Pleiotropy in complex traits: challenges and strategies

Improving genetic risk prediction by leveraging pleiotropy

An important task of human genetics studies is to predict accurately disease risks in individuals based on genetic markers, which allows for identifying individuals at high disease risks, and facilitating their disease treatment and prevention. Although hundreds of genome-wide association studies (GWAS) have been conducted on many complex human traits in recent years, there has been only limited success in translating these GWAS data into clinically useful risk prediction models. The predictive capability of GWAS data is largely bottlenecked by the available training sample size due to the presence of numerous variants carrying only small to modest effects. Recent studies have shown that different human traits may share common genetic bases. Therefore, an attractive strategy to increase the training sample size and hence improve the prediction accuracy is to integrate data from genetically correlated phenotypes. Yet, the utility of genetic correlation in risk prediction has not been explored in the literature. In this paper, we analyzed GWAS data for bipolar and related disorders and schizophrenia with a bivariate ridge regression method, and found that jointly predicting the two phenotypes could substantially increase prediction accuracy as measured by the area under the receiver operating characteristic curve. We also found similar prediction accuracy improvements when we jointly analyzed GWAS data for Crohn's disease and ulcerative colitis. The empirical observations were substantiated through our comprehensive simulation studies, suggesting that a gain in prediction accuracy can be obtained by combining phenotypes with relatively high genetic correlations. Through both real data and simulation studies, we demonstrated pleiotropy can be leveraged as a valuable asset that opens up a new opportunity to improve genetic risk prediction in the future.

Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies

Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.

Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies

Background

Mitochondria play a critical role in the cell and have DNA independent of the nuclear genome. There is much evidence that mitochondrial DNA (mtDNA) variation plays a role in human health and disease, however, this area of investigation has lagged behind research into the role of nuclear genetic variation on complex traits and phenotypic outcomes. Phenome-wide association studies (PheWAS) investigate the association between a wide range of traits and genetic variation. To date, this approach has not been used to investigate the relationship between mtDNA variants and phenotypic variation. Herein, we describe the development of a PheWAS framework for mtDNA variants (mt-PheWAS). Using the Metabochip custom genotyping array, nuclear and mitochondrial DNA variants were genotyped in 11,519 African Americans from the Vanderbilt University biorepository, BioVU. We employed both polygenic modeling and association testing with mitochondrial single nucleotide polymorphisms (mtSNPs) to explore the relationship between mtDNA variants and a group of eight cardiovascular-related traits obtained from de-identified electronic medical records within BioVU.

Results

Using polygenic modeling we found evidence for an effect of mtDNA variation on total cholesterol and type 2 diabetes (T2D). After performing comprehensive mitochondrial single SNP associations, we identified an increased number of single mtSNP associations with total cholesterol and T2D compared to the other phenotypes examined, which did not have more significantly associated SNPs than would be expected by chance. Among the mtSNPs significantly associated with T2D we identified variant mt16189, an association previously reported only in Asian and European-descent populations.

Conclusions

Our replication of previous findings and identification of novel associations from this initial study suggest that our mt-PheWAS approach is robust for investigating the relationship between mitochondrial genetic variation and a range of phenotypes, providing a framework for future mt-PheWAS.

A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V⁻¹t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.

We describe novel methods for finding significant associations between a genome wide panel of SNPs and multiple complex traits, and further for distinguishing between genes with effects on multiple traits and multiple linked genes affecting different traits. The method uses a meta-analysis based on estimates of SNP effects from independent single trait genome wide association studies (GWAS). The method could therefore be widely used to combine already published GWAS results. The method was applied to 32 traits that describe growth, body composition, feed intake and reproduction in 10,191 beef cattle genotyped for approximately 700,000 SNP. The genes found to be associated with these traits can be arranged into 4 groups that differ in their pattern of effects and hence presumably in their physiological mechanism of action. For instance, one group of genes affects weight and fatness in the opposite direction and can be described as a group of genes affecting mature size, while another group affects weight and fatness in the same direction.

Bipolar and schizophrenia network for intermediate phenotypes: outcomes across the psychosis continuum

Bipolar and schizophrenia network for intermediate phenotypes is a network of investigator-driven laboratories focused on developing phenotypes, genotypes, and biomarkers for psychosis. Over the last 5 years, the consortium has accomplished a dense phenotyping protocol using probands with a lifetime history of psychosis, their relatives, and healthy controls. This has established a library of biomarker information on individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. The founding goal of establishing disease biomarkers for current psychotic diagnoses has been poorly met, because the cognitive, electrophysiologic, eye movement, and brain imaging biomarkers did not regularly discriminate individuals with different DSM psychosis diagnoses. In future, we will use this biomarker information to establish a pathway to biomarker-based classification in psychoses.

MicroRNA sequence polymorphisms and the risk of different types of cancer

MicroRNAs (miRNAs) participate in diverse biological pathways and may act as oncogenes or tumor suppressors. Single nucleotide polymorphisms (SNPs) in miRNAs (MirSNPs) might promote carcinogenesis by affecting miRNA function and/or maturation; however, the association between MirSNPs reported and cancer risk remain inconsistent. Here, we investigated the association between nine common MirSNPs and cancer risk using data from large scale case-control studies. Eight precursor-miRNA (pre-miRNA) SNPs (rs2043556/miR-605, rs3746444/miR-499a/b, rs4919510/miR-608, rs2910164/miR-146a, rs11614913/miR-196a2, rs895819/miR-27a, rs2292832/miR-149, rs6505162/miR-423) and one primary-miRNA (pri-miRNA) SNP (rs1834306/miR-100) were analyzed in 16399 cases and 21779 controls from seven published studies in eight common cancers. With a novel statistic, Cross phenotype meta-analysis (CPMA) of the association of MirSNPs with multiple phenotypes indicated rs2910164 C (P = 1.11E-03), rs2043556 C (P = 0.0165), rs6505162 C (P = 2.05E-03) and rs895819 (P = 0.0284) were associated with a significant overall risk of cancer. In conclusion, MirSNPs might affect an individual's susceptibility to various types of cancer.

Current Insights into the Joint Genetic Basis of Type 2 Diabetes and Coronary Heart Disease

The large-scale genome-wide association studies conducted so far identified numerous allelic variants associated with type 2 diabetes (T2D), coronary heart disease (CHD) and related cardiometabolic traits. Many T2D- and some CHD-risk loci are also linked with metabolic traits that are hallmarks of insulin resistance (lipid profile, abdominal adiposity). Chromosome 9p21.3 and 2q36.3 are the most consistently replicated loci appearing to share genetic risk for both T2D and CHD. Although many glucose- or insulin-related trait variants are also linked with T2D risk, none of them is associated with CHD. Hence, while T2D and CHD are strongly clinically linked together, further ongoing analyses are needed to clarify the existence of a shared underlying genetic signature of these complex traits. The present review summarizes an updated picture of T2D-CHD genetics as of 2013, aiming to provide a platform for targeted studies dissecting the contribution of genetics to the phenotypic heterogeneity of T2D and CHD.

Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index

Phenome-wide association studies (PheWAS) have demonstrated utility in validating genetic associations derived from traditional genetic studies as well as identifying novel genetic associations. Here we used an electronic health record (EHR)-based PheWAS to explore pleiotropy of genetic variants in the fat mass and obesity associated gene (FTO), some of which have been previously associated with obesity and type 2 diabetes (T2D). We used a population of 10,487 individuals of European ancestry with genome-wide genotyping from the Electronic Medical Records and Genomics (eMERGE) Network and another population of 13,711 individuals of European ancestry from the BioVU DNA biobank at Vanderbilt genotyped using Illumina HumanExome BeadChip. A meta-analysis of the two study populations replicated the well-described associations between FTO variants and obesity (odds ratio [OR] = 1.25, 95% Confidence Interval = 1.11-1.24, p = 2.10 × 10(-9)) and FTO variants and T2D (OR = 1.14, 95% CI = 1.08-1.21, p = 2.34 × 10(-6)). The meta-analysis also demonstrated that FTO variant rs8050136 was significantly associated with sleep apnea (OR = 1.14, 95% CI = 1.07-1.22, p = 3.33 × 10(-5)); however, the association was attenuated after adjustment for body mass index (BMI). Novel phenotype associations with obesity-associated FTO variants included fibrocystic breast disease (rs9941349, OR = 0.81, 95% CI = 0.74-0.91, p = 5.41 × 10(-5)) and trends toward associations with non-alcoholic liver disease and gram-positive bacterial infections. FTO variants not associated with obesity demonstrated other potential disease associations including non-inflammatory disorders of the cervix and chronic periodontitis. These results suggest that genetic variants in FTO may have pleiotropic associations, some of which are not mediated by obesity.

Quantifying missing heritability at known GWAS loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Heritable diseases have an unknown underlying “genetic architecture” that defines the distribution of effect-sizes for disease-causing mutations. Understanding this genetic architecture is an important first step in designing disease-mapping studies, and many theories have been developed on the nature of this distribution. Here, we evaluate the hypothesis that additional heritable variation lies at previously known associated loci but is not fully explained by the single most associated marker. We develop methods based on variance-components analysis to quantify this type of “local” heritability, demonstrating that standard strategies can be falsely inflated or deflated due to correlation between neighboring markers and propose a robust adjustment. In analysis of nine common diseases we find a significant average increase of local heritability, consistent with multiple common causal variants at an average locus. Intriguingly, for autoimmune diseases we also observe significant local heritability in loci not associated with the specific disease but with other autoimmune diseases, implying a highly correlated underlying disease architecture. These findings have important implications to the design of future studies and our general understanding of common disease.

Genetic and epigenetic factors in etiology of diabetes mellitus type 1

Diabetes mellitus type 1 (T1D) is a complex disease resulting from the interplay of genetic, epigenetic, and environmental factors. Recent progress in understanding the genetic basis of T1D has resulted in an increased recognition of childhood diabetes heterogeneity. After the initial success of family-based linkage analyses, which uncovered the strong linkage and association between HLA gene variants and T1D, genome-wide association studies performed with high-density single-nucleotide polymorphism genotyping platforms provided evidence for a number of novel loci, although fine mapping and characterization of these new regions remains to be performed. T1D is one of the most heritable common diseases, and among autoimmune diseases it has the largest range of concordance rates in monozygotic twins. This fact, coupled with evidence of various epigenetic modifications of gene expression, provides convincing proof of the complex interplay between genetic and environmental factors. In T1D, epigenetic phenomena, such as DNA methylation, histone modifications, and microRNA dysregulation, have been associated with altered gene expression. Increasing epidemiologic and experimental evidence supports the role of genetic and epigenetic alterations in the etiopathology of diabetes. We discuss recent results related to the role of genetic and epigenetic factors involved in development of T1D.

Translational approaches to anxiety: focus on genetics, fear extinction and brain imaging

Anxiety disorders are highly prevalent and debilitating psychiatric disorders. Owing to the complex aetiology of anxiety disorders, translational studies involving multiple approaches, including human and animal genetics, molecular, endocrinological and imaging studies, are needed to get a converging picture of function or dysfunction of anxiety-related circuits. An advantage of anxiety disorders is that the neural circuitry of fear is comparatively well understood, with striking analogies between animal and human models, and this article aims to provide a brief overview of current translational approaches to anxiety. Experimental models that involve similar tasks in animals and humans, such as fear conditioning and extinction, seem particularly promising and can be readily integrated with imaging, behavioural and physiological readouts. The cross-validation between animal and human genetics models is essential to examine the relevance of candidate genes, as well as their neural pathways, for anxiety disorders; a recent example of such cross-validation work is provided by preclinical and clinical work on TMEM132D, which has been identified as a candidate gene for panic disorder. Further integration of epigenetic data and gene × environment interaction are promising approaches, as highlighted by FKPB5 and PACAP, early life trauma and stress-related anxiety disorders. Finally, connecting genetic and epigenetic data with functionally relevant imaging readouts will allow a comparison of overlap and differences across species in mechanistic pathways from genes to brain functioning and behaviour.

Clinical implications of shared genetics and pathogenesis in autoimmune diseases

Many endocrine diseases, including type 1 diabetes mellitus, Graves disease, Addison disease and Hashimoto disease, originate as an autoimmune reaction that affects disease-specific target organs. These autoimmune diseases are characterized by the development of specific autoantibodies and by the presence of autoreactive T cells. They are caused by a complex genetic predisposition that is attributable to multiple genetic variants, each with a moderate-to-low effect size. Most of the genetic variants associated with a particular autoimmune endocrine disease are shared between other systemic and organ-specific autoimmune and inflammatory diseases, such as rheumatoid arthritis, coeliac disease, systemic lupus erythematosus and psoriasis. Here, we review the shared and specific genetic background of autoimmune diseases, summarize their treatment options and discuss how identifying the genetic and environmental factors that predispose patients to an autoimmune disease can help in the diagnosis and monitoring of patients, as well as the design of new treatments.

Roles of the protein tyrosine phosphatase PTPN22 in immunity and autoimmunity

PTPN22 is a protein tyrosine phosphatase expressed by the majority of cells belonging to the innate and adaptive immune systems. Polymorphisms in PTPN22 are associated with several autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis and type 1 diabetes. This review discusses the role of PTPN22 in T and B cells, and its function in innate immune cells, such as monocytes, dendritic cells and NK cells. We focus particularly on the complexity that underlies the function of PTPN22 in the biological processes of the immune system; such complexity has led various research groups to produce rather conflicting data.

Assessment of genetic correlation between bacterial cold water disease resistance and spleen index in a domesticated population of rainbow trout: identification of QTL on chromosome Omy19

Selective breeding of animals for increased disease resistance is an effective strategy to reduce mortality in aquaculture. However, implementation of selective breeding programs is limited by an incomplete understanding of host resistance traits. We previously reported results of a rainbow trout selection program that demonstrated increased survival following challenge with Flavobacterium psychrophilum, the causative agent of bacterial cold water disease (BCWD). Mechanistic study of disease resistance identified a positive phenotypic correlation between post-challenge survival and spleen somatic-index (SI). Herein, we investigated the hypothesis of a genetic correlation between the two traits influenced by colocalizing QTL. We evaluated the inheritance and calculated the genetic correlation in five year-classes of odd- and even-year breeding lines. A total of 322 pedigreed families (n = 25,369 fish) were measured for disease resistance, and 251 families (n = 5,645 fish) were evaluated for SI. Spleen index was moderately heritable in both even-year (h(2)  = 0.56±0.18) and odd-year (h(2)  = 0.60±0.15) lines. A significant genetic correlation between SI and BCWD resistance was observed in the even-year line (rg  = 0.45±0.20, P = 0.03) but not in the odd-year line (rg  = 0.16±0.12, P = 0.19). Complex segregation analyses of the even-year line provided evidence of genes with major effect on SI, and a genome scan of a single family, 2008132, detected three significant QTL on chromosomes Omy19, 16 and 5, in addition to ten suggestive QTL. A separate chromosome scan for disease resistance in family 2008132 identified a significant BCWD QTL on Omy19 that was associated with time to death and percent survival. In family 2008132, Omy19 microsatellite alleles that associated with higher disease resistance also associated with increased spleen size raising the hypothesis that closely linked QTL contribute to the correlation between these traits. To our knowledge, this is the first estimation of spleen size heritability and evidence for genetic linkage with specific disease resistance in a teleost fish.

What is next after the genes for autoimmunity?

Clinical pathologies draw us to envisage disease as either an independent entity or a diverse set of traits governed by common physiopathological mechanisms, prompted by environmental assaults throughout life. Autoimmune diseases are not an exception, given they represent a diverse collection of diseases in terms of their demographic profile and primary clinical manifestations. Although they are pleiotropic outcomes of non-specific disease genes underlying similar immunogenetic mechanisms, research generally focuses on a single disease. Drastic technologic advances are leading research to organize clinical genomic multidisciplinary approaches to decipher the nature of human biological systems. Once the currently costly omic-based technologies become universally accessible, the way will be paved for a cleaner picture to risk quantification, prevention, prognosis and diagnosis, allowing us to clearly define better phenotypes always ensuring the integrity of the individuals studied. However, making accurate predictions for most autoimmune diseases is an ambitious challenge, since the understanding of these pathologies is far from complete. Herein, some pitfalls and challenges of the genetics of autoimmune diseases are reviewed, and an approximation to the future of research in this field is presented.