Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362-9367

Office of Population Genomics, Genome Technology Branch, National Human Genome Research Institute, and National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20892, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 07/2009; 106(23):9362-7. DOI: 10.1073/pnas.0903103106
Source: PubMed

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 x 10(-7)] and 5kb-promoter regions [OR = 2.3 (1.5-3.6), p = 3 x 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 x 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.

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Available from: Teri Manolio, Jan 12, 2014
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    • "In the past decade, genome-wide association studies (GWAS) have been conducted to study the genetic basis for thousands of phenotypes (Hindorff et al., 2009; Eicher et al., 2015), including diseases (e.g., the seven diseases from WTCCC, The Wellcome Trust Case Control Consortium, 2007), clinical traits (e.g., cholesterol levels), anthropometric traits (e.g., height, Wood et al., 2014), brain structures (Hibar et al., 2015) and social behaviors (e.g., educational attainment, Rietveld et al., 2013; marriage, Domingue et al., 2014). As of April, 2015, more than 15,000 single-nucleotide polymorphisms (SNPs) have been reported to be significantly associated (p < 5 × 10 −8 ) with at least one phenotype (see GWAS catalog, Welter et al., 2014). "
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    ABSTRACT: Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.
    Frontiers in Genetics 06/2015; 6:229. DOI:10.3389/fgene.2015.00229
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    • "data, but also of interaction data inferred by DNase I hypersensitivity assays or predicted based on TF binding sites. Most variants identified by GWAS reside in non-coding regions of the genome (Hindorff et al., 2009; Maurano et al., 2012). We propose that eY1H assays will provide a facile method with which differential TF interactions involving these variants can be analyzed. "
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    ABSTRACT: Gene regulatory networks (GRNs) comprising interactions between transcription factors (TFs) and regulatory loci control development and physiology. Numerous disease-associated mutations have been identified, the vast majority residing in non-coding regions of the genome. As current GRN mapping methods test one TF at a time and require the use of cells harboring the mutation(s) of interest, they are not suitable to identify TFs that bind to wild-type and mutant loci. Here, we use gene-centered yeast one-hybrid (eY1H) assays to interrogate binding of 1,086 human TFs to 246 enhancers, as well as to 109 non-coding disease mutations. We detect both loss and gain of TF interactions with mutant loci that are concordant with target gene expression changes. This work establishes eY1H assays as a powerful addition to the toolkit of mapping human GRNs and for the high-throughput characterization of genomic variants that are rapidly being identified by genome-wide association studies. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell 04/2015; 161(3):661-673. DOI:10.1016/j.cell.2015.03.003 · 33.12 Impact Factor
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    • "Over a hundred thousand genetic variants have been identified across a large number of Mendelian disorders (Amberger et al., 2011), complex traits (Hindorff et al., 2009), and cancer types (Chin et al., 2011). However, many fundamental questions regarding genotype-phenotype relationships remain unresolved (Vidal et al., 2011). "
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    ABSTRACT: How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell 04/2015; 161(3):647-660. DOI:10.1016/j.cell.2015.04.013 · 33.12 Impact Factor
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