Pleiotropy in complex traits: challenges and strategies

A univariate perspective of multivariate genome-wide association analysis

Multiple correlated phenotypes are frequently collected in genome-wide association studies (GWASs), and a systematic, simultaneous analysis of multiple phenotypes can integrate the signals from single phenotypes, therefore increasing the power of detecting genetic signals. However, fundamental questions remain open, including the conditions and reasons under which the multivariate analysis is beneficial, how a highly significant signal arises in the multivariate analysis. To understand these issues, we propose to decompose the multivariate model into a series of simple univariate models. This transformation offers a clearer quantitative analysis of the circumstances under which a multivariate approach can be beneficial for the bivariate phenotypes case. A real data analysis is employed to illustrate how to interpret how the signals arising from multivariate GWASs.

Embracing polygenicity: a review of methods and tools for psychiatric genetics research

The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

Genetics: An integrated genetic analysis of disease

A recent GWAS of quantitative traits (clinical lab measurements) was performed and incorporated with additional GWAS of a variety of complex diseases in Japanese subjects. Together, the data demonstrated pleiotropy, genetic correlation, and cell type specificity in regard to quantitative disease traits as predictors of multiple complex diseases.

Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the 'no horizontal pleiotropy' assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from -131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances.

Multi-polygenic score approach to trait prediction

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data

We propose genetic instrumental variable (GIV) regression—a method that controls for pleiotropic effects of genes on two variables. GIV regression is broadly applicable to study outcomes for which polygenic scores from large-scale genome-wide association studies are available. We explore the performance of GIV regression in the presence of pleiotropy across a range of scenarios and find that it yields more accurate estimates than alternative approaches such as ordinary least-squares regression or Mendelian randomization. When GIV regression is combined with proper controls for purely environmental sources of bias (e.g., using control variables and sibling fixed effects), it improves our understanding of the causal relationships between genetically correlated variables.

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.

A Systems Approach to Refine Disease Taxonomy by Integrating Phenotypic and Molecular Networks

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy.

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The International Classification of Diseases (ICD) lags behind the current molecular characteristics of disease.

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We quantified the limitations (specificity and blurred boundary) of ICD with integrated phenotypic and molecular profiles.

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An integrative disease network integrating phenotypic and genotypic profiles proposes a refined disease category framework.

Disease taxonomy is one of the foundations of medical science and healthcare solutions. The most widely used disease taxonomy in clinical settings is the International Classification of Diseases (ICD), a system established >100 years ago and maintained by the World Health Organization to track disease incidence. It is well recognized that ICD, which is based on clinical observations, largely lags behind the molecular achievements of this medical big data era. We quantified the limitations of ICD using integrated phenotypic and molecular profiles and proposed a refined disease taxonomy with possible applications for precision medicine.

Genome-wide pleiotropy and shared biological pathways for resistance to bovine pathogens

Host genetic architecture is a major factor in resistance to pathogens and parasites. The collection and analysis of sufficient data on both disease resistance and host genetics has, however, been a major obstacle to dissection the genetics of resistance to single or multiple pathogens. A severe challenge in the estimation of heritabilities and genetic correlations from pedigree-based studies has been the confounding effects of the common environment shared among relatives which are difficult to model in pedigree analyses, especially for health traits with low incidence rates. To circumvent this problem we used genome-wide single-nucleotide polymorphism data and implemented the Genomic-Restricted Maximum Likelihood (G-REML) method to estimate the heritabilities and genetic correlations for resistance to 23 different infectious pathogens in calves and cows in populations undergoing natural pathogen challenge. Furthermore, we conducted gene-based analysis and generalized gene-set analysis to understand the biological background of resistance to infectious diseases. The results showed relatively higher heritabilities of resistance in calves than in cows and significant pleiotropy (both positive and negative) among some calf and cow resistance traits. We also found significant pleiotropy between resistance and performance in both calves and cows. Finally, we confirmed the role of the B-lymphocyte pathway as one of the most important biological pathways associated with resistance to all pathogens. These results both illustrate the potential power of these approaches to illuminate the genetics of pathogen resistance in cattle and provide foundational information for future genomic selection aimed at improving the overall production fitness of cattle.

Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy

In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.

Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits

OBJECTIVE

Shared genetic background may explain phenotypic associations between depression and Type 2 diabetes (T2D). We aimed to study, on a genome-wide level, if genetic correlation and pleiotropic loci exist between depressive symptoms and T2D or glycemic traits.

METHODS

We estimated single-nucleotide polymorphism (SNP)-based heritability and analyzed genetic correlation between depressive symptoms and T2D and glycemic traits with the linkage disequilibrium score regression by combining summary statistics of previously conducted meta-analyses for depressive symptoms by CHARGE consortium (N = 51,258), T2D by DIAGRAM consortium (N = 34,840 patients and 114,981 controls), fasting glucose, fasting insulin, and homeostatic model assessment of β-cell function and insulin resistance by MAGIC consortium (N = 58,074). Finally, we investigated pleiotropic loci using a bivariate genome-wide association study approach with summary statistics from genome-wide association study meta-analyses and reported loci with genome-wide significant bivariate association p value (p < 5 × 10). Biological annotation and function of significant pleiotropic SNPs were assessed in several databases.

RESULTS

The SNP-based heritability ranged from 0.04 to 0.10 in each individual trait. In the linkage disequilibrium score regression analyses, depressive symptoms showed no significant genetic correlation with T2D or glycemic traits (p > 0.37). However, we identified pleiotropic genetic variations for depressive symptoms and T2D (in the IGF2BP2, CDKAL1, CDKN2B-AS, and PLEKHA1 genes), and fasting glucose (in the MADD, CDKN2B-AS, PEX16, and MTNR1B genes).

CONCLUSIONS

We found no significant overall genetic correlations between depressive symptoms, T2D, or glycemic traits suggesting major differences in underlying biology of these traits. However, several potential pleiotropic loci were identified between depressive symptoms, T2D, and fasting glucose, suggesting that previously established phenotypic associations may be partly explained by genetic variation in these specific loci.

A multi-trait Bayesian method for mapping QTL and genomic prediction

Background

Genomic prediction and quantitative trait loci (QTL) mapping typically analyze one trait at a time but this may ignore the possibility that one polymorphism affects multiple traits. The aim of this study was to develop a multivariate Bayesian approach that could be used for simultaneously elucidating genetic architecture, QTL mapping, and genomic prediction. Our approach uses information from multiple traits to divide markers into ‘unassociated’ (no association with any trait) and ‘associated’ (associated with one or more traits). The effect of associated markers is estimated independently for each trait to avoid the assumption that QTL effects follow a multi-variate normal distribution.

Results

Using simulated data, our multivariate method (BayesMV) detected a larger number of true QTL (with a posterior probability > 0.9) and increased the accuracy of genomic prediction compared to an equivalent univariate method (BayesR). With real data, accuracies of genomic prediction in validation sets for milk yield traits with high-density genotypes were approximately equal to those from equivalent single-trait methods. BayesMV tended to select a similar number of single nucleotide polymorphisms (SNPs) per trait for genomic prediction compared to BayesR (i.e. those with non-zero effects), but BayesR selected different sets of SNPs for each trait, whereas BayesMV selected a common set of SNPs across traits. Despite these two dramatically different estimates of genetic architecture (i.e. different SNPs affecting each trait vs. pleiotropic SNPs), both models indicated that 3000 to 4000 SNPs are associated with a trait. The BayesMV approach may be advantageous when the aim is to develop a low-density SNP chip that works well for a number of traits. SNPs for milk yield traits identified by BayesMV and BayesR were also found to be associated with detailed milk composition.

Conclusions

The BayesMV method simultaneously estimates the proportion of SNPs that are associated with a combination of traits. When applied to milk production traits, most of the identified SNPs were associated with all three traits (milk, fat and protein yield). BayesMV aims at exploiting pleiotropic QTL and selects a small number of SNPs that could be used to predict multiple traits.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0377-y) contains supplementary material, which is available to authorized users.

Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

A population genetic interpretation of GWAS findings for human quantitative traits

Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes-notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10-3.

Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies

Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research.

Optic nerve regeneration in the mouse is a complex trait modulated by genetic background

Purpose

The present study is designed to identify the influences of genetic background on optic nerve regeneration using the two parental strains (C57BL/6J and DBA/2J) and seven BXD recombinant inbred mouse strains.

Methods

To study regeneration in the optic nerve, Pten was knocked down in the retinal ganglion cells using adenoassociated virus (AAV) delivery of shRNA, and a mild inflammatory response was induced with an intravitreal injection of zymosan with CPT-cAMP. The axons of the retinal ganglion cells were damaged by optic nerve crush (ONC). Following a 12-day survival period, regenerating axons were labeled by cholera toxin B, and 2 days later, the regenerating axons within the optic nerve were examined. The number of axons at 0.5 mm and 1 mm from the crush site were counted. In addition, we measured the distance that five axons had grown down the nerve and the longest distance a single axon reached.

Results

The analysis revealed a considerable amount of differential axonal regeneration across the seven BXD strains and the parental strains. There was a statistically significant difference (p=0.014 Mann-Whitney U test) in the regenerative capacity in the number of axons reaching 0.5 mm from a low of 236.1±24.4 axons in the BXD102 mice to a high of 759.8±79.2 axons in the BXD29 mice. There were also statistically significant differences (p=0.014 Mann-Whitney U test) in the distance axons traveled. Looking at a minimum of five axons, the shortest distance was 787.2±46.5 µm in the BXD102 mice, and the maximum distance was 2025.5±223.3 µm in the BXD29 mice.

Conclusions

Differences in genetic background can have a profound effect on axonal regeneration causing a threefold increase in the number of regenerating axons at 0.5 mm from the crush site and a 2.5-fold increase in the distance traveled by at least five axons in the damaged optic nerve.

Genetic architecture: the shape of the genetic contribution to human traits and disease

Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.

Evaluation of shared genetic aetiology between osteoarthritis and bone mineral density identifies SMAD3 as a novel osteoarthritis risk locus

Osteoarthritis (OA) is a common complex disease with high public health burden and no curative therapy. High bone mineral density (BMD) is associated with an increased risk of developing OA, suggesting a shared underlying biology. Here, we performed the first systematic overlap analysis of OA and BMD on a genome wide scale. We used summary statistics from the GEFOS consortium for lumbar spine (n = 31,800) and femoral neck (n = 32,961) BMD, and from the arcOGEN consortium for three OA phenotypes (hip, n_cases=3,498; knee, n_cases=3,266; hip and/or knee, n_cases=7,410; n_controls=11,009). Performing LD score regression we found a significant genetic correlation between the combined OA phenotype (hip and/or knee) and lumbar spine BMD (r_g=0.18, P = 2.23 × 10⁻²), which may be driven by the presence of spinal osteophytes. We identified 143 variants with evidence for cross-phenotype association which we took forward for replication in independent large-scale OA datasets, and subsequent meta-analysis with arcOGEN for a total sample size of up to 23,425 cases and 236,814 controls. We found robustly replicating evidence for association with OA at rs12901071 (OR 1.08 95% CI 1.05–1.11, P_meta=3.12 × 10⁻¹⁰), an intronic variant in the SMAD3 gene, which is known to play a role in bone remodeling and cartilage maintenance. We were able to confirm expression of SMAD3 in intact and degraded cartilage of the knee and hip. Our findings provide the first systematic evaluation of pleiotropy between OA and BMD, highlight genes with biological relevance to both traits, and establish a robust new OA genetic risk locus at SMAD3.

Negative results are needed to show the specific value of a cultural explanation for g

Burkart et al. suggest that social learning can explain the cognitive positive manifold for social animals, including humans. We caution that simpler explanations of positive trait intercorrelations exist, such as genetic load. To test the suggested explanation's specificity, we also need to examine non-social species and traits, such as health, that are distal to cognitive abilities.

Identification of shared genetic variants between schizophrenia and lung cancer

Epidemiology studies suggest associations between schizophrenia and cancer. However, the underlying genetic mechanisms are not well understood, and difficult to identify from epidemiological data. We investigated if there is a shared genetic architecture between schizophrenia and cancer, with the aim to identify specific overlapping genetic loci. First, we performed genome-wide enrichment analysis and second, we analyzed specific loci jointly associated with schizophrenia and cancer by the conjunction false discovery rate. We analyzed the largest genome-wide association studies of schizophrenia and lung, breast, prostate, ovary, and colon-rectum cancer including more than 220,000 subjects, and included genetic association with smoking behavior. Polygenic enrichment of associations with lung cancer was observed in schizophrenia, and weak enrichment for the remaining cancer sites. After excluding the major histocompatibility complex region, we identified three independent loci jointly associated with schizophrenia and lung cancer. The strongest association included nicotinic acetylcholine receptors and is an established pleiotropic locus shared between lung cancer and smoking. The two other loci were independent of genetic association with smoking. Functional analysis identified downstream pleiotropic effects on epigenetics and gene-expression in lung and brain tissue. These findings suggest that genetic factors may explain partly the observed epidemiological association of lung cancer and schizophrenia.

Genetic overlap between epilepsy and schizophrenia: Evidence from cross phenotype analysis in Hong Kong Chinese population

Epilepsy and schizophrenia are common and typical neurological or mental illness respectively, and sometimes they comorbid in the same patients, however the underlying genetic relationship between the two brain diseases is still not fully understood. To investigate the possible genetic contribution to their comorbidity, we performed polygenic risk score (PRS) analyses and genetic correlation estimation so as to identify the overall genetic overlap between the two diseases. The global schizophrenia PRS is strongly associated with schizophrenia phenotype in Hong Kong population (odds ratio = 1.7, p = 2.26E-16), and focal epilepsy PRS is moderately associated with epilepsy phenotype in Hong Kong population (odds ratio = 1.14, p = 0.013). However the disease-specific PRS can only predict its own well-matched phenotype but not the other ones (p > 0.05). This pattern is further supported by non-significant pairwise genetic correlation and insufficient statistical power for PRS association from the cross-phenotype analyses. Our study reveals there's limited shared genetic aetiology between schizophrenia and epilepsy, and thus supports a model of shared environmental factors to explain the comorbidity between the two phenotypes.

Identification of Pleiotropic Cancer Susceptibility Variants from Genome-Wide Association Studies Reveals Functional Characteristics

Background: There exists compelling evidence that some genetic variants are associated with the risk of multiple cancer sites (i.e., pleiotropy). However, the biological mechanisms through which the pleiotropic variants operate are unclear.Methods: We obtained all cancer risk associations from the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog, and correlated cancer risk variants were clustered into groups. Pleiotropic variant groups and genes were functionally annotated. Associations of pleiotropic cancer risk variants with noncancer traits were also obtained.Results: We identified 1,431 associations between variants and cancer risk, comprised of 989 unique variants associated with 27 unique cancer sites. We found 20 pleiotropic variant groups (2.1%) composed of 33 variants (3.3%), including novel pleiotropic variants rs3777204 and rs56219066 located in the ELL2 gene. Relative to single-cancer risk variants, pleiotropic variants were more likely to be in genes (89.0% vs. 65.3%, P = 2.2 × 10-16), and to have somewhat larger risk allele frequencies (median RAF = 0.49 versus 0.39, P = 0.046). The 27 genes to which the pleiotropic variants mapped were suggestive for enrichment in response to radiation and hypoxia, alpha-linolenic acid metabolism, cell cycle, and extension of telomeres. In addition, we observed that 8 of 33 pleiotropic cancer risk variants were associated with 16 traits other than cancer.Conclusions: This study identified and functionally characterized genetic variants showing pleiotropy for cancer risk.Impact: Our findings suggest biological pathways common to different cancers and other diseases, and provide a basis for the study of genetic testing for multiple cancers and repurposing cancer treatments. Cancer Epidemiol Biomarkers Prev; 27(1); 75-85. ©2017 AACR.

LIPC variants as genetic determinants of adiposity status, visceral adiposity indicators, and triglyceride-glucose (TyG) index-related parameters mediated by serum triglyceride levels

BACKGROUND

Visceral adiposity indicators and the product of triglyceride and fasting plasma glucose (TyG) index-related parameters are effective surrogate markers for insulin resistance (IR) and are predictors of metabolic syndrome and diabetes mellitus. However, their genetic determinants have not been previously reported. Pleiotropic associations of LIPC variants have been observed in lipid profiles and atherosclerotic cardiovascular diseases. We aimed to investigate LIPC polymorphisms as the genetic determinants of adiposity status, visceral adiposity indicators and TyG index-related parameters.

METHODS

A total of 592 participants from Taiwan were genotyped for three LIPC single nucleotide polymorphisms (SNPs).

RESULTS

The LIPC SNPs rs2043085 and rs1532085 were significantly associated with body mass index (BMI), waist circumference (WC), lipid accumulation product, visceral adiposity index, and TyG index-related parameters [including the TyG index, TyG with adiposity status (TyG-BMI), and TyG-WC index], whereas the rs1800588 SNP was only significantly associated with the TyG index. The associations became nonsignificant after further adjustment for serum TG levels. No significant association was observed between any the studied LIPC SNPs and IR status.

CONCLUSION

Our data revealed a pleiotropic association between the LIPC variants and visceral adiposity indicators and TyG index-related parameters, which are mediated by serum TG levels.

Family history of venous thromboembolism as a risk factor and genetic research tool

Familial clustering of venous thromboembolism (VTE) was described as far back as 1905 by Briggs. Although Egeberg discovered inherited deficiency of antithrombin in 1965, it was not until Dahlback discovered resistance to activated protein C in 1993 that it became clear that genetic factors are common risk factors of VTE. Several genes have been linked to familial aggregation of VTE and genome-wide association studies have found several novel gene loci. Still, it has been estimated that much of the heritability for VTE remains to be discovered. Family history (FH) of VTE is therefore still important to determine whether a patient has an increased genetic risk of VTE. FH has the potential to represent the sum of effects and interactions between environmental and genetic factors. In this article the design, methodology, results, clinical and genetic implications of FH studies of VTE are reviewed. FH in first-degree relatives (siblings and/or parents) is associated with a 2–3 times increased familial relative risk (FRR). However, the FRR is dependent on age, number of affected relatives, and presentation of VTE (provoked/unprovoked). Especially high familial risks are observed in individuals with two or more affected siblings (FFR> 50). However, the familial risk for recurrent VTE is much lower or non-significant. Moreover, FH of VTE appears mainly to be important for venous diseases (i. e. VTE and varicose veins). The familial associations with other diseases are weaker. In conclusion, FH of VTE is an important research tool and a clinically potential useful risk factor for VTE.

Domesticated, Genetically Engineered, and Wild Plant Relatives Exhibit Unintended Phenotypic Differences: A Comparative Meta-Analysis Profiling Rice, Canola, Maize, Sunflower, and Pumpkin

Agronomic management of plants is a powerful evolutionary force acting on their populations. The management of cultivated plants is carried out by the traditional process of human selection or plant breeding and, more recently, by the technologies used in genetic engineering (GE). Even though crop modification through GE is aimed at specific traits, it is possible that other non-target traits can be affected by genetic modification due to the complex regulatory processes of plant metabolism and development. In this study, we conducted a meta-analysis profiling the phenotypic consequences of plant breeding and GE, and compared modified cultivars with wild relatives in five crops of global economic and cultural importance: rice, maize, canola, sunflower, and pumpkin. For these five species, we analyzed the literature with documentation of phenotypic traits that are potentially related to fitness for the same species in comparable conditions. The information was analyzed to evaluate whether the different processes of modification had influenced the phenotype in such a way as to cause statistical differences in the state of specific phenotypic traits or grouping of the organisms depending on their genetic origin [wild, domesticated with genetic engineering (domGE), and domesticated without genetic engineering (domNGE)]. In addition, we tested the hypothesis that, given that transgenic plants are a construct designed to impact, in many cases, a single trait of the plant (e.g., lepidopteran resistance), the phenotypic differences between domGE and domNGE would be either less (or inexistent) than between the wild and domesticated relatives (either domGE or domNGE). We conclude that (1) genetic modification (either by selective breeding or GE) can be traced phenotypically when comparing wild relatives with their domesticated relatives (domGE and domNGE) and (2) the existence and the magnitude of the phenotypic differences between domGE and domNGE of the same crop suggest consequences of genetic modification beyond the target trait(s).

Immunogenomic approaches to understand the function of immune disease variants

Mapping hundreds of genetic variants through genome wide association studies provided an opportunity to gain insights into the pathobiology of immune-mediated diseases. However, as most of the disease variants fall outside the gene coding sequences the functional interpretation of the exact role of the associated variants remains to be determined. The integration of disease-associated variants with large scale genomic maps of cell-type-specific gene regulation at both chromatin and transcript levels deliver examples of functionally prioritized causal variants and genes. In particular, the enrichment of disease variants with histone marks can point towards the cell types most relevant to disease development. Furthermore, chromatin contact maps that link enhancers to promoter regions in a direct way allow the identification of genes that can be regulated by the disease variants. Candidate genes implicated with such approaches can be further examined through the correlation of gene expression with genotypes. Additionally, in the context of immune-mediated diseases it is important to combine genomics with immunology approaches. Genotype correlations with the immune system as a whole, as well as with cellular responses to different stimuli, provide a valuable platform for understanding the functional impact of disease-associated variants. The intersection of immunogenomic resources with disease-associated variants paints a detailed picture of disease causal mechanisms. Here, we provide an overview of recent studies that combine these approaches to identify disease vulnerable pathways.

Current Scope and Challenges in Phenome-Wide Association Studies

PURPOSE OF REVIEW

Over many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS.

RECENT FINDINGS

PheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data.

SUMMARY

The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.

Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses

There is growing interest in testing genetic pleiotropy, which is when a single genetic variant influences multiple traits. Several methods have been proposed; however, these methods have some limitations. First, all the proposed methods are based on the use of individual-level genotype and phenotype data; in contrast, for logistical, and other, reasons, summary statistics of univariate SNP-trait associations are typically only available based on meta- or mega-analyzed large genome-wide association study (GWAS) data. Second, existing tests are based on marginal pleiotropy, which cannot distinguish between direct and indirect associations of a single genetic variant with multiple traits due to correlations among the traits. Hence, it is useful to consider conditional analysis, in which a subset of traits is adjusted for another subset of traits. For example, in spite of substantial lowering of low-density lipoprotein cholesterol (LDL) with statin therapy, some patients still maintain high residual cardiovascular risk, and, for these patients, it might be helpful to reduce their triglyceride (TG) level. For this purpose, in order to identify new therapeutic targets, it would be useful to identify genetic variants with pleiotropic effects on LDL and TG after adjusting the latter for LDL; otherwise, a pleiotropic effect of a genetic variant detected by a marginal model could simply be due to its association with LDL only, given the well-known correlation between the two types of lipids. Here, we develop a new pleiotropy testing procedure based only on GWAS summary statistics that can be applied for both marginal analysis and conditional analysis. Although the main technical development is based on published union-intersection testing methods, care is needed in specifying conditional models to avoid invalid statistical estimation and inference. In addition to the previously used likelihood ratio test, we also propose using generalized estimating equations under the working independence model for robust inference. We provide numerical examples based on both simulated and real data, including two large lipid GWAS summary association datasets based on ∼100,000 and ∼189,000 samples, respectively, to demonstrate the difference between marginal and conditional analyses, as well as the effectiveness of our new approach.

The Effect of Depression, Generalized Anxiety, and Selective Serotonin Reuptake Inhibitors on Change in Bone Metabolism in Adolescents and Emerging Adults

The purpose of this study was to prospectively examine the independent contribution of major depressive disorder (MDD), generalized anxiety disorder (GAD), and selective serotonin reuptake inhibitors (SSRIs) use to changes in bone metabolism in older adolescents and emerging adults. Medically healthy 15- to 20-year-olds who were unmedicated or within 1 month of starting an SSRI were prospectively followed. Psychiatric functioning and medication treatment were assessed monthly. Every 4 months, trabecular and cortical volumetric bone mineral density (vBMD) at the radius and markers of bone metabolism were evaluated. Every 8 months, total body less head areal bone mineral content and lumbar spine (LS) areal BMD (aBMD) were determined. Linear mixed-effects regression analysis examined associations between bone measures on the one hand and MDD, GAD, and SSRI indices on the other. A total of 264 participants were followed for 1.51 ± 0.76 years. After adjusting for age, sex, vitamin D concentration, physical activity, lean mass or grip strength, and time in the study, MDD severity was associated with increasing LS aBMD. Similarly, SSRI use was associated with increasing LS aBMD and bone formation in female participants. In contrast, SSRI use was associated with decreasing LS aBMD in males. After accounting for depression, GAD was independently, albeit weakly, associated with increased bone mineralization. In older adolescents and emerging adults, MDD and GAD are associated with increasing bone mass, particularly in the lumbar spine and in females, whereas SSRIs are associated with increasing bone mass in females but decreasing bone mass in males. © 2017 American Society for Bone and Mineral Research.

Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

Novel common variants associated with body mass index and coronary artery disease detected using a pleiotropic cFDR method

Genome-wide association studies (GWAS) have been successfully applied in identifying single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) and coronary heart disease (CAD). However, the SNPs to date can only explain a small percentage of the genetic variances of traits. Here, we applied a genetic pleiotropic conditional false discovery rate (cFDR) method that combines summary statistic p values from different multi-center GWAS datasets, to detect common genetic variants associated with these two traits. The enrichment of SNPs associated with BMI and CAD was assessed by conditional Q-Q plots and the common variants were identified by the cFDR method. By applying the cFDR level of 0.05, 7 variants were identified to be associated with CAD (2 variants being novel), 34 variants associated with BMI (11 variants being novel), and 3 variants associated with both BMI and CAD (2 variants being novel). The SNP rs653178 (ATXN2) is noteworthy as this variant was replicated in an independent analysis. SNP rs12411886 (CNNM2) and rs794356 (HIP1) were of note as the annotated genes may be associated with processes that are functionally important in lipid metabolism. In conclusion, the cFDR method identified novel variants associated with BMI and/or CAD by effectively incorporating different GWAS datasets.

A plethora of pleiotropy across complex traits

A new analysis has identified hundreds of loci that are associated with multiple traits or diseases by comparing genome-wide association study (GWAS) data for 42 complex traits. The study uses the power of GWAS to provide evidence of pairs of traits with a likely causal relationship.

Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202

Supplemental Digital Content is available in the text.

Background

High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatment laboratory phenotypes from antiretroviral therapy-naive patients who were randomized to initiate antiretroviral regimens in a prospective clinical trial, AIDS Clinical Trials Group protocol A5202.

Participants and methods

From among 5 9545 294 polymorphisms imputed genome-wide, we analyzed 2544, including 2124 annotated in the PharmGKB, and 420 previously associated with traits in the GWAS Catalog. We derived 774 phenotypes on the basis of context from six variables: plasma atazanavir (ATV) pharmacokinetics, plasma efavirenz (EFV) pharmacokinetics, change in the CD4+ T-cell count, HIV-1 RNA suppression, fasting low-density lipoprotein-cholesterol, and fasting triglycerides. Permutation testing assessed the likelihood of associations being by chance alone. Pleiotropy was assessed for polymorphisms with the lowest P-values.

Results

This analysis included 1181 patients. At P less than 1.5×10⁻⁴, most associations were not by chance alone. Polymorphisms with the lowest P-values for EFV pharmacokinetics (CYPB26 rs3745274), low-density lipoprotein -cholesterol (APOE rs7412), and triglyceride (APOA5 rs651821) phenotypes had been associated previously with those traits in previous studies. The association between triglycerides and rs651821 was present with ATV-containing regimens, but not with EFV-containing regimens. Polymorphisms with the lowest P-values for ATV pharmacokinetics, CD4 T-cell count, and HIV-1 RNA phenotypes had not been reported previously to be associated with that trait.

Conclusion

Using data from a prospective HIV clinical trial, we identified expected genetic associations, potentially novel associations, and at least one context-dependent association. This study supports high-throughput strategies that simultaneously explore multiple phenotypes from clinical trials’ datasets for genetic associations.

The role of gene variants in the pathogenesis of neurodegenerative disorders as revealed by next generation sequencing studies: a review

The clinical diagnosis of neurodegenerative disorders based on phenotype is difficult in heterogeneous conditions with overlapping symptoms. It does not take into account the disease etiology or the highly variable clinical course even amongst patients diagnosed with the same disorder. The advent of next generation sequencing (NGS) has allowed for a system-wide, unbiased approach to identify all gene variants in the genome simultaneously. With the plethora of new genes being identified, genetic rather than phenotype-based classification of Mendelian diseases such as spinocerebellar ataxia (SCA), hereditary spastic paraplegia (HSP) and Charcot-Marie-Tooth disease (CMT) has become widely accepted. It has also become clear that gene variants play a role in common and predominantly sporadic neurodegenerative diseases such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). The observation of pleiotropy has emerged, with mutations in the same gene giving rise to diverse phenotypes, which further increases the complexity of phenotype-genotype correlation. Possible mechanisms of pleiotropy include different downstream effects of different mutations in the same gene, presence of modifier genes, and oligogenic inheritance. Future directions include development of bioinformatics tools and establishment of more extensive public genotype/phenotype databases to better distinguish deleterious gene variants from benign polymorphisms, translation of genetic findings into pathogenic mechanisms through in-vitro and in-vivo studies, and ultimately finding disease-modifying therapies for neurodegenerative disorders.

A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics.

Identification and allelic dissection uncover roles of lncRNAs in secondary growth of Populus tomentosa

Long non-coding RNAs (lncRNAs) function in various biological processes. However, their roles in secondary growth of plants remain poorly understood. Here, 15,691 lncRNAs were identified from vascular cambium, developing xylem, and mature xylem of Populus tomentosa with high and low biomass using RNA-seq, including 1,994 lncRNAs that were differentially expressed (DE) among the six libraries. 3,569 cis-regulated and 3,297 trans-regulated protein-coding genes were predicted as potential target genes (PTGs) of the DE lncRNAs to participate in biological regulation. Then, 476 and 28 lncRNAs were identified as putative targets and endogenous target mimics (eTMs) of Populus known microRNAs (miRNAs), respectively. Genome re-sequencing of 435 individuals from a natural population of P. tomentosa found 34,015 single nucleotide polymorphisms (SNPs) within 178 lncRNA loci and 522 PTGs. Single-SNP associations analysis detected 2,993 associations with 10 growth and wood-property traits under additive and dominance model. Epistasis analysis identified 17,656 epistatic SNP pairs, providing evidence for potential regulatory interactions between lncRNAs and their PTGs. Furthermore, a reconstructed epistatic network, representing interactions of 8 lncRNAs and 15 PTGs, might enrich regulation roles of genes in the phenylpropanoid pathway. These findings may enhance our understanding of non-coding genes in plants.

Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function

IMPORTANCE

Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction.

OBJECTIVE

To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains.

DESIGN, SETTING, AND PARTICIPANTS

Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888).

MAIN OUTCOMES AND MEASURES

Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined.

RESULTS

Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10-7), general cognitive function (z score, -4.43; P = 9.42 × 10-6), and verbal-numerical reasoning (z score, -5.43; P = 5.64 × 10-8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain.

CONCLUSIONS AND RELEVANCE

The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.

Shared genetic risk between migraine and coronary artery disease: A genome-wide analysis of common variants

Migraine is a recurrent pain condition traditionally viewed as a neurovascular disorder, but little is known of its vascular basis. In epidemiological studies migraine is associated with an increased risk of cardiovascular disease, including coronary artery disease (CAD), suggesting shared pathogenic mechanisms. This study aimed to determine the genetic overlap between migraine and CAD, and to identify shared genetic risk loci, utilizing a conditional false discovery rate approach and data from two large-scale genome-wide association studies (GWAS) of CAD (C4D, 15,420 cases, 15,062 controls; CARDIoGRAM, 22,233 cases, 64,762 controls) and one of migraine (22,120 cases, 91,284 controls). We found significant enrichment of genetic variants associated with CAD as a function of their association with migraine, which was replicated across two independent CAD GWAS studies. One shared risk locus in the PHACTR1 gene (conjunctional false discovery rate for index SNP rs9349379 < 3.90 x 10⁻⁵), which was also identified in previous studies, explained much of the enrichment. Two further loci (in KCNK5 and AS3MT) showed evidence for shared risk (conjunctional false discovery rate < 0.05). The index SNPs at two of the three loci had opposite effect directions in migraine and CAD. Our results confirm previous reports that migraine and CAD share genetic risk loci in excess of what would be expected by chance, and highlight one shared risk locus in PHACTR1. Understanding the biological mechanisms underpinning this shared risk is likely to improve our understanding of both disorders.

The antihypertensive MTHFR gene polymorphism rs17367504-G is a possible novel protective locus for preeclampsia

OBJECTIVE

Preeclampsia is a complex heterogeneous disease commonly defined by new-onset hypertension and proteinuria in pregnancy. Women experiencing preeclampsia have increased risk for cardiovascular diseases (CVD) later in life. Preeclampsia and CVD share risk factors and pathophysiologic mechanisms, including dysregulated inflammation and raised blood pressure. Despite commonalities, little is known about the contribution of shared genes (pleiotropy) to these diseases. This study aimed to investigate whether genetic risk factors for hypertension or inflammation are pleiotropic by also being associated with preeclampsia.

METHODS

We genotyped 122 single nucleotide polymorphisms (SNPs) in women with preeclampsia (n = 1006) and nonpreeclamptic controls (n = 816) from the Norwegian HUNT Study. SNPs were chosen on the basis of previously reported associations with either nongestational hypertension or inflammation in genome-wide association studies. The SNPs were tested for association with preeclampsia in a multiple logistic regression model.

RESULTS

The minor (G) allele of the intronic SNP rs17367504 in the gene methylenetetrahydrofolate reductase (MTHFR) was associated with a protective effect on preeclampsia (odds ratio 0.65, 95% confidence interval 0.53-0.80) in the Norwegian cohort. This association did not replicate in an Australian preeclampsia case-control cohort (P = 0.68, odds ratio 1.05, 95% confidence interval 0.83-1.32, minor allele frequency = 0.15).

CONCLUSION

MTHFR is important for regulating transmethylation processes and is involved in regulation of folate metabolism. The G allele of rs17367504 has previously been shown to protect against nongestational hypertension. Our study suggests a novel association between this allele and reduced risk for preeclampsia. This is the first study associating the minor (G) allele of a SNP within the MTHFR gene with a protective effect on preeclampsia, and in doing so identifying a possible pleiotropic protective effect on preeclampsia and hypertension.

Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

Objectives: Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. Methods: We identify three reasons for the lack of integration. The first is that "Big Data" is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on "cloud-native" platforms that are outside the scope of most EMRs, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present Big Data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, applications need to be updated nightly. Results: A new architecture for EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. Conclusion: Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to a new ecosystem of applications (Apps) interacting with healthcare providers to fulfill a promise that is still to be determined.

Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons

The GLIS family zinc finger 3 isoform (GLIS3) is a risk gene for Type 1 and Type 2 diabetes, glaucoma and Alzheimer's disease endophenotype. We identified GLIS3 binding sites in insulin secreting cells (INS1) (FDR q<0.05; enrichment range 1.40-9.11 fold) sharing the motif wrGTTCCCArTAGs, which were enriched in genes involved in neuronal function and autophagy and in risk genes for metabolic and neuro-behavioural diseases. We confirmed experimentally Glis3-mediated regulation of the expression of genes involved in autophagy and neuron function in INS1 and neuronal PC12 cells. Naturally-occurring coding polymorphisms in Glis3 in the Goto-Kakizaki rat model of type 2 diabetes were associated with increased insulin production in vitro and in vivo, suggestive alteration of autophagy in PC12 and INS1 and abnormal neurogenesis in hippocampus neurons. Our results support biological pleiotropy of GLIS3 in pathologies affecting β-cells and neurons and underline the existence of trans‑nosology pathways in diabetes and its co-morbidities.

Multivariate discovery and replication of five novel loci associated with Immunoglobulin G N-glycosylation

Joint modeling of a number of phenotypes using multivariate methods has often been neglected in genome-wide association studies and if used, replication has not been sought. Modern omics technologies allow characterization of functional phenomena using a large number of related phenotype measures, which can benefit from such joint analysis. Here, we report a multivariate genome-wide association studies of 23 immunoglobulin G (IgG) N-glycosylation phenotypes. In the discovery cohort, our multi-phenotype method uncovers ten genome-wide significant loci, of which five are novel (IGH, ELL2, HLA-B-C, AZI1, FUT6-FUT3). We convincingly replicate all novel loci via multivariate tests. We show that IgG N-glycosylation loci are strongly enriched for genes expressed in the immune system, in particular antibody-producing cells and B lymphocytes. We empirically demonstrate the efficacy of multivariate methods to discover novel, reproducible pleiotropic effects.

Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes.

Body mass index, body dissatisfaction and adolescent smoking initiation

BACKGROUND

Smoking influences body weight, but there is little evidence as to whether body mass index (BMI) and body dissatisfaction increase smoking initiation in adolescents.

METHODS

We evaluated the association between measured BMI, body dissatisfaction and latent classes of smoking initiation (never smokers, experimenters, late onset regular smokers, early onset regular smokers) in the Avon Longitudinal Study of Parents and Children. In observational analyses we used BMI (N=3754) and body dissatisfaction at age 10.5 years (N=3349). In Mendelian randomisation (MR) analysis, we used a BMI genetic risk score of 76 single nucleotide polymorphisms (N=4017).

RESULTS

In females, higher BMI was associated with increased odds of early onset regular smoking (OR: 1.11, 95% CI: 1.04, 1.18) compared to being a never smoker, but not clearly associated with experimenting with smoking (OR: 1.04, 95% CI: 0.99, 1.10) or late onset regular smoking (OR: 1.01, 95% CI: 0.94, 1.09). No clear evidence was found for associations between BMI and smoking initiation classes in males (p-value for sex interaction≤0.001). Body dissatisfaction was associated with increased odds of late-onset regular smoking (OR: 1.71, 95% CI: 1.32, 1.99) in males and females combined (P-value for sex interaction=0.32). There was no clear evidence for an association between the BMI genetic risk score and smoking latent classes in males or females but estimates were imprecise.

CONCLUSIONS

BMI in females and body dissatisfaction in males and females are associated with increased odds of smoking initiation, highlighting these as potentially important factors for consideration in smoking prevention strategies.

Distinct genetic architectures for phenotype means and plasticities in Zea mays

Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in a maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single-nucleotide polymorphisms. We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function

Background

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic.

Results

The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes.

Conclusions

This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.