Defining the role of common variation in the genomic and biological architecture of adult human height

Estrogens and Androgens in Skeletal Physiology and Pathophysiology

Estrogens and androgens influence the growth and maintenance of the mammalian skeleton and are responsible for its sexual dimorphism. Estrogen deficiency at menopause or loss of both estrogens and androgens in elderly men contribute to the development of osteoporosis, one of the most common and impactful metabolic diseases of old age. In the last 20 years, basic and clinical research advances, genetic insights from humans and rodents, and newer imaging technologies have changed considerably the landscape of our understanding of bone biology as well as the relationship between sex steroids and the physiology and pathophysiology of bone metabolism. Together with the appreciation of the side effects of estrogen-related therapies on breast cancer and cardiovascular diseases, these advances have also drastically altered the treatment of osteoporosis. In this article, we provide a comprehensive review of the molecular and cellular mechanisms of action of estrogens and androgens on bone, their influences on skeletal homeostasis during growth and adulthood, the pathogenetic mechanisms of the adverse effects of their deficiency on the female and male skeleton, as well as the role of natural and synthetic estrogenic or androgenic compounds in the pharmacotherapy of osteoporosis. We highlight latest advances on the crosstalk between hormonal and mechanical signals, the relevance of the antioxidant properties of estrogens and androgens, the difference of their cellular targets in different bone envelopes, the role of estrogen deficiency in male osteoporosis, and the contribution of estrogen or androgen deficiency to the monomorphic effects of aging on skeletal involution.

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models.

Genetic and environmental influences on adult human height across birth cohorts from 1886 to 1994

Human height variation is determined by genetic and environmental factors, but it remains unclear whether their influences differ across birth-year cohorts. We conducted an individual-based pooled analysis of 40 twin cohorts including 143,390 complete twin pairs born 1886–1994. Although genetic variance showed a generally increasing trend across the birth-year cohorts, heritability estimates (0.69-0.84 in men and 0.53-0.78 in women) did not present any clear pattern of secular changes. Comparing geographic-cultural regions (Europe, North America and Australia, and East Asia), total height variance was greatest in North America and Australia and lowest in East Asia, but no clear pattern in the heritability estimates across the birth-year cohorts emerged. Our findings do not support the hypothesis that heritability of height is lower in populations with low living standards than in affluent populations, nor that heritability of height will increase within a population as living standards improve.

DOI:http://dx.doi.org/10.7554/eLife.20320.001

Novel genetic loci underlying human intracranial volume identified through genome-wide association

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

Genome-wide analysis identifies 12 loci influencing human reproductive behavior

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.

Genetics of Combined Pituitary Hormone Deficiency: Roadmap into the Genome Era

The genetic basis for combined pituitary hormone deficiency (CPHD) is complex, involving 30 genes in a variety of syndromic and nonsyndromic presentations. Molecular diagnosis of this disorder is valuable for predicting disease progression, avoiding unnecessary surgery, and family planning. We expect that the application of high throughput sequencing will uncover additional contributing genes and eventually become a valuable tool for molecular diagnosis. For example, in the last 3 years, six new genes have been implicated in CPHD using whole-exome sequencing. In this review, we present a historical perspective on gene discovery for CPHD and predict approaches that may facilitate future gene identification projects conducted by clinicians and basic scientists. Guidelines for systematic reporting of genetic variants and assigning causality are emerging. We apply these guidelines retrospectively to reports of the genetic basis of CPHD and summarize modes of inheritance and penetrance for each of the known genes. In recent years, there have been great improvements in databases of genetic information for diverse populations. Some issues remain that make molecular diagnosis challenging in some cases. These include the inherent genetic complexity of this disorder, technical challenges like uneven coverage, differing results from variant calling and interpretation pipelines, the number of tolerated genetic alterations, and imperfect methods for predicting pathogenicity. We discuss approaches for future research in the genetics of CPHD.

Treatment of growth hormone deficiency in children, adolescents and at the transitional age

Recombinant human growth hormone (rhGH) has been available since 1985. Before 1985 growth hormone (GH) was extracted from cadaveric pituitary glands, but this was stopped in most countries that year, following the recognition that it could transmit Creutzfeldt-Jacob disease. The primary goal of rhGH treatment in GHD patients is to normalize height during childhood and adolescence and attain an adult height within the normal range and within the target height range (genetic potential). Genome-wide association studies have been used increasingly to study the genetic influence on height. There is a wide response to rhGH therapy, likely due to compliance issues, severity of GH deficiency and patient's sensitivity to rhGH. While some pediatric endocrinologists will use a fixed dose of rhGH, most will use an auxology-based dosing approach. This will involve starting at the lower end of the dose range and then titrating upwards based on the patient's response to therapy with measurement of IGF-1 concentrations to ensure that the patient is not over treated or undertreated. Although treatment of children with GHD with rhGH has generally been safe, careful follow-up by a pediatric endocrinologist in partnership with the pediatrician or primary care physician is recommended. The aim of this paper is to review the strategies and recommendations for treatment of GHD in children and patients in the transition to adult care.

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

OBJECTIVE

Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations.

DESIGN AND METHODS

Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification.

RESULTS

The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes - SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR).

CONCLUSIONS

The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use.

Peripheral blood gene expression profiles linked to monoamine metabolite levels in cerebrospinal fluid

The blood-brain barrier separates circulating blood from the central nervous system (CNS). The scope of this barrier is not fully understood which limits our ability to relate biological measurements from peripheral to central phenotypes. For example, it is unknown to what extent gene expression levels in peripheral blood are reflective of CNS metabolism. In this study, we examine links between central monoamine metabolite levels and whole-blood gene expression to better understand the connection between peripheral systems and the CNS. To that end, we correlated the prime monoamine metabolites in cerebrospinal fluid (CSF) with whole-genome gene expression microarray data from blood (N=240 human subjects). We additionally applied gene-enrichment analysis and weighted gene co-expression network analyses (WGCNA) to identify modules of co-expressed genes in blood that may be involved with monoamine metabolite levels in CSF. Transcript levels of two genes were significantly associated with CSF serotonin metabolite levels after Bonferroni correction for multiple testing: THAP7 (P=2.8 × 10^-8, β=0.08) and DDX6 (P=2.9 × 10^-7, β=0.07). Differentially expressed genes were significantly enriched for genes expressed in the brain tissue (P=6.0 × 10^-52). WGCNA revealed significant correlations between serotonin metabolism and hub genes with known functions in serotonin metabolism, for example, HTR2A and COMT. We conclude that gene expression levels in whole blood are associated with monoamine metabolite levels in the human CSF. Our results, including the strong enrichment of brain-expressed genes, illustrate that gene expression profiles in peripheral blood can be relevant for quantitative metabolic phenotypes in the CNS.

Homocysteine-reducing B vitamins and ischemic heart disease: a separate-sample Mendelian randomization analysis

BACKGROUND/OBJECTIVES

Observationally, homocysteine is positively associated with ischemic heart disease (IHD) and unhealthy lipids; folate and vitamin B12, which reduce homocysteine, are associated with lower IHD risk and healthy lipids. Randomized controlled trials have shown no benefits of folate and vitamin B12 for IHD. To clarify the role of these potential targets of intervention in IHD we assessed how genetically determined homocysteine, folate and vitamin-B12-affected IHD and lipids.

SUBJECTS/METHODS

Separate-sample instrumental variable analysis with genetic instruments, that is, Mendelian randomization, was used to obtain unconfounded estimates (based on strongly related single-nucleotide polymorphisms (SNPs)) using CARDIoGRAMplusC4D, a large coronary artery disease/myocardial infarction (CAD/MI) case (n=64 374)-control (n=130 681) study with extensive genotyping, and the Global Lipids Genetics Consortium Results (n=196 475).

RESULTS

Homocysteine was unrelated to CAD/MI (odds ratio (OR) 1.07 per log-transformed s.d., 95% confidence interval (CI) 0.96 to 1.19) based on 14 SNPs, as was folate (OR 1.18 per s.d., 95% CI 0.80 to 1.75) based on rs153734, and vitamin B12 (OR 0.98 per log-transformed s.d., 95% CI 0.85 to 1.14) based on rs602662, rs9473555, rs526934 and rs11254363. Homocysteine and folate were not clearly associated with lipids, vitamin B12 was associated with higher inverse normal transformed low-density lipoprotein cholesterol (0.07, 95% CI 0.02 to 0.12) and triglycerides (0.05, 95% CI 0.004 to 0.09).

CONCLUSIONS

Our findings do not corroborate the observed positive association of homocysteine or negative associations of folate and vitamin B12 with CAD/MI. Vitamin B12 might be associated with an unfavorable lipid profile.

EZH1 and EZH2 promote skeletal growth by repressing inhibitors of chondrocyte proliferation and hypertrophy

Histone methyltransferases EZH1 and EZH2 catalyse the trimethylation of histone H3 at lysine 27 (H3K27), which serves as an epigenetic signal for chromatin condensation and transcriptional repression. Genome-wide associated studies have implicated EZH2 in the control of height and mutations in EZH2 cause Weaver syndrome, which includes skeletal overgrowth. Here we show that the combined loss of Ezh1 and Ezh2 in chondrocytes severely impairs skeletal growth in mice. Both of the principal processes underlying growth plate chondrogenesis, chondrocyte proliferation and hypertrophy, are compromised. The decrease in chondrocyte proliferation is due in part to derepression of cyclin-dependent kinase inhibitors Ink4a/b, while ineffective chondrocyte hypertrophy is due to the suppression of IGF signalling by the increased expression of IGF-binding proteins. Collectively, our findings reveal a critical role for H3K27 methylation in the regulation of chondrocyte proliferation and hypertrophy in the growth plate, which are the central determinants of skeletal growth.

Whole genome prediction and heritability of childhood asthma phenotypes

Introduction

While whole genome prediction (WGP) methods have recently demonstrated successes in the prediction of complex genetic diseases, they have not yet been applied to asthma and related phenotypes. Longitudinal patterns of lung function differ between asthmatics, but these phenotypes have not been assessed for heritability or predictive ability. Herein, we assess the heritability and genetic predictability of asthma‐related phenotypes.

Methods

We applied several WGP methods to a well‐phenotyped cohort of 832 children with mild‐to‐moderate asthma from CAMP. We assessed narrow‐sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre‐ and post‐bronchodilator forced expiratory volume in 1 sec (FEV₁), bronchodilator response, steroid responsiveness, and longitudinal patterns of lung function (normal growth, reduced growth, early decline, and their combinations). Prediction accuracy was evaluated using a training/testing set split of the cohort.

Results

We found that longitudinal lung function phenotypes demonstrated significant narrow‐sense heritability (reduced growth, 95%; normal growth with early decline, 55%). These same phenotypes also showed significant polygenic prediction (areas under the curve [AUCs] 56% to 62%). Including additional demographic covariates in the models increased prediction 4–8%, with reduced growth increasing from 62% to 66% AUC. We found that prediction with a genomic relatedness matrix was improved by filtering available SNPs based on chromatin evidence, and this result extended across cohorts.

Conclusions

Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP‐prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma‐related heritable traits.

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

Body height as risk factor for emphysema in COPD

Pulmonary emphysema is a phenotypic component of chronic obstructive pulmonary disease (COPD) which carries substantial morbidity and mortality. We explored the association between emphysema and body height in 726 patients with COPD using computed tomography as the reference diagnostic standard for emphysema. We applied univariate analysis to look for differences between patients with emphysema and those without, and multivariate logistic regression to identify significant predictors of the risk of emphysema. As covariates we included age, sex, body height, body mass index, pack-years of smoking, and forced expiratory volume in one second (FEV₁) as percent predicted. The overall prevalence of emphysema was 52%. Emphysemic patients were significantly taller and thinner than non-emphysemic ones, and featured significantly higher pack-years of smoking and lower FEV₁ (P < 0.001). The prevalence of emphysema rose linearly by 10-cm increase in body height (r² = 0.96). In multivariate analysis, the odds of emphysema increased by 5% (95% confidence interval, 3 to 7%) along with one-centimeter increase in body height, and remained unchanged after adjusting for all the potential confounders considered (P < 0.001). The odds of emphysema were not statistically different between males and females. In conclusion, body height is a strong, independent risk factor for emphysema in COPD.

Genetic variants linked to education predict longevity

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total ndeaths = 79,702) and ∼2.4% lower risk for fathers (total ndeaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.

The Impact of Early Life Stress on Growth and Cardiovascular Risk: A Possible Example for Autonomic Imprinting?

Introduction

Early life stress is imprinting regulatory properties with life-long consequences. We investigated heart rate variability in a group of small children with height below the third percentile, who experienced an episode of early life stress due to heart failure or intra uterine growth retardation. These children appear to develop autonomic dysfunction in later life.

Results

Compared to the healthy control group heart rate variability (HRV) is reduced on average in a group of 101 children with short stature. Low HRV correlates to groups of children born small for gestational age (SGA), children with cardiac growth failure and children with congenital syndromes, but not to those with constitutional growth delay (CGD), who had normal HRV. Reduced HRV indicated by lower RMSSD and High Frequency (HF)-Power is indicating reduced vagal activity as a sign of autonomic imbalance.

Conclusion

It is not short stature itself, but rather the underlying diseases that are the cause for reduced HRV in children with height below the third percentile. These high risk children—allocated in the groups with an adverse autonomic imprinting in utero or infancy (SGA, congenital heart disease and congenital syndromes)—have the highest risk for ‘stress diseases’ such as cardiovascular disease in later life. The incidence of attention deficit disorder is remarkably high in our group of short children.

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases.

Epigenetic and genetic components of height regulation

Adult height is a highly heritable trait. Here we identified 31.6 million sequence variants by whole-genome sequencing of 8,453 Icelanders and tested them for association with adult height by imputing them into 88,835 Icelanders. Here we discovered 13 novel height associations by testing four different models including parent-of-origin (|β|=0.4-10.6 cm). The minor alleles of three parent-of-origin signals associate with less height only when inherited from the father and are located within imprinted regions (IGF2-H19 and DLK1-MEG3). We also examined the association of these sequence variants in a set of 12,645 Icelanders with birth length measurements. Two of the novel variants, (IGF2-H19 and TET1), show significant association with both adult height and birth length, indicating a role in early growth regulation. Among the parent-of-origin signals, we observed opposing parental effects raising questions about underlying mechanisms. These findings demonstrate that common variations affect human growth by parental imprinting.

Height, height-related SNPs, and risk of non-melanoma skin cancer

Background:

Adult height has been associated with risk of several site-specific cancers, including melanoma. However, less attention has been given to non-melanoma skin cancer (NMSC).

Methods:

We prospectively examined the risk of squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) in relation to adult height in the Nurses' Health Study (NHS, n=117 863) and the Health Professionals Follow-up Study (HPFS, n=51 111). We also investigated the relationships between height-related genetic markers and risk of BCC and SCC in the genetic data sets of the NHS and HPFS (3898 BCC cases, and 8530 BCC controls; 527 SCC cases, and 8962 SCC controls).

Results:

After controlling for potential confounding factors, the hazard ratios were 1.09 (95% CI: 1.02, 1.15) and 1.10 (95% CI: 1.07, 1.13) for the associations between every 10 cm increase in height and risk of SCC and BCC respectively. None of the 687 height-related single-nucleotide polymorphisms (SNPs) was significantly associated with the risk of SCC or BCC, nor were the genetic scores combining independent height-related loci.

Conclusions:

Our data from two large cohorts provide further evidence that height is associated with an increased risk of NMSC. More studies on height-related genetic loci and early-life exposures may help clarify the underlying mechanisms.

Pigeonetics takes flight: Evolution, development, and genetics of intraspecific variation

Intensive artificial selection over thousands of years has produced hundreds of varieties of domestic pigeon. As Charles Darwin observed, the morphological differences among breeds can rise to the magnitude of variation typically observed among different species. Nevertheless, different pigeon varieties are interfertile, thereby enabling forward genetic and genomic approaches to identify genes that underlie derived traits. Building on classical genetic studies of pigeon variation, recent molecular investigations find a spectrum of coding and regulatory alleles controlling derived traits, including plumage color, feather growth polarity, and limb identity. Developmental and genetic analyses of pigeons are revealing the molecular basis of variation in a classic example of extreme intraspecific diversity, and have the potential to nominate genes that control variation among other birds and vertebrates in general.

Detection of human adaptation during the past 2000 years

Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

DNA methylation-based variation between human populations

Several studies have proved that DNA methylation affects regulation of gene expression and development. Epigenome-wide studies have reported variation in methylation patterns between populations, including Caucasians, non-Caucasians (Blacks), Hispanics, Arabs, and numerous populations of the African continent. Not only has DNA methylation differences shown to impact externally visible characteristics, but is also a potential biomarker for underlying racial health disparities between human populations. Ethnicity-related methylation differences set their mark during early embryonic development. Genetic variations, such as single-nucleotide polymorphisms and environmental factors, such as age, dietary folate, socioeconomic status, and smoking, impacts DNA methylation levels, which reciprocally impacts expression of phenotypes. Studies show that it is necessary to address these external influences when attempting to differentiate between populations since the relative impacts of these factors on the human methylome remain uncertain. The present review summarises several reported attempts to establish the contribution of differential DNA methylation to natural human variation, and shows that DNA methylation could represent new opportunities for risk stratification and prevention of several diseases amongst populations world-wide. Variation of methylation patterns between human populations is an exciting prospect which inspires further valuable research to apply the concept in routine medical and forensic casework. However, trans-generational inheritance needs to be quantified to decipher the proportion of variation contributed by DNA methylation. The future holds thorough evaluation of the epigenome to understand quantification, heritability, and the effect of DNA methylation on phenotypes. In addition, methylation profiling of the same ethnic groups across geographical locations will shed light on conserved methylation differences in populations.

Leveraging genetically simple traits to identify small-effect variants for complex phenotypes

Background

Polymorphisms underlying complex traits often explain a small part (less than 1 %) of the phenotypic variance (σ²_P). This makes identification of mutations underling complex traits difficult and usually only a subset of large-effect loci are identified. One approach to identify more loci is to increase sample size of experiments but here we propose an alternative. The aim of this paper is to use secondary phenotypes for genetically simple traits during the QTL discovery phase for complex traits. We demonstrate this approach in a dairy cattle data set where the complex traits were milk production phenotypes (fat, milk and protein yield; fat and protein percentage in milk) measured on thousands of individuals while secondary (potentially genetically simpler) traits are detailed milk composition traits (measurements of individual protein abundance, mineral and sugar concentrations; and gene expression).

Results

Quantitative trait loci (QTL) were identified using 11,527 Holstein cattle with milk production records and up to 444 cows with milk composition traits. There were eight regions that contained QTL for both milk production and a composition trait, including four novel regions. One region on BTAU1 affected both milk yield and phosphorous concentration in milk. The QTL interval included the gene SLC37A1, a phosphorous antiporter. The most significant imputed sequence variants in this region explained 0.001 σ²_P for milk yield, and 0.11 σ²_P for phosphorus concentration. Since the polymorphisms were non-coding, association mapping for SLC37A1 gene expression was performed using high depth mammary RNAseq data from a separate group of 371 lactating cows. This confirmed a strong eQTL for SLC37A1, with peak association at the same imputed sequence variants that were most significant for phosphorus concentration. Fitting any of these variants as covariables in the association analysis removed the QTL signal for milk production traits. Plausible causative mutations in the casein complex region were also identified using a similar strategy.

Conclusions

Milk production traits in dairy cows are typical complex traits where polymorphisms explain only a small portion of the phenotypic variance. However, here we show that these mutations can have larger effects on secondary traits, such as concentrations of minerals, proteins and sugars in the milk, and expression levels of genes in mammary tissue. These larger effects were used to successfully map variants for milk production traits. Genetically simple traits also provide a direct biological link between possible causal mutations and the effect of these mutations on milk production.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3175-3) contains supplementary material, which is available to authorized users.

Linkage and Association Mapping for Two Major Traits Used in the Maritime Pine Breeding Program: Height Growth and Stem Straightness

Background

Increasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches.

Results

The populations used for LA consisted of two unrelated three-generation full-sib families (n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait.

Conclusions

This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program.

Evolutionary History, Selective Sweeps, and Deleterious Variation in the Dog

The dog is our oldest domesticate and has experienced a wide variety of demographic histories, including a bottleneck associated with domestication and individual bottlenecks associated with the formation of modern breeds. Admixture with gray wolves, and among dog breeds and populations, has also occurred throughout its history. Likewise, the intensity and focus of selection have varied, from an initial focus on traits enhancing cohabitation with humans, to more directed selection on specific phenotypic characteristics and behaviors. In this review, we summarize and synthesize genetic findings from genome-wide and complete genome studies that document the genomic consequences of demography and selection, including the effects on adaptive and deleterious variation. Consistent with the evolutionary history of the dog, signals of natural and artificial selection are evident in the dog genome. However, conclusions from studies of positive selection are fraught with the problem of false positives given that demographic history is often not taken into account.

An Emerging Regulatory Landscape for Skeletal Development

Skeletal development creates the physical framework that shapes our body and its actions. In the past two decades, genetic studies have provided important insights into the molecular processes at play, including the roles of signaling pathways and transcriptional effectors that coordinate an orderly, progressive emergence and expansion of distinct cartilage and bone cell fates in an invariant temporal and spatial pattern for any given skeletal element within that specific vertebrate species. Genome-scale studies have provided additional layers of understanding, moving from individual genes to the gene regulatory landscape, integrating regulatory information through cis-regulatory modules into cell type-specific gene regulatory programs. This review discusses our current understanding of the transcriptional control of mammalian skeletal development, focusing on recent genome-scale studies.

Dietary Intake of Protein in Early Childhood Is Associated with Growth Trajectories between 1 and 9 Years of Age

BACKGROUND

High protein intake in infancy might lead to a higher body mass index (BMI) in childhood. However, whether these associations differ between different sources of protein is unclear.

OBJECTIVE

We investigated associations between the intake of total protein, protein from different sources, and individual amino acids in early childhood and repeatedly measured height, weight, and BMI up to the age of 9 y.

METHODS

This study was performed in 3564 children participating in the Generation R Study, a population-based prospective cohort study in Rotterdam, Netherlands. Intakes of total protein, animal protein, vegetable protein, and individual amino acids (including methionine, arginine, lysine, threonine, valine, leucine, isoleucine, phenylalanine, tryptophan, histidine, cysteine, tyrosine, alanine, asparagine, glutamine, glycine, proline, and serine) at 1 y were assessed by using a food-frequency questionnaire. Height and weight were measured at the approximate ages of 14, 18, 24, 30, 36, and 45 mo and at 6 and 9 y, and BMI was calculated.

RESULTS

After adjustment for confounders, linear mixed models showed that a 10-g higher total protein intake/d at 1 y was significantly associated with a 0.03-SD greater height (95% CI: 0.00, 0.06), a 0.06-SD higher weight (95% CI: 0.03, 0.09), and a 0.05-SD higher BMI (95% CI: 0.03, 0.08) up to the age of 9 y. Associations were stronger for animal than for vegetable protein intake but did not differ between dairy and nondairy animal protein or between specific amino acids.

CONCLUSIONS

A higher intake of protein, especially animal protein, at 1 y of age was associated with a greater height, weight, and BMI in childhood up to 9 y of age. Future studies should explore the role of growth hormones and investigate whether protein intake in early childhood affects health later in life.

HASE: Framework for efficient high-dimensional association analyses

High-throughput technology can now provide rich information on a person’s biological makeup and environmental surroundings. Important discoveries have been made by relating these data to various health outcomes in fields such as genomics, proteomics, and medical imaging. However, cross-investigations between several high-throughput technologies remain impractical due to demanding computational requirements (hundreds of years of computing resources) and unsuitability for collaborative settings (terabytes of data to share). Here we introduce the HASE framework that overcomes both of these issues. Our approach dramatically reduces computational time from years to only hours and also requires several gigabytes to be exchanged between collaborators. We implemented a novel meta-analytical method that yields identical power as pooled analyses without the need of sharing individual participant data. The efficiency of the framework is illustrated by associating 9 million genetic variants with 1.5 million brain imaging voxels in three cohorts (total N = 4,034) followed by meta-analysis, on a standard computational infrastructure. These experiments indicate that HASE facilitates high-dimensional association studies enabling large multicenter association studies for future discoveries.

Genome-Wide Meta-Analysis of Sciatica in Finnish Population

Sciatica or the sciatic syndrome is a common and often disabling low back disorder in the working-age population. It has a relatively high heritability but poorly understood molecular mechanisms. The Finnish population is a genetic isolate where small founder population and bottleneck events have led to enrichment of certain rare and low frequency variants. We performed here the first genome-wide association (GWAS) and meta-analysis of sciatica. The meta-analysis was conducted across two GWAS covering 291 Finnish sciatica cases and 3671 controls genotyped and imputed at 7.7 million autosomal variants. The most promising loci (p<1x10^-6) were replicated in 776 Finnish sciatica patients and 18,489 controls. We identified five intragenic variants, with relatively low frequencies, at two novel loci associated with sciatica at genome-wide significance. These included chr9:14344410:I (rs71321981) at 9p22.3 (NFIB gene; p = 1.30x10^-8, MAF = 0.08) and four variants at 15q21.2: rs145901849, rs80035109, rs190200374 and rs117458827 (MYO5A; p = 1.34x10^-8, MAF = 0.06; p = 2.32x10^-8, MAF = 0.07; p = 3.85x10^-8, MAF = 0.06; p = 4.78x10^-8, MAF = 0.07, respectively). The most significant association in the meta-analysis, a single base insertion rs71321981 within the regulatory region of the transcription factor NFIB, replicated in an independent Finnish population sample (p = 0.04). Despite identifying 15q21.2 as a promising locus, we were not able to replicate it. It was differentiated; the lead variants within 15q21.2 were more frequent in Finland (6–7%) than in other European populations (1–2%). Imputation accuracies of the three significantly associated variants (chr9:14344410:I, rs190200374, and rs80035109) were validated by genotyping. In summary, our results suggest a novel locus, 9p22.3 (NFIB), which may be involved in susceptibility to sciatica. In addition, another locus, 15q21.2, emerged as a promising one, but failed to replicate.

Phenotypically Enriched Genotypic Imputation in Genetic Association Tests

BACKGROUND

In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem.

METHODS

We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation.

RESULTS

Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause.

CONCLUSION

The PEGI method may improve power to detect an association over both CC and GI under many circumstances.

Mammalian axoneme central pair complex proteins: Broader roles revealed by gene knockout phenotypes

The axoneme genes, their encoded proteins, their functions and the structures they form are largely conserved across species. Much of our knowledge of the function and structure of axoneme proteins in cilia and flagella is derived from studies on model organisms like the green algae, Chlamydomonas reinhardtii. The core structure of cilia and flagella is the axoneme, which in most motile cilia and flagella contains a 9 + 2 configuration of microtubules. The two central microtubules are the scaffold of the central pair complex (CPC). Mutations that disrupt CPC genes in Chlamydomonas and other model organisms result in defects in assembly, stability and function of the axoneme, leading to flagellar motility defects. However, targeted mutations generated in mice in the orthologous CPC genes have revealed significant differences in phenotypes of mutants compared to Chlamydomonas. Here we review observations that support the concept of cell-type specific roles for the CPC genes in mice, and an expanded repertoire of functions for the products of these genes in cilia, including non-motile cilia, and other microtubule-associated cellular functions.

Genetic factor common to schizophrenia and HIV infection is associated with risky sexual behavior: antagonistic vs. synergistic pleiotropic SNPs enriched for distinctly different biological functions

Schizophrenia (SZ) and HIV infection are serious disorders with a complex phenotypic relationship. Observational studies have described their comorbidity; their genetic correlation is not well studied. We performed extensive analysis in search of common genetic factors for SZ and HIV, and their relationship with risky sexual behavior (RSB). Summary statistics from genome-wide association studies of HIV infection and schizophrenia were obtained and 2379 European Americans were genotyped and assessed for RSB score. Genetic relationships between traits were analyzed in three ways: linkage disequilibrium (LD) score regression to estimate genetic correlation; GPA (Genetic analysis incorporating Pleiotropy and Annotation) to test pleiotropy and identify pleiotropic loci; polygenic risk scores (PRS) of SZ and HIV to predict RSB using linear regression. We found significant pleiotropy (p = 5.31E - 28) and a positive genetic correlation (cor = 0.17, p = 0.002) for SZ and HIV infection. Pleiotropic SNPs with opposite effect directions (antagonistic) and SNPs with the same effect direction (synergistic) were enriched for distinctly different biological functions. SZ PRS computed with antagonistically pleiotropic SNPs consistently predicted RSB score with nominal significance, but SZ PRS based on either synergistically pleiotropic SNPs or all SNPs did not predict RSB. The epidemiologic correlation between schizophrenia and HIV can partly be explained by overlapping genetic risk factors, which are related to risky sexual behavior.

Genome-wide associations for birth weight and correlations with adult disease

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P < 5 × 10-8). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (Rg = -0.22, P = 5.5 × 10-13), T2D (Rg = -0.27, P = 1.1 × 10-6) and coronary artery disease (Rg = -0.30, P = 6.5 × 10-9). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P = 1.9 × 10-4). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated.

Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota

Human gut microbiota is an important determinant for health and disease, and recent studies emphasize the numerous factors shaping its diversity. Here we performed a genome-wide association study (GWAS) of the gut microbiota using two cohorts from northern Germany totaling 1,812 individuals. Comprehensively controlling for diet and non-genetic parameters, we identify genome-wide significant associations for overall microbial variation and individual taxa at multiple genetic loci, including the VDR gene (encoding vitamin D receptor). We observe significant shifts in the microbiota of Vdr^-/- mice relative to control mice and correlations between the microbiota and serum measurements of selected bile and fatty acids in humans, including known ligands and downstream metabolites of VDR. Genome-wide significant (P < 5 × 10^-8) associations at multiple additional loci identify other important points of host-microbe intersection, notably several disease susceptibility genes and sterol metabolism pathway components. Non-genetic and genetic factors each account for approximately 10% of the variation in gut microbiota, whereby individual effects are relatively small.

Multivariate Analysis of Anthropometric Traits Using Summary Statistics of Genome-Wide Association Studies from GIANT Consortium

Meta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this study, we performed CPASSOC analysis on the summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium using a novel method recently developed by our group. Sex-specific meta-analysis data for height, body mass index (BMI), and waist-to-hip ratio adjusted for BMI (WHRadjBMI) from discovery phase of the GIANT consortium study were combined using CPASSOC for each trait as well as 3 traits together. The conventional meta-analysis results from the discovery phase data of GIANT consortium studies were used to compare with that from CPASSOC analysis. The CPASSOC analysis was able to identify 17 loci associated with anthropometric traits that were missed by conventional meta-analysis. Among these loci, 16 have been reported in literature by including additional samples and 1 is novel. We also demonstrated that CPASSOC is able to detect pleiotropic effects when analyzing multiple traits.

Atlas of human diseases influenced by genetic variants with extreme allele frequency differences

Genetic variants with extreme allele frequency differences (EAFD) may underlie some human health disparities across populations. To identify EAFD loci, we systematically analyzed and characterized 81 million genomic variants from 2504 unrelated individuals of 26 world populations (phase III of the 1000 Genomes Project). Our analyses revealed a total of 434 genes, 15 pathways, and 18 diseases and traits influenced by EAFD variants from five continental populations. They included known EAFD genes, such as LCT (lactose tolerance), SLC24A5 (skin pigmentation), and EDAR (hair morphology). We found many novel EAFD genes, including TBC1D2B (autophagy mediator), TRIM40 (gastrointestinal inflammatory regulator), KRT71, KRT75, KRT83, and KRTAP10-1 (hair and epithelial keratin synthesis), PIK3R3 (insulin receptor interaction), DARS (neurological disorders), and NACA2 (skin inflammatory response). Our results also showed four complex diseases significantly associated with EAFD loci, including asthma (adjusted enrichment P = 4 × 10^-8), type I diabetes (P = 6 × 10^-9), alcohol consumption (P = 0.0002), and attention deficit/hyperactivity disorder (P = 0.003). This study provides a comprehensive atlas of genes, pathways, and human diseases significantly influenced by EAFD variants.

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans

RATIONALE

Obstructive sleep apnea is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. Although there is strong clinical and epidemiologic evidence supporting the importance of genetic factors in influencing obstructive sleep apnea, its genetic basis is still largely unknown. Prior genetic studies focused on traits defined using the apnea-hypopnea index, which contains limited information on potentially important genetically determined physiologic factors, such as propensity for hypoxemia and respiratory arousability.

OBJECTIVES

To define novel obstructive sleep apnea genetic risk loci for obstructive sleep apnea, we conducted genome-wide association studies of quantitative traits in Hispanic/Latino Americans from three cohorts.

METHODS

Genome-wide data from as many as 12,558 participants in the Hispanic Community Health Study/Study of Latinos, Multi-Ethnic Study of Atherosclerosis, and Starr County Health Studies population-based cohorts were metaanalyzed for association with the apnea-hypopnea index, average oxygen saturation during sleep, and average respiratory event duration.

MEASUREMENTS AND MAIN RESULTS

Two novel loci were identified at genome-level significance (rs11691765, GPR83, P = 1.90 × 10-8 for the apnea-hypopnea index, and rs35424364; C6ORF183/CCDC162P, P = 4.88 × 10-8 for respiratory event duration) and seven additional loci were identified with suggestive significance (P < 5 × 10-7). Secondary sex-stratified analyses also identified one significant and several suggestive associations. Multiple loci overlapped genes with biologic plausibility.

CONCLUSIONS

These are the first genome-level significant findings reported for obstructive sleep apnea-related physiologic traits in any population. These findings identify novel associations in inflammatory, hypoxia signaling, and sleep pathways.

Novel Genetic Variants for Cartilage Thickness and Hip Osteoarthritis

Osteoarthritis is one of the most frequent and disabling diseases of the elderly. Only few genetic variants have been identified for osteoarthritis, which is partly due to large phenotype heterogeneity. To reduce heterogeneity, we here examined cartilage thickness, one of the structural components of joint health. We conducted a genome-wide association study of minimal joint space width (mJSW), a proxy for cartilage thickness, in a discovery set of 13,013 participants from five different cohorts and replication in 8,227 individuals from seven independent cohorts. We identified five genome-wide significant (GWS, P≤5·0×10-8) SNPs annotated to four distinct loci. In addition, we found two additional loci that were significantly replicated, but results of combined meta-analysis fell just below the genome wide significance threshold. The four novel associated genetic loci were located in/near TGFA (rs2862851), PIK3R1 (rs10471753), SLBP/FGFR3 (rs2236995), and TREH/DDX6 (rs496547), while the other two (DOT1L and SUPT3H/RUNX2) were previously identified. A systematic prioritization for underlying causal genes was performed using diverse lines of evidence. Exome sequencing data (n = 2,050 individuals) indicated that there were no rare exonic variants that could explain the identified associations. In addition, TGFA, FGFR3 and PIK3R1 were differentially expressed in OA cartilage lesions versus non-lesioned cartilage in the same individuals. In conclusion, we identified four novel loci (TGFA, PIK3R1, FGFR3 and TREH) and confirmed two loci known to be associated with cartilage thickness.The identified associations were not caused by rare exonic variants. This is the first report linking TGFA to human OA, which may serve as a new target for future therapies.

The correlation between cognitive performance and retinal nerve fibre layer thickness is largely explained by genetic factors

Retinal nerve fibre layer (RNFL) thickness has been associated with cognitive function but it is unclear whether RNFL thinning is secondary to cortical loss, or if the same disease process affects both. We explored whether there is phenotypic sharing between RNFL thickness and cognitive traits, and whether such sharing is due to genetic factors. Detailed eye and cognitive examination were performed on 1602 twins (mean age: 56.4 years; range: 18-89) from the TwinsUK cohort. Associations between RNFL thickness and ophthalmic, cognitive and other predictors were assessed using linear regression or analysis of variance models. Heritability analyses were performed using uni- and bivariate Cholesky decomposition models. RNFL was thinner with increase in myopia and with decrease in disc area (p < 0.001). A thicker RNFL was associated with better performance on mini mental state examination (MMSE, F(5,883) = 5.8, p < 0.001), and with faster reaction time (RT, β = -0.01; p = 0.01); independent of the effects of age, refractive error and disc area (p < 0.05). RNFL thickness was highly heritable (82%) but there was low phenotypic sharing between RNFL thickness and MMSE (5%, 95% CI: 0-10%) or RT (7%, 95% CI: 1-12%). This sharing, however, was mostly due to additive genetic effects (67% and 92% of the shared variance respectively).

Genome-Wide Association Study of HIV Whole Genome Sequences Validated using Drug Resistance

Background

Genome-wide association studies (GWAS) have considerably advanced our understanding of human traits and diseases. With the increasing availability of whole genome sequences (WGS) for pathogens, it is important to establish whether GWAS of viral genomes could reveal important biological insights. Here we perform the first proof of concept viral GWAS examining drug resistance (DR), a phenotype with well understood genetics.

Method

We performed a GWAS of DR in a sample of 343 HIV subtype C patients failing 1^st line antiretroviral treatment in rural KwaZulu-Natal, South Africa. The majority and minority variants within each sequence were called using PILON, and GWAS was performed within PLINK. HIV WGS from patients failing on different antiretroviral treatments were compared to sequences derived from individuals naïve to the respective treatment.

Results

GWAS methodology was validated by identifying five associations on a genetic level that led to amino acid changes known to cause DR. Further, we highlighted the ability of GWAS to identify epistatic effects, identifying two replicable variants within amino acid 68 of the reverse transcriptase protein previously described as potential fitness compensatory mutations. A possible additional DR variant within amino acid 91 of the matrix region of the Gag protein was associated with tenofovir failure, highlighting GWAS’s ability to identify variants outside classical candidate genes. Our results also suggest a polygenic component to DR.

Conclusions

These results validate the applicability of GWAS to HIV WGS data even in relative small samples, and emphasise how high throughput sequencing can provide novel and clinically relevant insights. Further they suggested that for viruses like HIV, population structure was only minor concern compared to that seen in bacteria or parasite GWAS. Given the small genome length and reduced burden for multiple testing, this makes HIV an ideal candidate for GWAS.

Improved methods for multi-trait fine mapping of pleiotropic risk loci

MOTIVATION

Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

RESULTS

In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

AVAILABILITY AND IMPLEMENTATION

The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping

Background

Bayesian mixture models in which the effects of SNP are assumed to come from normal distributions with different variances are attractive for simultaneous genomic prediction and QTL mapping. These models are usually implemented with Monte Carlo Markov Chain (MCMC) sampling, which requires long compute times with large genomic data sets. Here, we present an efficient approach (termed HyB_BR), which is a hybrid of an Expectation-Maximisation algorithm, followed by a limited number of MCMC without the requirement for burn-in.

Results

To test prediction accuracy from HyB_BR, dairy cattle and human disease trait data were used. In the dairy cattle data, there were four quantitative traits (milk volume, protein kg, fat% in milk and fertility) measured in 16,214 cattle from two breeds genotyped for 632,002 SNPs. Validation of genomic predictions was in a subset of cattle either from the reference set or in animals from a third breeds that were not in the reference set. In all cases, HyB_BR gave almost identical accuracies to Bayesian mixture models implemented with full MCMC, however computational time was reduced by up to 1/17 of that required by full MCMC. The SNPs with high posterior probability of a non-zero effect were also very similar between full MCMC and HyB_BR, with several known genes affecting milk production in this category, as well as some novel genes. HyB_BR was also applied to seven human diseases with 4890 individuals genotyped for around 300 K SNPs in a case/control design, from the Welcome Trust Case Control Consortium (WTCCC). In this data set, the results demonstrated again that HyB_BR performed as well as Bayesian mixture models with full MCMC for genomic predictions and genetic architecture inference while reducing the computational time from 45 h with full MCMC to 3 h with HyB_BR.

Conclusions

The results for quantitative traits in cattle and disease in humans demonstrate that HyB_BR can perform equally well as Bayesian mixture models implemented with full MCMC in terms of prediction accuracy, but with up to 17 times faster than the full MCMC implementations. The HyB_BR algorithm makes simultaneous genomic prediction, QTL mapping and inference of genetic architecture feasible in large genomic data sets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3082-7) contains supplementary material, which is available to authorized users.

A rare variant in COL11A1 is strongly associated with adult height in Chinese Han population

Human height is a highly heritable trait in which multiple genes are involved. Recent genome-wide association studies (GWASs) have identified that COL11A1 is an important susceptibility gene for human height. To determine whether the variants of COL11A1 are associated with adult and children height, we analyzed splicing and coding single-nucleotide variants across COL11A1 through exome-targeted sequencing and two validation stages with a total 20,426 Chinese Han samples. A total of 105 variants were identified by exome-targeted sequencing, of which 30 SNPs were located in coding region. The strongest association signal was Chr1_103380393 with P value of 4.8 × 10(-7). Chr1_103380393 also showed nominal significance in the validation stage (P = 1.21 × 10(-6)). Combined analysis of 16,738 samples strengthened the original association of chr1_103380393 with adult height (Pcombined = 3.1 × 10(-8)), with an increased height of 0.292sd (standard deviation) per G allele (95% CI: 0.19-0.40). There was no evidence (P = 0.843) showing that chr1_103380393 altered child height in 3688 child samples. Only the group of 12-15 years showed slight significance with P value of 0.0258. This study firstly shows that genetic variants of COL11A1 contribute to adult height in Chinese Han population but not to children height, which expand our knowledge of the genetic factors underlying height variation and the biological regulation of human height.

Associations between childhood height and morphologically different variants of melanoma in adulthood

Aim of the study

Melanoma subtypes have different aetiological characteristics. Child height is positively associated with adult melanoma; however, a clarification of associations with specific melanoma variants is necessary for an improved understanding of risk factors underlying the histologic entities. This study investigated associations between childhood height and future development of cutaneous melanoma variants.

Method

A cohort study of 316,193 individuals from the Copenhagen School Health Records Register, with measured heights at ages 7–13 years who were born from 1930 to 1989. Melanoma cases were identified via linkage to the national Danish Cancer Registry and subdivided into subtypes. Cox proportional hazards regressions were performed.

Results

A total of 2223 cases of melanoma distributed as 60% superficial spreading melanoma (SSM), 27.5% melanoma not otherwise specified (NOS), 8.5% nodular melanoma (NM), and 2% lentigo maligna melanoma (LMM). The remaining rare melanoma forms were not analysed. Childhood height was positively and significantly associated with SSM, melanoma NOS, and NM, but not LMM, in adulthood. Per height z-score at age 13 years, the hazard ratios were 1.20 (95% confidence intervals [CI]: 1.13–1.27) for SSM, 1.19 (95% CI: 1.09–1.29) for melanoma NOS, and 1.21 (95% CI: 1.04–1.41) for NM. Further, growth patterns were linked to the melanoma variants with persistently tall children having an increased risk of developing SSM, melanoma NOS, or NM.

Conclusion

Childhood height is positively associated with the majority of the melanoma variants. These results suggest that the underlying processes contributing to childhood height and growth patterns interconnect early-life events with the predisposition to melanomagenesis in adulthood.

•

Childhood height was positively and significantly associated with the majority of melanoma subtypes.

•

Diverse growth patterns in childhood were differentially associated with future melanomagenesis.

•

Height and melanoma risk may be linked by biological mechanisms, and these remain to be elucidated.