiresan, S and Nilsson, P M and Deloukas, P and Thorsteinsdottir, U and Groop, L C and Stefansson, K and Hu, F and Pankow, J S and Dupuis, J and Meigs, J B and Altshuler, D and Boehnke, M and McCarthy, M I and Replication, D IAbetes Genetics and Meta-analysis, Consortium},
doi = {10.1038/ng.2383},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1546-1718},
journal = {Nat Genet},
keywords = {*Genetic Predisposition to Disease/genetics,Case-Control Studies,Diabetes Mellitus, Type 2/epidemiology/*genetics,Female,Genes/physiology,Genome-Wide Association Study/*statistics {\&} numeri,Humans,Linkage Disequilibrium,Male,Pakistan/epidemiology,Polymorphism, Single Nucleotide/physiology,Sex Factors},
number = {9},
pages = {981--990},
pmid = {22885922},
title = {{Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22885922},
volume = {44},
year = {2012}
}
@article{Gusev2015,
abstract = {Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS1-3. We applied our approaches to expression data from blood and adipose tissue measured in {\~{}}3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements4-6 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.},
author = {Gusev, Alexander and Ko, Arthur and Shi, Huwenbo and Bhatia, Gaurav and Chung, Wonil and Penninx, Brenda W J and Jansen, Rick and de Geus, Eco J C and Boomsma, Dorret I and Wright, Fred A and Sullivan, Patrick F and Nikkola, Elina and Alvarez, Marcus and Civelek, Mete and Lusis, Aldons J and Lehtimaki, Terho and Raitoharju, Emma and Kahonen, Mika and Seppala, Ilkka and Raitakari, Olli and Kuusisto, Johanna and Laakso, Markku and Price, Alkes L and Pajukanta, Paivi and Pasaniuc, Bogdan},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/gusev{\_}eQTLTEST{\_}2015.pdf:pdf},
journal = {bioRxiv},
month = {aug},
title = {{Integrative approaches for large-scale transcriptome-wide association studies}},
url = {http://biorxiv.org/content/early/2015/08/10/024083.abstract},
year = {2015}
}
@article{Reynisdottir2003,
abstract = {We report a genomewide linkage study of type 2 diabetes (T2D [MIM 125853]) in the Icelandic population. A list of type 2 diabetics was cross-matched with a computerized genealogical database clustering 763 type 2 diabetics into 227 families. The diabetic patients and their relatives were genotyped with 906 microsatellite markers. A nonparametric multipoint linkage analysis yielded linkage to 5q34-q35.2 (LOD = 2.90, P=1.29 x 10(-4)) in all diabetics. Since obesity, here defined as body mass index (BMI) > or =30 kg/m(2), is a key risk factor for the development of T2D, we studied the data either independently of BMI or by stratifying the patient group as obese (BMI > or =30) or nonobese (BMI <30). A nonparametric multipoint linkage analysis yielded linkage to 5q34-q35.2 (LOD = 3.64, P=2.12 x (10)-5) in the nonobese diabetics. No linkage was observed in this region for the obese diabetics. Linkage analysis conditioning on maternal transmission to the nonobese diabetics resulted in a LOD score of 3.48 (P=3.12 x 10(-5)) in the same region, whereas conditioning on paternal transmission led to a substantial drop in the LOD score. Finally, we observed potential interactions between the 5q locus and two T2D susceptibility loci, previously mapped in other populations.},
author = {Reynisdottir, Inga and Thorleifsson, Gudmar and Benediktsson, Rafn and Sigurdsson, Gunnar and Emilsson, Valur and Einarsdottir, Anna Sigurlin and Hjorleifsdottir, Eyrun Edda and Orlygsdottir, Gudbjorg Th and Bjornsdottir, Gudrun Thora and Saemundsdottir, Jona and Halldorsson, Skarphedinn and Hrafnkelsdottir, Soffia and Sigurjonsdottir, Steinunn Bjorg and Steinsdottir, Svana and Martin, Mitchell and Kochan, Jarema P and Rhees, Brian K and Grant, Struan F A and Frigge, Michael L and Kong, Augustine and Gudnason, Vilmundur and Stefansson, Kari and Gulcher, Jeffrey R},
doi = {10.1086/377139},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Reynisdottir{\_}linkageTCF7L2{\_}2003.pdf:pdf},
isbn = {0002-9297},
issn = {00029297},
journal = {American journal of human genetics},
pages = {323--335},
pmid = {12851856},
title = {{Localization of a susceptibility gene for type 2 diabetes to chromosome 5q34-q35.2.}},
volume = {73},
year = {2003}
}
@article{Hughes2014,
abstract = {Gene expression during development and differentiation is regulated in a cell- and stage-specific manner by complex networks of intergenic and intragenic cis-regulatory elements whose numbers and representation in the genome far exceed those of structural genes. Using chromosome conformation capture, it is now possible to analyze in detail the interaction between enhancers, silencers, boundary elements and promoters at individual loci, but these techniques are not readily scalable. Here we present a high-throughput approach (Capture-C) to analyze cis interactions, interrogating hundreds of specific interactions at high resolution in a single experiment. We show how this approach will facilitate detailed, genome-wide analysis to elucidate the general principles by which cis-acting sequences control gene expression. In addition, we show how Capture-C will expedite identification of the target genes and functional effects of SNPs that are associated with complex diseases, which most frequently lie in intergenic cis-acting regulatory elements.},
author = {Hughes, Jim R and Roberts, Nigel and McGowan, Simon and Hay, Deborah and Giannoulatou, Eleni and Lynch, Magnus and {De Gobbi}, Marco and Taylor, Stephen and Gibbons, Richard and Higgs, Douglas R},
doi = {10.1038/ng.2871},
isbn = {1061-4036},
issn = {1546-1718},
journal = {Nature genetics},
number = {2},
pages = {205--12},
pmid = {24413732},
title = {{Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24413732},
volume = {46},
year = {2014}
}
@article{Grant2006,
abstract = {We have previously reported suggestive linkage of type 2 diabetes mellitus to chromosome 10q. We genotyped 228 microsatellite markers in Icelandic individuals with type 2 diabetes and controls throughout a 10.5-Mb interval on 10q. A microsatellite, DG10S478, within intron 3 of the transcription factor 7-like 2 gene (TCF7L2; formerly TCF4) was associated with type 2 diabetes (P = 2.1 x 10(-9)). This was replicated in a Danish cohort (P = 4.8 x 10(-3)) and in a US cohort (P = 3.3 x 10(-9)). Compared with non-carriers, heterozygous and homozygous carriers of the at-risk alleles (38{\%} and 7{\%} of the population, respectively) have relative risks of 1.45 and 2.41. This corresponds to a population attributable risk of 21{\%}. The TCF7L2 gene product is a high mobility group box-containing transcription factor previously implicated in blood glucose homeostasis. It is thought to act through regulation of proglucagon gene expression in enteroendocrine cells via the Wnt signaling pathway.},
author = {Grant, Struan F a and Thorleifsson, Gudmar and Reynisdottir, Inga and Benediktsson, Rafn and Manolescu, Andrei and Sainz, Jesus and Helgason, Agnar and Stefansson, Hreinn and Emilsson, Valur and Helgadottir, Anna and Styrkarsdottir, Unnur and Magnusson, Kristinn P and Walters, G Bragi and Palsdottir, Ebba and Jonsdottir, Thorbjorg and Gudmundsdottir, Thorunn and Gylfason, Arnaldur and Saemundsdottir, Jona and Wilensky, Robert L and Reilly, Muredach P and Rader, Daniel J and Bagger, Yu and Christiansen, Claus and Gudnason, Vilmundur and Sigurdsson, Gunnar and Thorsteinsdottir, Unnur and Gulcher, Jeffrey R and Kong, Augustine and Stefansson, Kari},
doi = {10.1038/ng1732},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Grant{\_}TCF7L2{\_}2006.pdf:pdf},
isbn = {1061-4036 (Print) 1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nature genetics},
number = {3},
pages = {320--323},
pmid = {16415884},
title = {{Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes.}},
volume = {38},
year = {2006}
}
@misc{Visscher2012a,
abstract = {The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section. ?? 2012 The American Society of Human Genetics.},
author = {Visscher, Peter M. and Brown, Matthew A. and McCarthy, Mark I. and Yang, Jian},
booktitle = {American Journal of Human Genetics},
doi = {10.1016/j.ajhg.2011.11.029},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Visscher{\_}GWAS-Review{\_}2012.pdf:pdf},
isbn = {1537-6605 (Electronic)$\backslash$r0002-9297 (Linking)},
issn = {00029297},
number = {1},
pages = {7--24},
pmid = {22243964},
title = {{Five years of GWAS discovery}},
volume = {90},
year = {2012}
}
@article{Torres2014,
abstract = {Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a disproportionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to theWellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (???10{\%}); GWAS SNPs explained over 50{\%} of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index ??? 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.},
author = {Torres, Jason M. and Gamazon, Eric R. and Parra, Esteban J. and Below, Jennifer E. and Valladares-Salgado, Adan and Wacher, Niels and Cruz, Miguel and Hanis, Craig L. and Cox, Nancy J.},
doi = {10.1016/j.ajhg.2014.10.001},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Torres{\_}eQTLh2{\_}2014.pdf:pdf},
isbn = {1537-6605},
issn = {15376605},
journal = {American Journal of Human Genetics},
number = {5},
pages = {521--534},
pmid = {25439722},
title = {{Cross-tissue and tissue-specific eQTLs: Partitioning the heritability of a complex trait}},
volume = {95},
year = {2014}
}
@article{Musunuru2010,
annote = {10.1038/nature09266},
author = {Musunuru, Kiran and Strong, Alanna and Frank-Kamenetsky, Maria and Lee, Noemi E and Ahfeldt, Tim and Sachs, Katherine V and Li, Xiaoyu and Li, Hui and Kuperwasser, Nicolas and Ruda, Vera M and Pirruccello, James P and Muchmore, Brian and Prokunina-Olsson, Ludmila and Hall, Jennifer L and Schadt, Eric E and Morales, Carlos R and Lund-Katz, Sissel and Phillips, Michael C and Wong, Jamie and Cantley, William and Racie, Timothy and Ejebe, Kenechi G and Orho-Melander, Marju and Melander, Olle and Koteliansky, Victor and Fitzgerald, Kevin and Krauss, Ronald M and Cowan, Chad A and Kathiresan, Sekar and Rader, Daniel J},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Musunuro{\_}SORT1{\_}2010.pdf:pdf},
issn = {0028-0836},
journal = {Nature},
month = {aug},
number = {7307},
pages = {714--719},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus}},
url = {http://dx.doi.org/10.1038/nature09266 http://www.nature.com/nature/journal/v466/n7307/abs/nature09266.html{\#}supplementary-information},
volume = {466},
year = {2010}
}
@article{Cho2011,
abstract = {We conducted a three-stage genetic study to identify susceptibility loci for type 2 diabetes (T2D) in east Asian populations. We followed our stage 1 meta-analysis of eight T2D genome-wide association studies (6,952 cases with T2D and 11,865 controls) with a stage 2 in silico replication analysis (5,843 cases and 4,574 controls) and a stage 3 de novo replication analysis (12,284 cases and 13,172 controls). The combined analysis identified eight new T2D loci reaching genome-wide significance, which mapped in or near GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6 and ZFAND3. GLIS3, which is involved in pancreatic beta cell development and insulin gene expression, is known for its association with fasting glucose levels. The evidence of an association with T2D for PEPD and HNF4A has been shown in previous studies. KCNK16 may regulate glucose-dependent insulin secretion in the pancreas. These findings, derived from an east Asian population, provide new perspectives on the etiology of T2D.},
author = {Cho, Yoon Shin and Chen, Chien-Hsiun and Hu, Cheng and Long, Jirong and {Hee Ong}, Rick Twee and Sim, Xueling and Takeuchi, Fumihiko and Wu, Ying and Go, Min Jin and Yamauchi, Toshimasa and Chang, Yi-Cheng and Kwak, Soo Heon and Ma, Ronald C W and Yamamoto, Ken and Adair, Linda S and Aung, Tin and Cai, Qiuyin and Chang, Li-Ching and Chen, Yuan-Tsong and Gao, Yutang and Hu, Frank B and Kim, Hyung-Lae and Kim, Sangsoo and Kim, Young Jin and Lee, Jeannette Jen-Mai and Lee, Nanette R and Li, Yun and Liu, Jian Jun and Lu, Wei and Nakamura, Jiro and Nakashima, Eitaro and Ng, Daniel Peng-Keat and Tay, Wan Ting and Tsai, Fuu-Jen and Wong, Tien Yin and Yokota, Mitsuhiro and Zheng, Wei and Zhang, Rong and Wang, Congrong and So, Wing Yee and Ohnaka, Keizo and Ikegami, Hiroshi and Hara, Kazuo and Cho, Young Min and Cho, Nam H and Chang, Tien-Jyun and Bao, Yuqian and Hedman, {\AA}sa K and Morris, Andrew P and McCarthy, Mark I and Takayanagi, Ryoichi and Park, Kyong Soo and Jia, Weiping and Chuang, Lee-Ming and Chan, Juliana C N and Maeda, Shiro and Kadowaki, Takashi and Lee, Jong-Young and Wu, Jer-Yuarn and Teo, Yik Ying and Tai, E Shyong and Shu, Xiao Ou and Mohlke, Karen L and Kato, Norihiro and Han, Bok-Ghee and Seielstad, Mark},
doi = {10.1038/ng.1019},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nature Genetics},
number = {1},
pages = {67--72},
pmid = {22158537},
title = {{Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians}},
url = {http://dx.doi.org/10.1038/ng.1019},
volume = {44},
year = {2011}
}
@article{Zhu2016,
abstract = {Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.},
author = {Zhu, Zhihong and Zhang, Futao and Hu, Han and Bakshi, Andrew and Robinson, Matthew R and Powell, Joseph E and Montgomery, Grant W and Goddard, Michael E and Wray, Naomi R and Visscher, Peter M and Yang, Jian},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Visscher{\_}eQTLTEST{\_}2016.pdf:pdf},
issn = {1546-1718},
journal = {Nat Genet},
month = {mar},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets}},
url = {http://dx.doi.org/10.1038/ng.3538 10.1038/ng.3538 http://www.nature.com/ng/journal/vaop/ncurrent/abs/ng.3538.html{\#}supplementary-information},
volume = {advance on},
year = {2016}
}
@article{JianYangMichaelEGoddardPeterMVisscheretal.2010,
author = {{Jian Yang  Michael E Goddard, Peter M Visscher, et al.}, Beben Benyamin},
journal = {Nature Genetics},
pages = {565--569},
title = {{Common SNPs explain a large portion of the heritability for human height}},
volume = {42},
year = {2010}
}
@article{Gamazon2015,
abstract = {Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.},
author = {Gamazon, Eric R and Wheeler, Heather E and Shah, Kaanan P and Mozaffari, Sahar V and Aquino-Michaels, Keston and Carroll, Robert J and Eyler, Anne E and Denny, Joshua C and Nicolae, Dan L and Cox, Nancy J and Im, Hae Kyung},
doi = {10.1038/ng.3367},
file = {:Users/jtorres/Google Drive/Articles/gamazon-predixcan-2015.pdf:pdf},
isbn = {1061-4036},
issn = {1546-1718},
journal = {Nature genetics},
number = {9},
pages = {1091--1098},
pmid = {26258848},
title = {{A gene-based association method for mapping traits using reference transcriptome data.}},
url = {http://dx.doi.org/10.1038/ng.3367},
volume = {47},
year = {2015}
}
@article{Shu2009,
abstract = {Recent human genetics studies have revealed that common variants of the TCF7L2 (T-cell factor 7-like 2, formerly known as TCF4) gene are strongly associated with type 2 diabetes mellitus (T2DM). We have shown that TCF7L2 expression in the beta-cells is correlated with function and survival of the insulin-producing pancreatic beta-cell. In order to understand how variations in TCF7L2 influence diabetes progression, we investigated its mechanism of action in the beta-cell. We show robust differences in TCF7L2 expression between healthy controls and models of T2DM. While mRNA levels were approximately 2-fold increased in isolated islets from the diabetic db/db mouse, the Vancouver Diabetic Fatty (VDF) Zucker rat and the high fat/high sucrose diet-treated mouse compared with the non-diabetic controls, protein levels were decreased. A similar decrease was observed in pancreatic sections from patients with T2DM. In parallel, expression of the receptors for glucagon-like peptide 1 (GLP-1R) and glucose-dependent insulinotropic polypeptide (GIP-R) was decreased in islets from humans with T2DM as well as in isolated human islets treated with siRNA to TCF7L2 (siTCF7L2). Also, insulin secretion stimulated by glucose, GLP-1 and GIP, but not KCl or cyclic adenosine monophosphate (cAMP) was impaired in siTCF7L2-treated isolated human islets. Loss of TCF7L2 resulted in decreased GLP-1 and GIP-stimulated AKT phosphorylation, and AKT-mediated Foxo-1 phosphorylation and nuclear exclusion. Our findings suggest that beta-cell function and survival are regulated through an interplay between TCF7L2 and GLP-1R/GIP-R expression and signaling in T2DM.},
author = {Shu, Luan and Matveyenko, Aleksey V. and Kerr-Conte, Julie and Cho, Jae Hyoung and McIntosh, C. H S and Maedler, Kathrin},
doi = {10.1093/hmg/ddp178},
isbn = {1460-2083},
issn = {09646906},
journal = {Human Molecular Genetics},
number = {13},
pages = {2388--2399},
pmid = {19386626},
title = {{Decreased TCF7L2 protein levels in type 2 diabetes mellitus correlate with downregulation of GIP- and GLP-1 receptors and impaired beta-cell function}},
volume = {18},
year = {2009}
}
@article{Bates2015,
abstract = {Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen ``glue''.},
archivePrefix = {arXiv},
arxivId = {1406.5823},
author = {Bates, Douglas and M{\"{a}}chler, Martin and Bolker, Benjamin M. and Walker, Steven C.},
doi = {10.18637/jss.v067.i01},
eprint = {1406.5823},
isbn = {{\%}(},
issn = {1548-7660},
journal = {Journal of Statistical Software},
keywords = {Cholesky decomposition,linear mixed models,penalized least squares,sparse matrix methods},
number = {1},
pages = {1--48},
title = {{Fitting linear mixed-effects models using lme4}},
url = {http://cran.r-project.org/package=lme4$\backslash$nhttp://www.jstatsoft.org/index.php/jss/article/view/v067i01/v67i01.pdf},
volume = {67},
year = {2015}
}
@article{International2003,
abstract = {The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.},
archivePrefix = {arXiv},
arxivId = {1302.2710v1},
author = {International, The and Consortium, Hapmap},
doi = {10.1038/nature02168},
eprint = {1302.2710v1},
isbn = {1476-4687 (Electronic)$\backslash$r0028-0836 (Linking)},
issn = {0047-1852},
journal = {Nature},
keywords = {Biomedical and Behavioral Research,Empirical Approach,Genetics and Reproduction},
number = {6968},
pages = {789--796},
pmid = {14685227},
title = {{The International HapMap Project.}},
volume = {426},
year = {2003}
}
@article{Elbein2012,
abstract = {To date, 68 loci have been associated with type 2 diabetes (T2D) or glucose homeostasis traits. We report here the results of experiments aimed at functionally characterizing the SNPs replicated for T2D and glucose traits. We sought to determine whether these loci were associated with transcript levels in adipose, muscle, liver, lymphocytes, and pancreatic $\beta$-cells. We found an excess of trans, rather than cis, associations among these SNPs in comparison to what was expected in adipose and muscle. Among transcripts differentially expressed (FDR < 0.05) between muscle or adipose cells of insulin-sensitive individuals and those of insulin-resistant individuals (matched on BMI), trans-regulated transcripts, in contrast to the cis-regulated ones, were enriched. The paucity of cis associations with transcripts was confirmed in a study of liver transcriptome and was further supported by an analysis of the most detailed transcriptome map of pancreatic $\beta$-cells. Relative to location- and allele-frequency-matched random SNPs, both the 68 loci and top T2D-associated SNPs from two large-scale genome-wide studies were enriched for trans eQTLs in adipose and muscle but not in lymphocytes. Our study suggests that T2D SNPs have broad-reaching and tissue-specific effects that often extend beyond local transcripts and raises the question of whether patterns of cis or trans transcript regulation are a key feature of the architecture of complex traits.},
author = {Elbein, Steven C and Gamazon, Eric R and Das, Swapan K and Rasouli, Neda and Kern, Philip A and Cox, Nancy J},
doi = {10.1016/j.ajhg.2012.08.002},
file = {:Users/jtorres/Library/Application Support/Mendeley Desktop/Downloaded/Elbein et al. - 2012 - Genetic risk factors for type 2 diabetes a trans-regulatory genetic architecture.pdf:pdf},
issn = {1537-6605},
journal = {American journal of human genetics},
keywords = {Adipose Tissue,Adipose Tissue: metabolism,Adult,Diabetes Mellitus,Female,Gene Expression Profiling,Gene Expression Profiling: methods,Gene Expression Regulation,Glucose,Glucose: metabolism,Humans,Insulin Resistance,Insulin Resistance: genetics,Insulin-Secreting Cells,Insulin-Secreting Cells: metabolism,Liver,Liver: metabolism,Male,Middle Aged,Muscles,Muscles: metabolism,Organ Specificity,Polymorphism,Quantitative Trait Loci,Risk Factors,Single Nucleotide,Transcriptome,Type 2,Type 2: genetics,Young Adult},
pages = {466--77},
pmid = {22958899},
title = {{Genetic risk factors for type 2 diabetes: a trans-regulatory genetic architecture?}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3512001{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {91},
year = {2012}
}
@article{Degner2012,
abstract = {The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation. However, it remains difficult to identify the causal variants underlying eQTLs, and little is known about the regulatory mechanisms by which they act. Here we show that genetic variants that modify chromatin accessibility and transcription factor binding are a major mechanism through which genetic variation leads to gene expression differences among humans. We used DNase I sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines, for which genome-wide genotypes and estimates of gene expression levels are also available. We obtained a total of 2.7 billion uniquely mapped DNase I-sequencing (DNase-seq) reads, which allowed us to produce genome-wide maps of chromatin accessibility for each individual. We identified 8,902 locations at which the DNase-seq read depth correlated significantly with genotype at a nearby single nucleotide polymorphism or insertion/deletion (false discovery rate = 10{\%}). We call such variants 'DNase I sensitivity quantitative trait loci' (dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial fraction (16{\%}) of dsQTLs are also associated with variation in the expression levels of nearby genes (that is, these loci are also classified as eQTLs). Conversely, we estimate that as many as 55{\%} of eQTL single nucleotide polymorphisms are also dsQTLs. Our observations indicate that dsQTLs are highly abundant in the human genome and are likely to be important contributors to phenotypic variation.},
author = {Degner, Jacob F. and Pai, Athma A. and Pique-Regi, Roger and Veyrieras, Jean-Baptiste and Gaffney, Daniel J. and Pickrell, Joseph K. and {De Leon}, Sherryl and Michelini, Katelyn and Lewellen, Noah and Crawford, Gregory E. and Stephens, Matthew and Gilad, Yoav and Pritchard, Jonathan K.},
doi = {10.1038/nature10808},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Degner{\_}DNAse-QTLs{\_}2012.pdf:pdf},
isbn = {1476-4687 (Electronic)$\backslash$n0028-0836 (Linking)},
issn = {0028-0836},
journal = {Nature},
keywords = {Chromatin,Chromatin: genetics,Chromatin: metabolism,DNA,DNA Footprinting,Deoxyribonuclease I,Deoxyribonuclease I: metabolism,Gene Expression Profiling,Gene Expression Regulation,Gene Expression Regulation: genetics,Genetic Variation,Genetic Variation: genetics,Genome,Human,Human: genetics,Humans,Phenotype,Polymorphism,Quantitative Trait Loci,Quantitative Trait Loci: genetics,Sequence Analysis,Single Nucleotide,Single Nucleotide: genetics,Transcription Factors,Transcription Factors: metabolism},
number = {7385},
pages = {390--394},
pmid = {22307276},
title = {{DNase I sensitivity QTLs are a major determinant of human expression variation}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3501342{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {482},
year = {2012}
}
@article{Barbeira2016,
abstract = {To gain biological insight into the discoveries made by GWAS and meta-analysis studies, effective integration of functional data generated by large-scale efforts such as the GTEx Project is needed. PrediXcan is a gene-level approach that addresses this need by estimating the genetically determined component of gene expression. These predicted expression traits can then be tested for association with phenotype in order to test for mediating role of gene expression levels. Furthermore, due to the polygenic nature of many complex traits, efforts to aggregate multiple GWAS studies and conduct meta-analyses have successfully increased our ability to identify variants of small effect sizes. To take advantage of the results generated by these efforts and to avoid the problems associated with accessing and handling individual-level data (e.g. consent limitations, large computational/storage costs) we have developed an extension of PrediXcan. The new method, MetaXcan, infers the results of PrediXcan using only summary statistics from large-scale GWAS or meta-analyses. Here we show that the concordance between PrediXcan and MetaXcan is excellent when the right reference population is used (R2 {\&}gt; 0.95) and robust to population mismatches (R2 {\&}gt; 0.85). We provide open source local and web-based software for easy implementation through (https://github.com/hakyimlab/MetaXcan)},
author = {Barbeira, Alvaro and Shah, Kaanan P and Torres, Jason M and Wheeler, Heather E and Torstenson, Eric S and Edwards, Todd and Garcia, Tzintzuni and Bell, Graeme I and Nicolae, Dan and Cox, Nancy J and Im, Hae Kyung},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/MetaXcan{\_}2016.pdf:pdf},
journal = {bioRxiv},
month = {mar},
title = {{MetaXcan: Summary Statistics Based Gene-Level Association Method Infers Accurate PrediXcan Results}},
url = {http://biorxiv.org/content/early/2016/03/23/045260.abstract},
year = {2016}
}
@article{Hindorff2009,
abstract = {We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36{\%}, interquartile range (IQR) 21{\%}−53{\%}] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10−7] and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10−4] compared to SNPs randomly selected from genotyping arrays. Although 88{\%} of TASs were intronic (45{\%}) or intergenic (43{\%}), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10−9]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.},
author = {Hindorff, Lucia A and Sethupathy, Praveen and Junkins, Heather A and Ramos, Erin M and Mehta, Jayashri P and Collins, Francis S and Manolio, Teri A},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/PNAS-2009-Hindorff-9362-7.pdf:pdf},
journal = {Proceedings of the National Academy of Sciences},
month = {jun},
number = {23},
pages = {9362--9367},
title = {{Potential etiologic and functional implications of genome-wide association loci for human diseases and traits}},
url = {http://www.pnas.org/content/106/23/9362.abstract},
volume = {106},
year = {2009}
}
@article{Kooner2011,
abstract = {We carried out a genome-wide association study of type-2 diabetes (T2D) in individuals of South Asian ancestry. Our discovery set included 5,561 individuals with T2D (cases) and 14,458 controls drawn from studies in London, Pakistan and Singapore. We identified 20 independent SNPs associated with T2D at P < 10(-4) for testing in a replication sample of 13,170 cases and 25,398 controls, also all of South Asian ancestry. In the combined analysis, we identified common genetic variants at six loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) newly associated with T2D (P = 4.1 × 10(-8) to P = 1.9 × 10(-11)). SNPs at GRB14 were also associated with insulin sensitivity (P = 5.0 × 10(-4)), and SNPs at ST6GAL1 and HNF4A were also associated with pancreatic beta-cell function (P = 0.02 and P = 0.001, respectively). Our findings provide additional insight into mechanisms underlying T2D and show the potential for new discovery from genetic association studies in South Asians, a population with increased susceptibility to T2D.},
author = {Kooner, Jaspal S and Saleheen, Danish and Sim, Xueling and Sehmi, Joban and Zhang, Weihua and Frossard, Philippe and Been, Latonya F and Chia, Kee-Seng and Dimas, Antigone S and Hassanali, Neelam and Jafar, Tazeen and Jowett, Jeremy B M and Li, Xinzhong and Radha, Venkatesan and Rees, Simon D and Takeuchi, Fumihiko and Young, Robin and Aung, Tin and Basit, Abdul and Chidambaram, Manickam and Das, Debashish and Grundberg, Elin and Hedman, {\AA}sa K and Hydrie, Zafar I and Islam, Muhammed and Khor, Chiea-Chuen and Kowlessur, Sudhir and Kristensen, Malene M and Liju, Samuel and Lim, Wei-Yen and Matthews, David R and Liu, Jianjun and Morris, Andrew P and Nica, Alexandra C and Pinidiyapathirage, Janani M and Prokopenko, Inga and Rasheed, Asif and Samuel, Maria and Shah, Nabi and Shera, a Samad and Small, Kerrin S and Suo, Chen and Wickremasinghe, Ananda R and Wong, Tien Yin and Yang, Mingyu and Zhang, Fan and Abecasis, Goncalo R and Barnett, Anthony H and Caulfield, Mark and Deloukas, Panos and Frayling, Timothy M and Froguel, Philippe and Kato, Norihiro and Katulanda, Prasad and Kelly, M Ann and Liang, Junbin and Mohan, Viswanathan and Sanghera, Dharambir K and Scott, James and Seielstad, Mark and Zimmet, Paul Z and Elliott, Paul and Teo, Yik Ying and McCarthy, Mark I and Danesh, John and Tai, E Shyong and Chambers, John C},
doi = {10.1038/ng.921},
isbn = {1061-4036},
issn = {1061-4036},
journal = {Nature Genetics},
number = {10},
pages = {984--989},
pmid = {21874001},
title = {{Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci}},
volume = {43},
year = {2011}
}
@article{Young2010,
abstract = {We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.},
author = {Young, Matthew D and Wakefield, Matthew J and Smyth, Gordon K and Oshlack, Alicia},
doi = {10.1186/gb-2010-11-2-r14},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Go{\_}seq{\_}2010.pdf:pdf},
isbn = {1465-6906},
issn = {1465-6906},
journal = {Genome biology},
number = {2},
pages = {R14},
pmid = {20132535},
title = {{Gene ontology analysis for RNA-seq: accounting for selection bias.}},
volume = {11},
year = {2010}
}
@article{Feinerer2008,
abstract = {During the last decade text mining has become a widely used discipline utilizing sta- tistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facili- ties in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysismethods, text clustering, text classification and string kernels.},
author = {Feinerer, Ingo and Hornik, Kurt and Meyer, David},
doi = {citeulike-article-id:2842334},
isbn = {1548-7660},
issn = {15487660},
journal = {Journal Of Statistical Software},
keywords = {count based evaluation,r,string,text classification,text clustering,text mining},
number = {5},
pages = {1--54},
title = {{Text Mining Infrastructure in R}},
url = {http://www.jstatsoft.org/v25/i05},
volume = {25},
year = {2008}
}
@article{Voight2010a,
abstract = {By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.},
author = {Voight, Benjamin F and Scott, Laura J and Steinthorsdottir, Valgerdur and Andrew, P and Aulchenko, Yurii S and Thorleifsson, Gudmar and Mcculloch, Laura J and Ferreira, Teresa},
doi = {10.1038/ng.609.Twelve},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Voight{\_}t2d{\_}2010.pdf:pdf},
isbn = {1546-1718 (Electronic)$\backslash$r1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nat Genet},
number = {7},
pages = {579--589},
pmid = {20581827},
title = {{Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis}},
volume = {42},
year = {2010}
}
@article{Parra2011,
abstract = {We report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets.},
author = {Parra, E J and Below, J E and Krithika, S and Valladares, A and Barta, J L and Cox, N J and Hanis, C L and Wacher, N and Garcia-Mena, J and Hu, P and Shriver, M D and Kumate, J and McKeigue, P M and Escobedo, J and Cruz, M},
doi = {10.1007/s00125-011-2172-y},
file = {:Users/jtorres/Library/Application Support/Mendeley Desktop/Downloaded/Parra et al. - 2011 - Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-Ame.pdf:pdf},
isbn = {1432-0428 (Electronic)$\backslash$n0012-186X (Linking)},
issn = {0012-186X},
journal = {Diabetologia},
pages = {2038--2046},
pmid = {21573907},
title = {{Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas.}},
volume = {54},
year = {2011}
}
@article{Nicolae2010b,
abstract = {Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency-matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10(-4)-10(-8)), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.},
author = {Nicolae, Dan L. and Gamazon, Eric and Zhang, Wei and Duan, Shiwei and {Eileen Dolan}, M. and Cox, Nancy J.},
doi = {10.1371/journal.pgen.1000888},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Nicolae{\_}eQTLS-GWAS{\_}2010.PDF:PDF},
isbn = {1553-7404 (Electronic)$\backslash$r1553-7390 (Linking)},
issn = {15537390},
journal = {PLoS Genetics},
number = {4},
pmid = {20369019},
title = {{Trait-associated SNPs are more likely to be eQTLs: Annotation to enhance discovery from GWAS}},
volume = {6},
year = {2010}
}
@article{Fuchsberger2015,
abstract = {Summary: Genotype imputation is a key step in the analysis of genome-wide association studies. Upcoming very large reference panels, such as those from The 1000 Genomes Project and the Haplotype Consortium, will improve imputation quality of rare and less common variants, but will also increase the computational burden. Here, we demonstrate how the application of software engineering techniques can help to keep imputation broadly accessible. Overall, these improvements speed up imputation by an order of magnitude compared with our previous implementation.$\backslash$nAvailability and implementation: minimac2, including source code, documentation, and examples is available at http://genome.sph.umich.edu/wiki/Minimac2$\backslash$nContact: cfuchsb@umich.edu, goncalo@umich.edu},
author = {Fuchsberger, Christian and Abecasis, Gon??alo R. and Hinds, David A.},
doi = {10.1093/bioinformatics/btu704},
isbn = {1367-4811},
issn = {14602059},
journal = {Bioinformatics},
number = {5},
pages = {782--784},
pmid = {25338720},
title = {{Minimac2: Faster genotype imputation}},
volume = {31},
year = {2015}
}
@article{Gaulton2015,
abstract = {We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.},
author = {Gaulton, Kyle J and Ferreira, Teresa and Lee, Yeji and Raimondo, Anne and M{\"{a}}gi, Reedik and Reschen, Michael E and Mahajan, Anubha and Locke, Adam and {William Rayner}, N and Robertson, Neil and Scott, Robert A and Prokopenko, Inga and Scott, Laura J and Green, Todd and Sparso, Thomas and Thuillier, Dorothee and Yengo, Loic and Grallert, Harald and Wahl, Simone and Fr{\aa}nberg, Mattias and Strawbridge, Rona J and Kestler, Hans and Chheda, Himanshu and Eisele, Lewin and Gustafsson, Stefan and Steinthorsdottir, Valgerdur and Thorleifsson, Gudmar and Qi, Lu and Karssen, Lennart C and van Leeuwen, Elisabeth M and Willems, Sara M and Li, Man and Chen, Han and Fuchsberger, Christian and Kwan, Phoenix and Ma, Clement and Linderman, Michael and Lu, Yingchang and Thomsen, Soren K and Rundle, Jana K and Beer, Nicola L and van de Bunt, Martijn and Chalisey, Anil and Kang, Hyun Min and Voight, Benjamin F and Abecasis, Gon{\c{c}}alo R and Almgren, Peter and Baldassarre, Damiano and Balkau, Beverley and Benediktsson, Rafn and Bl{\"{u}}her, Matthias and Boeing, Heiner and Bonnycastle, Lori L and Bottinger, Erwin P and Burtt, No{\"{e}}l P and Carey, Jason and Charpentier, Guillaume and Chines, Peter S and Cornelis, Marilyn C and Couper, David J and Crenshaw, Andrew T and van Dam, Rob M and Doney, Alex S F and Dorkhan, Mozhgan and Edkins, Sarah and Eriksson, Johan G and Esko, Tonu and Eury, Elodie and Fadista, Jo{\~{a}}o and Flannick, Jason and Fontanillas, Pierre and Fox, Caroline and Franks, Paul W and Gertow, Karl and Gieger, Christian and Gigante, Bruna and Gottesman, Omri and Grant, George B and Grarup, Niels and Groves, Christopher J and Hassinen, Maija and Have, Christian T and Herder, Christian and Holmen, Oddgeir L and Hreidarsson, Astradur B and Humphries, Steve E and Hunter, David J and Jackson, Anne U and Jonsson, Anna and J{\o}rgensen, Marit E and J{\o}rgensen, Torben and Kao, Wen-Hong L and Kerrison, Nicola D and Kinnunen, Leena and Klopp, Norman and Kong, Augustine and Kovacs, Peter and Kraft, Peter and Kravic, Jasmina and Langford, Cordelia and Leander, Karin and Liang, Liming and Lichtner, Peter and Lindgren, Cecilia M and Lindholm, Eero and Linneberg, Allan and Liu, Ching-Ti and Lobbens, St{\'{e}}phane and Luan, Jian'an and Lyssenko, Valeriya and M{\"{a}}nnist{\"{o}}, Satu and McLeod, Olga and Meyer, Julia and Mihailov, Evelin and Mirza, Ghazala and M{\"{u}}hleisen, Thomas W and M{\"{u}}ller-Nurasyid, Martina and Navarro, Carmen and N{\"{o}}then, Markus M and Oskolkov, Nikolay N and Owen, Katharine R and Palli, Domenico and Pechlivanis, Sonali and Peltonen, Leena and Perry, John R B and Platou, Carl G P and Roden, Michael and Ruderfer, Douglas and Rybin, Denis and van der Schouw, Yvonne T and Sennblad, Bengt and Sigurðsson, Gunnar and Stan{\v{c}}{\'{a}}kov{\'{a}}, Alena and Steinbach, Gerald and Storm, Petter and Strauch, Konstantin and Stringham, Heather M and Sun, Qi and Thorand, Barbara and Tikkanen, Emmi and Tonjes, Anke and Trakalo, Joseph and Tremoli, Elena and Tuomi, Tiinamaija and Wennauer, Roman and Wiltshire, Steven and Wood, Andrew R and Zeggini, Eleftheria and Dunham, Ian and Birney, Ewan and Pasquali, Lorenzo and Ferrer, Jorge and Loos, Ruth J F and Dupuis, Jos{\'{e}}e and Florez, Jose C and Boerwinkle, Eric and Pankow, James S and van Duijn, Cornelia and Sijbrands, Eric and Meigs, James B and Hu, Frank B and Thorsteinsdottir, Unnur and Stefansson, Kari and Lakka, Timo A and Rauramaa, Rainer and Stumvoll, Michael and Pedersen, Nancy L and Lind, Lars and Keinanen-Kiukaanniemi, Sirkka M and Korpi-Hy{\"{o}}v{\"{a}}lti, Eeva and Saaristo, Timo E and Saltevo, Juha and Kuusisto, Johanna and Laakso, Markku and Metspalu, Andres and Erbel, Raimund and J{\"{o}}cke, Karl-Heinz and Moebus, Susanne and Ripatti, Samuli and Salomaa, Veikko and Ingelsson, Erik and Boehm, Bernhard O and Bergman, Richard N and Collins, Francis S and Mohlke, Karen L and Koistinen, Heikki and Tuomilehto, Jaakko and Hveem, Kristian and Nj{\o}lstad, Inger and Deloukas, Panagiotis and Donnelly, Peter J and Frayling, Timothy M and Hattersley, Andrew T and de Faire, Ulf and Hamsten, Anders and Illig, Thomas and Peters, Annette and Cauchi, Stephane and Sladek, Rob and Froguel, Philippe and Hansen, Torben and Pedersen, Oluf and Morris, Andrew D P and Palmer, Collin N A and Kathiresan, Sekar and Melander, Olle and Nilsson, Peter M and Groop, Leif C and Barroso, In{\^{e}}s and Langenberg, Claudia and Wareham, Nicholas J and O'Callaghan, Christopher A and Gloyn, Anna L and Altshuler, David and Boehnke, Michael and Teslovich, Tanya M and McCarthy, Mark I and Morris, Andrew D P and {DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium}},
doi = {10.1038/ng.3437},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Gaulton{\_}finemapT2D{\_}2015.pdf:pdf},
isbn = {1546-1718 (Electronic) 1061-4036 (Linking)},
issn = {1061-4036},
journal = {Nature Genetics},
number = {12},
pages = {1415--25},
pmid = {26551672},
title = {{Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26551672$\backslash$nhttp://www.nature.com/doifinder/10.1038/ng.3437},
volume = {47},
year = {2015}
}
@misc{Eckel2015,
abstract = {Short sleep duration and circadian misalignment are hypothesized to causally contribute to health problems including obesity, diabetes, metabolic syndrome, heart disease, mood disorders, cognitive impairment, and accidents [1-7]. Here, we investigated the influence of morning circadian misalignment induced by an imposed short nighttime sleep schedule on impaired insulin sensitivity, a precursor to diabetes. Imposed short sleep duration resulted in morning wakefulness occurring during the biological night (i.e., circadian misalignment)-a time when endogenous melatonin levels were still high indicating the internal circadian clock was still promoting sleep and related functions. We show the longer melatonin levels remained high after wake time, insulin sensitivity worsened. Overall, we find a simulated 5-day work week of 5-hr-per-night sleep opportunities and ad libitum food intake resulted in ???20{\%} reduced oral and intravenous insulin sensitivity in otherwise healthy men and women. Reduced insulin sensitivity was compensated by an increased insulin response to glucose, which may reflect an initial physiological adaptation to maintain normal blood sugar [8] levels during sleep loss. Furthermore, we find that transitioning from the imposed short sleep schedule to 9-hr sleep opportunities for 3 days restored oral insulin sensitivity to baseline, but 5 days with 9-hr sleep opportunities was insufficient to restore intravenous insulin sensitivity to baseline. These findings indicate morning wakefulness and eating during the biological night is a novel mechanism by which short sleep duration contributes to metabolic dysregulation and suggests food intake during the biological night may contribute to other health problems associated with short sleep duration. Short sleep duration is associated with elevated diabetes risk. Eckel et al. provide evidence showing short sleep results in morning wakefulness during the biological night (i.e., circadian misalignment). Such circadian misalignment was associated with lower insulin sensitivity, indicating a novel way by which short sleep may elevate diabetes risk.},
author = {Eckel, Robert H. and Depner, Christopher M. and Perreault, Leigh and Markwald, Rachel R. and Smith, Mark R. and McHill, Andrew W. and Higgins, Janine and Melanson, Edward L. and Wright, Kenneth P.},
booktitle = {Current Biology},
doi = {10.1016/j.cub.2015.10.011},
issn = {09609822},
keywords = {Sleep restriction},
pmid = {26549253},
title = {{Morning Circadian Misalignment during Short Sleep Duration Impacts Insulin Sensitivity}},
year = {2015}
}
@article{Willer2010,
abstract = {SUMMARY: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. AVAILABILITY AND IMPLEMENTATION: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/.},
author = {Willer, Cristen J. and Li, Yun and Abecasis, Gon??alo R.},
doi = {10.1093/bioinformatics/btq340},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Bioinformatics-2010-Willer-2190-1.pdf:pdf},
isbn = {1367-4803},
issn = {13674803},
journal = {Bioinformatics},
number = {17},
pages = {2190--2191},
pmid = {20616382},
title = {{METAL: Fast and efficient meta-analysis of genomewide association scans}},
volume = {26},
year = {2010}
}
@article{TheGeneOntologyConsortium2000,
abstract = {Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular componen},
archivePrefix = {arXiv},
arxivId = {10614036},
author = {{The Gene Ontology Consortium}},
doi = {10.1038/75556},
eprint = {10614036},
isbn = {1061-4036},
issn = {1061-4036},
journal = {Nature Genetics},
number = {may},
pages = {25--29},
pmid = {10802651},
title = {{Gene Ontology: tool for the unification of biology}},
url = {http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:Gene+Ontology:+tool+for+the+unification+of+biology{\#}0},
volume = {25},
year = {2000}
}
@article{Leung2005,
abstract = {In the past 4 years, RNA interference (RNAi) has become widely used as an experimental tool to analyse the function of mammalian genes, both in vitro and in vivo. By harnessing an evolutionary conserved endogenous biological pathway, first identified in plants and lower organisms, double-stranded RNA (dsRNA) reagents are used to bind to and promote the degradation of target RNAs, resulting in knockdown of the expression of specific genes. RNAi can be induced in mammalian cells by the introduction of synthetic double-stranded small interfering RNAs (siRNAs) 21–23 base pairs (bp) in length or by plasmid and viral vector systems that express double-stranded short hairpin RNAs (shRNAs) that are subsequently processed to siRNAs by the cellular machinery. RNAi has been widely used in mammalian cells to define the functional roles of individual genes, particularly in disease. In addition, siRNA and shRNA libraries have been developed to allow the systematic analysis of genes required for disease processes such as cancer using high throughput RNAi screens. RNAi has been used for the knockdown of gene expression in experimental animals, with the development of shRNA systems that allow tissue-specific and inducible knockdown of genes promising to provide a quicker and cheaper way to generate transgenic animals than conventional approaches. Finally, because of the ability of RNAi to silence disease-associated genes in tissue culture and animal models, the development of RNAi-based reagents for clinical applications is gathering pace, as technological enhancements that improve siRNA stability and delivery in vivo, while minimising off-target and nonspecific effects, are developed.},
author = {Leung, Ray K M and Whittaker, Paul A},
doi = {http://dx.doi.org/10.1016/j.pharmthera.2005.03.004},
file = {:Users/jtorres/Google Drive/Articles/Manuscript{\_}2016/Leung{\_}RNAi{\_}2005.pdf:pdf},
issn = {0163-7258},
journal = {Pharmacology {\&} Therapeutics},
keywords = {Disease,RNAi,Therapy,Validation,shRNA,siRNA},
month = {aug},
number = {2},
pages = {222--239},
title = {{RNA interference: From gene silencing to gene-specific therapeutics}},
url = {http://www.sciencedirect.com/science/article/pii/S016372580500062