Hundreds of variants clustered in genomic loci and biological pathways affect human height

The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits

Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the "Metabochip," a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.

Inhibin B and anti-Müllerian hormone/Müllerian-inhibiting substance may contribute to the male bias in autism

The autistic spectrum disorders have a significant male bias in incidence, which is unexplained. The Sertoli cells of the immature testes secrete supra-adult levels of Müllerian-inhibiting substance/anti-Müllerian hormone (AMH) and inhibin B (InhB), with both hormones being putative regulators of brain development. We report here, that 82 boys with an autism spectrum disorder have normal levels of InhB and AMH. However, the boys' level of InhB correlated with their autism diagnostic interview-revised (ADI-R) scores for the social interaction (R=0.29, P=0.009, N=82) and communication domains (R=0.29, P=0.022, N=63), and with the number of autistic traits the boys exhibited (R=0.34 and 0.27, respectively). The strengths of the abovementioned correlates were stronger in the boys with milder autism (R=0.42 and 0.50, respectively), with AMH exhibiting a significant negative correlation to the ADI-R score in these boys (R=-0.44 and R=-0.39, respectively). Neither hormone correlated to the incidence of stereotyped and repetitive behaviours. This suggests that the male bias in the autistic spectrum has multiple determinants, which modulate the effects of an otherwise non-dimorphic pathology. Furthermore, AMH and InhB have opposing effects on the SMAD1/5/8 pathway, and opposing correlates to autistic traits, implicating the SMAD pathways as a putative point of molecular convergence for the autistic spectrum.

Monocarboxylate transporter 10 functions as a thyroid hormone transporter in chondrocytes

Thyroid hormone is essential for normal proliferation and differentiation of chondrocytes. Thus, untreated congenital hypothyroidism is marked by severe short stature. The monocarboxylate transporter 8 (MCT8) is a highly specific transporter for thyroid hormone. The hallmarks of Allan-Herndon-Dudley syndrome, caused by MCT8 mutations, are severe psychomotor retardation and elevated T(3) levels. However, growth is mostly normal. We therefore hypothesized that growth plate chondrocytes use transporters other than MCT8 for thyroid hormone uptake. Extensive analysis of thyroid hormone transporter mRNA expression in mouse chondrogenic ATDC5 cells revealed that monocarboxylate transporter 10 (Mct10) was most abundantly expressed among the transporters known to be highly specific for thyroid hormone, namely Mct8, Mct10, and organic anion transporter 1c1. Expression levels of Mct10 mRNA diminished with chondrocyte differentiation in these cells. Accordingly, Mct10 mRNA was expressed most abundantly in the growth plate resting zone chondrocytes in vivo. Small interfering RNA-mediated knockdown of Mct10 mRNA in ATDC5 cells decreased [(125)I]T(3) uptake up to 44% compared with negative control (P < 0.05). Moreover, silencing Mct10 mRNA expression abolished the known effects of T(3), i.e. suppression of proliferation and enhancement of differentiation, in ATDC5 cells. These results suggest that Mct10 functions as a thyroid hormone transporter in chondrocytes and can explain at least in part why Allan-Herndon-Dudley syndrome patients do not exhibit significant growth impairment.

Study of the leptin levels and its gene polymorphisms in patients with idiopathic short stature and growth hormone deficiency

Leptin levels may regulate fat metabolism, skeletal growth, and puberty. Leptin gene variants affect risk of obesity, cancer, but their effect on onset of growth hormone deficiency (GHD) and idiopathic short stature (ISS) is unknown. We tested the hypothesis that the phenotype of GHD and ISS may be associated with polymorphism in the leptin gene. The prevalence of a single nucleotide polymorphism (SNP) in the leptin gene (LEP) promoter at -2548 and the leptin and insulin growth factor-1 (IGF-1) concentrations in GHD and ISS were compared to those of healthy controls. IGF-1 and leptin concentrations were significantly lower in both the GHD and ISS groups than in the control group. The ISS and GHD groups had a significantly different distribution of SNP alleles at the LEP -2548 (P = 0.010). Individuals with LEP -2548A/G or G/G genotype in ISS group (47.5%) showed a significantly lower weight and body mass index (BMI) (but not leptin levels) than individuals carrying the A/A genotype (52.5%). LEP -2548A/A in GHD patients (65.8%) was associated with lower weight, BMI, leptin concentrations than those of individuals carrying the A/G or G/G genotype (34.2%). These data suggest that the LEP -2548A polymorphism may associate with the weight and BMI of the children with ISS and GHD.

Mapping genetic variants associated with beta-adrenergic responses in inbred mice

β-blockers and β-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective β₁-blocker, Atenolol (ate), and a β-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the β-adrenergic system, and one locus for the responsiveness of QTc (p<10⁻⁸). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10⁻⁶). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to β-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied.

Evolutionary history and adaptation from high-coverage whole-genome sequences of diverse African hunter-gatherers

To reconstruct modern human evolutionary history and identify loci that have shaped hunter-gatherer adaptation, we sequenced the whole genomes of five individuals in each of three different hunter-gatherer populations at > 60× coverage: Pygmies from Cameroon and Khoesan-speaking Hadza and Sandawe from Tanzania. We identify 13.4 million variants, substantially increasing the set of known human variation. We found evidence of archaic introgression in all three populations, and the distribution of time to most recent common ancestors from these regions is similar to that observed for introgressed regions in Europeans. Additionally, we identify numerous loci that harbor signatures of local adaptation, including genes involved in immunity, metabolism, olfactory and taste perception, reproduction, and wound healing. Within the Pygmy population, we identify multiple highly differentiated loci that play a role in growth and anterior pituitary function and are associated with height.

Heritability in the genome-wide association era

Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the "missing heritability problem". In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.

Joint genotype calling with array and sequence data

Analysis of rare variants is currently a major focus of genetic studies of human disease. Single-nucleotide polymorphism (SNP) genotypes can be assayed using microarray genotyping or by sequencing, but neither technology produces perfect genotype calls, especially at rare SNPs. Studies that collect both types of data are becoming increasingly common, so it may be possible to combine data types to increase accuracy. We present a method, called Chiamante, which calls genotypes on individuals with either array data, sequence data, or both. The model adapts to data quality and can estimate when either the array or the sequence data should be ignored when calling the genotypes at each SNP. As a special case, our method will call genotypes from only array data and outperforms existing methods in this scenario. We have applied our method to array and sequence data from Phase I of the 1000 Genomes Project and show that it provides improved performance, especially at rare SNPs. This method provides a foundation for future efforts to fuse genetic data from different sources, for example, when combining data from exome sequencing and exome microarrays.

Evidence of inbreeding depression on human height

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 (χ² = 83.89, df = 1; p = 5.2×10⁻²⁰). 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.

Studies investigating the extent to which genetics influences human characteristics such as height have concentrated mainly on common variants of genes, where having one or two copies of a given variant influences the trait or risk of disease. This study explores whether a different type of genetic variant might also be important. We investigate the role of recessive genetic variants, where two identical copies of a variant are required to have an effect. By measuring genome-wide homozygosity—the phenomenon of inheriting two identical copies at a given point of the genome—in 35,000 individuals from 21 European populations, and by comparing this to individual height, we found that the more homozygous the genome, the shorter the individual. The offspring of first cousins (who have increased homozygosity) were predicted to be up to 3 cm shorter on average than the offspring of unrelated parents. Height is influenced by the combined effect of many recessive variants dispersed across the genome. This may also be true for other human characteristics and diseases, opening up a new way to understand how genetic variation influences our health.

Presence of multiple independent effects in risk loci of common complex human diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.

Allelic heterogeneity and trade-off shape natural variation for response to soil micronutrient

As sessile organisms, plants have to cope with diverse environmental constraints that may vary through time and space, eventually leading to changes in the phenotype of populations through fixation of adaptive genetic variation. To fully comprehend the mechanisms of evolution and make sense of the extensive genotypic diversity currently revealed by new sequencing technologies, we are challenged with identifying the molecular basis of such adaptive variation. Here, we have identified a new variant of a molybdenum (Mo) transporter, MOT1, which is causal for fitness changes under artificial conditions of both Mo-deficiency and Mo-toxicity and in which allelic variation among West-Asian populations is strictly correlated with the concentration of available Mo in native soils. In addition, this association is accompanied at different scales with patterns of polymorphisms that are not consistent with neutral evolution and show signs of diversifying selection. Resolving such a case of allelic heterogeneity helps explain species-wide phenotypic variation for Mo homeostasis and potentially reveals trade-off effects, a finding still rarely linked to fitness.

Plants are studied for their ability to adapt to their environment and especially to the physical constraints to which they are subjected. It is expected that they evolve in promoting genetic variants favorable under their native conditions, which could lead to negative consequences in other conditions. One approach to study the mechanisms and dynamics of these adaptations is to discover genetic variants that control potentially adaptive traits, and to study directly these variants in wild populations to try to reveal their evolutionary trajectory. We have identified a new polymorphism in a gene coding for a transporter of molybdenum (an essential micronutrient for the plant) in Arabidopsis; we show that this variant has strong phenotypic consequences at the level of plant growth and reproductive value in specific conditions, and that it explains a lot of the species diversity for these traits. Especially, the variant is associated with a clear negative effect under molybdenum-deficient conditions (caused by soil acidity) and with a subtle positive effect under molybdenum-plethoric conditions. Interestingly, the landscape distribution of the variant is not random among Asian populations and correlates well with the availability of molybdenum in the soil at the precise location where the plants are growing in the wild.

Candidate genes for obesity-susceptibility show enriched association within a large genome-wide association study for BMI

Before the advent of genome-wide association studies (GWASs), hundreds of candidate genes for obesity-susceptibility had been identified through a variety of approaches. We examined whether those obesity candidate genes are enriched for associations with body mass index (BMI) compared with non-candidate genes by using data from a large-scale GWAS. A thorough literature search identified 547 candidate genes for obesity-susceptibility based on evidence from animal studies, Mendelian syndromes, linkage studies, genetic association studies and expression studies. Genomic regions were defined to include the genes ±10 kb of flanking sequence around candidate and non-candidate genes. We used summary statistics publicly available from the discovery stage of the genome-wide meta-analysis for BMI performed by the genetic investigation of anthropometric traits consortium in 123 564 individuals. Hypergeometric, rank tail-strength and gene-set enrichment analysis tests were used to test for the enrichment of association in candidate compared with non-candidate genes. The hypergeometric test of enrichment was not significant at the 5% P-value quantile (P = 0.35), but was nominally significant at the 25% quantile (P = 0.015). The rank tail-strength and gene-set enrichment tests were nominally significant for the full set of genes and borderline significant for the subset without SNPs at P < 10(-7). Taken together, the observed evidence for enrichment suggests that the candidate gene approach retains some value. However, the degree of enrichment is small despite the extensive number of candidate genes and the large sample size. Studies that focus on candidate genes have only slightly increased chances of detecting associations, and are likely to miss many true effects in non-candidate genes, at least for obesity-related traits.

Four loci explain 83% of size variation in the horse

Horse body size varies greatly due to intense selection within each breed. American Miniatures are less than one meter tall at the withers while Shires and Percherons can exceed two meters. The genetic basis for this variation is not known. We hypothesize that the breed population structure of the horse should simplify efforts to identify genes controlling size. In support of this, here we show with genome-wide association scans (GWAS) that genetic variation at just four loci can explain the great majority of horse size variation. Unlike humans, which are naturally reproducing and possess many genetic variants with weak effects on size, we show that horses, like other domestic mammals, carry just a small number of size loci with alleles of large effect. Furthermore, three of our horse size loci contain the LCORL, HMGA2 and ZFAT genes that have previously been found to control human height. The LCORL/NCAPG locus is also implicated in cattle growth and HMGA2 is associated with dog size. Extreme size diversification is a hallmark of domestication. Our results in the horse, complemented by the prior work in cattle and dog, serve to pinpoint those very few genes that have played major roles in the rapid evolution of size during domestication.

Genetic architectures of psychiatric disorders: the emerging picture and its implications

Psychiatric disorders are among the most intractable enigmas in medicine. In the past 5 years, there has been unprecedented progress on the genetics of many of these conditions. In this Review, we discuss the genetics of nine cardinal psychiatric disorders (namely, Alzheimer's disease, attention-deficit hyperactivity disorder, alcohol dependence, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depressive disorder, nicotine dependence and schizophrenia). Empirical approaches have yielded new hypotheses about aetiology and now provide data on the often debated genetic architectures of these conditions, which have implications for future research strategies. Further study using a balanced portfolio of methods to assess multiple forms of genetic variation is likely to yield many additional new findings.

Bayesian method to predict individual SNP genotypes from gene expression data

RNA profiling can be used to capture the expression patterns of many genes that are associated with expression quantitative trait loci (eQTLs). Employing published putative cis eQTLs, we developed a Bayesian approach to predict SNP genotypes that is based only on RNA expression data. We show that predicted genotypes can accurately and uniquely identify individuals in large populations. When inferring genotypes from an expression data set using eQTLs of the same tissue type (but from an independent cohort), we were able to resolve 99% of the identities of individuals in the cohort at P(adjusted) ≤ 1 × 10(-5). When eQTLs derived from one tissue were used to predict genotypes using expression data from a different tissue, the identities of 90% of the study subjects could be resolved at P(adjusted) ≤ 1 × 10(-5). We discuss the implications of deriving genotypic information from RNA data deposited in the public domain.

Parental origin of the X-chromosome does not influence growth hormone treatment effect in Turner syndrome

CONTEXT

The parental origin of the intact X-chromosome has been reported to affect phenotype and response to GH treatment in Turner syndrome (TS).

OBJECTIVE

Our objective was to evaluate the influence of the parental origin of the X-chromosome on body growth and GH treatment effect in TS.

DESIGN AND SETTING

We conducted a population-based cohort study of TS patients previously treated with GH.

PARTICIPANTS

Participants included patients with a nonmosaic 45,X karyotype; 556 women were identified as eligible, 233 (49%) of whom participated, together with their mothers. Data were analyzed for 180 of these patients.

MAIN OUTCOME MEASURES

We performed fluorescence in situ hybridization analysis to exclude mosaicism and microsatellite analysis of nine polymorphic markers in DNA from the patients and their mothers. The influence on growth and effect of GH were analyzed by univariate and multivariate methods.

RESULTS

The X-chromosome was of paternal origin (X(pat)) in 52 (29%) of 180 and of maternal origin (X(mat)) in 128 (71%) of 180 patients. Height gain from the start of GH treatment to adult height was similar in X(mat) and X(pat) patients (+2.1 ± 0.9 vs. +2.2 ± 0.8 TS sd score, P = 0.45). The lack of influence of parental origin of the X-chromosome was confirmed in multivariate analysis. Parental origin of the X-chromosome also had no effect on the other growth characteristics studied, including growth velocity during the first year on GH treatment. Patient height was correlated with the heights of both parents and was not influenced by the parental origin of the X-chromosome.

CONCLUSION

In this, the largest such study carried out to date, the parental origin of the X-chromosome did not alter the effect of GH treatment or affect any other features of growth in TS.

C-type Natriuretic Peptide: a novel biomarker of steroid induced bone toxicity in children with acute lymphoblastic leukemia (ALL)

Impaired bone growth and mineralization, and osteonecrosis are significant and common long-term sequelae of chemotherapy for childhood acute lymphoblastic leukemia (ALL). Here we have evaluated the relationship between linear bone growth during chemotherapy for ALL and bone derived C-type Natriuretic Peptide (CNP). CNP is known to be critical to normal endochondral bone growth in both rodents and humans, and plasma concentration of the amino terminal pro CNP (NTproCNP) is strongly correlated with concurrent height velocity in children. Plasma NTproCNP and CNP were measured by radio-immunoassay in 12 children aged 2-9 years during induction and maintenance chemotherapy for children with ALL. Height velocity was calculated from stadiometer readings at intervals of 3-12 months and related to plasma NTproCNP during each growth interval. Plasma NTproCNP was markedly suppressed in all subjects during induction chemotherapy. Brief periods of NTproCNP decline and rapid rebound during maintenance treatment coincided with the use of dexamethasone but not with other chemotherapeutics. Height velocity was markedly reduced during ALL induction but unaffected in maintenance phase, and these changes in growth were strongly correlated with plasma NTproCNP concentration. Plasma NTproCNP has potential use as a biomarker of glucocorticoid-induced bone toxicity.

Correlation and Causation in the Study of Personality

Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences. Copyright © 2012 John Wiley & Sons, Ltd.

The Promises and Pitfalls of Genoeconomics*

This article reviews existing research at the intersection of genetics and economics, presents some new findings that illustrate the state of genoeconomics research, and surveys the prospects of this emerging field. Twin studies suggest that economic outcomes and preferences, once corrected for measurement error, appear to be about as heritable as many medical conditions and personality traits. Consistent with this pattern, we present new evidence on the heritability of permanent income and wealth. Turning to genetic association studies, we survey the main ways that the direct measurement of genetic variation across individuals is likely to contribute to economics, and we outline the challenges that have slowed progress in making these contributions. The most urgent problem facing researchers in this field is that most existing efforts to find associations between genetic variation and economic behavior are based on samples that are too small to ensure adequate statistical power. This has led to many false positives in the literature. We suggest a number of possible strategies to improve and remedy this problem: (a) pooling data sets, (b) using statistical techniques that exploit the greater information content of many genes considered jointly, and (c) focusing on economically relevant traits that are most proximate to known biological mechanisms.

Germline mutations in the oncogene EZH2 cause Weaver syndrome and increased human height

The biological processes controlling human growth are diverse, complex and poorly understood. Genetic factors are important and human height has been shown to be a highly polygenic trait to which common and rare genetic variation contributes. Weaver syndrome is a human overgrowth condition characterised by tall stature, dysmorphic facial features, learning disability and variable additional features. We performed exome sequencing in four individuals with Weaver syndrome, identifying a mutation in the histone methyltransferase, EZH2, in each case. Sequencing of EZH2 in additional individuals with overgrowth identified a further 15 mutations. The EZH2 mutation spectrum in Weaver syndrome shows considerable overlap with the inactivating somatic EZH2 mutations recently reported in myeloid malignancies. Our data establish EZH2 mutations as the cause of Weaver syndrome and provide further links between histone modifications and regulation of human growth.

Detecting association of rare and common variants by testing an optimally weighted combination of variants

Next-generation sequencing technology will soon allow sequencing the whole genome of large groups of individuals, and thus will make directly testing rare variants possible. Currently, most of existing methods for rare variant association studies are essentially testing the effect of a weighted combination of variants with different weighting schemes. Performance of these methods depends on the weights being used and no optimal weights are available. By putting large weights on rare variants and small weights on common variants, these methods target at rare variants only, although increasing evidence shows that complex diseases are caused by both common and rare variants. In this paper, we analytically derive optimal weights under a certain criterion. Based on the optimal weights, we propose a Variable Weight Test for testing the effect of an Optimally Weighted combination of variants (VW-TOW). VW-TOW aims to test the effects of both rare and common variants. VW-TOW is applicable to both quantitative and qualitative traits, allows covariates, can control for population stratification, and is robust to directions of effects of causal variants. Extensive simulation studies and application to the Genetic Analysis Workshop 17 (GAW17) data show that VW-TOW is more powerful than existing ones either for testing effects of both rare and common variants or for testing effects of rare variants only.

Ultraconserved elements in the human genome: association and transmission analyses of highly constrained single-nucleotide polymorphisms

Ultraconserved elements in the human genome likely harbor important biological functions as they are dosage sensitive and are able to direct tissue-specific expression. Because they are under purifying selection, variants in these elements may have a lower frequency in the population but a higher likelihood of association with complex traits. We tested a set of highly constrained SNPs (hcSNPs) distributed genome-wide among ultraconserved and nearly ultraconserved elements for association with seven traits related to reproductive (age at natural menopause, number of children, age at first child, and age at last child) and overall [longevity, body mass index (BMI), and height] fitness. Using up to 24,047 European-American samples from the National Heart, Lung, and Blood Institute Candidate Gene Association Resource (CARe), we observed an excess of associations with BMI and height. In an independent replication panel the most strongly associated SNPs showed an 8.4-fold enrichment of associations at the nominal level, including three variants in previously identified loci and one in a locus (DENND1A) previously shown to be associated with polycystic ovary syndrome. Finally, using 1430 family trios, we showed that the transmissions from heterozygous parents to offspring of the derived alleles of rare (frequency ≤ 0.5%) hcSNPs are not biased, particularly after adjusting for the rates of genotype missingness and error in the data. The lack of transmission bias ruled out an immediately and strongly deleterious effect due to the rare derived alleles, consistent with the observation that mice homozygous for the deletion of ultraconserved elements showed no overt phenotype. Our study also illustrated the importance of carefully modeling potential technical confounders when analyzing genotype data of rare variants.

Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort

BACKGROUND

Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets.

RESULTS

Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs.

CONCLUSION

Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.

Transethnic meta-analysis of genomewide association studies

The detection of loci contributing effects to complex human traits, and their subsequent fine-mapping for the location of causal variants, remains a considerable challenge for the genetics research community. Meta-analyses of genomewide association studies, primarily ascertained from European-descent populations, have made considerable advances in our understanding of complex trait genetics, although much of their heritability is still unexplained. With the increasing availability of genomewide association data from diverse populations, transethnic meta-analysis may offer an exciting opportunity to increase the power to detect novel complex trait loci and to improve the resolution of fine-mapping of causal variants by leveraging differences in local linkage disequilibrium structure between ethnic groups. However, we might also expect there to be substantial genetic heterogeneity between diverse populations, both in terms of the spectrum of causal variants and their allelic effects, which cannot easily be accommodated through traditional approaches to meta-analysis. In order to address this challenge, I propose novel transethnic meta-analysis methodology that takes account of the expected similarity in allelic effects between the most closely related populations, while allowing for heterogeneity between more diverse ethnic groups. This approach yields substantial improvements in performance, compared to fixed-effects meta-analysis, both in terms of power to detect association, and localization of the causal variant, over a range of models of heterogeneity between ethnic groups. Furthermore, when the similarity in allelic effects between populations is well captured by their relatedness, this approach has increased power and mapping resolution over random-effects meta-analysis. Genet. Epidemiol. 2011. © 2011 Wiley Periodicals, Inc.35: 809–;822, 2011.

Genome-wide association study of circulating estradiol, testosterone, and sex hormone-binding globulin in postmenopausal women

Genome-wide association studies (GWAS) have successfully identified common genetic variants that contribute to breast cancer risk. Discovering additional variants has become difficult, as power to detect variants of weaker effect with present sample sizes is limited. An alternative approach is to look for variants associated with quantitative traits that in turn affect disease risk. As exposure to high circulating estradiol and testosterone, and low sex hormone-binding globulin (SHBG) levels is implicated in breast cancer etiology, we conducted GWAS analyses of plasma estradiol, testosterone, and SHBG to identify new susceptibility alleles. Cancer Genetic Markers of Susceptibility (CGEMS) data from the Nurses’ Health Study (NHS), and Sisters in Breast Cancer Screening data were used to carry out primary meta-analyses among ∼1600 postmenopausal women who were not taking postmenopausal hormones at blood draw. We observed a genome-wide significant association between SHBG levels and rs727428 (joint β = -0.126; joint P = 2.09×10^–16), downstream of the SHBG gene. No genome-wide significant associations were observed with estradiol or testosterone levels. Among variants that were suggestively associated with estradiol (P<10^–5), several were located at the CYP19A1 gene locus. Overall results were similar in secondary meta-analyses that included ∼900 NHS current postmenopausal hormone users. No variant associated with estradiol, testosterone, or SHBG at P<10^–5 was associated with postmenopausal breast cancer risk among CGEMS participants. Our results suggest that the small magnitude of difference in hormone levels associated with common genetic variants is likely insufficient to detectably contribute to breast cancer risk.

Did a discrete event 200,000-100,000 years ago produce modern humans?

Scenarios for modern human origins are often predicated on the assumption that modern humans arose 200,000-100,000 years ago in Africa. This assumption implies that something 'special' happened at this point in time in Africa, such as the speciation that produced Homo sapiens, a severe bottleneck in human population size, or a combination of the two. The common thread is that after the divergence of the modern human and Neandertal evolutionary lineages ∼400,000 years ago, there was another discrete event near in time to the Middle-Late Pleistocene boundary that produced modern humans. Alternatively, modern human origins could have been a lengthy process that lasted from the divergence of the modern human and Neandertal evolutionary lineages to the expansion of modern humans out of Africa, and nothing out of the ordinary happened 200,000-100,000 years ago in Africa. Three pieces of biological (fossil morphology and DNA sequences) evidence are typically cited in support of discrete event models. First, living human mitochondrial DNA haplotypes coalesce ∼200,000 years ago. Second, fossil specimens that are usually classified as 'anatomically modern' seem to appear shortly afterward in the African fossil record. Third, it is argued that these anatomically modern fossils are morphologically quite different from the fossils that preceded them. Here I use theory from population and quantitative genetics to show that lengthy process models are also consistent with current biological evidence. That this class of models is a viable option has implications for how modern human origins is conceptualized.

Maps for the world of genomic medicine: the 2011 CSHL Personal Genomes meeting

The fourth Personal Genomes meeting was held at Cold Spring Harbor Laboratory, New York, from 30 September to 2 October and provided an exciting collection of science built on recent significant milestones in individual human genome sequencing, from the first personal genomes to thousands of human genomes sequenced. As ultra-high throughput sequencing platforms enable the production of more and more individual genomes, a growing number of scientists, physicians, and clinical geneticists are actively exploring the promise and the implications of these new data. Personal Genomes brought many of these pioneers together with nearly 200 scientists, physicians, ethicists, and others to discuss the progress and opportunities around the sequencing and medical interpretation of individual genome sequences.

Personalized medicine: hope or hype?

Medicine has always been personalized. For years, physicians have incorporated environmental, behavioural, and genetic factors that affect disease and drug response into patient management decisions. However, until recently, the 'genetic' data took the form of family history and self-reported race/ethnicity. As genome sequencing declines in cost, the availability of specific genomic information will no longer be limiting. Rather, our ability to parse these data and our decision whether to use it will become primary. As our understanding of genetic association with drug responses and diseases continues to improve, clinically useful genetic tests may emerge to improve upon our previous methods of assessing genetic risks. Indeed, genetic tests for monogenic disorders have already proven useful. Such changes may usher in a new era of personalized medicine. In this review, we will discuss the utility and limitations of personal genomic data in three domains: pharmacogenomics, assessment of genetic predispositions for common diseases, and identification of rare disease-causing genetic variants.

The insulin-like growth factors and growth disorders of childhood

Specific lesions of the growth hormone (GH)/insulin-like growth factor (IGF) axis have been identified in humans, each of which has distinctive auxologic and biochemical features. Measures of circulating IGF-I are useful in diagnosing growth disorders in childhood and in evaluating response to GH therapy. Recombinant human IGF-I is an effective treatment of severe primary IGF deficiency, which is typical of patients with GH receptor defects (Laron syndrome). Such treatment has been limited to a few severely affected patients. Future studies will provide new insight into IGF-I as treatment and into the nature of growth disorders that involve the IGF axis.

C-type natriuretic peptide forms in adult hyperthyroidism: correlation with thyroid hormones and markers of bone turnover

CONTEXT

Plasma C-type natriuretic peptide (CNP) forms correlate with linear growth velocity in juveniles. In hyperthyroid children, plasma CNP products fall in parallel with height velocity and thyroid hormones (TH) as euthyroidism is restored. The effect of TH on CNP forms after completion of endochondral growth is unknown.

OBJECTIVE

To determine the effect of restoring euthyroidism on plasma CNP forms and bone turnover markers (BTMs) in hyperthyroid adults.

DESIGN AND SETTING

We performed a prospective observational study in 20 adults (19 women) with acquired hyperthyroidism before and during carbimazole treatment. INTERVENTION AND MAIN OUTCOMES: Blood levels of CNP, amino-terminal propeptide of CNP (NTproCNP), TH and BTMs - bone-specific alkaline phosphatase, osteocalcin, procollagen type 1 amino-terminal propeptide and type 1 collagen C-telopeptide (CTx) - were measured before and during the first 6 months of carbimazole treatment and correlations determined.

RESULTS

Both CNP and NTproCNP were significantly correlated with TH at baseline. As in children, decreases in CNP forms were closely associated with fall in TH. Significant associations were found between CNP forms and CTx.

CONCLUSIONS

CNP production from tissues other than endochondral cartilage is responsive to TH. Strong temporal links with markers of bone resorption suggest that CNP may also participate in bone remodelling in the adult skeleton.

The role of estrogen receptor-α and its activation function-1 for growth plate closure in female mice

High estradiol levels in late puberty induce growth plate closure and thereby cessation of growth in humans. In mice, the growth plates do not fuse after sexual maturation, but old mice display reduced longitudinal bone growth and high-dose estradiol treatment induces growth plate closure. Estrogen receptor (ER)-α stimulates gene transcription via two activation functions (AFs), AF-1 and AF-2. To evaluate the role of ERα and its AF-1 for age-dependent reduction in longitudinal bone growth and growth plate closure, female mice with inactivation of ERα (ERα(-/-)) or ERαAF-1 (ERαAF-1(0)) were evaluated. Old (16- to 19-mo-old) female ERα(-/-) mice showed continued substantial longitudinal bone growth, resulting in longer bones (tibia: +8.3%, P < 0.01) associated with increased growth plate height (+18%, P < 0.05) compared with wild-type (WT) mice. In contrast, the longitudinal bone growth ceased in old ERαAF-1(0) mice (tibia: -4.9%, P < 0.01). Importantly, the proximal tibial growth plates were closed in all old ERαAF-1(0) mice while they were open in all WT mice. Growth plate closure was associated with a significantly altered balance between chondrocyte proliferation and apoptosis in the growth plate. In conclusion, old female ERα(-/-) mice display a prolonged and enhanced longitudinal bone growth associated with increased growth plate height, resembling the growth phenotype of patients with inactivating mutations in ERα or aromatase. In contrast, ERαAF-1 deletion results in a hyperactive ERα, altering the chondrocyte proliferation/apoptosis balance, leading to growth plate closure. This suggests that growth plate closure is induced by functions of ERα that do not require AF-1 and that ERαAF-1 opposes growth plate closure.

Investigating natural variation in Drosophila courtship song by the evolve and resequence approach

A primary goal of population genetics is to determine the genetic basis of natural trait variation. We could significantly advance this goal by developing comprehensive genome-wide approaches to link genotype and phenotype in model organisms. Here we combine artificial selection with population-based resequencing to investigate the genetic basis of variation in the interpulse interval (IPI) of Drosophila melanogaster courtship song. We performed divergent selection on replicate populations for only 14 generations, but had considerable power to differentiate alleles that evolved due to selection from those that evolved stochastically. We identified a large number of variants that changed frequency in response to selection for this simple behavior, and they are highly underrepresented on the X chromosome. Though our power was adequate using this experimental technique, the ability to differentiate causal variants from those affected by linked selection requires further development.

The interplay of genes and adolescent development in substance use disorders: leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption

The present study evaluated gene by development interaction in cigarettes smoked per day (CPD) in a longitudinal community-representative sample (N = 3,231) of Caucasian twins measured at ages 14, 17, 20, and 24. Biometric heritability analyses show strong heritabilities and shared environmental influences, as well as cross-age genetic and shared environmental correlations. Single nucleotide polymorphisms (SNPs) previously associated with CPD according to meta-analysis were summed to create a SNP score. At best, the SNP score accounted for 1 % of the variance in CPD. The results suggest developmental moderation with a larger significant SNP score effect on CPD at ages 20 and 24, and smaller non-significant effect at ages 14 and 17. These results are consistent with the notion that nicotine-specific genetic substance use risk is less important at younger ages, and becomes more important as individuals age into adulthood. In a complementary analysis, the same nicotine-relevant SNP score was unrelated to the frequency of alcohol use at ages 14, 17, 20, or 24. These results indicate that the SNP score is specific to nicotine in this small sample and that increased exposure to nicotine at ages 20 and 24 does not influence the extent of concurrent or later alcohol use. Increased sample sizes and replication or meta-analysis are necessary to confirm these results. The methods and results illustrate the importance and difficulty of considering developmental processes in understanding the interplay of genes and environment.

GH secretagogue receptor gene polymorphisms are associated with stature throughout childhood

CONTEXT

Ghrelin plays a major role in GH physiology and energy metabolism. Polymorphisms of its receptor (GH secretagogue receptor (GHSR)) may influence childhood growth and weight regulation.

OBJECTIVE

To correlate GHSR polymorphisms with auxological parameters throughout childhood in a healthy cohort.

STUDY DESIGN

Longitudinal retrospective population-based genetic association study.

SUBJECTS AND METHODS

GHSR genotypes were evaluated in 1362 children and compared with height/length, weight, and body mass index (BMI) data across an observation span of 10 years (0, 1, 3, 5, 8, and 10 years). Five different GHSR SNPs (rs2922126, rs2981464, rs482204, rs562416, and rs572169), minor allele frequency >0.1, were genotyped. Identification of potential genetic associations with height, weight, and BMI, using additive and dominant/recessive models, was optimized by comparing allele or genotype frequencies between the tallest and the shortest 27% of subjects for each auxological variable. Significance of association was evaluated by χ(2) test.

RESULTS

The rs482204 TT genotype, vs TC/CC, was associated with greater stature across the entire observation period (P<0.05). Similarly, the rs562416 TT genotype, vs TG/GG, correlated positively with tall stature at 3, 8, and 10 years. Other SNPs and genotypes showed no association with height at any age. No association was found between any tested SNPs and weight or BMI.

CONCLUSIONS

Longitudinal investigation between birth and 10 years in a population-based cohort revealed a significant association of the rs482204 and rs562416 GHSR polymorphisms on height, whereas no association between GHSR polymorphisms and weight or BMI was ascertainable.

A genome-wide association study of host genetic determinants of the antibody response to Anthrax Vaccine Adsorbed

Several lines of evidence have supported a host genetic contribution to vaccine response, but genome-wide assessments for specific determinants have been sparse. Here we describe a genome-wide association study (GWAS) of protective antigen-specific antibody (AbPA) responses among 726 European-Americans who received Anthrax Vaccine Adsorbed (AVA) as part of a clinical trial. After quality control, 736,996 SNPs were tested for association with the AbPA response to 3 or 4 AVA vaccinations given over a 6-month period. No SNP achieved the threshold of genome-wide significance (p=5 × 10(-8)), but suggestive associations (p<1 × 10(-5)) were observed for SNPs in or near the class II region of the major histocompatibility complex (MHC), in the promoter region of SPSB1, and adjacent to MEX3C. Multivariable regression modeling suggested that much of the association signal within the MHC corresponded to previously identified HLA DR-DQ haplotypes involving component HLA-DRB1 alleles of *15:01, *01:01, or *01:02. We estimated the proportion of additive genetic variance explained by common SNP variation for the AbPA response after the 6 month vaccination. This analysis indicated a significant, albeit imprecisely estimated, contribution of variation tagged by common polymorphisms (p=0.032). Future studies will be required to replicate these findings in European Americans and to further elucidate the host genetic factors underlying variable immune response to AVA.

The genetic architecture of economic and political preferences

Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic-based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.

Genome-wide association study identified three major QTL for carcass weight including the PLAG1-CHCHD7 QTN for stature in Japanese Black cattle

BACKGROUND

Significant quantitative trait loci (QTL) for carcass weight were previously mapped on several chromosomes in Japanese Black half-sib families. Two QTL, CW-1 and CW-2, were narrowed down to 1.1-Mb and 591-kb regions, respectively. Recent advances in genomic tools allowed us to perform a genome-wide association study (GWAS) in cattle to detect associations in a general population and estimate their effect size. Here, we performed a GWAS for carcass weight using 1156 Japanese Black steers.

RESULTS

Bonferroni-corrected genome-wide significant associations were detected in three chromosomal regions on bovine chromosomes (BTA) 6, 8, and 14. The associated single nucleotide polymorphisms (SNP) on BTA 6 were in linkage disequilibrium with the SNP encoding NCAPG Ile442Met, which was previously identified as a candidate quantitative trait nucleotide for CW-2. In contrast, the most highly associated SNP on BTA 14 was located 2.3-Mb centromeric from the previously identified CW-1 region. Linkage disequilibrium mapping led to a revision of the CW-1 region within a 0.9-Mb interval around the associated SNP, and targeted resequencing followed by association analysis highlighted the quantitative trait nucleotides for bovine stature in the PLAG1-CHCHD7 intergenic region. The association on BTA 8 was accounted for by two SNP on the BovineSNP50 BeadChip and corresponded to CW-3, which was simultaneously detected by linkage analyses using half-sib families. The allele substitution effects of CW-1, CW-2, and CW-3 were 28.4, 35.3, and 35.0 kg per allele, respectively.

CONCLUSION

The GWAS revealed the genetic architecture underlying carcass weight variation in Japanese Black cattle in which three major QTL accounted for approximately one-third of the genetic variance.

A genome-wide association study reveals loci influencing height and other conformation traits in horses

The molecular analysis of genes influencing human height has been notoriously difficult. Genome-wide association studies (GWAS) for height in humans based on tens of thousands to hundreds of thousands of samples so far revealed ∼200 loci for human height explaining only 20% of the heritability. In domestic animals isolated populations with a greatly reduced genetic heterogeneity facilitate a more efficient analysis of complex traits. We performed a genome-wide association study on 1,077 Franches-Montagnes (FM) horses using ∼40,000 SNPs. Our study revealed two QTL for height at withers on chromosomes 3 and 9. The association signal on chromosome 3 is close to the LCORL/NCAPG genes. The association signal on chromosome 9 is close to the ZFAT gene. Both loci have already been shown to influence height in humans. Interestingly, there are very large intergenic regions at the association signals. The two detected QTL together explain ∼18.2% of the heritable variation of height in horses. However, another large fraction of the variance for height in horses results from ECA 1 (11.0%), although the association analysis did not reveal significantly associated SNPs on this chromosome. The QTL region on ECA 3 associated with height at withers was also significantly associated with wither height, conformation of legs, ventral border of mandible, correctness of gaits, and expression of the head. The region on ECA 9 associated with height at withers was also associated with wither height, length of croup and length of back. In addition to these two QTL regions on ECA 3 and ECA 9 we detected another QTL on ECA 6 for correctness of gaits. Our study highlights the value of domestic animal populations for the genetic analysis of complex traits.

Dissection of complex adult traits in a mouse synthetic population

Finding the causative genetic variations that underlie complex adult traits is a significant experimental challenge. The unbiased search strategy of genome-wide association (GWAS) has been used extensively in recent human population studies. These efforts, however, typically find only a minor fraction of the genetic loci that are predicted to affect variation. As an experimental model for the analysis of adult polygenic traits, we measured a mouse population for multiple phenotypes and conducted a genome-wide search for effector loci. Complex adult phenotypes, related to body size and bone structure, were measured as component phenotypes, and each subphenotype was associated with a genomic spectrum of candidate effector loci. The strategy successfully detected several loci for the phenotypes, at genome-wide significance, using a single, modest-sized population (N = 505). The effector loci each explain 2%–10% of the measured trait variation and, taken together, the loci can account for over 25% of a trait's total population variation. A replicate population (N = 378) was used to confirm initially observed loci for one trait (femur length), and, when the two groups were merged, the combined population demonstrated increased power to detect loci. In contrast to human population studies, our mouse genome-wide searches find loci that individually explain a larger fraction of the observed variation. Also, the additive effects of our detected mouse loci more closely match the predicted genetic component of variation. The genetic loci discovered are logical candidates for components of the genetic networks having evolutionary conservation with human biology.

Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers

PURPOSE

To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers.

METHODS

From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non-patient cases to patient cases (false-positive ratio) for various risk thresholds.

RESULTS

Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively.

CONCLUSION

Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.

A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

Genome-wide association and functional studies identify the DOT1L gene to be involved in cartilage thickness and hip osteoarthritis

Hip osteoarthritis (HOA) is one of the most disabling and common joint disorders with a large genetic component that is, however, still ill-defined. To date, genome-wide association studies (GWAS) in osteoarthritis (OA) and specifically in HOA have yielded only few loci, which is partly explained by heterogeneity in the OA definition. Therefore, we here focused on radiographically measured joint-space width (JSW), a proxy for cartilage thickness and an important underlying intermediate trait for HOA. In a GWAS of 6,523 individuals on hip-JSW, we identified the G allele of rs12982744 on chromosome 19p13.3 to be associated with a 5% larger JSW (P = 4.8 × 10(-10)). The association was replicated in 4,442 individuals from three United Kingdom cohorts with an overall meta-analysis P value of 1.1 × 10(-11). The SNP was also strongly associated with a 12% reduced risk for HOA (P = 1 × 10(-4)). The SNP is located in the DOT1L gene, which is an evolutionarily conserved histone methyltransferase, recently identified as a potentially dedicated enzyme for Wnt target-gene activation in leukemia. Immunohistochemical staining of the DOT1L protein in mouse limbs supports a role for DOT1L in chondrogenic differentiation and adult articular cartilage. DOT1L is also expressed in OA articular chondrocytes. Silencing of Dot1l inhibited chondrogenesis in vitro. Dot1l knockdown reduces proteoglycan and collagen content, and mineralization during chondrogenesis. In the ATDC5 chondrogenesis model system, DOT1L interacts with TCF and Wnt signaling. These data are a further step to better understand the role of Wnt-signaling during chondrogenesis and cartilage homeostasis. DOT1L may represent a therapeutic target for OA.

A general framework for two-stage analysis of genome-wide association studies and its application to case-control studies

Two-stage analyses of genome-wide association studies have been proposed as a means to improving power for designs including family-based association and gene-environment interaction testing. In these analyses, all markers are first screened via a statistic that may not be robust to an underlying assumption, and the markers thus selected are then analyzed in a second stage with a test that is independent from the first stage and is robust to the assumption in question. We give a general formulation of two-stage designs and show how one can use this formulation both to derive existing methods and to improve upon them, opening up a range of possible further applications. We show how using simple regression models in conjunction with external data such as average trait values can improve the power of genome-wide association studies. We focus on case-control studies and show how it is possible to use allele frequencies derived from an external reference to derive a powerful two-stage analysis. An illustration involving the Wellcome Trust Case-Control Consortium data shows several genome-wide-significant associations, subsequently validated, that were not significant in the standard analysis. We give some analytic properties of the methods and discuss some underlying principles.

A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits

Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.

A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging

MOTIVATION

For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm.

RESULTS

We compare the performance of our method to the lasso and single marker analysis on simulated data and demonstrate that our approach has superior performance in terms of power and Type I error control. In addition, using the Women's Health Initiative (WHI) SNP Health Association Resource (SHARe) GWAS of African-Americans, we show that our method has power to detect additional novel associations with body height. These findings replicate by reaching a stringent cutoff of marginal association in a larger cohort.

AVAILABILITY

An R-package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is available at http://kooperberg.fhcrc.org/soft.html.