Pleiotropy in complex traits: challenges and strategies

NERI: network-medicine based integrative approach for disease gene prioritization by relative importance

Background

Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease.

Results

The goal of this work is to develop a methodology that integrates PPI networks with disease specific data sources, such as GWAS and gene expression, to find genes more specific of a given complex disease. After the integration of PPI networks and gene expression data, the resulting network is used to connect genes related to the disease through the shortest paths that have the greatest concordance between their gene expressions. Both case and control expression data are used separately and, at the end, the most altered genes between the two conditions are selected. To evaluate the method, schizophrenia was adopted as case study.

Conclusion

Results show that the proposed method successfully retrieves differentially coexpressed genes in two conditions, while avoiding the bias from literature. Moreover we were able to achieve a greater concordance in the selection of important genes from different microarray studies of the same disease and to produce a more specific gene set related to the studied disease.

Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.

GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2, http://jjwanglab.org/gwasdb) which provides comprehensive data curation and knowledge integration for GWAS TASs. These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations (P-value < 1.0×10⁻³) from each original publication, and generated a total of 252 530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website.

Linking Genes to Cardiovascular Diseases: Gene Action and Gene-Environment Interactions

A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the "post-genomic" era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how "modifier genes" influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many-practitioners and investigators-to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area.

Optimal multiple testing under a Gaussian prior on the effect sizes

We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p$\end{document}-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.

Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors

RATIONALE

Coronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes.

OBJECTIVE

We aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal-informed statistical framework.

METHODS AND RESULTS

Using genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate <0.01). Furthermore, we identified 53 loci with significant effects in both CAD and at least 1 of low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, systolic blood pressure, and type 1 diabetes mellitus.

CONCLUSIONS

The observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD.

Genome-wide association studies for feed intake and efficiency in two laying periods of chickens

Background

Feed contributes to over 60 % of the total production costs in the poultry industry. Increasing feed costs prompt geneticists to include feed intake and efficiency as selection goals in breeding programs. In the present study, we used an F₂ chicken population in a genome-wide association study (GWAS) to detect potential genetic variants and candidate genes associated with daily feed intake (FI) and feed efficiency, including residual feed intake (RFI) and feed conversion ratio (FCR).

Methods

A total of 1534 F₂ hens from a White Leghorn and Dongxiang reciprocal cross were phenotyped for feed intake and efficiency between 37 and 40 weeks (FI1, RFI1, and FCR1) and between 57 and 60 weeks (FI2, RFI2, and FCR2), and genotyped using the chicken 600 K single nucleotide polymorphism (SNP) genotyping array. Univariate, bivariate, and conditional genome-wide association studies (GWAS) were performed with GEMMA, a genome-wide efficient mixed model association algorithm. The statistical significance threshold for association was inferred by the simpleM method.

Results

We identified eight genomic regions that each contained at least one genetic variant that showed a significant association with FI. Genomic regions on Gallus gallus (GGA) chromosome 4 coincided with known quantitative trait loci (QTL) that affect feed intake of layers. Of particular interest, eight SNPs on GGA1 in the region between 169.23 and 171.55 Mb were consistently associated with FI in both univariate and bivariate GWAS, which explained 3.72 and 2.57 % of the phenotypic variance of FI1 and FI2, respectively. The CAB39L gene can be considered as a promising candidate for FI1. For RFI, a haplotype block on GGA27 harbored a significant SNP associated with RFI2. The major allele of rs315135692 was favorable for a lower RFI, with a phenotypic difference of 3.35 g/day between opposite homozygous genotypes. Strong signals on GGA1 were detected in the bivariate GWAS for FCR.

Conclusions

The results demonstrated the polygenic nature of feed intake. GWAS identified novel variants and confirmed a QTL that was previously reported for feed intake in chickens. Genetic variants associated with feed efficiency may be used in genomic breeding programs to select more efficient layers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0161-1) contains supplementary material, which is available to authorized users.

Obesity-Related Genetic Variants and their Associations with Physical Activity

BACKGROUND

Meta-analysis of genome-wide association studies identified obesity-related genetic variants. Due to the pleiotropic effects of related phenotypes, we tested six of these obesity-related genetic variants for their association with physical activity: fat mass and obesity-associated (FTO)(rs9939609)T>A, potassium channel tetramerization domain containing (KCTD15) (rs11084753)G>A, melanocortin receptor4 (MC4R)(rs17782313)T>C, neuronal growth regulator 1 (NEGR1)(rs2815752)A>G, SH2B adapter protein 1 (SH2B1)(rs7498665)A>G, and transmembrane protein18 (TMEM18)(rs6548238)C>T.

METHOD

European-American women (n = 263) and men (n = 229) (23.5 ± 0.3 years, 24.6 ± 0.2 kg/m2) were genotyped and completed the Paffenbarger physical activity Questionnaire. Physical activity volume in metabolic energy equivalents [MET]-hour/week was derived from the summed time spent (hour/week) times the given MET value for vigorous, moderate, and light intensity physical activity, and sitting and sleeping, respectively. Multivariable adjusted [(age, sex, and body mass index (BMI)] linear regression tested associations among genotype (dominant/recessive model) and the log of physical activity volume.

RESULT

MC4R (rs17782313)T>C explained 1.1 % (p = 0.02), TMEM18(rs6548238)C>T 1.2 % (p = 0.01), and SH2B1 (rs7498665)A>G 0.6 % (p = 0.08) of the variability in physical activity volume. Subjects with the MC4R C allele spent 3.5 % less MET-hour/week than those with the TT genotype (p = 0.02). Subjects with the TMEM18 T allele spent 4.1 % less MET-hour/week than those with the CC genotype (p = 0.01). Finally, subjects with the SH2B1 GG genotype spent 3.6 % less MET-hour/week than A allele carriers (p = 0.08).

CONCLUSION

Our findings suggest a shared genetic influence among some obesity-related gene loci and physical activity phenotypes that should be explored further. Physical activity volume differences by genotype have public health importance equating to 11-13 lb weight difference annually.

Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?

Positive associations between human intelligence and brain size have been suspected for more than 150 years. Nowadays, modern non-invasive measures of in vivo brain volume (Magnetic Resonance Imaging) make it possible to reliably assess associations with IQ. By means of a systematic review of published studies and unpublished results obtained by personal communications with researchers, we identified 88 studies examining effect sizes of 148 healthy and clinical mixed-sex samples (>8000 individuals). Our results showed significant positive associations of brain volume and IQ (r=.24, R(2)=.06) that generalize over age (children vs. adults), IQ domain (full-scale, performance, and verbal IQ), and sex. Application of a number of methods for detection of publication bias indicates that strong and positive correlation coefficients have been reported frequently in the literature whilst small and non-significant associations appear to have been often omitted from reports. We show that the strength of the positive association of brain volume and IQ has been overestimated in the literature, but remains robust even when accounting for different types of dissemination bias, although reported effects have been declining over time. While it is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability, we show that it is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences.

Genome-wide association study dissects genetic architecture underlying longitudinal egg weights in chickens

BACKGROUND

As a major economic trait in chickens, egg weight (EW) receives widespread interests in breeding, production and consumption. However, limited information is available for underlying genetic architecture of longitudinal trend in EW. Herein, we measured EWs at nine time points from onset of laying to 60 week of age, and conducted comprehensive genome-wide association studies (GWAS) in 1,534 F2 hens derived from reciprocal crosses between White Leghorn and Dongxiang chickens.

RESULTS

Egg weights at all ages except the first egg weight (FEW) exhibited high SNP-based heritability estimates (0.47~0.60). Strong pair-wise genetic correlations (0.77~1.00) were found among all EWs. Nine separate univariate genome-wide screens suggested 73 signals showing significant associations with longitudinal EWs. After multivariate and conditional analyses, four variants on three chromosomes remained independent contributions. The minor alleles at two loci exerted consistent and positive substitution effects on EWs, and other two were negative. The four loci together accounted for 3.84 % of the phenotypic variance for FEW and 7.29~11.06 % for EWs from 32 to 60 week of age. We obtained five candidate genes, of which NCAPG harbors a non-synonymous SNP (rs14491030) causing a valine-to-alanine amino-acid substitution. Genome partitioning analysis indicated a strong linear correlation between the variance explained by each chromosome and its length, which provided evidence that EW follows a highly polygenic nature of inheritance.

CONCLUSIONS

Identification of significant genetic causes that together implicate EWs at different ages will greatly advance our understanding of the genetic basis behind longitudinal EWs, and would be helpful to illuminate the future breeding direction on how to select desired egg size.

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

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

Heritabilities, proportions of heritabilities explained by GWAS findings, and implications of cross-phenotype effects on PR interval

Electrocardiogram (ECG) measurements are a powerful tool for evaluating cardiac function and are widely used for the diagnosis and prediction of a variety of conditions, including myocardial infarction, cardiac arrhythmias, and sudden cardiac death. Recently, genome-wide association studies (GWASs) identified a large number of genes related to ECG parameter variability, specifically for the QT, QRS, and PR intervals. The aims of this study were to establish the heritability of ECG traits, including indices of left ventricular hypertrophy, and to directly assess the proportion of those heritabilities explained by GWAS variants. These analyses were conducted in a large, Dutch family-based cohort study, the Erasmus Rucphen Family study using variance component methods implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) software package. Heritability estimates ranged from 34 % for QRS and Cornell voltage product to 49 % for 12-lead sum. Trait-specific GWAS findings for each trait explained a fraction of their heritability (17 % for QRS, 4 % for QT, 2 % for PR, 3 % for Sokolow–Lyon index, and 4 % for 12-lead sum). The inclusion of all ECG-associated single nucleotide polymorphisms explained an additional 6 % of the heritability of PR. In conclusion, this study shows that, although GWAS explain a portion of ECG trait variability, a large amount of heritability remains to be explained. In addition, larger GWAS for PR are likely to detect loci already identified, particularly those observed for QRS and 12-lead sum.

CPAG: software for leveraging pleiotropy in GWAS to reveal similarity between human traits links plasma fatty acids and intestinal inflammation

Meta-analyses of genome-wide association studies (GWAS) have demonstrated that the same genetic variants can be associated with multiple diseases and other complex traits. We present software called CPAG (Cross-Phenotype Analysis of GWAS) to look for similarities between 700 traits, build trees with informative clusters, and highlight underlying pathways. Clusters are consistent with pre-defined groups and literature-based validation but also reveal novel connections. We report similarity between plasma palmitoleic acid and Crohn's disease and find that specific fatty acids exacerbate enterocolitis in zebrafish. CPAG will become increasingly powerful as more genetic variants are uncovered, leading to a deeper understanding of complex traits. CPAG is freely available at www.sourceforge.net/projects/CPAG/.

The evolution of the human genome

Human genomes hold a record of the evolutionary forces that have shaped our species. Advances in DNA sequencing, functional genomics, and population genetic modeling have deepened our understanding of human demographic history, natural selection, and many other long-studied topics. These advances have also revealed many previously underappreciated factors that influence the evolution of the human genome, including functional modifications to DNA and histones, conserved 3D topological chromatin domains, structural variation, and heterogeneous mutation patterns along the genome. Using evolutionary theory as a lens to study these phenomena will lead to significant breakthroughs in understanding what makes us human and why we get sick.

Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS

Although some existing epidemiological observations and molecular experiments suggested that brain disorders in the realm of psychiatry may be influenced by immune dysregulation, the degree of genetic overlap between psychiatric disorders and immune disorders has not been well established. We investigated this issue by integrative analysis of genome-wide association studies of 18 complex human traits/diseases (five psychiatric disorders, seven immune disorders, and others) and multiple genome-wide annotation resources (central nervous system genes, immune-related expression-quantitative trait loci (eQTL) and DNase I hypertensive sites from 98 cell lines). We detected pleiotropy in 24 of the 35 psychiatric-immune disorder pairs. The strongest pleiotropy was observed for schizophrenia-rheumatoid arthritis with MHC region included in the analysis (p = 3.9 x 10(-285), and schizophrenia-Crohn's disease with MHC region excluded (p = 1.1 x 10(-36). Significant enrichment (> 1.4 fold) of immune-related eQTL was observed in four psychiatric disorders. Genomic regions responsible for pleiotropy between psychiatric disorders and immune disorders were detected. The MHC region on chromosome 6 appears to be the most important with other regions, such as cytoband 1p13.2, also playing significant roles in pleiotropy. We also found that most alleles shared between schizophrenia and Crohn's disease have the same effect direction, with similar trend found for other disorder pairs, such as bipolar-Crohn's disease. Our results offer a novel bird's-eye view of the genetic relationship and demonstrate strong evidence for pervasive pleiotropy between psychiatric disorders and immune disorders. Our findings might open new routes for prevention and treatment strategies for these disorders based on a new appreciation of the importance of immunological mechanisms in mediating risk of many psychiatric diseases.

How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity

Increasing proportions of elderly individuals in developed countries combined with substantial increases in related medical expenditures make the improvement of the health of the elderly a high priority today. If the process of aging by individuals is a major cause of age related health declines then postponing aging could be an efficient strategy for improving the health of the elderly. Implementing this strategy requires a better understanding of genetic and non-genetic connections among aging, health, and longevity. We review progress and problems in research areas whose development may contribute to analyses of such connections. These include genetic studies of human aging and longevity, the heterogeneity of populations with respect to their susceptibility to disease and death, forces that shape age patterns of human mortality, secular trends in mortality decline, and integrative mortality modeling using longitudinal data. The dynamic involvement of genetic factors in (i) morbidity/mortality risks, (ii) responses to stresses of life, (iii) multi-morbidities of many elderly individuals, (iv) trade-offs for diseases, (v) genetic heterogeneity, and (vi) other relevant aging-related health declines, underscores the need for a comprehensive, integrated approach to analyze the genetic connections for all of the above aspects of aging-related changes. The dynamic relationships among aging, health, and longevity traits would be better understood if one linked several research fields within one conceptual framework that allowed for efficient analyses of available longitudinal data using the wealth of available knowledge about aging, health, and longevity already accumulated in the research field.

Assessing the Causal Relationship of Maternal Height on Birth Size and Gestational Age at Birth: A Mendelian Randomization Analysis

BACKGROUND

Observational epidemiological studies indicate that maternal height is associated with gestational age at birth and fetal growth measures (i.e., shorter mothers deliver infants at earlier gestational ages with lower birth weight and birth length). Different mechanisms have been postulated to explain these associations. This study aimed to investigate the casual relationships behind the strong association of maternal height with fetal growth measures (i.e., birth length and birth weight) and gestational age by a Mendelian randomization approach.

METHODS AND FINDINGS

We conducted a Mendelian randomization analysis using phenotype and genome-wide single nucleotide polymorphism (SNP) data of 3,485 mother/infant pairs from birth cohorts collected from three Nordic countries (Finland, Denmark, and Norway). We constructed a genetic score based on 697 SNPs known to be associated with adult height to index maternal height. To avoid confounding due to genetic sharing between mother and infant, we inferred parental transmission of the height-associated SNPs and utilized the haplotype genetic score derived from nontransmitted alleles as a valid genetic instrument for maternal height. In observational analysis, maternal height was significantly associated with birth length (p = 6.31 × 10-9), birth weight (p = 2.19 × 10-15), and gestational age (p = 1.51 × 10-7). Our parental-specific haplotype score association analysis revealed that birth length and birth weight were significantly associated with the maternal transmitted haplotype score as well as the paternal transmitted haplotype score. Their association with the maternal nontransmitted haplotype score was far less significant, indicating a major fetal genetic influence on these fetal growth measures. In contrast, gestational age was significantly associated with the nontransmitted haplotype score (p = 0.0424) and demonstrated a significant (p = 0.0234) causal effect of every 1 cm increase in maternal height resulting in ~0.4 more gestational d. Limitations of this study include potential influences in causal inference by biological pleiotropy, assortative mating, and the nonrandom sampling of study subjects.

CONCLUSIONS

Our results demonstrate that the observed association between maternal height and fetal growth measures (i.e., birth length and birth weight) is mainly defined by fetal genetics. In contrast, the association between maternal height and gestational age is more likely to be causal. In addition, our approach that utilizes the genetic score derived from the nontransmitted maternal haplotype as a genetic instrument is a novel extension to the Mendelian randomization methodology in casual inference between parental phenotype (or exposure) and outcomes in offspring.

Phenome-Wide Association Studies: Embracing Complexity for Discovery

The inherent complexity of biological systems can be leveraged for a greater understanding of the impact of genetic architecture on outcomes, traits, and pharmacological response. The genome-wide association study (GWAS) approach has well-developed methods and relatively straight-forward methodologies; however, the bigger picture of the impact of genetic architecture on phenotypic outcome still remains to be elucidated even with an ever-growing number of GWAS performed. Greater consideration of the complexity of biological processes, using more data from the phenome, exposome, and diverse -omic resources, including considering the interplay of pleiotropy and genetic interactions, may provide additional leverage for making the most of the incredible wealth of information available for study. Here, we describe how incorporating greater complexity into analyses through the use of additional phenotypic data and widespread deployment of phenome-wide association studies may provide new insights into genetic factors influencing diseases, traits, and pharmacological response.

The detection and characterization of pleiotropy: discovery, progress, and promise

The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture.

Familiality and SNP heritability of age at onset and episodicity in major depressive disorder

BACKGROUND

Strategies to dissect phenotypic and genetic heterogeneity of major depressive disorder (MDD) have mainly relied on subphenotypes, such as age at onset (AAO) and recurrence/episodicity. Yet, evidence on whether these subphenotypes are familial or heritable is scarce. The aims of this study are to investigate the familiality of AAO and episode frequency in MDD and to assess the proportion of their variance explained by common single nucleotide polymorphisms (SNP heritability).

METHOD

For investigating familiality, we used 691 families with 2-5 full siblings with recurrent MDD from the DeNt study. We fitted (square root) AAO and episode count in a linear and a negative binomial mixed model, respectively, with family as random effect and adjusting for sex, age and center. The strength of familiality was assessed with intraclass correlation coefficients (ICC). For estimating SNP heritabilities, we used 3468 unrelated MDD cases from the RADIANT and GSK Munich studies. After similarly adjusting for covariates, derived residuals were used with the GREML method in GCTA (genome-wide complex trait analysis) software.

RESULTS

Significant familial clustering was found for both AAO (ICC = 0.28) and episodicity (ICC = 0.07). We calculated from respective ICC estimates the maximal additive heritability of AAO (0.56) and episodicity (0.15). SNP heritability of AAO was 0.17 (p = 0.04); analysis was underpowered for calculating SNP heritability of episodicity.

CONCLUSIONS

AAO and episodicity aggregate in families to a moderate and small degree, respectively. AAO is under stronger additive genetic control than episodicity. Larger samples are needed to calculate the SNP heritability of episodicity. The described statistical framework could be useful in future analyses.

Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.

Efficient set tests for the genetic analysis of correlated traits

Set tests are a powerful approach for genome-wide association testing between groups of genetic variants and quantitative traits. We describe mtSet (http://github.com/PMBio/limix), a mixed-model approach that enables joint analysis across multiple correlated traits while accounting for population structure and relatedness. mtSet effectively combines the benefits of set tests with multi-trait modeling and is computationally efficient, enabling genetic analysis of large cohorts (up to 500,000 individuals) and multiple traits.

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study (GWAS) approach, which has been used to identify single nucleotide polymorphisms (SNPs) associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype-phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

Loci associated with adult stature also affect calf birth survival in cattle

Background

Understanding the underlying pleiotropic relationships among quantitative traits is necessary in order to predict correlated responses to artificial selection. The availability of large-scale next-generation sequence data in cattle has provided an opportunity to examine whether pleiotropy is responsible for overlapping QTL in multiple economic traits. In the present study, we examined QTL affecting cattle stillbirth, calf size, and adult stature located in the same genomic region.

Results

A genome scan using imputed whole genome sequence variants revealed one QTL with large effects on the service sire calving index (SCI), and body conformation index (BCI) at the same location (~39 Mb) on chromosome 6 in Nordic Red cattle. The targeted region was analyzed for SCI and BCI component traits. The QTL peak included LCORL and NCAPG genes, which had been reported to influence fetal growth and adult stature in several species. The QTL exhibited large effects on calf size and stature in Nordic Red cattle. Two deviant haplotypes (HAP1 and HAP2) were resolved which increased calf size at birth, and affected adult body conformation. However, the haplotypes also resulted in increased calving difficulties and calf mortality due to increased calf size at birth. Haplotype locations overlapped, however linkage disequilibrium (LD) between the sites was low, suggesting that two independent mutations were responsible for similar effects. The difference in prevalence between the two haplotypes in Nordic Red subpopulations suggested independent origins in different populations.

Conclusions

Results of our study identified QTL with large effects on body conformation and service sire calving traits on chromosome 6 in cattle. We present robust evidence that variation at the LCORL and NCAPG locus affects calf size at birth and adult stature. We suggest the two deviant haplotypes within the QTL were due to two independent mutations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0202-3) contains supplementary material, which is available to authorized users.

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case.

NO RELATIONSHIP BETWEEN INTELLIGENCE AND FACIAL ATTRACTIVENESS IN A LARGE, GENETICALLY INFORMATIVE SAMPLE

Theories in both evolutionary and social psychology suggest that a positive correlation should exist between facial attractiveness and general intelligence, and several empirical observations appear to corroborate this expectation. Using highly reliable measures of facial attractiveness and IQ in a large sample of identical and fraternal twins and their siblings, we found no evidence for a phenotypic correlation between these traits. Likewise, neither the genetic nor the environmental latent factor correlations were statistically significant. We supplemented our analyses of new data with a simple meta-analysis that found evidence of publication bias among past studies of the relationship between facial attractiveness and intelligence. In view of these results, we suggest that previously published reports may have overestimated the strength of the relationship and that the theoretical bases for the predicted attractiveness-intelligence correlation may need to be reconsidered.

Genetic influence on left ventricular structure and function: a Korean twin and family study

Genetic factors have been suggested to be one of the determinants of the variation of left ventricular (LV) structure and function. However, the heritability range of LV structure varies across studies and the influence of genetics on LV function is not well established, especially in Asian populations. Study subjects were 1,642 healthy Korean adults from 426 families, consisting of 298 pairs of monozygotic twins, 62 pairs of dizygotic twins, one set of triplets, 567 siblings, and 354 parents. LV structure and function were measured by M-mode and 2D echocardiography, and conventional and tissue Doppler imaging (TDI). Pairwise intra-class correlations for various familial relationships and heritability were estimated for LV structure and function. The heritability of LV mass, LV ejection fraction (LVEF), left atrial volume index, the ratio between early and late diastolic velocity of mitral inflow (E/A ratio), and the ratio between early diastolic velocity of mitral inflow and early diastolic mitral annular velocities (E/Ea ratio) was 0.44, 0.27, 0.44, 0.25, and 0.33, respectively. Bivariate genetic analysis showed that LV structural and functional traits had significant genetic correlations with cardiovascular risk factors. Additive genetic correlation (ρG) of LV mass with body mass index, systolic blood pressure, and high density lipoprotein cholesterol were 0.49, 0.42, and -0.15 respectively. LVEF (ρG = 0.33) and left atrial volume index (ρG = 0.24) also had a significant genetic correlation with systolic blood pressure. These findings support the theory that genetic factors have significant influence on these traits and necessitate further work to identify the specific genes involved.

Mind the gap: why many geneticists and psychological scientists have discrepant views about gene-environment interaction (G×E) research

As our field seeks to elucidate the biopsychosocial etiologies of mental health disorders, many traditional psychological and social science researchers have added, or plan to add, genetic components to their programs of research. An understanding of the history, methods, and perspectives of the psychiatric genetics community is useful in this pursuit. In this article we provide a brief overview of psychiatric genetic methods and findings. This overview lays the groundwork for a more thorough review of gene-environment interaction (G×E) research and the candidate gene approach to G×E research that remains popular among many psychologists and social scientists. We describe the differences in perspective between psychiatric geneticists and psychological scientists that have contributed to a growing divide between the research cited and conducted by these two related disciplines. Finally, we outline a strategy for the future of research on gene-environment interactions that capitalizes on the relative strengths of each discipline. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Peripheral blood leukocyte telomere length and mortality among 64,637 individuals from the general population

BACKGROUND

Short telomeres in peripheral blood leukocytes are associated with older age and age-related diseases. We tested the hypotheses that short telomeres are associated with both increased cancer mortality and all-cause mortality.

METHODS

Individuals (n = 64637) were recruited from 1991 onwards from two Danish prospective cohort studies: the Copenhagen City Heart Study and the Copenhagen General Population Study. All had telomere length measured by quantitative polymerase chain reaction and the genotypes rs1317082 (TERC), rs7726159 (TERT), and rs2487999 (OBFC1) determined. The sum of telomere-shortening alleles from these three genotypes was calculated. We conducted Cox regression analyses and instrumental variable analyses using the allele sum as an instrument. All statistical tests were two-sided.

RESULTS

Among 7607 individuals who died during follow-up (0-22 years, median = 7 years), 2420 had cancer and 2633 had cardiovascular disease as causes of death. Decreasing telomere length deciles were associated with increasing all-cause mortality (P(trend) = 2*10(-15)). The multivariable-adjusted hazard ratio of all-cause mortality was 1.40 (95% confidence interval [CI] = 1.25 to 1.57) for individuals in the shortest vs the longest decile. Results were similar for cancer mortality and cardiovascular mortality. Telomere length decreased 69 base pairs (95% CI = 61 to 76) per allele for the allele sum, and the per-allele hazard ratio for cancer mortality was 0.95 (95% CI = 0.91 to 0.99). Allele sum was not associated with cardiovascular, other, or all-cause mortality.

CONCLUSION

Short telomeres in peripheral blood leukocytes were associated with high mortality in association analyses. In contrast, genetically determined short telomeres were associated with low cancer mortality but not with all-cause mortality.

Genome-wide association analyses for boar taint components and testicular traits revealed regions having pleiotropic effects

Background

The aim of this study was to perform a genome-wide association analyses (GWAS) for androstenone, skatole and indole in different Pietrain sire lines and compare the results with previous findings in purebred populations. Furthermore, the genetic relationship of androstenone and skatole were investigated with respect to pleiotropy. In order to characterize the performance of intact boars, crossbred progenies of 136 Pietrain boars mated to crossbred sows from three different breeding companies were tested on four test stations. A total of 598 boars were performance tested according to the rules of stationary performance testing in Germany. Beside common fattening and carcass composition traits, the concentrations of the boar taint components and testicular size parameters were recorded. All boars were genotyped with the PorcineSNP60 Illumina BeadChip. The GWAS were performed using the whole data set as well as in sub groups according to the line of origin. Besides an univariate GWAS approach, principal component (PC) techniques were applied to identify common expression pattern affecting the biosynthesis and the metabolism of androstenone.

Results

In total, 33 SNPs were significantly associated with at least one of the boar taint components. Only one SNP was identified being significant in both subgroups. The analyses of the testes size parameters revealed 31 significant associations. The numbers of significant SNPs within the genetic groups evidenced the strong population specific effects. A multivariate approach using PC revealed 33 significant associations for five different PC.

Conclusions

Based on Pietrain sired cross bred boars, the mayor objective of our study was to identify QTL for boar taint components and to detect pleiotropy among boar taint and testes traits. The high number of identified QTL revealed that boar taint traits are influenced by a large number of loci. Analyzing pleiotropy allowed identifying a QTL affecting androstenone and the gonasomatic index. In this region, QTL for ovulation rate and age at puberty of sows have been described in literature. This supports the physiological findings that the androstenone level of boars and reproduction performance of sows might be linked by an antagonistic relationship.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0194-z) contains supplementary material, which is available to authorized users.

TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3x10^-21), A928V (rs35018800, OR = 0.53, P = 1.2x10^-9), and I684S (rs12720356, OR = 0.86, P = 4.6x10^-7). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, P_omnibus = 6x10^-18), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; P_omnibus = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.

A genome-wide pleiotropy scan for prostate cancer risk

BACKGROUND

No single-nucleotide polymorphisms (SNPs) specific for aggressive prostate cancer have been identified in genome-wide association studies (GWAS).

OBJECTIVE

To test if SNPs associated with other traits may also affect the risk of aggressive prostate cancer.

DESIGN, SETTING, AND PARTICIPANTS

SNPs implicated in any phenotype other than prostate cancer (p≤10(-7)) were identified through the catalog of published GWAS and tested in 2891 aggressive prostate cancer cases and 4592 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). The 40 most significant SNPs were followed up in 4872 aggressive prostate cancer cases and 24,534 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Odds ratios (ORs) and 95% confidence intervals (CIs) for aggressive prostate cancer were estimated.

RESULTS AND LIMITATIONS

A total of 4666 SNPs were evaluated by the BPC3. Two signals were seen in regions already reported for prostate cancer risk. rs7014346 at 8q24.21 was marginally associated with aggressive prostate cancer in the BPC3 trial (p=1.6×10(-6)), whereas after meta-analysis by PRACTICAL the summary OR was 1.21 (95% CI 1.16-1.27; p=3.22×10(-18)). rs9900242 at 17q24.3 was also marginally associated with aggressive disease in the meta-analysis (OR 0.90, 95% CI 0.86-0.94; p=2.5×10(-6)). Neither of these SNPs remained statistically significant when conditioning on correlated known prostate cancer SNPs. The meta-analysis by BPC3 and PRACTICAL identified a third promising signal, marked by rs16844874 at 2q34, independent of known prostate cancer loci (OR 1.12, 95% CI 1.06-1.19; p=4.67×10(-5)); it has been shown that SNPs correlated with this signal affect glycine concentrations. The main limitation is the heterogeneity in the definition of aggressive prostate cancer between BPC3 and PRACTICAL.

CONCLUSIONS

We did not identify new SNPs for aggressive prostate cancer. However, rs16844874 may provide preliminary genetic evidence on the role of the glycine pathway in prostate cancer etiology.

PATIENT SUMMARY

We evaluated whether genetic variants associated with several traits are linked to the risk of aggressive prostate cancer. No new such variants were identified.

Genome-wide association scan in north Indians reveals three novel HLA-independent risk loci for ulcerative colitis

OBJECTIVE

Over 100 ulcerative colitis (UC) loci have been identified by genome-wide association studies (GWASs) primarily in Caucasians (CEUs). Many of them have weak effects on disease susceptibility, and the bulk of the heritability cannot be ascribed to these loci. Very little is known about the genetic background of UC in non-CEU groups. Here we report the first GWAS on UC in a genetically distinct north Indian (NI) population.

DESIGN

A genome-wide scan was performed on 700 cases and 761 controls. 18 single-nucleotide polymorphisms (SNPs) (p<5×10(-5)) were genotyped in an independent cohort of 733 cases and 1148 controls. A linear mixed model was used for case-control association tests.

RESULTS

Seven novel human leucocyte antigen (HLA)-independent SNPs from chromosome 6, located in 3.8-1, BAT2, MSH5, HSPA1L, SLC44A4, CFB and NOTCH4, exceeded p<5×10(-8) in the combined analysis. To assess the independent biological contribution of such genes from the extended HLA region, we determined the percentage alternative pathway activity of complement factor B (CFB), the top novel hit. The activity was significantly different (p=0.01) between the different genotypes at rs12614 in UC cases. Transethnic comparisons revealed a shared contribution of a fraction of UC risk genes between NI and CEU populations, in addition to genetic heterogeneity.

CONCLUSIONS

This study shows varying contribution of the HLA region to UC in different populations. Different environmental exposures and the characteristic genetic structure of the HLA locus across ethnic groups collectively make it amenable to the discovery of causative alleles by transethnic resequencing. This may lead to an improved understanding of the molecular mechanisms underlying UC.

Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10^-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.

Heredity and self-organization: partners in the generation and evolution of phenotypes

In this review we examine the role of self-organization in the context of the evolution of morphogenesis. We provide examples to show that self-organized behavior is ubiquitous, and suggest it is a mechanism that can permit high levels of biodiversity without the invention of ever-increasing numbers of genes. We also examine the implications of self-organization for understanding the "internal descriptions" of organisms and the concept of a genotype-phenotype map.

The importance of anxiety states in bipolar disorder

Anxiety symptoms and syndromes are common in bipolar disorders, occurring in over half of all subjects with bipolar disorder type I. Despite methodological and diagnostic inconsistencies, most studies have shown a robust association between the presence of a broadly defined comorbid anxiety disorder and important indices of clinical morbidity in bipolar disorder, including a greater number of depressive episodes, worse treatment outcomes, and elevated risk of attempting suicide. Anxiety symptoms and/or syndromes often precede the onset of bipolar disorder and may represent a clinical phenotype of increased risk in subjects with prodromal symptoms. Although the causal relationship between anxiety and bipolar disorders remains unresolved, the multifactorial nature of most psychiatric phenotypes suggests that even with progress towards more biologically valid phenotypes, the "phenomenon" of comorbidity is likely to remain a clinical reality. Treatment studies of bipolar patients with comorbid anxiety have begun to provide preliminary evidence for the role of specific pharmacological and psychotherapeutic treatments, but these need to be confirmed in more definitive trials. Hence, there is an immediate need for further research to help guide assessment and help identify appropriate treatments for comorbid conditions.

Recent genetic findings in schizophrenia and their therapeutic relevance

Over 100 loci are now associated with schizophrenia risk as identified by single nucleotide polymorphisms (SNPs) in genome-wide association studies. These findings mean that 'genes for schizophrenia' have unquestionably been found. However, many questions remain unanswered, including several which affect their therapeutic significance. The SNPs individually have minor effects, and even cumulatively explain only a modest fraction of the genetic predisposition. The remainder likely results from many more loci, from rare variants, and from gene-gene and gene-environment interactions. The risk SNPs are almost all non-coding, meaning that their biological significance is unclear; probably their effects are mediated via an influence on gene regulation, and emerging evidence suggests that some key molecular events occur during early brain development. The loci include novel genes of unknown function as well as genes and pathways previously implicated in the pathophysiology of schizophrenia, e.g. NMDA receptor signalling. Genes in the latter category have the clearer therapeutic potential, although even this will be a challenging process because of the many complexities concerning the genetic architecture and mediating mechanisms. This review summarises recent schizophrenia genetic findings and some key issues they raise, particularly with regard to their implications for identifying and validating novel drug targets.

Genetic architecture of a hormonal response to gene knockdown in honey bees

Variation in endocrine signaling is proposed to underlie the evolution and regulation of social life histories, but the genetic architecture of endocrine signaling is still poorly understood. An excellent example of a hormonally influenced set of social traits is found in the honey bee (Apis mellifera): a dynamic and mutually suppressive relationship between juvenile hormone (JH) and the yolk precursor protein vitellogenin (Vg) regulates behavioral maturation and foraging of workers. Several other traits cosegregate with these behavioral phenotypes, comprising the pollen hoarding syndrome (PHS) one of the best-described animal behavioral syndromes. Genotype differences in responsiveness of JH to Vg are a potential mechanistic basis for the PHS. Here, we reduced Vg expression via RNA interference in progeny from a backcross between 2 selected lines of honey bees that differ in JH responsiveness to Vg reduction and measured JH response and ovary size, which represents another key aspect of the PHS. Genetic mapping based on restriction site-associated DNA tag sequencing identified suggestive quantitative trait loci (QTL) for ovary size and JH responsiveness. We confirmed genetic effects on both traits near many QTL that had been identified previously for their effect on various PHS traits. Thus, our results support a role for endocrine control of complex traits at a genetic level. Furthermore, this first example of a genetic map of a hormonal response to gene knockdown in a social insect helps to refine the genetic understanding of complex behaviors and the physiology that may underlie behavioral control in general.

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.

Genomic predictors of combat stress vulnerability and resilience in U.S. Marines: A genome-wide association study across multiple ancestries implicates PRTFDC1 as a potential PTSD gene

BACKGROUND

Research on the etiology of post-traumatic stress disorder (PTSD) has rapidly matured, moving from candidate gene studies to interrogation of the entire human genome in genome-wide association studies (GWAS). Here we present the results of a GWAS performed on samples from combat-exposed U.S. Marines and Sailors from the Marine Resiliency Study (MRS) scheduled for deployment to Iraq and/or Afghanistan. The MRS is a large, prospective study with longitudinal follow-up designed to identify risk and resiliency factors for combat-induced stress-related symptoms. Previously implicated PTSD risk loci from the literature and polygenic risk scores across psychiatric disorders were also evaluated in the MRS cohort.

METHODS

Participants (N=3494) were assessed using the Clinician-Administered PTSD Scale and diagnosed using the DSM-IV diagnostic criterion. Subjects with partial and/or full PTSD diagnosis were called cases, all other subjects were designated controls, and study-wide maximum CAPS scores were used for longitudinal assessments. Genomic DNA was genotyped on the Illumina HumanOmniExpressExome array. Individual genetic ancestry was determined by supervised cluster analysis for subjects of European, African, Hispanic/Native American, and other descent. To test for association of SNPs with PTSD, logistic regressions were performed within each ancestry group and results were combined in meta-analyses. Measures of childhood and adult trauma were included to test for gene-by-environment (GxE) interactions. Polygenic risk scores from the Psychiatric Genomic Consortium were used for major depressive disorder (MDD), bipolar disorder (BPD), and schizophrenia (SCZ).

RESULTS

The array produced >800K directly genotyped and >21M imputed markers in 3494 unrelated, trauma-exposed males, of which 940 were diagnosed with partial or full PTSD. The GWAS meta-analysis identified the phosphoribosyl transferase domain containing 1 gene (PRTFDC1) as a genome-wide significant PTSD locus (rs6482463; OR=1.47, SE=0.06, p=2.04×10(-9)), with a similar effect across ancestry groups. Association of PRTFDC1 with PTSD in an independent military cohort showed some evidence for replication. Loci with suggestive evidence of association (n=25 genes, p<5×10(-6)) further implicated genes related to immune response and the ubiquitin system, but these findings remain to be replicated in larger GWASs. A replication analysis of 25 putative PTSD genes from the literature found nominally significant SNPs for the majority of these genes, but associations did not remain significant after correction for multiple comparison. A cross-disorder analysis of polygenic risk scores from GWASs of BPD, MDD, and SCZ found that PTSD diagnosis was associated with risk sores of BPD, but not with MDD or SCZ.

CONCLUSIONS

This first multi-ethnic/racial GWAS of PTSD highlights the potential to increase power through meta-analyses across ancestry groups. We found evidence for PRTFDC1 as a potential novel PTSD gene, a finding that awaits further replication. Our findings indicate that the genetic architecture of PTSD may be determined by many SNPs with small effects, and overlap with other neuropsychiatric disorders, consistent with current findings from large GWAS of other psychiatric disorders.

Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols

Background.  Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods.  Data from 4 AIDS Clinical Trials Group (ACTG) studies were split into 2 datasets: Dataset I (1181 individuals from protocol A5202) and Dataset II (1366 from protocols A5095, ACTG 384, and A5142). Final analyses involved 2547 individuals and 5 954 294 imputed polymorphisms. We calculated comprehensive associations between these polymorphisms and 27 baseline laboratory phenotypes. Results.  A total of 10 584 (0.17%) polymorphisms had associations with P < .01 in both datasets and with the same direction of association. Twenty polymorphisms replicated associations with identical or related phenotypes reported in the Catalog of Published Genome-Wide Association Studies, including several not previously reported in HIV-positive cohorts. We also identified several possibly novel associations. Conclusions.  These analyses define PheWAS properties and principles with baseline laboratory data from HIV clinical trials. This approach may be useful for evaluating on-treatment HIV clinical trials data for associations with various clinical phenotypes.

Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33

Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.