1 Enriching the gold dust: extreme-value based genome-wide association in the post-gwas era




Название1 Enriching the gold dust: extreme-value based genome-wide association in the post-gwas era
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Дата21.09.2012
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Enriching the gold dust: extreme-value based genome-wide association in the post-GWAS era


Dalin Li (1), David V. Conti (2)

(1) University of Southern California
(2) david.conti@keck.usc.edu

Based on the "common disease-common variant" hypothesis, great progress has been made in recent GWAS. However current findings are far from fully explaining the heredity of the traits studied, suggesting rare variants may contribute significantly to the genetic predisposition of human traits. With traditional GWAS design very large sample size is required to detect rare variants and the cost would be forbidding, particularly when we might rely on sequencing to explore the rare variants across the genome. Here we propose the extreme-value based GWAS in which only individuals with extreme phenotypes are used for whole genome genotyping or sequencing. Our simulations show that this extreme-value design is highly efficient in detecting rare variants with a sample size 10 to 20 times smaller than the comparable full study design. Furthermore the impact of bias effects does not increase correspondingly and the disturbance from unknown confounding and measurement bias can be significantly reduced. Moreover with the small sample size in the extreme-value design, it would be practically feasible to combine the information from the DNA sequence, DNA or histone methylation as well as RNA expression in the study. We propose an analysis framework for the extreme-value design based on maximum likelihood theory. A corresponding power calculation approach and a guideline for optimizing the extreme-value design conditional on the phenotyping/genotyping cost ratio are further proposed.

2
Fine mapping of common and rare variants associated with low-density lipoprotein cholesterol (LDL-C) via sequencing candidate loci following genome-wide scans


Bingshan Li (1), Yun Li (1), David Schlessinger (2), Samer Najjar (2), Angelo Scuteri (2), Ed Lakkata (2), Serena Sanna (2), Mike Boehnke (1), Goncalo Abecasis (1), Manuela Uda (2)

(1) Center for Statistical Genetics, Dept. of Biostatistics University of Michigan
(2) Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, Italy

Coronary artery disease is one of leading causes of morbidity and mortality in developed countries and strong associations have been established between lipoprotein levels and coronary heart disease. Our previous studies of >8,000 individuals through genome-wide association scans identified a number of loci associated with LDL-C levels, including previously reported and also newly implicated loci. To further understand the genetic contributions of both common and rare variants to the LDL-C level, we sequenced exons of 9 genes in associated loci in 256 unrelated Sardinian individuals with either extremely low or high LDL-C levels, along with 120 HapMap samples. Among all variants identified, 71% (81/121) nonsynonymous and 56.3% (40/71) synonymous mutations have frequency below 1%. In addition, two frame shift (in APOB) and two truncation mutations (in PCSK9) were identified. Comparisons between high LDL-C and low LDL-C groups showed that rare coding variants are enriched in one of the two groups for a set of genes (APOB, LDLR, PCSK9, SORT1). To increase power of detecting associations of variants in coding regions with LDL-C levels, we are using imputation to extend our findings to additional genotyped individuals in our 6148 sample Sardinian cohort. Equipped with this larger amount of data after imputation, fine mapping and evaluation of potential functional variants should be achieved with greater power.

3
An Integration of Genome-Wide Association Study and Gene Expression Profiling to Prioritize the Discovery of Novel Susceptibility Loci for Osteoporosis Related Traits


Yi-Hsiang Hsu (1), M. Carola Zillikens (2), Scott G. Wilson (3), Charles R. Farber (4), Serkalem Demissie (5), Estelle N. Bianchi (6), Liming Liang (7), J. Brent Richards (8), Karol Estrada (2), Yanhua Zhou (5), Nicole Soranzo (9), Atila van Nas (10), Miriam F. Moffatt (11), Guangju Zhai (12), Albert Hofman (13), Joyce B. van Meurs (2), Roger I. Price (3), L. Adrienne Cupples (5), Aldons J. Lusis (14), Eric E. Schadt (15), Serge Ferrari (6), André G. Uitterlinden (2), Fernando Rivadeneira (2), Tim D. Spector (12), David Karasik (1), Douglas P. Kiel (1)

(1) Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, 02131 MA, USA.
(2) Department of Internal Medicine, Erasmus MC, Rotterdam, 3015GE, The Netherlands
(3) Departments of Endocrinology & Diabetes and Medical Technology & Physics, Sir Charles Gairdner Hospital, Western Australia.
(4) Dep. Medicine, Cardiovascular Medicine and Center for Public Health Genomics, University of Virginia, Virginia, USA
(5) Department of Biostatistics, School of Public Health, Boston University, Boston, MA, 02118 USA.
(6) Service of Bone Diseases, Department of Rehabilitation and Geriatrics, University Geneva Hospital, Switzerland
(7) Center for Statistical Genetics, Department of Biostatistics, School of public Health, Ann Arbor, Michigan 48109-2029, USA.
(8) Departments of Medicine and Human Genetics, Lady Davis Institute, McGill University, Montreal, QC, Canada H3T 1E2.
(9) Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK.
(10) Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA.
(11) National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK.
(12) Department of Twin Research and Genetic Epidemiology, King's College London, London, UK Se1 7EH.
(13) Department of Epidemiology, Erasmus MC, Rotterdam, 3015GE, The Netherlands.
(14) Department of Medicine, Department of Human Genetics, Department of Microbiology, Immunology, and Molecular Genetics, UCLA
(15) Rosetta Inpharmatics/Merck, Inc., Seattle, WA 98109, USA

Although genome-wide association study (GWAS) is a power tool, the identification of disease-susceptibility genes by means of statistical significance provides limited information to predict their biological processes involved in diseases' pathophysiology. To overcome this challenge, we integrated expression profiling experiments with GWAS. We first performed GWAS for osteoporosis-related traits (bone mineral density and hip geometry indices) in the Framingham Osteoporosis Study and then replicated top findings in two additional studies. Meta-analyses were performed in 7634 women and 3657 men. To identify potential biological links to bone metabolism and prioritize candidate genes, we (1) analyzed the expression QTL (eSNP) in several human tissues; (2) conducted expression profiling in cellular models for parathyroid hormone stimulated osteoclastogenesis and for osteoblastogenesis of embryonic stem cells; (3) performed likelihood-based causality model selection (LCMS) in a different mice experiment to identify genes causally related to bone phenotypes; and (4) constructed functional interaction networks based on biological information from available bioinformatics databases. We have discovered four novel loci and highlighted the efficiency of subsequent functional characterization using these experiments to prioritize candidate genes and generate new hypotheses for further investigation.
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