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 (σ
g2)
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 σ
g2 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 σ
g2s 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.
