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

Identification of Common Genetic Variants Influencing Spontaneous Dizygotic Twinning and Female Fertility

Spontaneous dizygotic (DZ) twinning occurs in 1%-4% of women, with familial clustering and unknown physiological pathways and genetic origin. DZ twinning might index increased fertility and has distinct health implications for mother and child. We performed a GWAS in 1,980 mothers of spontaneous DZ twins and 12,953 control subjects. Findings were replicated in a large Icelandic cohort and tested for association across a broad range of fertility traits in women. Two SNPs were identified (rs11031006 near FSHB, p = 1.54 × 10(-9), and rs17293443 in SMAD3, p = 1.57 × 10(-8)) and replicated (p = 3 × 10(-3) and p = 1.44 × 10(-4), respectively). Based on ∼90,000 births in Iceland, the risk of a mother delivering twins increased by 18% for each copy of allele rs11031006-G and 9% for rs17293443-C. A higher polygenic risk score (PRS) for DZ twinning, calculated based on the results of the DZ twinning GWAS, was significantly associated with DZ twinning in Iceland (p = 0.001). A higher PRS was also associated with having children (p = 0.01), greater lifetime parity (p = 0.03), and earlier age at first child (p = 0.02). Allele rs11031006-G was associated with higher serum FSH levels, earlier age at menarche, earlier age at first child, higher lifetime parity, lower PCOS risk, and earlier age at menopause. Conversely, rs17293443-C was associated with later age at last child. We identified robust genetic risk variants for DZ twinning: one near FSHB and a second within SMAD3, the product of which plays an important role in gonadal responsiveness to FSH. These loci contribute to crucial aspects of reproductive capacity and health.

The genetic basis for ecological adaptation of the Atlantic herring revealed by genome sequencing

Ecological adaptation is of major relevance to speciation and sustainable population management, but the underlying genetic factors are typically hard to study in natural populations due to genetic differentiation caused by natural selection being confounded with genetic drift in subdivided populations. Here, we use whole genome population sequencing of Atlantic and Baltic herring to reveal the underlying genetic architecture at an unprecedented detailed resolution for both adaptation to a new niche environment and timing of reproduction. We identify almost 500 independent loci associated with a recent niche expansion from marine (Atlantic Ocean) to brackish waters (Baltic Sea), and more than 100 independent loci showing genetic differentiation between spring- and autumn-spawning populations irrespective of geographic origin. Our results show that both coding and non-coding changes contribute to adaptation. Haplotype blocks, often spanning multiple genes and maintained by selection, are associated with genetic differentiation.

Divergent associations of height with cardiometabolic disease and cancer: epidemiology, pathophysiology, and global implications

Among chronic non-communicable diseases, cardiometabolic diseases and cancer are the most important causes of morbidity and mortality worldwide. Although high BMI and waist circumference, as estimates of total and abdominal fat mass, are now accepted as predictors of the increasing incidence of these diseases, adult height, which also predicts mortality, has been neglected. Interestingly, increasing evidence suggests that height is associated with lower cardiometabolic risk, but higher cancer risk, associations supported by mendelian randomisation studies. Understanding the complex epidemiology, biology, and pathophysiology related to height, and its association with cardiometabolic diseases and cancer, is becoming even more important because average adult height has increased substantially in many countries during recent generations. Among the mechanisms driving the increase in height and linking height with cardiometabolic diseases and cancer are insulin and insulin-like growth factor signalling pathways. These pathways are thought to be activated by overnutrition, especially increased intake of milk, dairy products, and other animal proteins during different stages of child development. Limiting overnutrition during pregnancy, early childhood, and puberty would avoid not only obesity, but also accelerated growth in children-and thus might reduce risk of cancer in adulthood.

RNA splicing is a primary link between genetic variation and disease

Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

Physical and neurobehavioral determinants of reproductive onset and success

The ages of puberty, first sexual intercourse and first birth signify the onset of reproductive ability, behavior and success, respectively. In a genome-wide association study of 125,667 UK Biobank participants, we identify 38 loci associated (P < 5 × 10(-8)) with age at first sexual intercourse. These findings were taken forward in 241,910 men and women from Iceland and 20,187 women from the Women's Genome Health Study. Several of the identified loci also exhibit associations (P < 5 × 10(-8)) with other reproductive and behavioral traits, including age at first birth (variants in or near ESR1 and RBM6-SEMA3F), number of children (CADM2 and ESR1), irritable temperament (MSRA) and risk-taking propensity (CADM2). Mendelian randomization analyses infer causal influences of earlier puberty timing on earlier first sexual intercourse, earlier first birth and lower educational attainment. In turn, likely causal consequences of earlier first sexual intercourse include reproductive, educational, psychiatric and cardiometabolic outcomes.

Monte Carlo simulation of body height in a spatial network

BACKGROUND/OBJECTIVES

Recent evidence suggests clustering of human body height. We want to assess the consequences of connectedness in a spatial network on height clustering in an artificial society.

SUBJECTS/METHODS

We used an agent-based computer modelling technique (Monte Carlo simulation) and compared simulated height in a spatial network with characteristics of the observed geographic height distribution of three historic cohorts of Swiss military conscripts (conscripted in 1884-1891; 1908-1910; and 2004-2009).

RESULTS

Conscript height shows several characteristic features: (1) height distributions are overdispersed. (2) Conscripts from districts with direct inter-district road connections tend to be similar in height. (3) Clusters of tall and clusters of short stature districts vary over time. Autocorrelations in height between late 19th and early 21st century districts are low. (4) Mean district height depends on the number of connecting roads and on the number of conscripts per district. Using Monte Carlo simulation, we were able to generate these natural characteristics in an artificial society. Already 5% height information from directly connected districts is sufficient to simulate the characteristics of natural height distribution. Very similar observations in regular rectangular networks indicate that the characteristics of Swiss conscript height distributions do not so much result from the particular Swiss geography but rather appear to be general features of spatial networks.

CONCLUSIONS

Spatial connectedness can affect height clustering in an artificial society, similar to that seen in natural cohorts of military conscripts, and strengthen the concept of connectedness being involved in the regulation of human height.

Discordant Haplotype Sequencing Identifies Functional Variants at the 2q33 Breast Cancer Risk Locus

The findings from genome-wide association studies hold enormous potential for novel insight into disease mechanisms. A major challenge in the field is to map these low-risk association signals to their underlying functional sequence variants (FSV). Simple sequence study designs are insufficient, as the vast numbers of statistically comparable variants and a limited knowledge of noncoding regulatory elements complicate prioritization. Furthermore, large sample sizes are typically required for adequate power to identify the initial association signals. One important question is whether similar sample sizes need to be sequenced to identify the FSVs. Here, we present a proof-of-principle example of an extreme discordant design to map FSVs within the 2q33 low-risk breast cancer locus. Our approach employed DNA sequencing of a small number of discordant haplotypes to efficiently identify candidate FSVs. Our results were consistent with those from a 2,000-fold larger, traditional imputation-based fine-mapping study. To prioritize further, we used expression-quantitative trait locus analysis of RNA sequencing from breast tissues, gene regulation annotations from the ENCODE consortium, and functional assays for differential enhancer activities. Notably, we implicate three regulatory variants at 2q33 that target CASP8 (rs3769823, rs3769821 in CASP8, and rs10197246 in ALS2CR12) as functionally relevant. We conclude that nested discordant haplotype sequencing is a promising approach to aid mapping of low-risk association loci. The ability to include more efficient sequencing designs into mapping efforts presents an opportunity for the field to capitalize on the potential of association loci and accelerate translation of association signals to their underlying FSVs. Cancer Res; 76(7); 1916-25. ©2016 AACR.

MECHANISMS IN ENDOCRINOLOGY: Novel genetic causes of short stature

The fast technological development, particularly single nucleotide polymorphism array, array-comparative genomic hybridization, and whole exome sequencing, has led to the discovery of many novel genetic causes of growth failure. In this review we discuss a selection of these, according to a diagnostic classification centred on the epiphyseal growth plate. We successively discuss disorders in hormone signalling, paracrine factors, matrix molecules, intracellular pathways, and fundamental cellular processes, followed by chromosomal aberrations including copy number variants (CNVs) and imprinting disorders associated with short stature. Many novel causes of GH deficiency (GHD) as part of combined pituitary hormone deficiency have been uncovered. The most frequent genetic causes of isolated GHD are GH1 and GHRHR defects, but several novel causes have recently been found, such as GHSR, RNPC3, and IFT172 mutations. Besides well-defined causes of GH insensitivity (GHR, STAT5B, IGFALS, IGF1 defects), disorders of NFκB signalling, STAT3 and IGF2 have recently been discovered. Heterozygous IGF1R defects are a relatively frequent cause of prenatal and postnatal growth retardation. TRHA mutations cause a syndromic form of short stature with elevated T3/T4 ratio. Disorders of signalling of various paracrine factors (FGFs, BMPs, WNTs, PTHrP/IHH, and CNP/NPR2) or genetic defects affecting cartilage extracellular matrix usually cause disproportionate short stature. Heterozygous NPR2 or SHOX defects may be found in ∼3% of short children, and also rasopathies (e.g., Noonan syndrome) can be found in children without clear syndromic appearance. Numerous other syndromes associated with short stature are caused by genetic defects in fundamental cellular processes, chromosomal abnormalities, CNVs, and imprinting disorders.

Analyzing the Role of MicroRNAs in Schizophrenia in the Context of Common Genetic Risk Variants

IMPORTANCE

The recent implication of 108 genomic loci in schizophrenia marked a great advancement in our understanding of the disease. Against the background of its polygenic nature there is a necessity to identify how schizophrenia risk genes interplay. As regulators of gene expression, microRNAs (miRNAs) have repeatedly been implicated in schizophrenia etiology. It is therefore of interest to establish their role in the regulation of schizophrenia risk genes in disease-relevant biological processes.

OBJECTIVE

To examine the role of miRNAs in schizophrenia in the context of disease-associated genetic variation.

DESIGN, SETTING, AND PARTICIPANTS

The basis of this study was summary statistics from the largest schizophrenia genome-wide association study meta-analysis to date (83 550 individuals in a meta-analysis of 52 genome-wide association studies) completed in 2014 along with publicly available data for predicted miRNA targets. We examined whether schizophrenia risk genes were more likely to be regulated by miRNA. Further, we used gene set analyses to identify miRNAs that are regulators of schizophrenia risk genes.

MAIN OUTCOMES AND MEASURES

Results from association tests for miRNA targetomes and related analyses.

RESULTS

In line with previous studies, we found that similar to other complex traits, schizophrenia risk genes were more likely to be regulated by miRNAs (P < 2 × 10-16). Further, the gene set analyses revealed several miRNAs regulating schizophrenia risk genes, with the strongest enrichment for targets of miR-9-5p (P = .0056 for enrichment among the top 1% most-associated single-nucleotide polymorphisms, corrected for multiple testing). It is further of note that MIR9-2 is located in a genomic region showing strong evidence for association with schizophrenia (P = 7.1 × 10-8). The second and third strongest gene set signals were seen for the targets of miR-485-5p and miR-137, respectively.

CONCLUSIONS AND RELEVANCE

This study provides evidence for a role of miR-9-5p in the etiology of schizophrenia. Its implication is of particular interest as the functions of this neurodevelopmental miRNA tie in with established disease biology: it has a regulatory loop with the fragile X mental retardation homologue FXR1 and regulates dopamine D2 receptor density.

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.

Variants near CHRNA3/5 and APOE have age- and sex-related effects on human lifespan

Lifespan is a trait of enormous personal interest. Research into the biological basis of human lifespan, however, is hampered by the long time to death. Using a novel approach of regressing (272,081) parental lifespans beyond age 40 years on participant genotype in a new large data set (UK Biobank), we here show that common variants near the apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 genes are associated with lifespan. The effects are strongly sex and age dependent, with APOE ɛ4 differentially influencing maternal lifespan (P=4.2 × 10⁻¹⁵, effect −1.24 years of maternal life per imputed risk allele in parent; sex difference, P=0.011), and a locus near CHRNA3/5 differentially affecting paternal lifespan (P=4.8 × 10⁻¹¹, effect −0.86 years per allele; sex difference P=0.075). Rare homozygous carriers of the risk alleles at both loci are predicted to have 3.3–3.7 years shorter lives.

Understanding the genetic influences on human aging requires a large number of subjects for a study of sufficient power. Here, Jim Wilson and colleagues use information on parental ages at death to show that common variants near the genes for apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 are associated with longer lifespan.

Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.

Population-based approaches to genetics of migraine

BACKGROUND

While the most accurate diagnosis of migraine typically requires a clinical interview guided by strict diagnostic criteria, an alternative approach that ascertains migraine by questionnaire in population-based settings has been instrumental in the discovery of common genetic variants influencing migraine risk. This result may be surprising. Population-based approaches are often criticized for limited ability to distinguish migraine from other forms of primary headache. It is thus useful to revisit prevailing ideas about population-based ascertainment of migraine to evaluate the extent to which this approach has potential for additional insights into migraine genetics and therefore pathophysiology.

OVERVIEW

We review recent findings suggesting that the success of the population-based approach is derived from the possibility of collecting much larger samples than in the clinic-based setting even at the risk of introducing phenotypic and genetic heterogeneity. The findings are also consistent with new appreciations for the genetic basis of many other common, complex clinical characteristics. However, clinic-based ascertainment and other settings will remain more effective than population-based approaches for investigating certain, often very specific aspects of migraine genetics.

CONCLUSION

We argue that the detailed genetic architecture of migraine, various aspects of methodology, and the ultimate sample size achieved by population-based ascertainment will be critical determinants of the future success of this approach to genetic analysis of migraine and its comorbidities.

Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genes

Over 100 associated genetic loci have been robustly associated with schizophrenia. Gene prioritization and pathway analysis have focused on a priori hypotheses and thus may have been unduly influenced by prior assumptions and missed important causal genes and pathways. Using a data-driven approach, we show that genes in associated loci: (1) are highly expressed in cortical brain areas; (2) are enriched for ion channel pathways (false discovery rates <0.05); and (3) contain 62 genes that are functionally related to each other and hence represent promising candidates for experimental follow up. We validate the relevance of the prioritized genes by showing that they are enriched for rare disruptive variants and de novo variants from schizophrenia sequencing studies (odds ratio 1.67, P = 0.039), and are enriched for genes encoding members of mouse and human postsynaptic density proteomes (odds ratio 4.56, P = 5.00 × 10(-4); odds ratio 2.60, P = 0.049).The authors wish it to be known that, in their opinion, the first 2 authors should be regarded as joint First Author.

Detailed phenotyping identifies genes with pleiotropic effects on body composition

Background

Genetic variation in both the composition and distribution of fat and muscle in the body is important to human health as well as the healthiness and value of meat from cattle and sheep. Here we use detailed phenotyping and a multi-trait approach to identify genes explaining variation in body composition traits.

Results

A multi-trait genome wide association analysis of 56 carcass composition traits measured on 10,613 sheep with imputed and real genotypes on 510,174 SNPs was performed. We clustered 71 significant SNPs into five groups based on their pleiotropic effects across the 56 traits. Among these 71 significant SNPs, one group of 11 SNPs affected the fatty acid profile of the muscle and were close to 8 genes involved in fatty acid or triglyceride synthesis. Another group of 23 SNPs had an effect on mature size, based on their pattern of effects across traits, but the genes near this group of SNPs did not share any obvious function. Many of the likely candidate genes near SNPs with significant pleiotropic effects on the 56 traits are involved in intra-cellular signalling pathways. Among the significant SNPs were some with a convincing candidate gene due to the function of the gene (e.g. glycogen synthase affecting glycogen concentration) or because the same gene was associated with similar traits in other species.

Conclusions

Using a multi-trait analysis increased the power to detect associations between SNP and body composition traits compared with the single trait analyses. Detailed phenotypic information helped to identify a convincing candidate in some cases as did information from other species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2538-0) contains supplementary material, which is available to authorized users.

Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank

OBJECTIVE

To determine whether height and body mass index (BMI) have a causal role in five measures of socioeconomic status.

DESIGN

Mendelian randomisation study to test for causal effects of differences in stature and BMI on five measures of socioeconomic status. Mendelian randomisation exploits the fact that genotypes are randomly assigned at conception and thus not confounded by non-genetic factors.

SETTING

UK Biobank.

PARTICIPANTS

119,669 men and women of British ancestry, aged between 37 and 73 years.

MAIN OUTCOME MEASURES

Age completed full time education, degree level education, job class, annual household income, and Townsend deprivation index.

RESULTS

In the UK Biobank study, shorter stature and higher BMI were observationally associated with several measures of lower socioeconomic status. The associations between shorter stature and lower socioeconomic status tended to be stronger in men, and the associations between higher BMI and lower socioeconomic status tended to be stronger in women. For example, a 1 standard deviation (SD) higher BMI was associated with a £210 (€276; $300; 95% confidence interval £84 to £420; P=6 × 10(-3)) lower annual household income in men and a £1890 (£1680 to £2100; P=6 × 10(-15)) lower annual household income in women. Genetic analysis provided evidence that these associations were partly causal. A genetically determined 1 SD (6.3 cm) taller stature caused a 0.06 (0.02 to 0.09) year older age of completing full time education (P=0.01), a 1.12 (1.07 to 1.18) times higher odds of working in a skilled profession (P=6 × 10(-7)), and a £1130 (£680 to £1580) higher annual household income (P=4 × 10(-8)). Associations were stronger in men. A genetically determined 1 SD higher BMI (4.6 kg/m(2)) caused a £2940 (£1680 to £4200; P=1 × 10(-5)) lower annual household income and a 0.10 (0.04 to 0.16) SD (P=0.001) higher level of deprivation in women only.

CONCLUSIONS

These data support evidence that height and BMI play an important partial role in determining several aspects of a person's socioeconomic status, especially women's BMI for income and deprivation and men's height for education, income, and job class. These findings have important social and health implications, supporting evidence that overweight people, especially women, are at a disadvantage and that taller people, especially men, are at an advantage.

Small island, big genetic discoveries

Three new studies have identified new genes and sequence variants implicated in blood lipids, inflammatory markers, hemoglobin levels and adult height variation in Sardinia. These reports highlight the usefulness of large-scale genotype imputation based on whole-genome sequencing, particularly in isolated populations, in studying the genetics of complex human phenotypes.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

The Hippo signal transduction network for exercise physiologists

The ubiquitous transcriptional coactivators Yap (gene symbol Yap1) and Taz (gene symbol Wwtr1) regulate gene expression mainly by coactivating the Tead transcription factors. Being at the center of the Hippo signaling network, Yap and Taz are regulated by the Hippo kinase cassette and additionally by a plethora of exercise-associated signals and signaling modules. These include mechanotransduction, the AKT-mTORC1 network, the SMAD transcription factors, hypoxia, glucose homeostasis, AMPK, adrenaline/epinephrine and angiotensin II through G protein-coupled receptors, and IL-6. Consequently, exercise should alter Hippo signaling in several organs to mediate at least some aspects of the organ-specific adaptations to exercise. Indeed, Tead1 overexpression in muscle fibers has been shown to promote a fast-to-slow fiber type switch, whereas Yap in muscle fibers and cardiomyocytes promotes skeletal muscle hypertrophy and cardiomyocyte adaptations, respectively. Finally, genome-wide association studies in humans have linked the Hippo pathway members LATS2, TEAD1, YAP1, VGLL2, VGLL3, and VGLL4 to body height, which is a key factor in sports.

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.

Adult height, nutrition, and population health

In this review, the potential causes and consequences of adult height, a measure of cumulative net nutrition, in modern populations are summarized. The mechanisms linking adult height and health are examined, with a focus on the role of potential confounders. Evidence across studies indicates that short adult height (reflecting growth retardation) in low- and middle-income countries is driven by environmental conditions, especially net nutrition during early years. Some of the associations of height with health and social outcomes potentially reflect the association between these environmental factors and such outcomes. These conditions are manifested in the substantial differences in adult height that exist between and within countries and over time. This review suggests that adult height is a useful marker of variation in cumulative net nutrition, biological deprivation, and standard of living between and within populations and should be routinely measured. Linkages between adult height and health, within and across generations, suggest that adult height may be a potential tool for monitoring health conditions and that programs focused on offspring outcomes may consider maternal height as a potentially important influence.

Significant impact of miRNA-target gene networks on genetics of human complex traits

The impact of microRNA (miRNA) on the genetics of human complex traits, especially in the context of miRNA-target gene networks, has not been fully assessed. Here, we developed a novel analytical method, MIGWAS, to comprehensively evaluate enrichment of genome-wide association study (GWAS) signals in miRNA–target gene networks. We applied the method to the GWAS results of the 18 human complex traits from >1.75 million subjects, and identified significant enrichment in rheumatoid arthritis (RA), kidney function, and adult height (P < 0.05/18 = 0.0028, most significant enrichment in RA with P = 1.7 × 10⁻⁴). Interestingly, these results were consistent with current literature-based knowledge of the traits on miRNA obtained through the NCBI PubMed database search (adjusted P = 0.024). Our method provided a list of miRNA and target gene pairs with excess genetic association signals, part of which included drug target genes. We identified a miRNA (miR-4728-5p) that downregulates PADI2, a novel RA risk gene considered as a promising therapeutic target (rs761426, adjusted P = 2.3 × 10⁻⁹). Our study indicated the significant impact of miRNA–target gene networks on the genetics of human complex traits, and provided resources which should contribute to drug discovery and nucleic acid medicine.

Integrated analyses of gene expression and genetic association studies in a founder population

Genome-wide association studies (GWASs) have become a standard tool for dissecting genetic contributions to disease risk. However, these studies typically require extraordinarily large sample sizes to be adequately powered. Strategies that incorporate functional information alongside genetic associations have proved successful in increasing GWAS power. Following this paradigm, we present the results of 20 different genetic association studies for quantitative traits related to complex diseases, conducted in the Hutterites of South Dakota. To boost the power of these association studies, we collected RNA-sequencing data from lymphoblastoid cell lines for 431 Hutterite individuals. We then used Sherlock, a tool that integrates GWAS and expression quantitative trait locus (eQTL) data, to identify weak GWAS signals that are also supported by eQTL data. Using this approach, we found novel associations with quantitative phenotypes related to cardiovascular disease, including carotid intima-media thickness, left atrial volume index, monocyte count and serum YKL-40 levels.

Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits

BACKGROUND

Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants.

RESULTS

We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.

CONCLUSIONS

BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.

Whole-genome sequence analyses of Western Central African Pygmy hunter-gatherers reveal a complex demographic history and identify candidate genes under positive natural selection

African Pygmies practicing a mobile hunter-gatherer lifestyle are phenotypically and genetically diverged from other anatomically modern humans, and they likely experienced strong selective pressures due to their unique lifestyle in the Central African rainforest. To identify genomic targets of adaptation, we sequenced the genomes of four Biaka Pygmies from the Central African Republic and jointly analyzed these data with the genome sequences of three Baka Pygmies from Cameroon and nine Yoruba famers. To account for the complex demographic history of these populations that includes both isolation and gene flow, we fit models using the joint allele frequency spectrum and validated them using independent approaches. Our two best-fit models both suggest ancient divergence between the ancestors of the farmers and Pygmies, 90,000 or 150,000 yr ago. We also find that bidirectional asymmetric gene flow is statistically better supported than a single pulse of unidirectional gene flow from farmers to Pygmies, as previously suggested. We then applied complementary statistics to scan the genome for evidence of selective sweeps and polygenic selection. We found that conventional statistical outlier approaches were biased toward identifying candidates in regions of high mutation or low recombination rate. To avoid this bias, we assigned P-values for candidates using whole-genome simulations incorporating demography and variation in both recombination and mutation rates. We found that genes and gene sets involved in muscle development, bone synthesis, immunity, reproduction, cell signaling and development, and energy metabolism are likely to be targets of positive natural selection in Western African Pygmies or their recent ancestors.

Unravelling the human genome-phenome relationship using phenome-wide association studies

Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.

Genome-wide association analysis of self-reported events in 6135 individuals and 252 827 controls identifies 8 loci associated with thrombosis

Thrombotic diseases are among the leading causes of morbidity and mortality in the world. To add insights into the genetic regulation of thrombotic disease, we conducted a genome-wide association study (GWAS) of 6135 self-reported blood clots events and 252 827 controls of European ancestry belonging to the 23andMe cohort of research participants. Eight loci exceeded genome-wide significance. Among the genome-wide significant results, our study replicated previously known venous thromboembolism (VTE) loci near the F5, FGA-FGG, F11, F2, PROCR and ABO genes, and the more recently discovered locus near SLC44A2 In addition, our study reports for the first time a genome-wide significant association between rs114209171, located upstream of the F8 structural gene, and thrombosis risk. Analyses of expression profiles and expression quantitative trait loci across different tissues suggested SLC44A2, ILF3 and AP1M2 as the three most plausible candidate genes for the chromosome 19 locus, our only genome-wide significant thrombosis-related locus that does not harbor likely coagulation-related genes. In addition, we present data showing that this locus also acts as a novel risk factor for stroke and coronary artery disease (CAD). In conclusion, our study reveals novel common genetic risk factors for VTE, stroke and CAD and provides evidence that self-reported data on blood clots used in a GWAS yield results that are comparable with those obtained using clinically diagnosed VTE. This observation opens up the potential for larger meta-analyses, which will enable elucidation of the genetics of thrombotic diseases, and serves as an example for the genetic study of other diseases.

Integrative approaches for large-scale transcriptome-wide association studies

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

A study of common Mendelian disease carriers across ageing British cohorts: meta-analyses reveal heterozygosity for alpha 1-antitrypsin deficiency increases respiratory capacity and height

BACKGROUND

Several recessive Mendelian disorders are common in Europeans, including cystic fibrosis (CFTR), medium-chain-acyl-Co-A-dehydrogenase deficiency (ACADM), phenylketonuria (PAH) and alpha 1-antitrypsin deficiency (SERPINA1).

METHODS

In a multicohort study of >19,000 older individuals, we investigated the relevant phenotypes in heterozygotes for these genes: lung function (forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC)) for CFTR and SERPINA1; cognitive measures for ACADM and PAH; and physical capability for ACADM, PAH and SERPINA1.

RESULTS

Findings were mostly negative but lung function in SERPINA1 (protease inhibitor (PI) Z allele, rs28929474) showed enhanced FEV1 and FVC (0.13 z-score increase in FEV1 (p=1.7 × 10(-5)) and 0.16 z-score increase in FVC (p=5.2 × 10(-8))) in PI-MZ individuals. Height adjustment (a known, strong correlate of FEV1 and FVC) revealed strong positive height associations of the Z allele (1.50 cm increase in height (p=3.6 × 10(-10))).

CONCLUSIONS

The PI-MZ rare (2%) SNP effect is nearly four times greater than the 'top' common height SNP in HMGA2. However, height only partially attenuates the SERPINA1-FEV1 or FVC association (around 50%) and vice versa. Height SNP variants have recently been shown to be positively selected collectively in North versus South Europeans, while the Z allele high frequency is localised to North Europe. Although PI-ZZ is clinically disadvantageous to lung function, PI-MZ increases both height and respiratory function; potentially a balanced polymorphism. Partial blockade of PI could conceivably form part of a future poly-therapeutic approach in very short children. The notion that elastase inhibition should benefit patients with chronic obstructive pulmonary disease may also merit re-evaluation. PI is already a therapeutic target: our findings invite a reconsideration of the optimum level in respiratory care and novel pathway potential for development of agents for the management of growth disorders.

Stunting at 5 Years Among SGA Newborns

OBJECTIVE

To compare risk of stunting at 5 years across etiological subgroups of small for gestational age (SGA) newborns.

METHODS

We analyzed data of a subsample (N = 1100) of the Early Childhood Longitudinal Study-Birth Cohort. We defined SGA as birth weight <10th percentile, then classified subjects into etiological subgroups by each of 8 risk factors (ie, maternal prepregnancy underweight, short stature, smoking during pregnancy, alcohol use during pregnancy, inadequate gestational weight gain [GWG], hypertension, genital herpes infection, and multiple births) or by cooccurrence of 2 often intertwined risk factors (smoking and inadequate GWG). We defined stunting as 5 years height-for-age z score below -2. We fitted logistic regression models to test whether the risk of stunting differed across SGA subgroups, adjusting for confounders.

RESULTS

SGA subgroup with maternal short stature (odds ratio [OR] = 3.88; 95% confidence interval [CI] = 2.16-6.96) or inadequate GWG (OR = 2.18; 95% CI = 1.23-3.84) had higher risk of stunting at 5 years, compared with the SGA subgroup without the corresponding risk factor. SGA newborns with both maternal smoking and inadequate GWG during pregnancy had much higher risk of stunting at 5 years (OR = 3.10; 95% CI = 1.21-7.91), compared with SGA newborns without any of these 2 SGA risk factors.

CONCLUSIONS

Etiological subgroups of SGA differed in risk of stunting at 5 years. SGA newborns of inadequate GWG mothers who smoke and SGA newborns of short mothers were at particularly high risk of stunting.

Higher methylation of the IGF1 P2 promoter is associated with idiopathic short stature

BACKGROUND

Idiopathic short stature (ISS) has a strong familial component, but genetics explains only part of it. Indeed, environmental factors act on human growth either directly or through epigenetic factors that remain to be determined. Given the importance of the GH/IGF1 axis for child growth, we suspected that such epigenetic factors could involve the CG methylation at the IGF1 gene P2 promoter, which was recently shown to be a transcriptional regulator for IGF1 gene and a major contributor to GH sensitivity.

OBJECTIVE

Explore whether the methylation of the two IGF1 low-CG-rich promoters (P1 and P2) is associated with ISS.

SUBJECTS AND METHODS

A total of 94 children with ISS were compared with 119 age-matched children of normal height for the methylation of CGs located within the IGF1 promoters measured with bisulphite PCR pyrosequencing.

RESULTS

The methylation of 5 CGs of the P2 promoter was higher in ISS children, notably CG-137 (49 ± 4% in ISS vs 46 ± 4 % in control children, P = 9 × 10^-5 ). This was also true for CG-611 of the P1 promoter (93 ± 3% vs 91 ± 3% P = 10^-4 ). The CG methylation of the IGF1 promoters thus takes place among the multifactorial factors that are associated with ISS.

New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10(-8)), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.

Common polygenic variation in coeliac disease and confirmation of ZNF335 and NIFA as disease susceptibility loci

Coeliac disease (CD) is a chronic immune-mediated disease triggered by the ingestion of gluten. It has an estimated prevalence of approximately 1% in European populations. Specific HLA-DQA1 and HLA-DQB1 alleles are established coeliac susceptibility genes and are required for the presentation of gliadin to the immune system resulting in damage to the intestinal mucosa. In the largest association analysis of CD to date, 39 non-HLA risk loci were identified, 13 of which were new, in a sample of 12,014 individuals with CD and 12 228 controls using the Immunochip genotyping platform. Including the HLA, this brings the total number of known CD loci to 40. We have replicated this study in an independent Irish CD case-control population of 425 CD and 453 controls using the Immunochip platform. Using a binomial sign test, we show that the direction of the effects of previously described risk alleles were highly correlated with those reported in the Irish population, (P=2.2 × 10(-16)). Using the Polygene Risk Score (PRS) approach, we estimated that up to 35% of the genetic variance could be explained by loci present on the Immunochip (P=9 × 10(-75)). When this is limited to non-HLA loci, we explain a maximum of 4.5% of the genetic variance (P=3.6 × 10(-18)). Finally, we performed a meta-analysis of our data with the previous reports, identifying two further loci harbouring the ZNF335 and NIFA genes which now exceed genome-wide significance, taking the total number of CD susceptibility loci to 42.

Which Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?

Large experimental efforts are characterizing the regulatory genome, yet we are still missing a systematic definition of functional and silent genetic variants in non-coding regions. Here, we integrated DNaseI footprinting data with sequence-based transcription factor (TF) motif models to predict the impact of a genetic variant on TF binding across 153 tissues and 1,372 TF motifs. Each annotation we derived is specific for a cell-type condition or assay and is locally motif-driven. We found 5.8 million genetic variants in footprints, 66% of which are predicted by our model to affect TF binding. Comprehensive examination using allele-specific hypersensitivity (ASH) reveals that only the latter group consistently shows evidence for ASH (3,217 SNPs at 20% FDR), suggesting that most (97%) genetic variants in footprinted regulatory regions are indeed silent. Combining this information with GWAS data reveals that our annotation helps in computationally fine-mapping 86 SNPs in GWAS hit regions with at least a 2-fold increase in the posterior odds of picking the causal SNP. The rich meta information provided by the tissue-specificity and the identity of the putative TF binding site being affected also helps in identifying the underlying mechanism supporting the association. As an example, the enrichment for LDL level-associated SNPs is 9.1-fold higher among SNPs predicted to affect HNF4 binding sites than in a background model already including tissue-specific annotation.

Variations in the high-mobility group-A2 gene (HMGA2) are associated with idiopathic short stature

BACKGROUND

Several association studies confirmed high-mobility group-A2 gene (HMGA2) polymorphisms as the most relevant variants contributing to height variability. Animal models and deletions in humans suggest that alterations of HMGA2 might be relevant in causing short stature. Together, these observations led us to investigate the involvement of HMGA2 in idiopathic short stature (ISS) through an association study and a mutation screening.

METHODS

We conducted an association study (155 ISS patients and 318 normal stature controls) with three HMGA2 single-nucleotide polymorphisms (SNPs) (SNPs rs1042725, rs7968682, and rs7968902) using a TaqMan-based assay. The patients were then analyzed by direct sequencing and multiplex ligation-dependent probe amplification (MLPA) to detect point mutations and genomic micro-rearrangements.

RESULTS

Considering a recessive model, an OR value >1 was observed for genotypes rs7968682 TT (Odds ratio (OR) = 1.72, confidence interval (CI): 1.14-2.58) and rs1042725 TT (OR = 1.51, CI: 1.00-2.28) in accordance to the effect exhibited by the single alleles in the general population. None of the patients carried possibly causative HMGA2 mutations.

CONCLUSION

Besides the already known role in determining variability in human height, HMGA2 polymorphisms also contribute to susceptibility to ISS. Moreover, we here report the first mutation screening performed in ISS concluding that HMGA2 has not a significant impact on the monogenic form of ISS.

Genomic insights into growth and its disorders: an update

PURPOSE OF REVIEW

This article provides an update of the most striking new developments in the field of growth genetics over the past 12 months.

RECENT FINDINGS

A number of large genome-wide association studies have identified new genetic loci and pathways associated to human growth and adult height as well as related traits and comorbidities. New genetic causes of primordial dwarfism and several short stature syndromes have been elucidated. Moreover, a breakthrough finding of Xq26 microduplications as a cause of pituitary gigantism was made. Several new developments in imprinted growth-related genes (including the first human mutation in insulin-like growth factor II) and novel insights into the epigenetic regulation of growth have been reported.

SUMMARY

Genomic investigations continue to provide new insights into the genetic basis of human growth as well as its disorders.

Searching for the human genetic factors standing in the way of universally effective vaccines

Vaccines have revolutionized modern public health. The effectiveness of some vaccines is limited by the variation in response observed between individuals and across populations. There is compelling evidence that a significant proportion of this variability can be attributed to human genetic variation, especially for those vaccines administered in early life. Identifying and understanding the determinants of this variation could have a far-reaching influence upon future methods of vaccine design and deployment. In this review, we summarize the genetic studies that have been undertaken attempting to identify the genetic determinants of response heterogeneity for the vaccines against hepatitis B, measles and rubella. We offer a critical appraisal of these studies and make a series of suggestions about how modern genetic techniques, including genome-wide association studies, could be used to characterize the genetic architecture of vaccine response heterogeneity. We conclude by suggesting how the findings from such studies could be translated to improve vaccine effectiveness and target vaccination in a more cost-effective manner.

Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study

BACKGROUND

The discovery of disease-associated loci through genome-wide association studies (GWAS) is the leading genetic approach to the identification of novel biological pathways underlying diseases in humans. Until recently, GWAS in ischaemic stroke have been limited by small sample sizes and have yielded few loci associated with ischaemic stroke. We did a large-scale GWAS to identify additional susceptibility genes for stroke and its subtypes.

METHODS

To identify genetic loci associated with ischaemic stroke, we did a two-stage GWAS. In the first stage, we included 16 851 cases with state-of-the-art phenotyping data and 32 473 stroke-free controls. Cases were aged 16 to 104 years, recruited between 1989 and 2012, and subtypes of ischaemic stroke were recorded by centrally trained and certified investigators who used the web-based protocol, Causative Classification of Stroke (CCS). We constructed case-control strata by identifying samples that were genotyped on nearly identical arrays and were of similar genetic ancestral background. We cleaned and imputed data by use of dense imputation reference panels generated from whole-genome sequence data. We did genome-wide testing to identify stroke-associated loci within each stratum for each available phenotype, and we combined summary-level results using inverse variance-weighted fixed-effects meta-analysis. In the second stage, we did in-silico lookups of 1372 single nucleotide polymorphisms identified from the first stage GWAS in 20 941 cases and 364 736 unique stroke-free controls. The ischaemic stroke subtypes of these cases had previously been established with the Trial of Org 10 172 in Acute Stroke Treatment (TOAST) classification system, in accordance with local standards. Results from the two stages were then jointly analysed in a final meta-analysis.

FINDINGS

We identified a novel locus (G allele at rs12122341) at 1p13.2 near TSPAN2 that was associated with large artery atherosclerosis-related stroke (first stage odds ratio [OR] 1·21, 95% CI 1·13-1·30, p=4·50 × 10^-8; joint OR 1·19, 1·12-1·26, p=1·30 × 10^-9). Our results also supported robust associations with ischaemic stroke for four other loci that have been reported in previous studies, including PITX2 (first stage OR 1·39, 1·29-1·49, p=3·26 × 10^-19; joint OR 1·37, 1·30-1·45, p=2·79 × 10^-32) and ZFHX3 (first stage OR 1·19, 1·11-1·27, p=2·93 × 10^-7; joint OR 1·17, 1·11-1·23, p=2·29 × 10^-10) for cardioembolic stroke, and HDAC9 (first stage OR 1·29, 1·18-1·42, p=3·50 × 10^-8; joint OR 1·24, 1·15-1·33, p=4·52 × 10^-9) for large artery atherosclerosis stroke. The 12q24 locus near ALDH2, which has previously been associated with all ischaemic stroke but not with any specific subtype, exceeded genome-wide significance in the meta-analysis of small artery stroke (first stage OR 1·20, 1·12-1·28, p=6·82 × 10^-8; joint OR 1·17, 1·11-1·23, p=2·92 × 10^-9). Other loci associated with stroke in previous studies, including NINJ2, were not confirmed.

INTERPRETATION

Our results suggest that all ischaemic stroke-related loci previously implicated by GWAS are subtype specific. We identified a novel gene associated with large artery atherosclerosis stroke susceptibility. Follow-up studies will be necessary to establish whether the locus near TSPAN2 can be a target for a novel therapeutic approach to stroke prevention. In view of the subtype-specificity of the associations detected, the rich phenotyping data available in the Stroke Genetics Network (SiGN) are likely to be crucial for further genetic discoveries related to ischaemic stroke.

FUNDING

US National Institute of Neurological Disorders and Stroke, National Institutes of Health.

Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics

Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association studies (GWAS) across genes and pathways is a strategy to improve statistical power and gain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful tool for computing gene and pathway scores from SNP-phenotype association summary statistics. For gene score computation, we implemented analytic and efficient numerical solutions to calculate test statistics. We examined in particular the sum and the maximum of chi-squared statistics, which measure the strongest and the average association signals per gene, respectively. For pathway scoring, we use a modified Fisher method, which offers not only significant power improvement over more traditional enrichment strategies, but also eliminates the problem of arbitrary threshold selection inherent in any binary membership based pathway enrichment approach. We demonstrate the marked increase in power by analyzing summary statistics from dozens of large meta-studies for various traits. Our extensive testing indicates that our method not only excels in rigorous type I error control, but also results in more biologically meaningful discoveries.

Genome-wide association studies (GWAS) typically generate lists of trait- or disease-associated SNPs. Yet, such output sheds little light on the underlying molecular mechanisms and tools are needed to extract biological insight from the results at the SNP level. Pathway analysis tools integrate signals from multiple SNPs at various positions in the genome in order to map associated genomic regions to well-established pathways, i.e., sets of genes known to act in concert. The nature of GWAS association results requires specifically tailored methods for this task. Here, we present Pascal (Pathway scoring algorithm), a tool that allows gene and pathway-level analysis of GWAS association results without the need to access the original genotypic data. Pascal was designed to be fast, accurate and to have high power to detect relevant pathways. We extensively tested our approach on a large collection of real GWAS association results and saw better discovery of confirmed pathways than with other popular methods. We believe that these results together with the ease-of-use of our publicly available software will allow Pascal to become a useful addition to the toolbox of the GWAS community.

Complex disease and phenotype mapping in the domestic dog

The domestic dog is becoming an increasingly valuable model species in medical genetics, showing particular promise to advance our understanding of cancer and orthopaedic disease. Here we undertake the largest canine genome-wide association study to date, with a panel of over 4,200 dogs genotyped at 180,000 markers, to accelerate mapping efforts. For complex diseases, we identify loci significantly associated with hip dysplasia, elbow dysplasia, idiopathic epilepsy, lymphoma, mast cell tumour and granulomatous colitis; for morphological traits, we report three novel quantitative trait loci that influence body size and one that influences fur length and shedding. Using simulation studies, we show that modestly larger sample sizes and denser marker sets will be sufficient to identify most moderate- to large-effect complex disease loci. This proposed design will enable efficient mapping of canine complex diseases, most of which have human homologues, using far fewer samples than required in human studies.

The domestic dog is an important model organism for our understanding of cancer and other diseases. Here the authors conduct a genome-wide association study across multiple breeds and identify novel loci significantly associated with several complex diseases and morphological traits.

Genetic determination of height-mediated mate choice

BACKGROUND

Numerous studies have reported positive correlations among couples for height. This suggests that humans find individuals of similar height attractive. However, the answer to whether the choice of a mate with a similar phenotype is genetically or environmentally determined has been elusive.

RESULTS

Here we provide an estimate of the genetic contribution to height choice in mates in 13,068 genotyped couples. Using a mixed linear model we show that 4.1% of the variation in the mate height choice is determined by a person's own genotype, as expected in a model where one's height determines the choice of mate height. Furthermore, the genotype of an individual predicts their partners' height in an independent dataset of 15,437 individuals with 13% accuracy, which is 64% of the theoretical maximum achievable with a heritability of 0.041. Theoretical predictions suggest that approximately 5% of the heritability of height is due to the positive covariance between allelic effects at different loci, which is caused by assortative mating. Hence, the coupling of alleles with similar effects could substantially contribute to the missing heritability of height.

CONCLUSIONS

These estimates provide new insight into the mechanisms that govern mate choice in humans and warrant the search for the genetic causes of choice of mate height. They have important methodological implications and contribute to the missing heritability debate.