ASHG 2012 abstracts (2): physical traits
Chromosome X revisited - Variants in Xq21.1 associate with adult stature in a meta-analysis of 14,700 Finns. T. Tukiainen1, J. Kettunen1,2, A.-P. Sarin1,2, J. G. Eriksson3,4,5,6,7, A. Jula8, V. Salomaa3, O. T. Raitakari9,10, M.-R. Järvelin11,12, S. Ripatti1,2,13 1) Institute for Molecular Medicine Finland FIMM, University of Helsinki, Finland; 2) Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland; 3) Department of Chronic Disease Prevention, National Institute for Health and Welfare, Finland; 4) Department of General Practice and Primary Healthcare, University of Helsinki, Finland; 5) Unit of General Practice, Helsinki University Central Hospital, Finland; 6) Folkhälsan Research Center, Helsinki, Finland; 7) Vaasa Central Hospital, Vaasa, Finland; 8) Population Studies Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Turku, Finland; 9) Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland; 10) Department of Clinical Physiology, Turku University Hospital, Finland; 11) Department of Epidemiology and Biostatistics, Faculty of Medicine, Imperial College London, United Kingdom; 12) Institute of Health Sciences, Biocenter Oulu, University of Oulu, Finland; 13) Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
Genome-wide association studies (GWAS) provide a powerful tool to assess genetic associations between common marker alleles and complex traits in large numbers of individuals. Typically these studies have focused on testing the markers in the 22 autosomal chromosomes while the X-chromosome has been omitted from the analyses. The chromosome X, however, constitutes approximately 5% of genomic DNA encoding for more than 1000 genes, and thus also likely contains genetic variation contributing to common traits and disorders.
We set to test associations between 560,000 genotyped and imputed SNP markers and eight anthropometric (BMI, stature, WHR) and biochemical (CRP, HDL, LDL, TC, TG) traits in 14,710 individuals (7468 males, 7242 females) from five Finnish cohorts.
A region in chromosome Xq21.1 was associated with adult stature (meta-analysis p-value = 3.32×10-10). The lead SNP in the locus explained up to 0.55% of the variance in height in 31-year-old women corresponding to 1.09 cm difference between minor and major allele homozygotes. The associated lead variant (MAF = 0.31) is located upstream of ITM2A, a gene encoding for a membrane protein that plays a role in osteo- and chondrogenic differentiation. As this is among the first studies using the X chromosome reference haplotypes from the 1000 Genomes project, we are currently validating the imputation with genotyping methods.
The findings pinpoint the value of including chromosome X in the GWAS of complex traits to identify further relevant gene regions that also account for some of the missing heritability. The study illustrates that the 1000 Genomes reference haplotypes allow for high-resolution investigations of the genetic variants in chromosome X even with a relative modest sample sizes compared to the current-day GWAS meta-analyses. Our finding demonstrates that the same analysis strategy is also likely to be useful in the meta-analyses of the large consortia with complex traits.
Dissection of polygenic variation for human height into individual variants, specific loci and biological pathways from a GWAS meta-analysis of 250,000 individuals. T. Esko1, A. R. Wood2, S. Vedantam3,4,5, J. Yang6, S. Gustaffsson7, S. I. Berndt8, J. Karjalainen9, H. M. Kang10, A. E. Locke11, A. Scherag12, D. C. Croteau-Chonka13, F. Day14, R. Magi1, T. Ferreira15, J. Randall15, T. W. Winkler16, T. Fall7, Z. Kutalik17, T. Workalemahu18, G. Abecasis10, M. E. Goddard6, L. Franke9, R. J. F. Loos14,19, M. N. Weedon2, E. Ingelsson7, P. M. Visscher6, J. N. Hirschhorn3,4,5, T. M. Frayling2, GIANT Consortium 1) Estonian Genome Center, University of Tartu, Tartu, Tartumaa, Estonia; 2) Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK; 3) Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital, Boston, Massachusetts 02115, USA; 4) Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA; 5) Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA; 6) University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia; 7) Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden; 8) Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA; 9) Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; 10) Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA; 11) Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; 12) Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Germany; 13) Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA; 14) MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK; 15) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK; 16) Public Health and Gender Studies, Institute of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany; 17) Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland; 18) Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA; 19) Mount Sinai School of Medicine, New York, NY, USA.
Adult human height is a highly heritable polygenic trait. Previous genome-wide analyses have identified 180 independent loci explaining an estimated 1/8th of the heritable component (80%). Our aims were a) to increase the understanding of the role of common genetic variation in a model quantitative trait, and b) to help understand the biology of normal growth and development. Within the GIANT consortium, we performed a GWAS of ~250,000 individuals of European ancestry. We tested for the presence of multiple signals at individual loci using an approximate conditional and joint multiple SNP regression analysis. We identified 698 independent variants associated with height at p<5x10-8, which fell in 424 loci (+/-500kb from lead SNP) and altogether explained 1/4 of the inherited component in adult height. Half of the loci contained multiple signals of association. By applying a novel pathway analysis approach that uses co-expression data from 80,000 samples to predict the biological function of poorly annotated genes, we observed enrichment for novel and biologically relevant pathways in these loci. For example, for more than 10 % of the loci a gene was found in their vicinity with a predicted "regulation of ossification" function (GO:0030278, WMW P < 10-34), including newly identified genes such as PRRX1and SNAI1. Other genes and pathways newly highlighted by pathway analysis include WNT (WNT2B, WNT4, WNT7A) and FGF (FGF2, FGF18) signaling and osteoglycin. We also noted an excess of signals across the entire genome, with the median test statistic twice that expected under null (lambda = 2.0). This result is consistent with either a very deep polygenic component to height that covers most of the genome or population stratification contributing partly to the results, or a combination of the two. Encouragingly, initial results from family based analyses and mixed models that correct for distant relatedness across samples indicate that a large proportion of the discovered signals are genuine height-associated variants rather than confounded by stratification. In conclusion, data from 250,000 individuals show that adult height is highly polygenic with, typically, multiple signals of association per locus now accounting for ¼ of heritability. Furthermore, these results suggest that increasing GWAS sample sizes can continue to uncover substantial new insights into the aetiological pathways involved in common human phenotypes.
Over 250 novel associations with human morphological traits. N. Eriksson, C. B. Do, J. Y. Tung, A. K. Kiefer, D. A. Hinds, J. L. Mountain, U. Francke 23andMe, Mountain View, CA.
External morphological features are by definition visible and are typically easy to measure. They also generally happen to be highly heritable. As such, they have played a fundamental role in the development of the field of genetics. As morphological traits have frequently been the target of natural selection, their genetics may also provide clues into our evolutionary history. Many rare diseases include dysmorphologic features among their symptoms. However, aside from height and BMI, currently little is known about the genetics of common variation in human morphology. Here we present a series of genome-wide association studies across 18 self-reported morphological traits in a total of over 55,000 people of European ancestry from the customer base of 23andMe. The phenotypes studied include hair traits (baldness, unibrow, hair curl, upper and lower back hair, widow’s peak), as well as many soft tissue and skeletal traits (chin dimple, nose shape, dimples, earlobe attachment, nose-wiggling ability, the presence of a gap between the top incisors, joint hypermobility, finger and toe relative lengths, arch height, foot direction, height-normalized shoe size). Across the 18 phenotypes, we find a total of 281 genome-wide significant associations (including 53 for unibrow and 29 each for hair curl and chin dimple). Almost all of these associations are novel; we believe this is the largest set of novel associations ever described in a single report. Many of these SNPs show pleiotropic effects, e.g., a SNP near GDF5 is associated with hypermobility, arch height, relative toe length, shoe size, and foot direction; another near AUTS is associated with both back hair and baldness. Nearby genes are significantly enriched to be transcription factors (p<1e-14) and to be involved in rare disorders that cause cleft palate, ear, limb, or skull abnormalities (p<1e-7). A SNP near ZEB2 is associated with both widow’s peak and chin dimple; mutations in ZEB2 cause Mowat-Wilson syndrome, which includes distinctive facial features such as a pronounced chin. Morphology-associated SNPs are also enriched within regions that have been identified as undergoing selection since the divergence from Neanderthals (18 associations in 11 regions, p = 4e-5). The abundance of these SNPs, which include the ZEB2 and GDF5 associations above, suggest that physical traits may have played a significant role in driving the natural selection processes that gave rise to modern humans.
Genome-wide association study of Tanner puberty staging in males and females. D. Cousminer1, N. Timpson2, D. Berry3, W. Ang4, I. Ntalla5, M. Groen-Blokhuis6, M. Guxens7, M. Kähönen8, J. Viikari9, T. Lehtimäki10, K. Panoutsopoulou11, D. Boomsma6, E. Zeggini11, G. Dedoussis5, C. Pennell4, O. Raitakari12, E. Hyppönen3, G. Davey Smith2, M. McCarthy13, E. Widén1, The Early Growth Genetics (EGG) Consortium 1) Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland; 2) The Medical Research Council (MRC) Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK; 3) Centre for Paediatric Epidemiology and Biostatistics, MRC Centre for Epidemiology of Child Health, UCL Institute of Child Health, London, UK; 4) University of Western Australia, Perth, Western Australia, Australia; 5) Harokopio University of Athens, Department of Dietetics and Nutrition, Athens; 6) Netherlands Twin Register, Department of Biological Psychology, VU University, Amsterdam, The Netherlands; 7) Center for Research in Environmental Epidemiology (CREAL), Barcelona, Catalonia, Spain; 8) Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Finland; 9) Department of Medicine, University of Turku, Finland; 10) Department of Clinical Chemistry, Fimlab Laboratories, University Hospital and University of Tampere, Finland; 11) Wellcome Trust Sanger Institute, Hinxton, UK; 12) Department of Clinical Physiology and Nuclear Medicine, University of Turku, Finland; 13) Wellcome Trust Centre for Human Genetics, Roosevelt Drive, University of Oxford, Oxford, UK.
Puberty is a complex trait with large variation in timing and tempo in the population, and extremes in pubertal timing are a common cause for referral to pediatric specialists. Recently, large genome-wide association studies (GWAS) have revealed 42 common variant loci associated with age at menarche (AAM), and some implicated genes are known from severe single-gene disorders. However, little remains known of the genetic architecture underlying normal variation in the onset of puberty, especially in males.
Tanner staging, a 5-stage scale assessing female breast and male genital development, is a commonly used measure of pubertal development. While AAM is a late event during puberty, Tanner staging during mid-puberty may correlate more closely with the central activation of puberty. With Tanner scale data at the comparable ages of 11-12 yrs in girls and 13-14 yrs in boys, we performed GWAS meta-analyses across 10 cohorts with up to 9,900 samples. The combined male and female analysis showed evidence for association near LIN28B (P=1.95x10-8), previously implicated in AAM and height growth in both sexes. Our data confirms that this locus is also important for male pubertal development and may be part of the pubertal initiation program upstream of sex-specific mechanisms. A novel signal (P= 4.95 x 10-8) with a consistent direction of effect across contributing datasets locates on chromosome 1 at an intronic transcription factor binding-site cluster within the gene CAMTA1. Furthermore, the primary analyses revealed suggestive evidence for male-specific loci, e.g. nearby MKL2 (P=4.68 x 10-7), which may be confirmed by follow-up genotyping. MAGENTA gene-set enrichment analysis of the combined-gender GWAS results showed enrichment of genes involved in expected pathways given the known biology underlying activation of puberty via the HPG axis. Novel genes near suggestively associated loci may also pinpoint novel regulatory mechanisms; CAMTA1 is a calmodulin-binding transcriptional activator, while MKL2 is also a transcriptional activator involved in cell differentiation and development. These results suggest the presence of multiple real signals beneath the genome-wide significant threshold, and further exploration of enriched pathways may reveal new insights into the biology of pubertal development.
Heritability estimation of height from common genetic variants in a large sample of African Americans. F. Chen1, G. K. Chen1, R. C. Millikan2, E. M. John3,4, C. B. Ambrosone5, L. Berstein6, W. Zheng7, J. J. Hu8, R. G. Ziegler9, S. L. Deming7, E. V. Bandera10, W. J. Blot7, 11, S. S. Strom12, S. I. Berndt9, R. A. Kittles13, B. A. Rybicki14, W. Issacs15, S. A. Ingles1, J. L. Stanford16, W. R. Diver17, J. S. Witte18, L. B. Signorello7,11, S. J. Chanock9, L. Le Marchand19, L. N. Kolonel19, B. E. Henderson1, C. A. Haiman1, D. O. Stram1 1) Preventive Medicine, University of Southern California, Los Angeles, CA; 2) Epidemiology, Gillings School of Global Public Health, and Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC; 3) Northern California Cancer Center, Fremont, CA; 4) School of Medicine, Stanford University, and Stanford Cancer Center, Stanford, CA; 5) Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY; 6) Cancer Etiology, Population Science, Beckman Research Institute, City of Hope, CA; 7) Epidemiology, Vanderbilt Epidemiology Center and Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN; 8) Sylvester Comprehensive Cancer Center, Department of Epidemiology and Public Health, University of Miami, Miami, FL; 9) Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bathesda, MD; 10) The Cancer Institute of New Jersey, New Brunswick, NJ; 11) International Epidemiology Institute, Rockville, MD; 12) Epidemiology, The University of Texas M.D. Anderson Cancer Center, Huston, TX; 13) Medicine, University of Illinois at Chicago, Chicago, IL; 14) Biostatistics and Research Epidemiology, Henry Ford Hospital, Detroit, MI; 15) James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institutions, Baltimore, MD; 16) Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA; 17) Epidemiology Research, American Cancer Society, Atlanta, GA; 18) Institute of Human Genetics, Dept of Epidemiology and Biostatistics, University of California, San Francisco, CA; 19) Epidemiology, Cancer Research Center, University of Hawaii, Honolulu, HI.
Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. Each of these common variants has a very modest effect, and only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In this large study of African-American men and women, we genotyped and analyzed 975,519 autosomal SNPs across the entire genome using a variance components approach, and found that 46.4% of phenotypic variation can be explained by these SNPs in a sample of 9,779 evidently unrelated individuals. We noted that in two samples of close relatives defined by probability of identical-by-descent (IBD) alleles sharing (Pr (IBD=1)>=0.3 and Pr (IBD=1)>=0.4), the proportion of phenotypic variation explained by the same set of SNPs increased to 75.5% (se: 14.8%) and 70.3% (26.9%), respectively. We conclude that the additive component of genetic variation for height may have been overestimated in earlier studies (~80%) and argue that this proportion also includes variation from epistatic effects. Using simulation, we showed that by using common SNPs that are only weakly correlated with causal SNPs, the model could explain a large proportion of heritability. We therefore argue that the heritability estimate from the variance components approach is not necessarily the variation explained by a given set of SNPs, but also possibly reflects distant relatedness between nominally unrelated participants. Finally, we explored the performance of the variance components approach and concluded that the approach fails when a large number of independent variables are included in the model as the structure of the two components becomes similar. Thus some degree of population stratification seems to be required in order for the method to perform well for very large numbers of SNPs; however when modest stratification is present there is a risk of miss-attribution of effects of unmeasured (and untagged) variants to measured variants.
A multi-SNP locus-association method reveals a substantial fraction of the missing heritability. Z. Kutalik1,2, G. Ehret3,4, D. Lamparter1,2, C. Hoggart5, J. Whittaker6, J. Beckmann1,7, GIANT consortium 1) Med Gen, Univ Lausanne, Lausanne, Switzerland; 2) Swiss Institute of Bioinformatics, Switzerland; 3) Division of Cardiology, Geneva University Hospital, Geneva, Switzerland; 4) McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America; 5) Department of Pediatrics, Imperial College London, London, United Kingdom; 6) Quantitative Sciences, GlaxoSmithKline, Stevenage, UK; 7) Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzer- land.
There are many known examples of multiple (semi-)independent associations at individual loci, which may arise either because of true allelic heterogeneity or imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex trait GWAS. We describe a multi-SNP association method that estimates the effect of loci harbouring multiple association signals using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (GIANT), we show that for height, BMI, and waist-hip-ratio (WHR) 10%, 9%, and 8% of additional phenotypic variance can be explained respectively on top of the previously reported 10%, 1.5%, 1%. The method also permitted to substantially increase the number of loci that replicate in a discovery-validation design. Specifically, we identified in total 263 loci at which the multi-SNP explains significantly more variance than the best individual SNP at the locus. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs not in LD with the lead SNP suggesting a major contribution of allelic heterogeneity to the missing heritability.
Hundreds of loci contribute to body fat distribution and central adiposity. A. E. Locke1, D. Shungin2,3,4, T. Ferreira5, T. W. Winkler6, D. C. Croteau-Chonka7, R. Magi5,8, T. Workalemahu9, K. Fischer8, J. Wu10, R. J. Strawbridge11, A. Justice12, F. Day13, N. Heard-Costa14,15, C. S. Fox14, M. C. Zillikens16, E. K. Speliotes17,18, H. Völzke19, L. Qi9, I. Barroso20,21, I. M. Heid6, K. E. North12, P. W. Franks2,4,9, M. I. McCarthy22, J. N. Hirschhorn23, L. A. Cupples10,14, E. Ingelsson24, A. P. Morris5, R. J. F. Loos13,25, C. M. Lindgren5, K. L. Mohlke7, Genetic Investigation of ANthropometric Traits (GIANT) Consortium 1) Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI; 2) Genetic and Molecular Epidemiology Group, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; 3) Department of Odontology, Umeå University, Umeå, Sweden; 4) Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden; 5) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; 6) Regensburg University Medical Center, Department of Epidemiology and Preventive Medicine, Regensburg, Germany; 7) Department of Genetics, University of North Carolina, Chapel Hill, NC; 8) Estonian Genome Center, University of Tartu, Estonia; 9) Department of Nutrition, Harvard School of Public Health, Boston, MA; 10) Department of Biostatistics, School of Public Health, Boston University, Boston, MA; 11) Cardiovasvular Genetics and Genomics Group, Karolinska Institutet, Stockholm Sweden; 12) Department of Epidemiology and Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC; 13) MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK; 14) National Heart, Lung, and Blood Institute, Framingham, MA; 15) Department of Neurology, Boston University School of Medicine, Boston, MA; 16) Department of Internal Medicine, Erasmus MC Rotterdam, the Netherlands; 17) Department of Internal Medicine, Division of Gastroenterology, and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI; 18) Broad Institute, Cambridge, MA; 19) Institute for Community Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany; 20) Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK; 21) University of Cambridge Metabolic Research Labs, Institute of Metabolic Sciences,; 22) University of Oxford, Oxford, UK; 23) Department of Genetics, Harvard Medical School, Boston, MA; 24) Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 25) Charles R. Bronfman Institute of Personalized Medicine, Child Health and Development Institute, Department of Preventive Medicine, Mount Sinai School of Medicine, New York, NY.
Central adiposity and body fat distribution are risk factors for type 2 diabetes and cardiovascular disease and can be measured using waist circumference (WC), hip circumference (HIP), and waist-to-hip ratio (WHR). Adjusting for body mass index (BMI) differentiates effects from those for overall obesity. We performed fixed effects inverse variance meta-analysis for these traits with 72,919 individuals from 30 studies in a prior genome-wide association study (GWAS) meta-analysis, 71,139 individuals from 24 additional GWAS, and 67,163 individuals from 28 studies genotyped on Metabochip by the GIANT consortium. We identified 48 independent genome-wide significant (p<5x10-8) associations for WHR adjusted for BMI, including all 14 previously published signals. Twelve signals are located near genes for transcription factors, including developmental homeobox-containing proteins. Among them, two are in the HOXC gene cluster near HOXC8 and miR-196a2. HOXC8 is expressed in white adipose tissue and is a regulator of brown adipogenesis, while miR-196a inhibits Hoxc8 expression. Signals are located near PPARG, encoding a transcription factor known to regulate adipocyte differentiation, and near HMGA1 and CEPBA, encoding transcription factors that act downstream of insulin receptor and leptin signaling, respectively. Further novel signals are located near genes involved in angiogenesis (PLXND1, VEGFB, and MEIS1). Among the other five traits, we estimate that a significant proportion of the genetic effects for WC and HIP adjusted for BMI are correlated with height (0.59, p<5x10-79 and 0.83, p<2x10-40, respectively). Despite this strong correlation, an appreciable proportion of the genetic contributions to these traits will be independent of height. Association meta-analysis for the five additional traits identified an additional 148 independent signals (p<5x10-8), 32 of which have not been reported previously for an anthropometric trait. These novel signals suggest regulation of adipose gene expression (KLF14) and transcriptional control of cell patterning and differentiation in early development (HLX, SOX11, ZNF423, and HMGXB4) affect fat distribution. Meta-analyses for WHR, WC, and HIP, with and without adjustment for BMI, identified a total of 196 independent loci, 66 novel, affecting fat deposition and body shape, and implicating genes involved in development, adipose gene expression and tissue differentiation, response to metabolic signaling, and angiogenesis.
Prediction of human height with large panels of SNPs - insights into genetic architecture. Y. C. Klimentidis1, A. I. Vazquez1, G. de los Campos2 1) Energetics, University of Alabama at Birmingham, Birmingham, AL; 2) Biostatistics, University of Alabama at Birmingham, Birmingham, AL.
Prediction of complex traits from genetic information is an area of major clinical and scientific interest. Height is a model trait since it is highly heritable and easily measured. Substantial strides in understanding the genetic basis of height have recently been made through genome-wide association studies (GWAS), and whole-genome prediction (WGP) which fits thousands of SNPs jointly. Here, we attempt to gain insight into the genetic architecture of human height by examining how WGP accuracy is affected by the choice of single-nucleotide polymorphism (SNPs). Specifically, we compare the prediction accuracy of models using: 1) SNPs selected based on the ‘top hits’ of the GIANT consortium meta-analysis for height at different p-value thresholds, and 2) SNPs in genomic regions that surround the most significant ‘top hits’. We use the Framingham Heart Study and GENEVA datasets, imputed up to 10 million SNPs with 1000 Genomes reference data. The predictive accuracy of each model was evaluated in cross-validation. We find that prediction accuracy increases up to a certain point with the inclusion of more ‘top hits’ from the GIANT study, that including SNPs from the regions surrounding ‘top hits’ contributes minimally to prediction accuracy, and that prediction accuracy increases with the size of the training dataset. Finally, we find that prediction accuracy is greatest for individuals at the phenotypic extremes of height. Our results suggest that improvement of genomic prediction models will require the use of information from a large number of selected SNPs, and that these models may be most useful at the phenotypic extremes.
Evidence of Inbreeding Depression on Human Height. J. F. Wilson1, N. Eklund2,3, N. Pirastu4, M. Kuningas5, B. P. McEvoy6, T. Esko7, T. Corre8, G. Davies9, P. d'Adamo4, N. D. Hastie10, U. Gyllensten11, A. F. Wright10, C. M. van Duijn5, M. Dunlop10, I. Rudan1, P. Gasparini4, P. P. Pramstaller12, I. J. Deary9, D. Toniolo8, J. G. Eriksson3, A. Jula3, O. T. Raitakari13, A. Metspalu7, M. Perola2,3,7, M. R. Jarvelin14,15, A. Uitterlinden5, P. M. Visscher6, H. Campbell1, R. McQuillan1, ROHgen 1) Centre for Population Health Sciences, Univ Edinburgh, Edinburgh, United Kingdom; 2) Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland; 3) Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; 4) Institute for Maternal and Child Health, IRCCS “Burlo Garofolo”, Trieste, University of Trieste, Italy; 5) Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; 6) Queensland Institute of Medical Research, 300 Herston Road, Brisbane, Queensland 4006, Australia; 7) Estonian Genome Center, University of Tartu, Tartu, Estonia; 8) Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano, Italy; 9) Department of Psychology, The University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK; 10) MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, Scotland; 11) Department of Immunology, Genetics and Pathology, SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden; 12) Centre for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany; 13) Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; 14) Biocenter Oulu, University of Oulu, Finland; 15) Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, MRC Health Protection Agency (HPA) Centre for Environment and Health, Imperial College London, London, UK.
Stature is a classical and highly heritable complex trait, with 80-90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder. There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.
Empirical and theoretical studies on genetic variance of rare variants for complex traits using whole genome sequencing in the CHARGE Consortium. C. Zhu1, A. Morrison2, J. Reid3, C. J. O’Donnell4, B. Psaty5, L. A. Cupples4,6, R. Gibbs3, E. Boerwinkle2,3, X. Liu2 1) Department of Agronomy, Kansas State University , Manhattan, KS; 2) Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX; 3) Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX; 4) NHLBI Framingham Heart Study, Framingham, MA; 5) Cardiovascular Health Research Unit, University of Washington, Seattle, WA; 6) Department of Biostatistics, Boston University School of Public Health, Boston, MA.
As the frontier of human genetic studies have shifted from genome-wide association studies (GWAS) towards whole exome and whole genome sequencing studies, we have witnessed an explosion of new DNA variants, especially rare variants. An important but not yet answered question is the contribution of rare variants to the heritabilities of complex traits, which determine, in part, the gain in power from rare variants to discover new disease-associated genes. Here we present theoretical and empirical results on this question.
Our theoretical study was based upon the distribution of allele frequencies incorporating mutation, random genetic drift, and the possibility of purifying selection against susceptibility mutations. It shows that in most cases rare variants only contribute a small proportion to the overall genetic variance of a trait, but under certain conditions they may explain as much as 50% of additive genetic variance when both susceptible alleles are under purifying selection and the rate of mutations compensating the susceptible alleles (i.e. repair rate) is high.
In our empirical study, we estimated the proportion of additive genetic variances (σg2) of rare variants contributed to the total phenotypic variances of six complex traits (BMI, height, LDL-C, HDL-C, triglyceride and total cholesterol) using whole genome sequences (8x coverage) of 962 European Americans from the Charge-S study. The results show that the estimated σg2 of rare variants (MAF≤1%) ranged from 2% to 8% across the six traits. However, the standard errors (s.e.) of the estimated variance components from rare variants are relatively large compared to those of common variants. Using HDL-C as an example, the estimated σg2s are 0.08 (s.e. 0.10), 0.05 (s.e. 0.05) and 0.58 (s.e. 0.05) for rare, low-frequency (1%<MAF≤5%) and common (MAF>5%) variants, respectively.
Leveraging admixture analysis to resolve missing and cross-population heritability in GWAS. N. Zaitlen1, A. Gusev1, B. Pasaniuc1, G. Bhatia2, S. Pollack1, A. Tandon3, E. Stahl3, R. Do4, B. Vilhjalmsson1, E. Akylbekova5, A. Cupples6, M. Fornage7, L. Kao8, L. Lange9, S. Musani5, G. Papanicolaou10, J. Rotter11, I. Ruczinksi12, D. Siscovick13, X. Zhu14, S. McCarroll3, G. Lettre15, J. Hirschhorn16, N. Patterson4, D. Reich3, J. Wilson5, S. Kathiresan4, A. Price1, CAC. CARe Analysis Core5 1) Genetic Epidemiology, Harvard School of Public Health, Boston, MA; 2) Harvard-MIT Division of Health, Science and Technology; 3) Department of Genetics, Harvard Medical School, Boston, MA, USA; 4) Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA; 5) Jackson Heart Study, Jackson State University, Jackson, MS, USA; 6) Boston University, Boston, MA, USA; 7) Institute of Molecular Medicine and Division of Epidemiology School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX, 77030, USA; 8) Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America; 9) University of North Carolina, Chapel Hill, NC, USA; 10) National Heart, Lung, and Blood Institute (NHLBI), Division of Cardiovascular Sciences, NIH, Bethesda, MD 20892, USA; 11) Cedars-Sinai Medical Center, Medical Genetics Institute, Los Angeles, CA, USA; 12) Johns Hopkins University, Baltimore, Maryland, United States of America; 13) University of Washington, Seattle, WA, USA; 14) Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, USA; 15) Département de Médecine, Université de Montréal, C.P. 6128, succursale CentrePville, Montréal, Québec, Canada; 16) Divisions of Genetics and Endocrinology and Program in Genomics, Children’s Hospital Boston, Boston, MA, USA2.
Resolving missing heritability, the difference between phenotypic variance explained by associated SNPs and estimates of narrow-sense heritability (h2), will inform strategies for disease mapping and prediction of complex traits. Possible explanations for missing heritability include rare variants not captured by genotyping arrays, or biased estimates of h2 due to epistatic interactions [Zuk et al. 2012]. Here, we develop a novel approach to estimating h2 based on sharing of local ancestry segments between pairs of unrelated individuals in an admixed population. Unlike recent approaches for estimating the heritability explained by genotyped markers (h2g) [Yang et al. 2010], our approach captures the total h2, because local ancestry estimated from genotyping array data captures the effects of all variants—not just those on the array. Our approach uses only unrelated individuals, and is thus not susceptible to biases caused by epistatic interactions or shared environment that can confound genealogy-based estimates of h2. Theory and simulations show that the variance explained by local ancestry (h2γ) is related to h2, Fst, and genome-wide ancestry proportion (θ): h2γ = h2*2*Fst*θ*(1-θ). Thus, we can estimate h2γ and then infer h2 from h2γ. We apply our method to 5,040 African Americans from the CARe cohort and estimate the autosomal h2 for HDL cholesterol (0.39±0.11), LDL cholesterol (0.18±0.09), and height (0.55±0.13). As expected these h2 estimates were higher than estimates of h2g from the same data using standard approaches: 0.22±0.07, 0.16±0.07 and 0.31±0.07, consistent with previous estimates. The difference between h2 and h2g suggests that rare variants contribute substantial missing heritability that can be quantified using local ancestry information. Larger sample sizes will sizes will enable h2 estimates with even lower standard errors, so that the possible contribution of epistasis to previous estimates of h2 can be precisely quantified. We additionally use local ancestry to estimate the fraction of phenotypic variance shared between European and African genomes that is explained by genotyped markers, by estimating h2g in European segments, h2g in African segments, and h2g shared between European and African segments. Given that most GWAS to date have been carried out in individuals of European descent, these estimates shed light on the importance of collecting data from non-European populations for mapping disease in those populations.
Genome-wide association meta-analyses in over 210,000 individuals identify 20 sexually dimorphic genetic variants for body fat distribution. T. W. Winkler1, D. C. Croteau-Chonka2, T. Ferreira3, K. Fischer4, A. E. Locke5, R. Mägi3,4, D. Shungin6,7,8, T. Workalemahu9, J. Wu10, F. Day11, A. U. Jackson5, A. Justice12, R. Strawbridge13, H. Völzke14, L. Qi9, M. C. Zillikens15, C. S. Fox16, E. K. Speliotes17,18, I. Barroso19,20, E. Ingelsson21, J. N. Hirschhorn22, M. I. McCarthy23, P. W. Franks6,8,9, A. P. Morris3, L. A. Cupples10,24, K. E. North12, K. L. Mohlke2, R. J. F. Loos11,25, I. M. Heid1, C. M. Lindgren3, GIANT Consortium 1) Public Health and Gender Studies, Institute of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany; 2) Department of Genetics, University of North Carolina, Chapel Hill, NC; 3) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; 4) Estonian Genome Center, University of Tartu, Tartu, Estonia; 5) Department of Biostatistics, University of Michigan, Ann Arbor, MI; 6) Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden; 7) Department of Odontology, Umeå University, Umeå, Sweden; 8) Genetic and Molecular Epidemiology Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden; 9) Department of Nutrition, Harvard School of Public Health, Boston, MA; 10) Department of Biostatistics, School of Public Health, Boston University, Boston, MA; 11) MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK; 12) Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC; 13) Cardiovascular Genetics and Genomics Group, Karolinska Institute, Stockholm, Sweden; 14) Institute for Community Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany; 15) Department of Internal Medicine, Erasmus MC Rotterdam, the Netherlands; 16) National Heart, Lung, and Blood Institute, Framingham, MA; 17) Broad Institute, Cambridge, MA; 18) Department of Internal Medicine, Division of Gastroenterology, and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI; 19) University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital, Cambridge, UK; 20) Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK; 21) Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 22) Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA; 23) University of Oxford, Oxford, UK; 24) Framingham Heart Study, Framingham, MA; 25) Charles R. Bronfman Institute of Personalized Medicine, Child Health and Development Institute, Department of Preventive medicine, Mount Sinai School of Medicine, New York, NY 10029, USA.
It is well-known that body fat distribution differs between men and women, a circumstance that may be due to innate, genetic differences between sexes. Previously, we performed a large-scale meta-analysis of GWAS of waist-to-hip ratio adjusted for BMI (WHR), a measure of body fat distribution independent of overall adiposity and found that of the 14 loci established in men and women combined, seven showed a significant sex-difference. In a subsequent genome-wide analysis that was specifically tailored to detect sex-differential genetic effects for WHR, we identified two additional loci with significant sex-difference. Despite these findings, the genetic basis affecting the sexual dimorphism of WHR as well as the genetic architecture of WHR in general are still poorly understood. We therefore conducted sex-combined and sex-stratified meta-analyses comprising >210,000 individuals (>116,000 women; >94,000 men) of European ancestry from 57 GWAS studies and 28 studies genotyped on the MetaboChip within the GIANT consortium. The sex-combined analysis yielded 39 loci with genome-wide significant association (P<5x10-8), of which 11 loci showed significant sex-difference (Bonferroni-corrected P<0.05/39). Six of these loci influence WHR in women only without any effect in men (near COBLL1, LYPLAL1, PPARG, PLXND1, MACROD1, FAM13A); four loci have an effect in women and a less pronounced effect in men (near VEGFA, ADAMTS9, HOXC13, RSPO3); and one locus has only an effect in men (near GDF5). The sex-stratified analyses identified nine additional female-specific loci that had been missed in the sex-combined analysis due to the lack of effect in men (near MAP3K1, BCL2, TNFAIP8, CMIP, NKX3-1, NMU, SFXN2, HMGA1, KCNJ2). No additional loci were identified in the male-specific analysis. We confirmed all previously established sexually dimorphic variants for WHR. Of particular interest is the PPARG region that is a well-known target in type 2 diabetes treatments and shows a female-specific association with WHR. The enrichment of female-specific associations, i.e. 19 of the 20 sexually dimorphic loci, is consistent with the heritability of WHR as estimated in the Framingham Heart study; we found that WHR is more heritable in women (h2~46%) compared to men (h2~19%). Our results highlight the importance of sex-stratified analyses and can help to better understand the genetics underpinning the sex-differences of body fat distribution.