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

Weaver Syndrome-Associated EZH2 Protein Variants Show Impaired Histone Methyltransferase Function In Vitro

Weaver syndrome (WS) is a rare congenital disorder characterized by generalized overgrowth, macrocephaly, specific facial features, accelerated bone age, intellectual disability, and susceptibility to cancers. De novo mutations in the enhancer of zeste homolog 2 (EZH2) have been shown to cause WS. EZH2 is a histone methyltransferase that acts as the catalytic agent of the polycomb‐repressive complex 2 (PRC2) to maintain gene repression via methylation of lysine 27 on histone H3 (H3K27). Functional studies investigating histone methyltransferase activity of mutant EZH2 from various cancers have been reported, whereas WS‐associated mutations remain poorly characterized. To investigate the role of EZH2 in WS, we performed functional studies using artificially assembled PRC2 complexes containing mutagenized human EZH2 that reflected the codon changes predicted from patients with WS. We found that WS‐associated amino acid alterations reduce the histone methyltransferase function of EZH2 in this in vitro assay. Our results support the hypothesis that WS is caused by constitutional mutations in EZH2 that alter the histone methyltransferase function of PRC2. However, histone methyltransferase activities of different EZH2 variants do not appear to correlate directly with the phenotypic variability between WS patients and individuals with a common c.553G>C (p.Asp185His) polymorphism in EZH2.

Genome-Wide Analysis Reveals Novel Regulators of Growth in Drosophila melanogaster

Organismal size depends on the interplay between genetic and environmental factors. Genome-wide association (GWA) analyses in humans have implied many genes in the control of height but suffer from the inability to control the environment. Genetic analyses in Drosophila have identified conserved signaling pathways controlling size; however, how these pathways control phenotypic diversity is unclear. We performed GWA of size traits using the Drosophila Genetic Reference Panel of inbred, sequenced lines. We find that the top associated variants differ between traits and sexes; do not map to canonical growth pathway genes, but can be linked to these by epistasis analysis; and are enriched for genes and putative enhancers. Performing GWA on well-studied developmental traits under controlled conditions expands our understanding of developmental processes underlying phenotypic diversity.

Genetic studies in Drosophila have elucidated conserved signaling pathways and environmental factors that together control organismal size. In humans, hundreds of genes are associated with height variation, but these associations have not been performed in a controlled environment. As a result we are still lacking an understanding of the mechanisms creating size variability within a species. Here, under carefully controlled environmental conditions, we identify naturally occurring genetic variants that are associated with size diversity in Drosophila. We identify a cluster of associations close to the kek1 locus, a well-characterized growth regulator, but otherwise find that most variants are located in or close to genes that do not belong to the conserved pathways but may interact with these in a biological network. We validate 33 novel growth regulatory genes that participate in diverse cellular processes, most notably cellular metabolism and cell polarity. This study is the first genome-wide association analysis of natural variants underlying size in Drosophila and our results complement the knowledge we have accumulated on this trait from mutational studies of single genes.

Genotype Imputation with Millions of Reference Samples

We present a genotype imputation method that scales to millions of reference samples. The imputation method, based on the Li and Stephens model and implemented in Beagle v.4.1, is parallelized and memory efficient, making it well suited to multi-core computer processors. It achieves fast, accurate, and memory-efficient genotype imputation by restricting the probability model to markers that are genotyped in the target samples and by performing linear interpolation to impute ungenotyped variants. We compare Beagle v.4.1 with Impute2 and Minimac3 by using 1000 Genomes Project data, UK10K Project data, and simulated data. All three methods have similar accuracy but different memory requirements and different computation times. When imputing 10 Mb of sequence data from 50,000 reference samples, Beagle's throughput was more than 100× greater than Impute2's throughput on our computer servers. When imputing 10 Mb of sequence data from 200,000 reference samples in VCF format, Minimac3 consumed 26× more memory per computational thread and 15× more CPU time than Beagle. We demonstrate that Beagle v.4.1 scales to much larger reference panels by performing imputation from a simulated reference panel having 5 million samples and a mean marker density of one marker per four base pairs.

Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures

Bone mineral density (BMD) is a measure of osteoporosis and is useful in evaluating the risk of fracture. In a genome-wide association study of BMD among 20,100 Icelanders, with follow-up in 10,091 subjects of European and East-Asian descent, we found a new BMD locus that harbours the PTCH1 gene, represented by rs28377268 (freq. 11.4–22.6%) that associates with reduced spine BMD (P=1.0 × 10⁻¹¹, β=−0.09). We also identified a new spine BMD signal in RSPO3, rs577721086 (freq. 6.8%), that associates with increased spine BMD (P=6.6 × 10⁻¹⁰, β=0.14). Importantly, both variants associate with osteoporotic fractures and affect expression of the PTCH1 and RSPO3 genes that is in line with their influence on BMD and known biological function of these genes. Additional new BMD signals were also found at the AXIN1 and SOST loci and a new lead SNP at the EN1 locus.

Bone mineral density (BMD) is the best predictor of osteoporotic fracture risk. Here, the authors perform a genome wide association study in Icelanders and people of European and East-Asian descent, and identify a new allele in intron 15 of the PTCH1 gene that associates with reduced BMD.

Genome-Wide Estimates of Heritability for Social Demographic Outcomes

An increasing number of studies that are widely used in the demographic research community have collected genome-wide data from their respondents. It is therefore important that demographers have a proper understanding of some of the methodological tools needed to analyze such data. This article details the underlying methodology behind one of the most common techniques for analyzing genome-wide data, genome-wide complex trait analysis (GCTA). GCTA models provide heritability estimates for health, health behaviors, or indicators of attainment using data from unrelated persons. Our goal was to describe this model, highlight the utility of the model for biodemographic research, and demonstrate the performance of this approach under modifications to the underlying assumptions. The first set of modifications involved changing the nature of the genetic data used to compute genetic similarities between individuals (the genetic relationship matrix). We then explored the sensitivity of the model to heteroscedastic errors. In general, GCTA estimates are found to be robust to the modifications proposed here, but we also highlight potential limitations of GCTA estimates.

Examining non-syndromic autosomal recessive intellectual disability (NS-ARID) genes for an enriched association with intelligence differences

Two themes are emerging regarding the molecular genetic aetiology of intelligence. The first is that intelligence is influenced by many variants and those that are tagged by common single nucleotide polymorphisms account for around 30% of the phenotypic variation. The second, in line with other polygenic traits such as height and schizophrenia, is that these variants are not randomly distributed across the genome but cluster in genes that work together. Less clear is whether the very low range of cognitive ability (intellectual disability) is simply one end of the normal distribution describing individual differences in cognitive ability across a population. Here, we examined 40 genes with a known association with non-syndromic autosomal recessive intellectual disability (NS-ARID) to determine if they are enriched for common variants associated with the normal range of intelligence differences. The current study used the 3511 individuals of the Cognitive Ageing Genetics in England and Scotland (CAGES) consortium. In addition, a text mining analysis was used to identify gene sets biologically related to the NS-ARID set. Gene-based tests indicated that genes implicated in NS-ARID were not significantly enriched for quantitative trait loci (QTL) associated with intelligence. These findings suggest that genes in which mutations can have a large and deleterious effect on intelligence are not associated with variation across the range of intelligence differences.

•

Only three loci have been associated with intelligence.

•

In traits such as height common variants are found in the same genes as rare variants.

•

We hypothesise that intelligence may also follow this trend.

•

We examine genes where rare variants can produce large deleterious effects on IQ.

•

No enrichment was found for the non-syndromic intellectual disability gene set.

The Relevance of Genomic Signatures at Adhesion GPCR Loci in Humans

Adhesion G protein-coupled receptors (aGPCRs) have a long evolutionary history dating back to very basal unicellular eukaryotes. Almost every vertebrate is equipped with a set of different aGPCRs. Genomic sequence data of several hundred extinct and extant species allows for reconstruction of aGPCR phylogeny in vertebrates and non-vertebrates in general but also provides a detailed view into the recent evolutionary history of human aGPCRs. Mining these sequence sources with bioinformatic tools can unveil many facets of formerly unappreciated aGPCR functions. In this review, we extracted such information from the literature and open public sources and provide insights into the history of aGPCR in humans. This includes comprehensive analyses of signatures of selection, variability of human aGPCR genes, and quantitative traits at human aGPCR loci. As indicated by a large number of genome-wide genotype-phenotype association studies, variations in aGPCR contribute to specific human phenotypes. Our survey demonstrates that aGPCRs are significantly involved in adaptation processes, phenotype variations, and diseases in humans.

Common variants in DRD2 are associated with sleep duration: the CARe consortium

Sleep duration is implicated in the etiologies of chronic diseases and premature mortality. However, the genetic basis for sleep duration is poorly defined. We sought to identify novel genetic components influencing sleep duration in a multi-ethnic sample. Meta-analyses were conducted of genetic associations with self-reported, habitual sleep duration from seven Candidate Gene Association Resource (CARe) cohorts of over 25 000 individuals of African, Asian, European and Hispanic American ancestry. All individuals were genotyped for ∼50 000 SNPs from 2000 candidate heart, lung, blood and sleep genes. African-Americans had additional genome-wide genotypes. Four cohorts provided replication. A SNP (rs17601612) in the dopamine D2 receptor gene (DRD2) was significantly associated with sleep duration (P = 9.8 × 10(-7)). Conditional analysis identified a second DRD2 signal with opposite effects on sleep duration. In exploratory analysis, suggestive association was observed for rs17601612 with polysomnographically determined sleep latency (P = 0.002). The lead DRD2 signal was recently identified in a schizophrenia GWAS, and a genetic risk score of 11 additional schizophrenia GWAS loci genotyped on the IBC array was also associated with longer sleep duration (P = 0.03). These findings support a role for DRD2 in influencing sleep duration. Our work motivates future pharmocogenetics research on alerting agents such as caffeine and modafinil that interact with the dopaminergic pathway and further investigation of genetic overlap between sleep and neuro-psychiatric traits.

Dissecting the Genetic Basis of a Complex cis-Regulatory Adaptation

Although single genes underlying several evolutionary adaptations have been identified, the genetic basis of complex, polygenic adaptations has been far more challenging to pinpoint. Here we report that the budding yeast Saccharomyces paradoxus has recently evolved resistance to citrinin, a naturally occurring mycotoxin. Applying a genome-wide test for selection on cis-regulation, we identified five genes involved in the citrinin response that are constitutively up-regulated in S. paradoxus. Four of these genes are necessary for resistance, and are also sufficient to increase the resistance of a sensitive strain when over-expressed. Moreover, cis-regulatory divergence in the promoters of these genes contributes to resistance, while exacting a cost in the absence of citrinin. Our results demonstrate how the subtle effects of individual regulatory elements can be combined, via natural selection, into a complex adaptation. Our approach can be applied to dissect the genetic basis of polygenic adaptations in a wide range of species.

Chronic gastroesophageal reflux disease shares genetic background with esophageal adenocarcinoma and Barrett's esophagus

Esophageal adenocarcinoma (EA) is a rapidly fatal cancer with rising incidence in the developed world. Most EAs arise in a metaplastic epithelium, Barrett's esophagus (BE), which is associated with greatly increased risk of EA. One of the key risk factors for both BE and EA is chronic gastroesophageal reflux disease (GERD). This study used the linkage disequilibrium (LD) score regression and genomic profile risk scoring approaches to investigate the contribution of multiple common single-nucleotide polymorphisms (SNPs) to the risk of GERD, and the extent of genetic overlap between GERD and BE or EA. Using LD score regression, we estimated an overall phenotypic variance of 7% (95% CI 3–11%) for GERD explained by all the genotyped SNPs. A genetic correlation of 77% (s.e. = 24%, P = 0.0012) between GERD and BE and 88% between GERD and EA (s.e. = 25%, P = 0.0004) was estimated using the LD score regression approach. Results from the genomic profile risk scoring approach, as a robustness check, were broadly similar to those from the LD score regression. This study provides the first evidence for a polygenic basis for GERD and supports for a polygenic overlap between GERD and BE, and GERD and EA.

A prospective study of measured body size and height and risk of keratinocyte cancers and melanoma

BACKGROUND

The potential influence of measured body weight and height on keratinocyte skin cancer risk has scarcely been studied. Some evidence indicates melanoma risk increases as self-reported height increases, but an association with body mass index (BMI) is less certain.

METHODS

We measured body weight and height of 1171 Australian men and women in a community-based skin cancer study in Queensland and prospectively examined the association of BMI, body surface area (BSA) and height and incidence of basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and melanoma while accounting for skin phenotype, sun exposure, clinical/cutaneous signs of chronic photodamage and other risk factors.

RESULTS

During 16 years of follow-up, 334 and 188 participants newly developed BCC and SCC, respectively; 28 participants were diagnosed with primary melanoma. BMI and BSA were unrelated to skin cancer incidence. After full adjustment, height was significantly associated with SCC development in men (relative risk (RR)=1.66; 95% confidence interval (CI)=1.11-2.48, for ≥175cm vs ≤171cm, Ptrend=0.017), and BCC in women (Ptrend=0.043). Melanoma in men, was similarly positively associated with height (RR per 5cm increment=1.55; 95%CI 0.97-2.47, P=0.067) though not significantly.

CONCLUSION

This study shows that after adjusting for sun exposure tall stature may be a risk factor for the most common types of skin cancer BCC, SCC, and melanoma, while body mass and surface area appear unrelated to risk.

A new method for estimating effect size distribution and heritability from genome-wide association summary results

Accurately estimating the distribution and heritability of SNP effects across the genome could help explain the mystery of missing heritability. In this study, we propose a novel statistical method for estimating the distribution and heritability of SNP effects from genome-wide association studies (GWASs), and compare its performance to several existing methods using both simulations and real data. Specifically, we study the full range of GWAS summary results and link observed p values and unobserved effect sizes by (non-central) Chi-square distribution. By modeling the observed full set of association signals using a multinomial distribution, we build a likelihood function of SNP effect sizes using parametric and non-parametric maximum likelihood frameworks. Simulation studies show that the proposed method can accurately estimate effect sizes and the number of associated SNPs. As real applications, we analyze publicly available GWAS summary results for height, body mass index (BMI), and bone mineral density (BMD). Our analyses show that there are over 10,000 SNPs that might be associated with height, and the total heritability attributable to these SNPs exceeds 70 %. The heritabilities for BMI and BMD are ~10 and ~15 %, respectively. The results indicate that the proposed method has the potential to improve the accuracy of estimates of heritability and effect size for common SNPs in large-scale GWAS meta-analyses. These improved estimates may contribute to an enhanced understanding of the genetic basis of complex traits.

Integrating Diverse Types of Genomic Data to Identify Genes that Underlie Adverse Pregnancy Phenotypes

Progress in understanding complex genetic diseases has been bolstered by synthetic approaches that overlay diverse data types and analyses to identify functionally important genes. Pre-term birth (PTB), a major complication of pregnancy, is a leading cause of infant mortality worldwide. A major obstacle in addressing PTB is that the mechanisms controlling parturition and birth timing remain poorly understood. Integrative approaches that overlay datasets derived from comparative genomics with function-derived ones have potential to advance our understanding of the genetics of birth timing, and thus provide insights into the genes that may contribute to PTB. We intersected data from fast evolving coding and non-coding gene regions in the human and primate lineage with data from genes expressed in the placenta, from genes that show enriched expression only in the placenta, as well as from genes that are differentially expressed in four distinct PTB clinical subtypes. A large fraction of genes that are expressed in placenta, and differentially expressed in PTB clinical subtypes (23–34%) are fast evolving, and are associated with functions that include adhesion neurodevelopmental and immune processes. Functional categories of genes that express fast evolution in coding regions differ from those linked to fast evolution in non-coding regions. Finally, there is a surprising lack of overlap between fast evolving genes that are differentially expressed in four PTB clinical subtypes. Integrative approaches, especially those that incorporate evolutionary perspectives, can be successful in identifying potential genetic contributions to complex genetic diseases, such as PTB.

Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation

Lung function measures are used in the diagnosis of chronic obstructive pulmonary disease. In 38,199 European ancestry individuals, we studied genome-wide association of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC with 1000 Genomes Project (phase 1)-imputed genotypes and followed up top associations in 54,550 Europeans. We identify 14 novel loci (P<5 × 10(-8)) in or near ENSA, RNU5F-1, KCNS3, AK097794, ASTN2, LHX3, CCDC91, TBX3, TRIP11, RIN3, TEKT5, LTBP4, MN1 and AP1S2, and two novel signals at known loci NPNT and GPR126, providing a basis for new understanding of the genetic determinants of these traits and pulmonary diseases in which they are altered.

Early life trauma, depression and the glucocorticoid receptor gene--an epigenetic perspective

BACKGROUND

Hopes to identify genetic susceptibility loci accounting for the heritability seen in unipolar depression have not been fully realized. Family history remains the 'gold standard' for both risk stratification and prognosis in complex phenotypes such as depression. Meanwhile, the physiological mechanisms underlying life-event triggers for depression remain opaque. Epigenetics, comprising heritable changes in gene expression other than alterations of the nucleotide sequence, may offer a way to deepen our understanding of the aetiology and pathophysiology of unipolar depression and optimize treatments. A heuristic target for exploring the relevance of epigenetic changes in unipolar depression is the hypothalamic-pituitary-adrenal (HPA) axis. The glucocorticoid receptor (GR) gene (NR3C1) has been found to be susceptible to epigenetic modification, specifically DNA methylation, in the context of environmental stress such as early life trauma, which is an established risk for depression later in life.

METHOD

In this paper we discuss the progress that has been made by studies that have investigated the relationship between depression, early trauma, the HPA axis and the NR3C1 gene. Difficulties with the design of these studies are also explored.

RESULTS

Future efforts will need to comprehensively address epigenetic natural histories at the population, tissue, cell and gene levels. The complex interactions between the epigenome, genome and environment, as well as ongoing nosological difficulties, also pose significant challenges.

CONCLUSIONS

The work that has been done so far is nevertheless encouraging and suggests potential mechanistic and biomarker roles for differential DNA methylation patterns in NR3C1 as well as novel therapeutic targets.

Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1-Mb genomic regions harbor ≥1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

Protein profiling reveals consequences of lifestyle choices on predicted biological aging

Ageing is linked to a number of changes in how the body and its organs function. On a molecular level, ageing is associated with a reduction of telomere length, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern. Lifestyle factors such as smoking or stress can impact some of these molecular processes and thereby affect the ageing of an individual. Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference. The plasma protein profile can also be used to identify lifestyle factors that accelerate and decelerate ageing. We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2–6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount. This method can be applied to dried blood spots and may thus be useful in forensic medicine to provide basic anthropometrical measures for an individual based on a biological evidence sample.

Short and tall stature: a new paradigm emerges

In the past, the growth hormone (GH)-insulin-like growth factor 1 (IGF-1) axis was often considered to be the main system that regulated childhood growth and, therefore, determined short stature and tall stature. However, findings have now revealed that the GH-IGF-1 axis is just one of many regulatory systems that control chondrogenesis in the growth plate, which is the biological process that drives height gain. Consequently, normal growth in children depends not only on GH and IGF-1 but also on multiple hormones, paracrine factors, extracellular matrix molecules and intracellular proteins that regulate the activity of growth plate chondrocytes. Mutations in the genes that encode many of these local proteins cause short stature or tall stature. Similarly, genome-wide association studies have revealed that the normal variation in height seems to be largely due to genes outside the GH-IGF-1 axis that affect growth at the growth plate through a wide variety of mechanisms. These findings point to a new conceptual framework for understanding short and tall stature that is centred not on two particular hormones but rather on the growth plate, which is the structure responsible for height gain.

The epigenomic landscape of African rainforest hunter-gatherers and farmers

The genetic history of African populations is increasingly well documented, yet their patterns of epigenomic variation remain uncharacterized. Moreover, the relative impacts of DNA sequence variation and temporal changes in lifestyle and habitat on the human epigenome remain unknown. Here we generate genome-wide genotype and DNA methylation profiles for 362 rainforest hunter-gatherers and sedentary farmers. We find that the current habitat and historical lifestyle of a population have similarly critical impacts on the methylome, but the biological functions affected strongly differ. Specifically, methylation variation associated with recent changes in habitat mostly concerns immune and cellular functions, whereas that associated with historical lifestyle affects developmental processes. Furthermore, methylation variation—particularly that correlated with historical lifestyle—shows strong associations with nearby genetic variants that, moreover, are enriched in signals of natural selection. Our work provides new insight into the genetic and environmental factors affecting the epigenomic landscape of human populations over time.

Genetic and environmental factors affect genome-wide patterns of epigenetic variation. Here, the authors show that while current habitat and historical lifestyle impact the methylome of rainforest hunter-gatherers and sedentary farmers, the biological functions affected and the degree of genetic control differ.

Uncovering the Genetic Architectures of Quantitative Traits

The aim of a genome-wide association study (GWAS) is to identify loci in the human genome affecting a phenotype of interest. This review summarizes some recent work on conceptual and methodological aspects of GWAS. The average effect of gene substitution at a given causal site in the genome is the key estimand in GWAS, and we argue for its fundamental importance. Implicit in the definition of average effect is a linear model relating genotype to phenotype. The fraction of the phenotypic variance ascribable to polymorphic sites with nonzero average effects in this linear model is called the heritability, and we describe methods for estimating this quantity from GWAS data. Finally, we show that the theory of compressed sensing can be used to provide a sharp estimate of the sample size required to identify essentially all sites contributing to the heritability of a given phenotype.

Schizophrenia

Schizophrenia is a chronic psychiatric disorder with a heterogeneous genetic and neurobiological background that influences early brain development, and is expressed as a combination of psychotic symptoms - such as hallucinations, delusions and disorganization - and motivational and cognitive dysfunctions. The mean lifetime prevalence of the disorder is just below 1%, but large regional differences in prevalence rates are evident owing to disparities in urbanicity and patterns of immigration. Although gross brain pathology is not a characteristic of schizophrenia, the disorder involves subtle pathological changes in specific neural cell populations and in cell-cell communication. Schizophrenia, as a cognitive and behavioural disorder, is ultimately about how the brain processes information. Indeed, neuroimaging studies have shown that information processing is functionally abnormal in patients with first-episode and chronic schizophrenia. Although pharmacological treatments for schizophrenia can relieve psychotic symptoms, such drugs generally do not lead to substantial improvements in social, cognitive and occupational functioning. Psychosocial interventions such as cognitive-behavioural therapy, cognitive remediation and supported education and employment have added treatment value, but are inconsistently applied. Given that schizophrenia starts many years before a diagnosis is typically made, the identification of individuals at risk and those in the early phases of the disorder, and the exploration of preventive approaches are crucial.

Mapping of Craniofacial Traits in Outbred Mice Identifies Major Developmental Genes Involved in Shape Determination

The vertebrate cranium is a prime example of the high evolvability of complex traits. While evidence of genes and developmental pathways underlying craniofacial shape determination is accumulating, we are still far from understanding how such variation at the genetic level is translated into craniofacial shape variation. Here we used 3D geometric morphometrics to map genes involved in shape determination in a population of outbred mice (Carworth Farms White, or CFW). We defined shape traits via principal component analysis of 3D skull and mandible measurements. We mapped genetic loci associated with shape traits at ~80,000 candidate single nucleotide polymorphisms in ~700 male mice. We found that craniofacial shape and size are highly heritable, polygenic traits. Despite the polygenic nature of the traits, we identified 17 loci that explain variation in skull shape, and 8 loci associated with variation in mandible shape. Together, the associated variants account for 11.4% of skull and 4.4% of mandible shape variation, however, the total additive genetic variance associated with phenotypic variation was estimated in ~45%. Candidate genes within the associated loci have known roles in craniofacial development; this includes 6 transcription factors and several regulators of bone developmental pathways. One gene, Mn1, has an unusually large effect on shape variation in our study. A knockout of this gene was previously shown to affect negatively the development of membranous bones of the cranial skeleton, and evolutionary analysis shows that the gene has arisen at the base of the bony vertebrates (Eutelostomi), where the ossified head first appeared. Therefore, Mn1 emerges as a key gene for both skull formation and within-population shape variation. Our study shows that it is possible to identify important developmental genes through genome-wide mapping of high-dimensional shape features in an outbred population.

Formation of the face, mandible, and skull is determined in part by genetic factors, but the relationship between genetic variation and craniofacial development is not well understood. We demonstrate how recent advances in mouse genomics and statistical methods can be used to identify genes involved in craniofacial development. We use outbred mice together with a dense panel of genetic markers to identify genetic loci affecting craniofacial shape. Some of the loci we identify are also known from past studies to contribute to craniofacial development and bone formation. For example, the top candidate gene identified in this study, Mn1, is a gene that appeared at a time when animals started to form bony skulls, suggesting that it may be a key gene in this evolutionary innovation. This further suggests that Mn1 and other genes involved in head formation are also responsible for more fine-grained regulation of its shape. Our results confirm that the outbred mouse population used in this study is suitable to identify single genetic factors even under conditions where many genes cooperate to generate a complex phenotype.

Population genetic differentiation of height and body mass index across Europe

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10(-8); BMI, P < 5.95 × 10(-4)), and we find an among-population genetic correlation for tall and slender individuals (r = -0.80, 95% CI = -0.95, -0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58).

Height-reducing variants and selection for short stature in Sardinia

We report sequencing-based whole-genome association analyses to evaluate the impact of rare and founder variants on stature in 6,307 individuals on the island of Sardinia. We identify two variants with large effects. One variant, which introduces a stop codon in the GHR gene, is relatively frequent in Sardinia (0.87% versus <0.01% elsewhere) and in the homozygous state causes Laron syndrome involving short stature. We find that this variant reduces height in heterozygotes by an average of 4.2 cm (-0.64 s.d.). The other variant, in the imprinted KCNQ1 gene (minor allele frequency (MAF) = 7.7% in Sardinia versus <1% elsewhere) reduces height by an average of 1.83 cm (-0.31 s.d.) when maternally inherited. Additionally, polygenic scores indicate that known height-decreasing alleles are at systematically higher frequencies in Sardinians than would be expected by genetic drift. The findings are consistent with selection for shorter stature in Sardinia and a suggestive human example of the proposed 'island effect' reducing the size of large mammals.

Height and Breast Cancer Risk: Evidence From Prospective Studies and Mendelian Randomization

BACKGROUND

Epidemiological studies have linked adult height with breast cancer risk in women. However, the magnitude of the association, particularly by subtypes of breast cancer, has not been established. Furthermore, the mechanisms of the association remain unclear.

METHODS

We performed a meta-analysis to investigate associations between height and breast cancer risk using data from 159 prospective cohorts totaling 5216302 women, including 113178 events. In a consortium with individual-level data from 46325 case patients and 42482 control patients, we conducted a Mendelian randomization analysis using a genetic score that comprised 168 height-associated variants as an instrument. This association was further evaluated in a second consortium using summary statistics data from 16003 case patients and 41335 control patients.

RESULTS

The pooled relative risk of breast cancer was 1.17 (95% confidence interval [CI] = 1.15 to 1.19) per 10cm increase in height in the meta-analysis of prospective studies. In Mendelian randomization analysis, the odds ratio of breast cancer per 10cm increase in genetically predicted height was 1.22 (95% CI = 1.13 to 1.32) in the first consortium and 1.21 (95% CI = 1.05 to 1.39) in the second consortium. The association was found in both premenopausal and postmenopausal women but restricted to hormone receptor-positive breast cancer. Analyses of height-associated variants identified eight new loci associated with breast cancer risk after adjusting for multiple comparisons, including three loci at 1q21.2, DNAJC27, and CCDC91 at genome-wide significance level P < 5×10(-8).

CONCLUSIONS

Our study provides strong evidence that adult height is a risk factor for breast cancer in women and certain genetic factors and biological pathways affecting adult height have an important role in the etiology of breast cancer.

Prediction of male-pattern baldness from genotypes

The global demand for products that effectively prevent the development of male-pattern baldness (MPB) has drastically increased. However, there is currently no established genetic model for the estimation of MPB risk. We conducted a prediction analysis using single-nucleotide polymorphisms (SNPs) identified from previous GWASs of MPB in a total of 2725 German and Dutch males. A logistic regression model considering the genotypes of 25 SNPs from 12 genomic loci demonstrates that early-onset MPB risk is predictable at an accuracy level of 0.74 when 14 SNPs were included in the model, and measured using the area under the receiver-operating characteristic curves (AUC). Considering age as an additional predictor, the model can predict normal MPB status in middle-aged and elderly individuals at a slightly lower accuracy (AUC 0.69-0.71) when 6-11 SNPs were used. A variance partitioning analysis suggests that 55.8% of early-onset MPB genetic liability can be explained by common autosomal SNPs and 23.3% by X-chromosome SNPs. For normal MPB status in elderly individuals, the proportion of explainable variance is lower (42.4% for autosomal and 9.8% for X-chromosome SNPs). The gap between GWAS findings and the variance partitioning results could be explained by a large body of common DNA variants with small effects that will likely be identified in GWAS of increased sample sizes. Although the accuracy obtained here has not reached a clinically desired level, our model was highly informative for up to 19% of Europeans, thus may assist decision making on early MPB intervention actions and in forensic investigations.

Genome-wide genetic homogeneity between sexes and populations for human height and body mass index

Sex-specific genetic effects have been proposed to be an important source of variation for human complex traits. Here we use two distinct genome-wide methods to estimate the autosomal genetic correlation (rg) between men and women for human height and body mass index (BMI), using individual-level (n = ∼44 000) and summary-level (n = ∼133 000) data from genome-wide association studies. Results are consistent and show that the between-sex genetic correlation is not significantly different from unity for both traits. In contrast, we find evidence of genetic heterogeneity between sexes for waist-hip ratio (rg = ∼0.7) and between populations for BMI (rg = ∼0.9 between Europe and the USA) but not for height. The lack of evidence for substantial genetic heterogeneity for body size is consistent with empirical findings across traits and species.

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (∼0.03-0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.

A Non-Synonymous HMGA2 Variant Decreases Height in Shetland Ponies and Other Small Horses

The identification of quantitative trait loci (QTL) such as height and their underlying causative variants is still challenging and often requires large sample sizes. In humans hundreds of loci with small effects control the heritable portion of height variability. In domestic animals, typically only a few loci with comparatively large effects explain a major fraction of the heritability. We investigated height at withers in Shetland ponies and mapped a QTL to ECA 6 by genome-wide association (GWAS) using a small cohort of only 48 animals and the Illumina equine SNP70 BeadChip. Fine-mapping revealed a shared haplotype block of 793 kb in small Shetland ponies. The HMGA2 gene, known to be associated with height in horses and many other species, was located in the associated haplotype. After closing a gap in the equine reference genome we identified a non-synonymous variant in the first exon of HMGA2 in small Shetland ponies. The variant was predicted to affect the functionally important first AT-hook DNA binding domain of the HMGA2 protein (c.83G>A; p.G28E). We assessed the functional impact and found impaired DNA binding of a peptide with the mutant sequence in an electrophoretic mobility shift assay. This suggests that the HMGA2 variant also affects DNA binding in vivo and thus leads to reduced growth and a smaller stature in Shetland ponies. The identified HMGA2 variant also segregates in several other pony breeds but was not found in regular-sized horse breeds. We therefore conclude that we identified a quantitative trait nucleotide for height in horses.

New insights into craniofacial malformations

Development of the human skull and face is a highly orchestrated and complex three-dimensional morphogenetic process, involving hundreds of genes controlling the coordinated patterning, proliferation and differentiation of tissues having multiple embryological origins. Craniofacial malformations that occur because of abnormal development (including cleft lip and/or palate, craniosynostosis and facial dysostoses), comprise over one-third of all congenital birth defects. High-throughput sequencing has recently led to the identification of many new causative disease genes and functional studies have clarified their mechanisms of action. We present recent findings in craniofacial genetics and discuss how this information together with developmental studies in animal models is helping to increase understanding of normal craniofacial development.

Season of birth is associated with birth weight, pubertal timing, adult body size and educational attainment: a UK Biobank study

Season of birth, a marker of in utero vitamin D exposure, has been associated with a wide range of health outcomes. Using a dataset of ∼450,000 participants from the UK Biobank study, we aimed to assess the impact of this seasonality on birth weight, age at menarche, adult height and body mass index (BMI). Birth weight, age at menarche and height, but not BMI, were highly significantly associated with season of birth. Individuals born in summer (June-July-August) had higher mean birth weight (P = 8 × 10^-10), later pubertal development (P = 1.1 × 10^-45) and taller adult height (P = 6.5 × 10^-9) compared to those born in all other seasons. Concordantly, those born in winter (December-January-February) showed directionally opposite differences in these outcomes. A secondary comparison of the extreme differences between months revealed higher odds ratios [95% confidence intervals (CI)] for low birth weight in February vs. September (1.23 [1.15-1.32], P = 4.4 × 10^-10), for early puberty in September vs. July (1.22 [1.16-1.28], P = 7.3 × 10^-15) and for short stature in December vs. June (1.09 [1.03-1.17], P = 0.006). The above associations were also seen with total hours of sunshine during the second trimester, but not during the first three months after birth. Additional associations were observed with educational attainment; individuals born in autumn vs. summer were more likely to continue in education post age 16 years (P = 1.1 × 10^-91) or attain a degree-level qualification (P = 4 × 10^-7). However, unlike other outcomes, an abrupt difference was seen between those born in August vs. September, which flank the start of the school year. Our findings provide support for the 'fetal programming' hypothesis, refining and extending the impact that season of birth has on childhood growth and development. Whilst other mechanisms may contribute to these associations, these findings are consistent with a possible role of in utero vitamin D exposure.

Understanding Language from a Genomic Perspective

Language is a defining characteristic of the human species, but its foundations remain mysterious. Heritable disorders offer a gateway into biological underpinnings, as illustrated by the discovery that FOXP2 disruptions cause a rare form of speech and language impairment. The genetic architecture underlying language-related disorders is complex, and although some progress has been made, it has proved challenging to pinpoint additional relevant genes with confidence. Next-generation sequencing and genome-wide association studies are revolutionizing understanding of the genetic bases of other neurodevelopmental disorders, like autism and schizophrenia, and providing fundamental insights into the molecular networks crucial for typical brain development. We discuss how a similar genomic perspective, brought to the investigation of language-related phenotypes, promises to yield equally informative discoveries. Moreover, we outline how follow-up studies of genetic findings using cellular systems and animal models can help to elucidate the biological mechanisms involved in the development of brain circuits supporting language.

Monitoring human growth and development: a continuum from the womb to the classroom

A comprehensive set of fully integrated anthropometric measures is needed to evaluate human growth from conception to infancy so that consistent judgments can be made about the appropriateness of fetal and infant growth. At present, there are 2 barriers to this strategy. First, descriptive reference charts, which are derived from local, unselected samples with inadequate methods and poor characterization of their putatively healthy populations, commonly are used rather than prescriptive standards. The use of prescriptive standards is justified by the extensive biologic, genetic, and epidemiologic evidence that skeletal growth is similar from conception to childhood across geographic populations, when health, nutrition, environmental, and health care needs are met. Second, clinicians currently screen fetuses, newborn infants, and infants at all levels of care with a wide range of charts and cutoff points, often with limited appreciation of the underlying population or quality of the study that generated the charts. Adding to the confusion, infants are evaluated after birth with a single prescriptive tool: the World Health Organization Child Growth Standards, which were derived from healthy, breastfed newborn infants, infants, and young children from populations that have been exposed to few growth-restricting factors. The International Fetal and Newborn Growth Consortium for the 21st Century Project addressed these issues by providing international standards for gestational age estimation, first-trimester fetal size, fetal growth, newborn size for gestational age, and postnatal growth of preterm infants, all of which complement the World Health Organization Child Growth Standards conceptually, methodologically, and analytically. Hence, growth and development can now, for the first time, be monitored globally across the vital first 1000 days and all the way to 5 years of age. It is clear that an integrative approach to monitoring growth and development from pregnancy to school age is desirable, scientifically supported, and likely to improve care, referral patterns, and reporting systems. Such integration can be achieved only through the use of international growth standards, especially in increasingly diverse, mixed ancestry populations. Resistance to new scientific developments has been hugely problematic in medicine; however, we are confident that the obstetric and neonatal communities will join their pediatric colleagues worldwide in the adoption of this integrative strategy.

Predictors of Pulmonary Infarction

In the setting of acute pulmonary embolism (PE), pulmonary infarction is deemed to occur primarily in individuals with compromised cardiac function.The current study was undertaken to establish the prevalence of pulmonary infarction in patients with acute PE, and the relationship between infarction and: age, body height, body mass index (BMI), smoking habits, clot burden, and comorbidities.The authors studied prospectively 335 patients with acute PE diagnosed by computed tomographic angiography (CT) in 18 hospitals throughout central Italy. The diagnosis of pulmonary infarction on CT was based on Hampton and Castleman's criteria (cushion-like or hemispherical consolidation lying along the visceral pleura). Multivariable logistic regression was used to model the relationship between covariates and the probability of pulmonary infarction.The prevalence of pulmonary infarction was 31%. Patients with infarction were significantly younger and with significantly lower prevalence of cardiovascular disease than those without (P < 0.001). The frequency of infarction increased linearly with increasing height, and decreased with increasing BMI. In logistic regression, the covariates significantly associated with the probability of infarction were age, body height, BMI, and current smoking. The risk of infarction grew with age, peaked at approximately age 40, and decreased afterwards. Increasing body height and current smoking were significant amplifiers of the risk of infarction, whereas increasing BMI appeared to confer some protection.Our data indicate that pulmonary infarction occurs in nearly one-third of the patients with acute PE. Those with infarction are often young and otherwise healthy. Increasing body height and active smoking are predisposing risk factors.

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Clinical and genetic characterization of pituitary gigantism: an international collaborative study in 208 patients

Despite being a classical growth disorder, pituitary gigantism has not been studied previously in a standardized way. We performed a retrospective, multicenter, international study to characterize a large series of pituitary gigantism patients. We included 208 patients (163 males; 78.4%) with growth hormone excess and a current/previous abnormal growth velocity for age or final height >2 s.d. above country normal means. The median onset of rapid growth was 13 years and occurred significantly earlier in females than in males; pituitary adenomas were diagnosed earlier in females than males (15.8 vs 21.5 years respectively). Adenomas were ≥10 mm (i.e., macroadenomas) in 84%, of which extrasellar extension occurred in 77% and invasion in 54%. GH/IGF1 control was achieved in 39% during long-term follow-up. Final height was greater in younger onset patients, with larger tumors and higher GH levels. Later disease control was associated with a greater difference from mid-parental height (r=0.23, P=0.02). AIP mutations occurred in 29%; microduplication at Xq26.3 - X-linked acrogigantism (X-LAG) - occurred in two familial isolated pituitary adenoma kindreds and in ten sporadic patients. Tumor size was not different in X-LAG, AIP mutated and genetically negative patient groups. AIP-mutated and X-LAG patients were significantly younger at onset and diagnosis, but disease control was worse in genetically negative cases. Pituitary gigantism patients are characterized by male predominance and large tumors that are difficult to control. Treatment delay increases final height and symptom burden. AIP mutations and X-LAG explain many cases, but no genetic etiology is seen in >50% of cases.

Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index

We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60-70% for height and 30-40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis

The identification of small molecules that target specific CFTR variants has ushered in a new era of treatment for cystic fibrosis (CF), yet optimal, individualized treatment of CF will require identification and targeting of disease modifiers. Here we use genome-wide association analysis to identify genetic modifiers of CF lung disease, the primary cause of mortality. Meta-analysis of 6,365 CF patients identifies five loci that display significant association with variation in lung disease. Regions on chr3q29 (MUC4/MUC20; P=3.3 × 10(-11)), chr5p15.3 (SLC9A3; P=6.8 × 10(-12)), chr6p21.3 (HLA Class II; P=1.2 × 10(-8)) and chrXq22-q23 (AGTR2/SLC6A14; P=1.8 × 10(-9)) contain genes of high biological relevance to CF pathophysiology. The fifth locus, on chr11p12-p13 (EHF/APIP; P=1.9 × 10(-10)), was previously shown to be associated with lung disease. These results provide new insights into potential targets for modulating lung disease severity in CF.

Approximately independent linkage disequilibrium blocks in human populations

We present a method to identify approximately independent blocks of linkage disequilibrium in the human genome. These blocks enable automated analysis of multiple genome-wide association studies.

AVAILABILITY AND IMPLEMENTATION

code: http://bitbucket.org/nygcresearch/ldetect; data: http://bitbucket.org/nygcresearch/ldetect-data.

CONTACT

tberisa@nygenome.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

De novo mutations in ARID1B associated with both syndromic and non-syndromic short stature

Background

Human height is a complex trait with a strong genetic basis. Recently, a significant association between rare copy number variations (CNVs) and short stature has been identified, and candidate genes in these rare CNVs are being explored. This study aims to evaluate the association between mutations in ARID1B gene and short stature, both the syndromic and non-syndromic form.

Results

Based on a case-control study of whole genome chromosome microarray analysis (CMA), three overlapping CNVs were identified in patients with developmental disorders who exhibited short stature. ARID1B, a causal gene for Coffin Siris syndrome, is the only gene encompassed by all three CNVs. A following retrospective genotype-phenotype analysis based on a literature review confirmed that short stature is a frequent feature in those Coffin-Siris syndrome patients with ARID1B mutations. Mutation screening of ARID1B coding regions was further conducted in a cohort of 48 non-syndromic short stature patients,andfour novel missense variants including two de novo mutations were found.

Conclusion

These results suggest that haploinsufficient mutations of ARID1B are associated with syndromic short stature including Coffin-Siris syndrome and intellectual disability, while rare missense variants in ARID1B are associated with non-syndromic short stature. This study supports the notion that mutations in genes related to syndromic short stature may exert milder effect and contribute to short stature in the general population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1898-1) contains supplementary material, which is available to authorized users.

Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P < 10(-16)), the R(2) for TA increased by 123% (0.0154 to 0.0344; P < 10(-16)), and the liability-scale R(2) for BCC increased by 68% (0.0138-0.0232; P < 10(-16)) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.