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

Analysis of case-control association studies with known risk variants

MOTIVATION

The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature.

RESULTS

We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants.

AVAILABILITY

LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/.

Identification of QTL for UV-protective eye area pigmentation in cattle by progeny phenotyping and genome-wide association analysis

Pigmentation patterns allow for the differentiation of cattle breeds. A dominantly inherited white head is characteristic for animals of the Fleckvieh (FV) breed. However, a minority of the FV animals exhibits peculiar pigmentation surrounding the eyes (ambilateral circumocular pigmentation, ACOP). In areas where animals are exposed to increased solar ultraviolet radiation, ACOP is associated with a reduced susceptibility to bovine ocular squamous cell carcinoma (BOSCC, eye cancer). Eye cancer is the most prevalent malignant tumour affecting cattle. Selection for animals with ACOP rapidly reduces the incidence of BOSCC. To identify quantitative trait loci (QTL) underlying ACOP, we performed a genome-wide association study using 658,385 single nucleotide polymorphisms (SNPs). The study population consisted of 3579 bulls of the FV breed with a total of 320,186 progeny with phenotypes for ACOP. The proportion of progeny with ACOP was used as a quantitative trait with high heritability (h² = 0.79). A variance component based approach to account for population stratification uncovered twelve QTL regions on seven chromosomes. The identified QTL point to MCM6, PAX3, ERBB3, KITLG, LEF1, DKK2, KIT, CRIM1, ATRN, GSDMC, MITF and NBEAL2 as underlying genes for eye area pigmentation in cattle. The twelve QTL regions explain 44.96% of the phenotypic variance of the proportion of daughters with ACOP. The chromosomes harbouring significantly associated SNPs account for 54.13% of the phenotypic variance, while another 19.51% of the phenotypic variance is attributable to chromosomes without identified QTL. Thus, the missing heritability amounts to 7% only. Our results support a polygenic inheritance pattern of ACOP in cattle and provide the basis for efficient genomic selection of animals that are less susceptible to serious eye diseases.

Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses

In this article, we review some of the data that contribute to our understanding of the genetic architecture of psychiatric disorders. These include results from evolutionary modelling (hence no data), the observed recurrence risk to relatives and data from molecular markers. We briefly discuss the common-disease common-variant hypothesis, the success (or otherwise) of genome-wide association studies, the evidence for polygenic variance and the likely success of exome and whole-genome sequencing studies. We conclude that the perceived dichotomy between 'common' and 'rare' variants is not only false, but unhelpful in making progress towards increasing our understanding of the genetic basis of psychiatric disorders. Strong evidence has been accumulated that is consistent with the contribution of many genes to risk of disease, across a wide range of allele frequencies and with a substantial proportion of genetic variation in the population in linkage disequilibrium with single-nucleotide polymorphisms (SNPs) on commercial genotyping arrays. At the same time, most causal variants that segregate in the population are likely to be rare and in total these variants also explain a significant proportion of genetic variation. It is the combination of allele frequency, effect size and functional characteristics that will determine the success of new experimental paradigms such as whole exome/genome sequencing to detect such loci. Empirical results suggest that roughly half the genetic variance is tagged by SNPs on commercial genome-wide chips, but that individual causal variants have a small effect size, on average. We conclude that larger experimental sample sizes are essential to further our understanding of the biology underlying psychiatric disorders.

Quantitative genetics in the genomics era

The genetic analysis of quantitative or complex traits has been based mainly on statistical quantities such as genetic variances and heritability. These analyses continue to be developed, for example in studies of natural populations. Genomic methods are having an impact on progress and prospects. Actual relationships of individuals can be estimated enabling novel quantitative analyses. Increasing precision of linkage mapping is feasible with dense marker panels and designed stocks allowing multiple generations of recombination, and large SNP panels enable the use of genome wide association analysis utilising historical recombination. Whilst such analyses are identifying many loci for disease genes and traits such as height, typically each individually contributes a small amount of the variation. Only by fitting all SNPs without regard to significance can a high proportion be accounted for, so a classical polygenic model with near infinitesimally small effects remains a useful one. Theory indicates that a high proportion of variants will have low minor allele frequency, making detection difficult. Genomic selection, based on simultaneously fitting very dense markers and incorporating these with phenotypic data in breeding value prediction is revolutionising breeding programmes in agriculture and has a major potential role in human disease prediction.

A genome-wide meta-analysis of association studies of Cloninger's Temperament Scales

Temperament has a strongly heritable component, yet multiple independent genome-wide studies have failed to identify significant genetic associations. We have assembled the largest sample to date of persons with genome-wide genotype data, who have been assessed with Cloninger's Temperament and Character Inventory. Sum scores for novelty seeking, harm avoidance, reward dependence and persistence have been measured in over 11,000 persons collected in four different cohorts. Our study had >80% power to identify genome-wide significant loci (P<1.25 × 10(-8), with correction for testing four scales) accounting for ≥0.4% of the phenotypic variance in temperament scales. Using meta-analysis techniques, gene-based tests and pathway analysis we have tested over 1.2 million single-nucleotide polymorphisms (SNPs) for association to each of the four temperament dimensions. We did not discover any SNPs, genes, or pathways to be significantly related to the four temperament dimensions, after correcting for multiple testing. Less than 1% of the variability in any temperament dimension appears to be accounted for by a risk score derived from the SNPs showing strongest association to the temperament dimensions. Elucidation of genetic loci significantly influencing temperament and personality will require potentially very large samples, and/or a more refined phenotype. Item response theory methodology may be a way to incorporate data from cohorts assessed with multiple personality instruments, and might be a method by which a large sample of a more refined phenotype could be acquired.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.

Evolutionary molecular medicine

Evolution has long provided a foundation for population genetics, but some major advances in evolutionary biology from the twentieth century that provide foundations for evolutionary medicine are only now being applied in molecular medicine. They include the need for both proximate and evolutionary explanations, kin selection, evolutionary models for cooperation, competition between alleles, co-evolution, and new strategies for tracing phylogenies and identifying signals of selection. Recent advances in genomics are transforming evolutionary biology in ways that create even more opportunities for progress at its interfaces with genetics, medicine, and public health. This article reviews 15 evolutionary principles and their applications in molecular medicine in hopes that readers will use them and related principles to speed the development of evolutionary molecular medicine.

Patterns of ancestry, signatures of natural selection, and genetic association with stature in Western African pygmies

African Pygmy groups show a distinctive pattern of phenotypic variation, including short stature, which is thought to reflect past adaptation to a tropical environment. Here, we analyze Illumina 1M SNP array data in three Western Pygmy populations from Cameroon and three neighboring Bantu-speaking agricultural populations with whom they have admixed. We infer genome-wide ancestry, scan for signals of positive selection, and perform targeted genetic association with measured height variation. We identify multiple regions throughout the genome that may have played a role in adaptive evolution, many of which contain loci with roles in growth hormone, insulin, and insulin-like growth factor signaling pathways, as well as immunity and neuroendocrine signaling involved in reproduction and metabolism. The most striking results are found on chromosome 3, which harbors a cluster of selection and association signals between approximately 45 and 60 Mb. This region also includes the positional candidate genes DOCK3, which is known to be associated with height variation in Europeans, and CISH, a negative regulator of cytokine signaling known to inhibit growth hormone-stimulated STAT5 signaling. Finally, pathway analysis for genes near the strongest signals of association with height indicates enrichment for loci involved in insulin and insulin-like growth factor signaling.

Africa is thought to be the location of origin of modern humans within the past 200,000 years and the source of our dispersion across the globe within the past 100,000 years. Africa is also a region of extreme environmental, cultural, linguistic, and phenotypic diversity, and human populations living there show the highest levels of genetic diversity in the world. Yet little is known about the genetic basis of the observed phenotypic variation in Africa or how local adaptation and demography have influenced these patterns in the recent past. Here, we analyze a set of admixing Bantu-speaking agricultural and Western Pygmy hunter-gatherer populations that show extreme differences in stature; Pygmies are ∼17 cm shorter on average than their Bantu neighbors and among the shortest populations globally. Our multifaceted approach identified several genomic regions that may have been targets of natural selection and so may harbor variants underlying the unique anatomy and physiology of Western African Pygmies. One region of chromosome three, in particular, harbors strong signals of natural selection, population differentiation, and association with height. This region also contains a significant association with height in Europeans as well as a candidate gene known to regulate growth hormone signaling.

Polygenic effects of common single-nucleotide polymorphisms on life span: when association meets causality

Recently we have shown that the human life span is influenced jointly by many common single-nucleotide polymorphisms (SNPs), each with a small individual effect. Here we investigate further the polygenic influence on life span and discuss its possible biological mechanisms. First we identified six sets of prolongevity SNP alleles in the Framingham Heart Study 550K SNPs data, using six different statistical procedures (normal linear, Cox, and logistic regressions; generalized estimation equation; mixed model; gene frequency method). We then estimated joint effects of these SNPs on human survival. We found that alleles in each set show significant additive influence on life span. Twenty-seven SNPs comprised the overlapping set of SNPs that influenced life span, regardless of the statistical procedure. The majority of these SNPs (74%) were within genes, compared to 40% of SNPs in the original 550K set. We then performed a review of current literature on functions of genes closest to these 27 SNPs. The review showed that the respective genes are largely involved in aging, cancer, and brain disorders. We concluded that polygenic effects can explain a substantial portion of genetic influence on life span. Composition of the set of prolongevity alleles depends on the statistical procedure used for the allele selection. At the same time, there is a core set of longevity alleles that are selected with all statistical procedures. Functional relevance of respective genes to aging and major diseases supports causal relationships between the identified SNPs and life span. The fact that genes found in our and other genetic association studies of aging/longevity have similar functions indicates high chances of true positive associations for corresponding genetic variants.

A two-platform design for next generation genome-wide association studies

Genome-wide association studies (GWAS) have been successful in their search for common genetic variants associated with complex traits and diseases. With new advances in array technologies together with available genetic reference sets, the next generation of GWAS will extend the search for associations with uncommon SNPs (1% ≤ MAF ≤ 10%). Two possible approaches are genotyping all participants, a prohibitively expensive option for large GWAS, or using a combination of genotyping and imputation. Here, we consider a two platform method that genotypes all participants on a standard genotyping array, designed to identify common variants, and then supplements that data by genotyping only a small proportion of the participants on a platform that has higher coverage for uncommon SNPs. This subset of the study population is then included as part of the imputation reference set. To demonstrate the use of this two-platform design, we evaluate its potential efficiency using a newly available dataset containing 756 individuals genotyped on both the Illumina Human OmniExpress and Omni2.5 Quad. Although genotyping all individuals on the denser array would be ideal, we find that genotyping only 100 individuals on this array, in combination with imputation, leads to only a modest loss of power for detecting associations. However, the loss of power due to imputation can be more substantial if the relative risks for rare variants are significantly larger than those previously observed for common variants.

Common variants at 12q15 and 12q24 are associated with infant head circumference

To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 × 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 × 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height, their effects on infant head circumference were largely independent of height (P = 3.8 × 10(-7) for rs7980687 and P = 1.3 × 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 × 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume, Parkinson's disease and other neurodegenerative diseases, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.

Identification of common variants associated with human hippocampal and intracranial volumes

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).

Common variants at 6q22 and 17q21 are associated with intracranial volume

During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study (GWAS) in 8,175 community-dwelling elderly persons did not reveal any associations at genome-wide significance (P < 5 × 10(-8)) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (P = 3.4 × 10(-11)), a known height-associated locus on chromosome 6q22, and rs9915547 (P = 1.5 × 10(-12)), localized to the inversion on chromosome 17q21. We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 elderly persons (P = 1.1 × 10(-3) for 6q22 and 1.2 × 10(-3) for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age of 14.5 months). Our data identify two loci associated with head size, with the inversion at 17q21 also likely to be involved in attaining maximal brain size.

Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

Parameters in dynamic models of complex traits are containers of missing heritability

Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology.

Despite an ever-increasing number of genome locations reported to be associated with complex human diseases or quantitative traits, only a small proportion of phenotypic variations in a typical quantitative trait can be explained by the discovered variants. We argue that this problem can partly be resolved by combining the statistical methods of quantitative genetics with computational biology. We demonstrate this for the in silico genotype-to-phenotype map of a model heart cell in conjunction with publically accessible genomic data. We show that genome wide association studies (GWAS) on model parameters identify more causal variants and can build better prediction models for the higher-level phenotypes than by performing GWAS on the higher-level phenotypes themselves. Since model parameters are in principle measurable physiological phenotypes, our findings suggest that development of future phenotyping technologies could be guided by mathematical models of the biological systems being targeted.

Integrating pathway analysis and genetics of gene expression for genome-wide association study of basal cell carcinoma

Genome-wide association studies (GWASs) have primarily focused on marginal effects for individual markers and have incorporated external functional information only after identifying robust statistical associations. We applied a new approach combining the genetics of gene expression and functional classification of genes to the GWAS of basal cell carcinoma (BCC) to identify potential biological pathways associated with BCC. We first identified 322,324 expression-associated single-nucleotide polymorphisms (eSNPs) from two existing GWASs of global gene expression in lymphoblastoid cell lines (n = 955), and evaluated the association of these functionally annotated SNPs with BCC among 2,045 BCC cases and 6,013 controls in Caucasians. We then grouped them into 99 KEGG pathways for pathway analysis and identified two pathways associated with BCC with p value <0.05 and false discovery rate (FDR) <0.5: the autoimmune thyroid disease pathway (mainly HLA class I and II antigens, p < 0.001, FDR = 0.24) and Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway (p = 0.02, FDR = 0.49). Seventy-nine (25.7%) out of 307 significant eSNPs in the JAK-STAT pathway were associated with BCC risk (p < 0.05) in an independent replication set of 278 BCC cases and 1,262 controls. In addition, the association of JAK-STAT signaling pathway was marginally validated using 16,691 eSNPs identified from 110 normal skin samples (p = 0.08). Based on the evidence of biological functions of the JAK-STAT pathway on oncogenesis, it is plausible that this pathway is involved in BCC pathogenesis.

Child development and molecular genetics: 14 years later

Fourteen years ago, the first article on molecular genetics was published in this journal: Child Development, Molecular Genetics, andWhat to Do With Genes Once They Are Found (R. Plomin & M. Rutter, 1998). The goal of the article was to outline what developmentalists can do with genes once they are found. These new directions for developmental research are still relevant today. The problem lies with the phrase “once they are found”: It has been much more difficult than expected to identify genes responsible for the heritability of complex traits and common disorders, the so-called missing heritability problem. The present article considers reasons for the missing heritability problem and possible solutions.

Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits

We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h²) and heritability explained by the common SNPs (h_g²) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h² accounted for by common SNPs to be 58% of h² for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.

The narrow-sense heritability of a trait such as body-mass index is a measure of the variability of the trait between people that is accounted for by their additive genetic differences. Knowledge of these genetic differences provides insight into biological mechanisms and hence treatments for diseases. Genome-wide association studies (GWAS) survey a large set of genetic markers common to the population. They have identified several single markers that are associated with traits and diseases. However, these markers do not seem to account for all of the known narrow-sense heritability. Here we used a recently developed model to quantify the genetic information contained in GWAS for single traits and shared between traits. We specifically investigated metabolic syndrome traits that are associated with type 2 diabetes and heart disease, and we found that for the majority of these traits much of the previously unaccounted for heritability is contained within common markers surveyed in GWAS. We also computed the genetic correlation between traits, which is a measure of the genetic components shared by traits. We found that the genetic correlation between these traits could be predicted from their phenotypic correlation.

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.

A genome-wide search for genetic influences and biological pathways related to the brain's white matter integrity

A genome-wide search for genetic variants influencing the brain's white matter integrity in old age was conducted in the Lothian Birth Cohort 1936 (LBC1936). At ∼73 years of age, members of the LBC1936 underwent diffusion MRI, from which 12 white matter tracts were segmented using quantitative tractography, and tract-averaged water diffusion parameters were determined (n = 668). A global measure of white matter tract integrity, g(FA), derived from principal components analysis of tract-averaged fractional anisotropy measurements, accounted for 38.6% of the individual differences across the 12 white matter tracts. A genome-wide search was performed with g(FA) on 535 individuals with 542,050 single nucleotide polymorphisms (SNPs). No single SNP association was genome-wide significant (all p > 5 × 10(-8)). There was genome-wide suggestive evidence for two SNPs, one in ADAMTS18 (p = 1.65 × 10(-6)), which is related to tumor suppression and hemostasis, and another in LOC388630 (p = 5.08 × 10(-6)), which is of unknown function. Although no gene passed correction for multiple comparisons in single gene-based testing, biological pathways analysis suggested evidence for an over-representation of neuronal transmission and cell adhesion pathways relating to g(FA).

Analysis of microsatellite variation in Drosophila melanogaster with population-scale genome sequencing

Genome sequencing technologies promise to revolutionize our understanding of genetics, evolution, and disease by making it feasible to survey a broad spectrum of sequence variation on a population scale. However, this potential can only be realized to the extent that methods for extracting and interpreting distinct forms of variation can be established. The error profiles and read length limitations of early versions of next-generation sequencing technologies rendered them ineffective for some sequence variant types, particularly microsatellites and other tandem repeats, and fostered the general misconception that such variants are inherently inaccessible to these platforms. At the same time, tandem repeats have emerged as important sources of functional variation. Tandem repeats are often located in and around genes, and frequent mutations in their lengths exert quantitative effects on gene function and phenotype, rapidly degrading linkage disequilibrium between markers and traits. Sensitive identification of these variants in large-scale next-gen sequencing efforts will enable more comprehensive association studies capable of revealing previously invisible associations. We present a population-scale analysis of microsatellite repeats using whole-genome data from 158 inbred isolates from the Drosophila Genetics Reference Panel, a collection of over 200 extensively phenotypically characterized isolates from a single natural population, to uncover processes underlying repeat mutation and to enable associations with behavioral, morphological, and life-history traits. Analysis of repeat variation from next-generation sequence data will also enhance studies of genome stability and neurodegenerative diseases.

Adaptive evolution of loci covarying with the human African Pygmy phenotype

African Pygmies are hunter-gatherer populations from the equatorial rainforest that present the lowest height averages among humans. The biological basis and the putative adaptive role of the short stature of Pygmy populations has been one of the most intriguing topics for human biologists in the last century, which still remains elusive. Worldwide convergent evolution of the Pygmy size suggests the presence of strong selective pressures on the phenotype. We developed a novel approach to survey the genetic architecture of phenotypes and applied it to study the genomic covariation between allele frequencies and height measurements among Pygmy and non-Pygmy populations. Among the regions that were most associated with the phenotype, we identified a significant excess of genes with pivotal roles in bone homeostasis, such as PPPT3B and the height associated SUPT3H-RUNX2. We hypothesize that skeletal remodeling could be a key biological process underlying the Pygmy phenotype. In addition, we showed that these regions have most likely evolved under positive selection. These results constitute the first genetic hint of adaptive evolution in the African Pygmy phenotype, which is consistent with the independent emergence of the Pygmy height in other continents with similar environments.

Allelic expression analysis of the osteoarthritis susceptibility locus that maps to MICAL3

Background

A genome-wide association scan with subsequent replication study that involved over 67,000 individuals of European ancestry has produced evidence of association of single nucleotide polymorphism rs2277831 to primary osteoarthritis (OA) with a P-value of 2.9 × 10^-5. rs2277831, an A/G transition, is located in an intron of MICAL3. This gene is located on chromosome 22q11.21 and the association signal encompasses two additional genes, BCL2L13 and BID. It is becoming increasingly apparent that many common complex traits are mediated by cis-acting regulatory polymorphisms that influence, in a tissue-specific manner, gene expression or transcript stability.

Methods

We used total and allelic expression analysis to assess whether the OA association to rs2277831 is mediated by an influence on MICAL3, BCL2L13 or BID expression. Using RNA extracted from joint tissues of 60 patients who had undergone elective joint replacement surgery, we assessed whether rs2277831 correlated with allelic expression of either of the three genes by: 1) measuring the expression of each gene by quantitative PCR and then stratifying the data by genotype at rs2277831 and 2) accurately discriminating and quantifying the mRNA synthesised from the alleles of OA patients using allelic-quantitative PCR.

Results

We found no evidence for a correlation between gene expression and genotype at rs2277831, with P-values of 0.09 for BCL2L13, 0.07 for BID and 0.33 for MICAL3. In the allelic expression analysis we observed several examples of significant (p < 0.05) allelic imbalances, with an allelic expression ratio of 2.82 observed in BCL2L13 (P = 0.004), 2.09 at BID (P = 0.001) and the most extreme case being at MICAL3, with an allelic expression ratio of 5.47 (P = 0.001). However, there was no correlation observed between the pattern of allelic expression and the genotype at rs2277831.

Conclusions

In the tissues that we have studied, our data do not support our hypothesis that the association between rs2277831 and OA is due to the effect this SNP has on MICAL3, BCL2L13 or BID gene expression. Instead, our data point towards other functional effects accounting for the OA associated signal.

Meta-analysis of genome-wide association studies for personality

Personality can be thought of as a set of characteristics that influence people's thoughts, feelings and behavior across a variety of settings. Variation in personality is predictive of many outcomes in life, including mental health. Here we report on a meta-analysis of genome-wide association (GWA) data for personality in 10 discovery samples (17,375 adults) and five in silico replication samples (3294 adults). All participants were of European ancestry. Personality scores for Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness were based on the NEO Five-Factor Inventory. Genotype data of ≈ 2.4M single-nucleotide polymorphisms (SNPs; directly typed and imputed using HapMap data) were available. In the discovery samples, classical association analyses were performed under an additive model followed by meta-analysis using the weighted inverse variance method. Results showed genome-wide significance for Openness to Experience near the RASA1 gene on 5q14.3 (rs1477268 and rs2032794, P=2.8 × 10(-8) and 3.1 × 10(-8)) and for Conscientiousness in the brain-expressed KATNAL2 gene on 18q21.1 (rs2576037, P=4.9 × 10(-8)). We further conducted a gene-based test that confirmed the association of KATNAL2 to Conscientiousness. In silico replication did not, however, show significant associations of the top SNPs with Openness and Conscientiousness, although the direction of effect of the KATNAL2 SNP on Conscientiousness was consistent in all replication samples. Larger scale GWA studies and alternative approaches are required for confirmation of KATNAL2 as a novel gene affecting Conscientiousness.

Update: consequences of abnormal fetal growth

Intrauterine growth restriction (IUGR) is prevalent worldwide and affects children and adults in multiple ways. These include predisposition to type 2 diabetes mellitus, the metabolic syndrome, cardiovascular disease, persistent reduction in stature, and possibly changes in the pattern of puberty. A review of recent literature confirms that the metabolic effects of being born small for gestational age are evident in the very young, persist with age, and are amplified by adiposity. Furthermore, the pattern of growth in the first few years of life has a significant bearing on a person's later health, with those that show increasing weight gain being at the greatest risk for future metabolic dysfunction. Treatment with exogenous human GH is used to improve height in children who remain short after being small for gestational age at birth, but the response of individuals remains variable and difficult to predict. The mechanisms involved in the metabolic programming of IUGR children are just beginning to be explored. It appears that IUGR leads to widespread changes in DNA methylation and that specific "epigenetic signatures" for IUGR are likely to be found in various fetal tissues. The challenge is to link such alterations with modifications in gene expression and ultimately the metabolic abnormalities of adulthood, and it represents one of the frontiers for research in the field.

Genome-wide association studies of schizophrenia: does bigger lead to better results?

PURPOSE OF REVIEW

Numerous genome-wide association studies (GWAS) of schizophrenia have been published in the past 6 years, with a number of key reports published in the last year. The studies have evolved in scale from small individual samples to large collaborative endeavors. This review aims to critically assess whether the results have improved as the sample size and scale of genetic association studies have grown.

RECENT FINDINGS

Genomic genotyping and increasing sample sizes for schizophrenia association studies have led to parallel increases in the number of risk genes discovered with high statistical confidence. Nearly 20 genes or loci have surpassed the genome-wide significance threshold (P = 5 × 10) in a single study, and several have been replicated in more than one GWAS.

SUMMARY

Identifying the genetic underpinnings of complex diseases offers insight into the etiological mechanisms leading to manifestation of the disease. New and more effective treatments for schizophrenia are desperately needed, and the ability to target the relevant biological processes grows with our understanding of the genes involved. As the size of GWAS samples has increased, more genes have been identified with high confidence that have begun to provide insight into the etiological and pathophysiological foundations of this disorder.

Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes

Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D_ABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules (ABD: 0.48 versus 0.18, P = 0.08; GLU: 0.54 versus 0.20, P = 7.8×10⁻⁴). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10⁻⁴); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10⁻⁴) and BMI–adjusted waist-to-hip ratio (P = 2.4×10⁻⁴). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.

Metabolic Syndrome (MetS) is a highly prevalent disorder with considerable public health concern, but its underlying genetic factors remain elusive. Given that most cellular components exert their functions through interactions with other cellular components, even the largest of genome-wide association (GWA) studies may often not detect their effects, nor necessarily provide insight into the complex molecular mechanisms of the disease. Rather than focusing on individual genes, the analysis of coexpression networks can be used for finding clusters (modules) of correlated expression levels across samples. In this study, we used a gene network–based approach for integrating clinical MetS, genotypic, and gene expression data from abdominal and gluteal adipose tissue and whole blood. We identified modules of genes related to MetS significantly enriched for immune response and oxidative phosphorylation pathways. We tested SNPs for association with MetS–associated expression (eSNPs), and tested prioritised eSNPs for association with MetS–related phenotypes in two large-scale GWA datasets. We identified two loci, neither of which had reached genome-wide significance levels in GWAs, associated with expression levels of RARRES2 and HLA-DRB1 and with MetS–related phenotypes, demonstrating that the integrated analysis of genotype and expression data from relevant multiple tissues can identify novel associations with complex traits such as MetS.

A survey of genomic studies supports association of circadian clock genes with bipolar disorder spectrum illnesses and lithium response

Circadian rhythm abnormalities in bipolar disorder (BD) have led to a search for genetic abnormalities in circadian “clock genes” associated with BD. However, no significant clock gene findings have emerged from genome-wide association studies (GWAS). At least three factors could account for this discrepancy: complex traits are polygenic, the organization of the clock is more complex than previously recognized, and/or genetic risk for BD may be shared across multiple illnesses. To investigate these issues, we considered the clock gene network at three levels: essential “core” clock genes, upstream circadian clock modulators, and downstream clock controlled genes. Using relaxed thresholds for GWAS statistical significance, we determined the rates of clock vs. control genetic associations with BD, and four additional illnesses that share clinical features and/or genetic risk with BD (major depression, schizophrenia, attention deficit/hyperactivity). Then we compared the results to a set of lithium-responsive genes. Associations with BD-spectrum illnesses and lithium-responsiveness were both enriched among core clock genes but not among upstream clock modulators. Associations with BD-spectrum illnesses and lithium-responsiveness were also enriched among pervasively rhythmic clock-controlled genes but not among genes that were less pervasively rhythmic or non-rhythmic. Our analysis reveals previously unrecognized associations between clock genes and BD-spectrum illnesses, partly reconciling previously discordant results from past GWAS and candidate gene studies.

Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs

Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 cases, 12,171 controls and 915,354 imputed SNPs from the Psychiatric GWA Consortium for schizophrenia (PGC-SCZ) we estimate that 23% (s.e. 1%) of variation in liability to schizophrenia is captured by SNPs. We show that an important proportion of this variation must be due to common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, p = 2.6 × 10⁻⁸), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2725 genes expressed in the central nervous system (CNS) (p = 7.6 ×10⁻⁸). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.

Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans

Visual cortical surface area varies two- to threefold between human individuals, is highly heritable, and has been correlated with visual acuity and visual perception. However, it is still largely unknown what specific genetic and environmental factors contribute to normal variation in the area of visual cortex. To identify SNPs associated with the proportional surface area of visual cortex, we performed a genome-wide association study followed by replication in two independent cohorts. We identified one SNP (rs6116869) that replicated in both cohorts and had genome-wide significant association (P(combined) = 3.2 × 10(-8)). Furthermore, a metaanalysis of imputed SNPs in this genomic region identified a more significantly associated SNP (rs238295; P = 6.5 × 10(-9)) that was in strong linkage disequilibrium with rs6116869. These SNPs are located within 4 kb of the 5' UTR of GPCPD1, glycerophosphocholine phosphodiesterase GDE1 homolog (Saccharomyces cerevisiae), which in humans, is more highly expressed in occipital cortex compared with the remainder of cortex than 99.9% of genes genome-wide. Based on these findings, we conclude that this common genetic variation contributes to the proportional area of human visual cortex. We suggest that identifying genes that contribute to normal cortical architecture provides a first step to understanding genetic mechanisms that underlie visual perception.

Genetic variation in myosin 1H contributes to mandibular prognathism

INTRODUCTION

Several candidate loci have been suggested as influencing mandibular prognathism (1p22.1, 1p22.2, 1p36, 3q26.2, 5p13-p12, 6q25, 11q22.2-q22.3, 12q23, 12q13.13, and 19p13.2). The goal of this study was to replicate these results in a well-characterized homogeneous sample set.

METHODS

Thirty-three single nucleotide polymorphisms spanning all candidate regions were studied in 44 prognathic and 35 Class I subjects from the University of Pittsburgh School of Dental Medicine Dental Registry and DNA Repository. The 44 subjects with mandibular prognathism had an average age of 18.4 years; 31 were female and 13 male; and 24 were white, 15 African American, 2 Hispanic, and 3 Asian. The 36 Class I subjects had an average age of 17.6 years; 27 were female and 9 male; and 27 were white, 6 African American, 1 Hispanic, and 2 Asian. Skeletal mandibular prognathism diagnosis included cephalometric values indicative of Class III such as an ANB smaller than 2°, a negative Wits appraisal, and a positive A-B plane. Additional mandibular prognathism criteria included negative overjet and visually prognathic (concave) profile as determined by the subject's clinical evaluation. Orthognathic subjects without jaw deformations were used as the comparison group. The mandibular prognathic and orthognathic subjects were matched by race, sex, and age. Genetic markers were tested by polymerase chain reaction with TaqMan chemistry. Chi-square and Fisher exact tests were used to determine overrepresentation of marker allele with an alpha of 0.05.

RESULTS

An association was unveiled between a marker in MYO1H (rs10850110) and the mandibular prognathism phenotype (P = 0.03). MYO1H is a Class I myosin that is in a different protein group than the myosin isoforms of muscle sarcomeres, which are the basis of skeletal muscle fiber typing. Class I myosins are necessary for cell motility, phagocytosis, and vesicle transport.

CONCLUSIONS

More strict clinical definitions might increase homogeneity and aid the studies of genetic susceptibility to malocclusions. We provide evidence that MYO1H can contribute to mandibular prognathism.

Permutation-based approaches do not adequately allow for linkage disequilibrium in gene-wide multi-locus association analysis

Additional information about risk genes or risk pathways for diseases can be extracted from genome-wide association studies through analyses of groups of markers. The most commonly employed approaches involve combining individual marker data by adding the test statistics, or summing the logarithms of their P-values, and then using permutation testing to derive empirical P-values that allow for the statistical dependence of single-marker tests arising from linkage disequilibrium (LD). In the present study, we use simulated data to show that these approaches fail to reflect the structure of the sampling error, and the effect of this is to give undue weight to correlated markers. We show that the results obtained are internally inconsistent in the presence of strong LD, and are externally inconsistent with the results derived from multi-locus analysis. We also show that the results obtained from regression and multivariate Hotelling T(2) (H-T2) testing, but not those obtained from permutations, are consistent with the theoretically expected distributions, and that the H-T2 test has greater power to detect gene-wide associations in real datasets. Finally, we show that while the results from permutation testing can be made to approximate those from regression and multivariate Hotelling T(2) testing through aggressive LD pruning of markers, this comes at the cost of loss of information. We conclude that when conducting multi-locus analyses of sets of single-nucleotide polymorphisms, regression or multivariate Hotelling T(2) testing, which give equivalent results, are preferable to the other more commonly applied approaches.

DNase I sensitivity QTLs are a major determinant of human expression variation

The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation^1-5. However, it remains difficult to identify the causal variants underlying eQTLs and little is known about the regulatory mechanisms by which they act. To address this gap, we used DNaseI sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines (LCLs), for which genome-wide genotypes and estimates of gene expression levels are also available^6-8. We obtained a total of 2.7 billion uniquely mapped DNase-seq reads, which allowed us to produce genome-wide maps of chromatin accessibility for each individual. We identified 9,595 locations at which DNase-seq read depth correlates significantly with genotype at a nearby SNP or indel (FDR=10%). We call such variants “DNaseI sensitivity Quantitative Trait Loci” (dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial fraction (16%) of dsQTLs are also associated with variation in the expression levels of nearby genes, (namely, these loci are also classified as eQTLs). Conversely, we estimate that as many as 55% of eQTL SNPs are also dsQTLs. Our observations indicate that dsQTLs are highly abundant in the human genome, and are likely to be important contributors to phenotypic variation.

The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals

The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.

Pharmacodynamic responses of plasma and tissue C-type natriuretic peptide to GH: correlation with linear growth in GH-deficient rats

Studies from genetic modification and spontaneous mutations show that C-type natriuretic peptide (CNP) signalling plays an essential part in postnatal endochondral growth, but measurement of CNP proteins and changes in their abundance in tissues and plasma during normal growth has not been reported. Using rodent pups with GH deficiency, we now describe the pharmacodynamic response of CNP and rat amino-terminal proCNP (NTproCNP) in plasma and tissues, and relate these to changes in linear growth (nose-tail length, tibial length and tibial growth plate width) during the course of 1 week of GH or saline (control) administration. Compared with saline, significant increases in plasma and tissue CNP forms were observed after 24 h in GH-treated pups and before any detectable change in linear growth. Whereas CNP abundance was increased in most tissues (muscle, heart and liver) by GH, enrichment was the greatest in extracts from growth plates and kidney. Plasma and tissue concentrations in GH-treated pups were sustained or further increased at 1 week when strong positive associations were found between plasma NTproCNP and linear growth or tissue concentrations. High content of NTproCNP in kidney tissue strongly correlated with plasma concentrations, which is consistent with previous data showing renal extraction of the peptide. In showing a prompt and significant increase in CNP in tissues driving normal endochondral growth, these findings provide further rationale for CNP agonists in the treatment of growth disorders resistant to current therapies and support the use of CNP concentrations as biomarkers of linear growth.

Meta-analysis of new genome-wide association studies of colorectal cancer risk

Colorectal cancer is the second leading cause of cancer death in developed countries. Genome-wide association studies (GWAS) have successfully identified novel susceptibility loci for colorectal cancer. To follow up on these findings, and try to identify novel colorectal cancer susceptibility loci, we present results for GWAS of colorectal cancer (2,906 cases, 3,416 controls) that have not previously published main associations. Specifically, we calculated odds ratios and 95% confidence intervals using log-additive models for each study. In order to improve our power to detect novel colorectal cancer susceptibility loci, we performed a meta-analysis combining the results across studies. We selected the most statistically significant single nucleotide polymorphisms (SNPs) for replication using ten independent studies (8,161 cases and 9,101 controls). We again used a meta-analysis to summarize results for the replication studies alone, and for a combined analysis of GWAS and replication studies. We measured ten SNPs previously identified in colorectal cancer susceptibility loci and found eight to be associated with colorectal cancer (p value range 0.02 to 1.8 × 10(-8)). When we excluded studies that have previously published on these SNPs, five SNPs remained significant at p < 0.05 in the combined analysis. No novel susceptibility loci were significant in the replication study after adjustment for multiple testing, and none reached genome-wide significance from a combined analysis of GWAS and replication. We observed marginally significant evidence for a second independent SNP in the BMP2 region at chromosomal location 20p12 (rs4813802; replication p value 0.03; combined p value 7.3 × 10(-5)). In a region on 5p33.15, which includes the coding regions of the TERT-CLPTM1L genes and has been identified in GWAS to be associated with susceptibility to at least seven other cancers, we observed a marginally significant association with rs2853668 (replication p value 0.03; combined p value 1.9 × 10(-4)). Our study suggests a complex nature of the contribution of common genetic variants to risk for colorectal cancer.

A functional haplotype in EIF2AK3, an ER stress sensor, is associated with lower bone mineral density

EIF2AK3 is a type I transmembrane protein that functions as an endoplasmic reticulum (ER) stress sensor to regulate global protein synthesis. Rare mutations in EIF2AK3 cause Wolcott-Rallison syndrome (OMIM 226980), an autosomal recessive disorder characterized by diabetes, epiphyseal dysplasia, osteoporosis, and growth retardation. To investigate the role of common genetic variation in EIF2AK3 as a determinant of bone mineral density (BMD) and osteoporosis, we sequenced all exons and flanking regions, then genotyped six potentially functional single nucleotide polymorphisms (SNPs) in this gene in 997 Amish subjects for association analysis, and attempted replication in 887 Mexican Americans. We found that the minor allele of a nonsynonymous SNP rs13045 had borderline associations with decreased forearm BMD in both discovery and replication cohorts (unadjusted p = 0.036 and β = -0.007 for the Amish; unadjusted p = 0.031 and β = -0.008 for Mexican Americans). A meta-analysis indicated this association achieved statistical significance in the combined sample (unadjusted p = 0.003; Bonferroni corrected p = 0.009). Rs13045 and three other potentially functional SNPs, a promoter SNP (rs6547787) and two nonsynonymous SNPs (rs867529 and rs1805165), formed two haplotypes: a low-BMD associated haplotype, denoted haplotype B [minor allele frequency (MAF) = 0.311] and a common haplotype A (MAF = 0.676). There were no differences in mRNA expression in lymphoblastoid cell lines between the two haplotypes. However, after treating lymphoblastoid cell lines with thapsigargin to induce ER stress, cell lines with haplotype B showed increased sensitivity to ER stress (p = 0.014) compared with cell lines with haplotype A. Taken together, our results suggest that common nonsynonymous sequence variants in EIF2AK3 have a modest effect on ER stress response and may contribute to the risk for low BMD through this mechanism.

Dissecting the regulatory architecture of gene expression QTLs

Background

Expression quantitative trait loci (eQTLs) are likely to play an important role in the genetics of complex traits; however, their functional basis remains poorly understood. Using the HapMap lymphoblastoid cell lines, we combine 1000 Genomes genotypes and an extensive catalogue of human functional elements to investigate the biological mechanisms that eQTLs perturb.

Results

We use a Bayesian hierarchical model to estimate the enrichment of eQTLs in a wide variety of regulatory annotations. We find that approximately 40% of eQTLs occur in open chromatin, and that they are particularly enriched in transcription factor binding sites, suggesting that many directly impact protein-DNA interactions. Analysis of core promoter regions shows that eQTLs also frequently disrupt some known core promoter motifs but, surprisingly, are not enriched in other well-known motifs such as the TATA box. We also show that information from regulatory annotations alone, when weighted by the hierarchical model, can provide a meaningful ranking of the SNPs that are most likely to drive gene expression variation.

Conclusions

Our study demonstrates how regulatory annotation and the association signal derived from eQTL-mapping can be combined into a single framework. We used this approach to further our understanding of the biology that drives human gene expression variation, and of the putatively causal SNPs that underlie it.

Perioperative genomics

Since the completion of the Human Genome Project 10 years ago, the world has witnessed an incredible progress in human genetics and genomics.(1) This progress was largely driven by the availability of better, faster and cheaper sequencing technology.(2) While it took more than 10 years and more than 1 billion dollars to complete the Human Genome Project,(3-5) an individual in the year 2011 can have his whole genome sequenced within a week for less than $30,000. With cheaper and faster sequencing came a wealth of novel discoveries which makes it timely to review how these newly found insights into the human genome are relevant for perioperative medicine. This article summarises the basics of genetic inheritance, the human genome and modern sequencing methods, as well as genetic variation and how this knowledge may be applied to patient care and research in the perioperative setting.

Common genetic factors for hematological traits in humans

Hematological traits are essential biomedical indicators that are widely used in clinical practice. The elucidation of the etiology that determines an individual's hematological traits would have a substantial impact. Hematological traits are known to be heritable, and it has been suggested that genetic factors contribute significantly to the inter-individual variance of these traits. Here, we review our current knowledge regarding the genetic architecture of hematological traits in humans, most of which has been obtained through recent developments in genome-wide association studies (GWAS). In addition to current knowledge, which is based on the hematological traits of the three major blood-cell lineages (white blood cells; WBC, red blood cells; RBC, and platelets; PLT), we propose future approaches that would be useful as a next step in the post-GWAS era.

Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture

Improvements in technology have reduced the cost of DNA sequencing to the point that the limiting factor for many experiments is the time and reagent cost of sample preparation. We present an approach in which 192 sequencing libraries can be produced in a single day of technician time at a cost of about $15 per sample. These libraries are effective not only for low-pass whole-genome sequencing, but also for simultaneously enriching them in pools of approximately 100 individually barcoded samples for a subset of the genome without substantial loss in efficiency of target capture. We illustrate the power and effectiveness of this approach on about 2000 samples from a prostate cancer study.