A plethora of pleiotropy across complex traits

Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.

Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Pleiotropic phenotypic effects of the TaCYP78A family on multiple yield-related traits in wheat

Increasing crop yield depends on selecting and utilizing pleiotropic genes/alleles to improve multiple yield-related traits (YRTs) during crop breeding. However, synergistic improvement of YRTs is challenging due to the trade-offs between YRTs in breeding practices. Here, the favourable haplotypes of the TaCYP78A family are identified by analysing allelic variations in 1571 wheat accessions worldwide, demonstrating the selection and utilization of pleiotropic genes to improve yield and related traits during wheat breeding. The TaCYP78A family members, including TaCYP78A3, TaCYP78A5, TaCYP78A16, and TaCYP78A17, are organ size regulators expressed in multiple organs, and their allelic variations associated with various YRTs. However, due to the trade-offs between YRTs, knockdown or overexpression of TaCYP78A family members does not directly increase yield. Favourable haplotypes of the TaCYP78A family, namely A3/5/16/17Ap-Hap II, optimize the expression levels of TaCYP78A3/5/16/17-A across different wheat organs to overcome trade-offs and improve multiple YRTs. Different favourable haplotypes have both complementary and specific functions in improving YRTs, and their aggregation in cultivars under strong artificial selection greatly increase yield, even under various planting environments and densities. These findings provide new support and valuable genetic resources for molecular breeding of wheat and other crops in the era of Breeding 4.0.

Parthenogenetic Stick Insects Exhibit Signatures of Preservation in the Molecular Architecture of Male Reproduction

After the loss of a trait, theory predicts that the molecular machinery underlying its phenotypic expression should decay. Yet, empirical evidence is contrasting. Here, we test the hypotheses that (i) the molecular ground plan of a lost trait could persist due to pleiotropic effects on other traits and (ii) that gene co-expression network architecture could constrain individual gene expression. Our testing ground has been the Bacillus stick insect species complex, which contains close relatives that are either bisexual or parthenogenetic. After the identification of genes expressed in male reproductive tissues in a bisexual species, we investigated their gene co-expression network structure in two parthenogenetic species. We found that gene co-expression within the male gonads was partially preserved in parthenogens. Furthermore, parthenogens did not show relaxed selection on genes upregulated in male gonads in the bisexual species. As these genes were mostly expressed in female gonads, this preservation could be driven by pleiotropic interactions and an ongoing role in female reproduction. Connectivity within the network also played a key role, with highly connected-and more pleiotropic-genes within male gonad also having a gonad-biased expression in parthenogens. Our findings provide novel insight into the mechanisms which could underlie the production of rare males in parthenogenetic lineages; more generally, they provide an example of the cryptic persistence of a lost trait molecular architecture, driven by gene pleiotropy on other traits and within their co-expression network.

Mendelian randomization analysis of 34,497 German Holstein cows to infer causal associations between milk production and health traits

BACKGROUND

Claw diseases and mastitis represent the most important health issues in dairy cattle with a frequently mentioned connection to milk production. Although many studies have aimed at investigating this connection in more detail by estimating genetic correlations, they do not provide information about causality. An alternative is to carry out Mendelian randomization (MR) studies using genetic variants to investigate the effect of an exposure on an outcome trait mediated by genetic variants. No study has yet investigated the causal association of milk yield (MY) with health traits in dairy cattle. Hence, we performed a MR analysis of MY and seven health traits using imputed whole-genome sequence data from 34,497 German Holstein cows. We applied a method that uses summary statistics and removes horizontal pleiotropic variants (having an effect on both traits), which improves the power and unbiasedness of MR studies. In addition, genetic correlations between MY and each health trait were estimated to compare them with the estimates of causal effects that we expected.

RESULTS

All genetic correlations between MY and each health trait were negative, ranging from - 0.303 (mastitis) to - 0.019 (digital dermatitis), which indicates a reduced health status as MY increases. The only non-significant correlation was between MY and digital dermatitis. In addition, each causal association was negative, ranging from - 0.131 (mastitis) to - 0.034 (laminitis), but the number of significant associations was reduced to five nominal and two experiment-wide significant results. The latter were between MY and mastitis and between MY and digital phlegmon. Horizontal pleiotropic variants were identified for mastitis, digital dermatitis and digital phlegmon. They were located within or nearby variants that were previously reported to have a horizontal pleiotropic effect, e.g., on milk production and somatic cell count.

CONCLUSIONS

Our results confirm the known negative genetic connection between health traits and MY in dairy cattle. In addition, they provide new information about causality, which for example points to the negative energy balance mediating the connection between these traits. This knowledge helps to better understand whether the negative genetic correlation is based on pleiotropy, linkage between causal variants for both trait complexes, or indeed on a causal association.

Development of genome-wide association studies on childhood obesity and its indicators: A scoping review and enrichment analysis

BACKGROUND

The progress of genome-wide association studies (GWAS) in childhood obesity and its indicators is challenging and there are differences in genetic studies in children and adults.

OBJECTIVE

To illustrate the history of the development of GWAS in childhood obesity and its indicators and summarize the GWAS loci.

METHODS

PubMed, Web of Science, Embase and GWAS Catalog databases were systematically searched from 1 January 2005 to 19 October 2022 for literature related to GWAS of childhood BMI, body fatness and obesity. The nearest genes were used as positional genes to perform gene set analyses including the enrichment of pathways, tissues and diseases.

RESULTS

Twenty articles published between 2007 and 2021 were included in this scoping review, which identified 116 SNPs reaching genome-wide significance with childhood BMI (n = 50), body fatness (n = 31) and obesity (n = 35). The study populations were European in 16 studies, non-European in three studies (1 East Asian; 1 American; 1 Mexican) and trans-ancestry in one study. Several enriched pathways, tissues and diseases were identified through enrichment analysis of genes associated with childhood obesity and its indicators.

CONCLUSIONS

The innovations in tools and methods enable GWAS to better explore the genetic characteristics of obesity in children and adolescents. However, the number of GWAS in American, Asian and African populations is limited compared to the European population.

Familial aggregation of multimorbidity in Sweden: national explorative family study

Objectives

To examine whether multimorbidity aggregates in families in Sweden.

Design

National explorative family study.

Setting

Swedish Multigeneration Register linked to the National Patient Register, 1997-2015. Multimorbidity was assessed with a modified counting method of 45 chronic non-communicable diseases according to ICD-10 (international classification of diseases, 10th revision) diagnoses.

Participants

2 694 442 Swedish born individuals (48.73% women) who could be linked to their Swedish born first, second, and third degree relatives. Twins were defined as full siblings born on the same date.

Main outcome measures

Multimorbidity was defined as two or more non-communicable diseases. Familial associations for one, two, three, four, and five or more non-communicable diseases were assessed to examine risks depending on the number of non-communicable diseases. Familial adjusted odds ratios for multimorbidity were calculated for individuals with a diagnosis of multimorbidity compared with relatives of individuals unaffected by multimorbidity (reference). An initial principal component decomposition followed by a factor analysis with a principal factor method and an oblique promax rotation was used on the correlation matrix of tetrachoric correlations between 45 diagnoses in patients to identify disease clusters.

Results

The odds ratios for multimorbidity were 2.89 in twins (95% confidence interval 2.56 to 3.25), 1.81 in full siblings (1.78 to 1.84), 1.26 in half siblings (1.24 to 1.28), and 1.13 in cousins (1.12 to 1.14) of relatives with a diagnosis of multimorbidity. The odds ratios for multimorbidity increased with the number of diseases in relatives. For example, among twins, the odds ratios for multimorbidity were 1.73, 2.84, 4.09, 4.63, and 6.66 for an increasing number of diseases in relatives, from one to five or more, respectively. Odds ratios were highest at younger ages: in twins, the odds ratio was 3.22 for those aged ≤20 years, 3.14 for those aged 21-30 years, and 2.29 for those aged >30 years at the end of follow-up. Nine disease clusters (factor clusters 1-9) were identified, of which seven aggregated in families. The first three disease clusters in the principal component decomposition were cardiometabolic disease (factor 1), mental health disorders (factor 2), and disorders of the digestive system (factor 3). Odds ratios for multimorbidity in twins, siblings, half siblings, and cousins for the factor 1 cluster were 2.79 (95% confidence interval 0.97 to 8.06), 2.62 (2.39 to 2.88), 1.52 (1.34 to 1.73), and 1.31 (1.23 to 1.39), and for the factor 2 cluster, 5.79 (4.48 to 7.48) 3.24 (3.13 to 3.36), 1.51 (1.45 to 1.57), and 1.37 (1.341.40).

Conclusions

The results of this explorative family study indicated that multimorbidity aggregated in Swedish families. The findings suggest that map clusters of diseases should be used for the genetic study of common diseases to show new genetic patterns of non-communicable diseases.

Molecular bases of rice grain size and quality for optimized productivity

The accomplishment of further optimization of crop productivity in grain yield and quality is a great challenge. Grain size is one of the crucial determinants of rice yield and quality; all of these traits are typical quantitative traits controlled by multiple genes. Research advances have revealed several molecular and developmental pathways that govern these traits of agronomical importance. This review provides a comprehensive summary of these pathways, including those mediated by G-protein, the ubiquitin-proteasome system, mitogen-activated protein kinase, phytohormone, transcriptional regulators, and storage product biosynthesis and accumulation. We also generalize the excellent precedents for rice variety improvement of grain size and quality, which utilize newly developed gene editing and conventional gene pyramiding capabilities. In addition, we discuss the rational and accurate breeding strategies, with the aim of better applying molecular design to breed high-yield and superior-quality varieties.

A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle

Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (-0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.

Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.

Using genetic designs to identify likely causal environmental contributions to psychopathology

The multifactorial nature of psychopathology, whereby both genetic and environmental factors contribute risk, has long been established. In this paper, we provide an update on genetically informative designs that are utilized to disentangle genetic and environmental contributions to psychopathology. We provide a brief reminder of quantitative behavioral genetic research designs that have been used to identify potentially causal environmental processes, accounting for genetic contributions. We also provide an overview of recent molecular genetic approaches that utilize genome-wide association study data which are increasingly being applied to questions relevant to psychopathology research. While genetically informative designs typically have been applied to investigate the origins of psychopathology, we highlight how these approaches can also be used to elucidate potential causal environmental processes that contribute to developmental course and outcomes. We highlight the need to use genetically sensitive designs that align with intervention and prevention science efforts, by considering strengths-based environments to investigate how positive environments can mitigate risk and promote children's strengths.

Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data

BACKGROUND

Numerous genes are implicated in autism spectrum disorder (ASD). ASD encompasses a wide-range and severity of symptoms and co-occurring conditions; however, the details of how genetic variation contributes to phenotypic differences are unclear. This creates a challenge for translating genetic evidence into clinically useful knowledge. Sleep disturbances are particularly prevalent co-occurring conditions in ASD, and genetics may inform treatment. Identifying convergent mechanisms with evidence for dysfunction that connect ASD and sleep biology could help identify better treatments for sleep disturbances in these individuals.

METHODS

To identify mechanisms that influence risk for ASD and co-occurring sleep disturbances, we analyzed whole exome sequence data from individuals in the Simons Simplex Collection (n = 2380). We predicted protein damaging variants (PDVs) in genes currently implicated in either ASD or sleep duration in typically developing children. We predicted a network of ASD-related proteins with direct evidence for interaction with sleep duration-related proteins encoded by genes with PDVs. Overrepresentation analyses of Gene Ontology-defined biological processes were conducted on the resulting gene set. We calculated the likelihood of dysfunction in the top overrepresented biological process. We then tested if scores reflecting genetic dysfunction in the process were associated with parent-reported sleep duration.

RESULTS

There were 29 genes with PDVs in the ASD dataset where variation was reported in the literature to be associated with both ASD and sleep duration. A network of 108 proteins encoded by ASD and sleep duration candidate genes with PDVs was identified. The mechanism overrepresented in PDV-containing genes that encode proteins in the interaction network with the most evidence for dysfunction was cerebral cortex development (GO:0,021,987). Scores reflecting dysfunction in this process were associated with sleep durations; the largest effects were observed in adolescents (p = 4.65 × 10^-3).

CONCLUSIONS

Our bioinformatic-driven approach detected a biological process enriched for genes encoding a protein-protein interaction network linking ASD gene products with sleep duration gene products where accumulation of potentially damaging variants in individuals with ASD was associated with sleep duration as reported by the parents. Specifically, genetic dysfunction impacting development of the cerebral cortex may affect sleep by disrupting sleep homeostasis which is evidenced to be regulated by this brain region. Future functional assessments and objective measurements of sleep in adolescents with ASD could provide the basis for more informed treatment of sleep problems in these individuals.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Statistical methods for Mendelian randomization in genome-wide association studies: A review

Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.

Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size

Crop genetic improvement requires balancing complex tradeoffs caused by gene pleiotropy and linkage drags, as exemplified by IPA1 (Ideal Plant Architecture 1), a typical pleiotropic gene in rice that increases grains per panicle but reduces tillers. In this study, we identified a 54-base pair cis-regulatory region in IPA1 via a tiling-deletion-based CRISPR-Cas9 screen that, when deleted, resolves the tradeoff between grains per panicle and tiller number, leading to substantially enhanced grain yield per plant. Mechanistic studies revealed that the deleted fragment is a target site for the transcription factor An-1 to repress IPA1 expression in panicles and roots. Targeting gene regulatory regions should help dissect tradeoff effects and provide a rich source of targets for breeding complementary beneficial traits.

MEF2C gene variations are associated with ADHD in the Chinese Han population: a case-control study

Myocyte enhancer factor 2C (MEF2C) is associated with hyperactivity and might be a novel risk gene for susceptibility to attention deficit hyperactivity disorder (ADHD). Therefore, this study aimed to explore the association between MEF2C genetic variants and ADHD in the Chinese Han population. A total of 215 patients with ADHD and 233 controls were recruited for this study. The Swanson, Nolan, and Pelham version IV questionnaire was used to evaluate the clinical features of ADHD. In silico analysis was used to annotate the biological functions of the promising single nucleotide polymorphisms. Our findings indicated that MEF2C rs587490 was significantly associated with ADHD in the multiplicative model (OR = 0.640, p = 0.002). Participants with the rs587490 TT allele exhibited less hyperactivity/impulsivity than those with the rs587490 CC allele. Furthermore, the expression quantitative trait loci analysis suggested that rs587490 could regulate the gene expression of MEF2C in the hippocampus, putamen, thalamus, and frontal white matter. Our study concluded that the MEF2C rs587490 T allele is significantly associated with a reduced risk of ADHD in the Chinese Han population, which provides new insight into the genetic etiology of ADHD.

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation, and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multitrait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between nonpleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

A Multilayer Interactome Network Constructed in a Forest Poplar Population Mediates the Pleiotropic Control of Complex Traits

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

Major Depressive Disorder and Lifestyle: Correlated Genetic Effects in Extended Twin Pedigrees

In recent years, evidence has accumulated with regard to the ubiquity of pleiotropy across the genome, and shared genetic etiology is thought to play a large role in the widespread comorbidity among psychiatric disorders and risk factors. Recent methods investigate pleiotropy by estimating genetic correlation from genome-wide association summary statistics. More comprehensive estimates can be derived from the known relatedness between genetic relatives. Analysis of extended twin pedigree data allows for the estimation of genetic correlation for additive and non-additive genetic effects, as well as a shared household effect. Here we conduct a series of bivariate genetic analyses in extended twin pedigree data on lifetime major depressive disorder (MDD) and three indicators of lifestyle, namely smoking behavior, physical inactivity, and obesity, decomposing phenotypic variance and covariance into genetic and environmental components. We analyze lifetime MDD and lifestyle data in a large multigenerational dataset of 19,496 individuals by variance component analysis in the ‘Mendel’ software. We find genetic correlations for MDD and smoking behavior (r_G = 0.249), physical inactivity (r_G = 0.161), body-mass index (r_G = 0.081), and obesity (r_G = 0.155), which were primarily driven by additive genetic effects. These outcomes provide evidence in favor of a shared genetic etiology between MDD and the lifestyle factors.

Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.

A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

BACKGROUND

Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets.

METHODS

We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D).

RESULTS

Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies.

CONCLUSION

The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

Meta-Analysis Identifies Pleiotropic Loci Controlling Phenotypic Trade-offs in Sorghum

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

An Update on Precision Medicine Advances In Neurodevelopmental Disorders

Neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyper-activity disorder (ADHD), represent a group of conditions that manifest early in child development and produce impairments across multiple domains of functioning. Although a number of pharmacological and psychosocial treatments exist to improve the symptoms associated with these syndromes, treatment advances have lagged. The Precision Medicine Initiative was launched with the goal of revolutionizing medicine by progressing beyond the historical one-size-fits-all approach. In this review, we evaluate current research efforts to personalize treatments for ASD and ADHD. Most pharmacogenetic testing has focused on the cytochrome P450 enzyme family with a particular focus on CYP2D6 and CYP2C19, which are genes that produce an enzyme that acts as a key metabolizer of many prescribed medications. This article provides an update on the state of the field of pharmacogenetics and "therapy-genetics" in the context of ASD and ADHD, and it also encourages clinicians to follow US Food and Drug Administration recommendations regarding pharmacogenetic testing.

Conserved pleiotropy of an ancient plant homeobox gene uncovered by cis-regulatory dissection

Divergence of gene function is a hallmark of evolution, but assessing functional divergence over deep time is not trivial. The few alleles available for cross-species studies often fail to expose the entire functional spectrum of genes, potentially obscuring deeply conserved pleiotropic roles. Here, we explore the functional divergence of WUSCHEL HOMEOBOX9 (WOX9), suggested to have species-specific roles in embryo and inflorescence development. Using a cis-regulatory editing drive system, we generate a comprehensive allelic series in tomato, which revealed hidden pleiotropic roles for WOX9. Analysis of accessible chromatin and conserved cis-regulatory sequences identifies the regions responsible for this pleiotropic activity, the functions of which are conserved in groundcherry, a tomato relative. Mimicking these alleles in Arabidopsis, distantly related to tomato and groundcherry, reveals new inflorescence phenotypes, exposing a deeply conserved pleiotropy. We suggest that targeted cis-regulatory mutations can uncover conserved gene functions and reduce undesirable effects in crop improvement.

How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?

Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

Do myeloproliferative neoplasms and multiple myeloma share the same genetic susceptibility loci?

Myeloproliferative neoplasms (MPNs) are a group of diseases that cause myeloid hematopoietic cells to overproliferate. Epidemiological and familial studies suggest that genetic factors contribute to the risk of developing MPN, but the genetic susceptibility of MPN is still not well known. Indeed, only few loci are known to have a clear role in the predisposition to this disease. Some studies reported a diagnosis of MPNs and multiple myeloma (MM) in the same patients, but the biological causes are still unclear. We tested the hypothesis that the two diseases share at least partly the same genetic risk loci. In the context of a European multicenter study with 460 cases and 880 controls, we analyzed the effect of the known MM risk loci, individually and in a polygenic risk score (PRS). The most significant result was obtained among patients with chronic myeloid leukemia (CML) for PS0RS1C1-rs2285803, which showed to be associated with an increased risk (OR = 3.28, 95% CI 1.79-6.02, P = .00012, P = .00276 when taking into account multiple testing). Additionally, the PRS showed an association with MPN risk when comparing the last with the first quartile of the PRS (OR = 2.39, 95% CI 1.64-3.48, P = 5.98 × 10^-6 ). In conclusion, our results suggest a potential common genetic background between MPN and MM, which needs to be further investigated.

Quantitative and Qualitative Role of Antagonistic Heterogeneity in Genetics of Blood Lipids

Prevailing strategies in genome-wide association studies (GWAS) mostly rely on principles of medical genetics emphasizing one gene, one function, one phenotype concept. Here, we performed GWAS of blood lipids leveraging a new systemic concept emphasizing complexity of genetic predisposition to such phenotypes. We focused on total cholesterol, low- and high-density lipoprotein cholesterols, and triglycerides available for 29,902 individuals of European ancestry from seven independent studies, men and women combined. To implement the new concept, we leveraged the inherent heterogeneity in genetic predisposition to such complex phenotypes and emphasized a new counter intuitive phenomenon of antagonistic genetic heterogeneity, which is characterized by misalignment of the directions of genetic effects and the phenotype correlation. This analysis identified 37 loci associated with blood lipids but only one locus, FBXO33, was not reported in previous top GWAS. We, however, found strong effect of antagonistic heterogeneity that leaded to profound (quantitative and qualitative) changes in the associations with blood lipids in most, 25 of 37 or 68%, loci. These changes suggested new roles for some genes, which functions were considered as well established such as GCKR, SIK3 (APOA1 locus), LIPC, LIPG, among the others. The antagonistic heterogeneity highlighted a new class of genetic associations emphasizing beneficial and adverse trade-offs in predisposition to lipids. Our results argue that rigorous analyses dissecting heterogeneity in genetic predisposition to complex traits such as lipids beyond those implemented in current GWAS are required to facilitate translation of genetic discoveries into health care.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture

Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

Empirical comparisons of multiple Mendelian randomization approaches in the presence of assortative mating

BACKGROUND

Mendelian randomization (MR) is widely used to unravel causal relationships in epidemiological studies. Whereas multiple MR methods have been developed to control for bias due to horizontal pleiotropy, their performance in the presence of other sources of bias, like non-random mating, has been mostly evaluated using simulated data. Empirical comparisons of MR estimators in such scenarios have yet to be conducted. Pleiotropy and non-random mating have been shown to account equally for the genetic correlation between height and educational attainment. Previous studies probing the causal nature of this association have produced conflicting results.

METHODS

We estimated the causal effect of height on educational attainment in various MR models, including the MR-Egger and the MR-Direction of Causation (MR-DoC) models that correct for, or explicitly model, horizontal pleiotropy.

RESULTS

We reproduced the weak but positive association between height and education in the Netherlands Twin Register sample (P= 3.9 × 10-6). All MR analyses suggested that height has a robust, albeit small, causal effect on education. We showed via simulations that potential assortment for height and education had no effect on the causal parameter in the MR-DoC model. With the pleiotropic effect freely estimated, MR-DoC yielded a null finding.

CONCLUSIONS

Non-random mating may have a bearing on the results of MR studies based on unrelated individuals. Family data enable tests of causal relationships to be conducted more rigorously, and are recommended to triangulate results of MR studies assessing pairs of traits leading to non-random mate selection.

Breed Differences in Dog Cognition Associated with Brain-Expressed Genes and Neurological Functions

Given their remarkable phenotypic diversity, dogs present a unique opportunity for investigating the genetic bases of cognitive and behavioral traits. Our previous work demonstrated that genetic relatedness among breeds accounts for a substantial portion of variation in dog cognition. Here, we investigated the genetic architecture of breed differences in cognition, seeking to identify genes that contribute to variation in cognitive phenotypes. To do so, we combined cognitive data from the citizen science project Dognition.com with published breed-average genetic polymorphism data, resulting in a dataset of 1654 individuals with cognitive phenotypes representing 49 breeds. We conducted a breed-average genome-wide association study to identify specific polymorphisms associated with breed differences in inhibitory control, communication, memory, and physical reasoning. We found five single nucleotide polymorphisms (SNPs) that reached genome-wide significance after Bonferroni correction, located in EML1, OR52E2, HS3ST5, a U6 spliceosomal RNA, and a long noncoding RNA. When we combined results across multiple SNPs within the same gene, we identified 188 genes implicated in breed differences in cognition. This gene set included more genes than expected by chance that were (1) differentially expressed in brain tissue and (2) involved in nervous system functions including peripheral nervous system development, Wnt signaling, presynapse assembly, and synaptic vesicle exocytosis. These results advance our understanding of the genetic underpinnings of complex cognitive phenotypes and identify specific genetic variants for further research.

Statistical Methods in Genome-Wide Association Studies

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization

Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology's single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

Polygenic risk scores: pleiotropy and the effect of environment

Polygenic risk scores (PRSs) discriminate trait risks better than single genetic markers because they aggregate the effects of risk alleles from multiple genetic loci. Constructing pleiotropic PRSs and understanding heterogeneity, and the replication of PRS-trait associations can strengthen its applications. By using variational Bayesian multivariate high-dimensional regression, we constructed pleiotropic PRSs jointly associated with body mass index, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol in a sample of 18,108 Caucasians from three independent cohorts. We found that dissecting heterogeneity associated with birth year, which is a proxy of exogenous exposures, improved the replication of significant PRS-trait associations from 37.5% (6 of 16) in the entire sample to 90% (18 of 20) in the more homogeneous sample of individuals born before the year 1925. Our findings suggest that secular changes in exogenous exposures may substantially modify pleiotropic risk profiles affecting translation of genetic discoveries into health care.

The dark genome and pleiotropy: challenges for precision medicine

Surprisingly we remain ignorant of the function of the majority of genes in the human and mouse genomes. The dark genome is a major obstacle to the interpretation of the function of human genetic variation and its impact on disease. At the same time, pleiotropy, how individual variants influence multiple phenotypes, is key to understanding gene function and the role of genes and genetic networks in disease systems. Both understanding the genetics of disease and developing new therapeutic approaches and advances in precision medicine are all compromised by our limited knowledge of gene function and pleiotropic effects. Illuminating the dark genome and revealing pleiotropy across the genome requires a highly coordinated and international effort to acquire and analyse high-dimensional phenotype data from model organisms. We describe briefly how the International Mouse Phenotyping Consortium is addressing these challenges and the novel features of the pleiotropic landscape that are revealed by functional genomics programmes at genome-wide scale.

Genes-in-Dyads: A Study of Relationship Quality

OBJECTIVES

Using dyadic genetic information on older couples, this study queried associations of a polygenic score for well-being with one's own as well as a partner's relationship experiences.

METHOD

Data were from the 2010 wave of the U.S. Health and Retirement Study. Analysis was through structural equation modeling.

RESULTS

Especially among women, the genetic score was associated with individuals' own relationship experiences. Genetic externalities-linkages of one's genes with a partner's experiences-were also observed. No significant gender variations emerged.

DISCUSSION

Contrary to conceptions implicit in much of existing genetics literature-which focuses on individuals' own gene-trait associations-the interpersonal environments most crucial to life course and health outcomes are shaped by the genes of all involved actors. Genetic externalities are a central component. Implications for the life course and gene-environment literatures are discussed.

Next-generation drug repurposing using human genetics and network biology

Drug repurposing has attracted increased attention, especially in the context of drug discovery rates that remain too low despite a recent wave of approvals for biological therapeutics (e.g. gene therapy). These new biological entities-based treatments have high costs that are difficult to justify for small markets that include rare diseases. Drug repurposing, involving the identification of single or combinations of existing drugs based on human genetics data and network biology approaches represents a next-generation approach that has the potential to increase the speed of drug discovery at a lower cost. This Pharmacological Perspective reviews progress and perspectives in combining human genetics, especially genome-wide association studies, with network biology to drive drug repurposing for rare and common diseases with monogenic or polygenic etiologies. Also, highlighted here are important features of this next generation approach to drug repurposing, which can be combined with machine learning methods to meet the challenges of personalized medicine.

Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology

Nonalcoholic fatty liver disease (NAFLD) affects around a quarter of the global population, paralleling worldwide increases in obesity and metabolic syndrome. NAFLD arises in the context of systemic metabolic dysfunction that concomitantly amplifies the risk of cardiovascular disease and diabetes. These interrelated conditions have long been recognized to have a heritable component, and advances using unbiased association studies followed by functional characterization have created a paradigm for unravelling the genetic architecture of these conditions. A novel perspective is to characterize the shared genetic basis of NAFLD and other related disorders. This information on shared genetic risks and their biological overlap should in future enable the development of precision medicine approaches through better patient stratification, and enable the identification of preventive and therapeutic strategies. In this Review, we discuss current knowledge of the genetic basis of NAFLD and of possible pleiotropy between NAFLD and other liver diseases as well as other related metabolic disorders. We also discuss evidence of causality in NAFLD and other related diseases and the translational significance of such evidence, and future challenges from the study of genetic pleiotropy.

A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in southern Chinese men

BACKGROUND

Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort.

RESULT

A total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson's test, Jaccard's index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10^- 4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10^- 8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3 T3-L1 preadipocytes.

CONCLUSION

All these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.

Polygenic risk score for disability and insights into disability-related molecular mechanisms

Late life disability is a highly devastating condition affecting 20% or more of persons aged 65 years and older in the USA; it is an important determinant of acute medical and long-term care costs which represent a growing burden on national economies. Disability is a multifactorial trait that contributes substantially to decline of health/wellbeing. Accordingly, gaining insights into the genetics of disability could help in identifying molecular mechanisms of this devastating condition and age-related processes contributing to a large fraction of specific geriatric conditions, concordantly with geroscience. We performed a genome-wide association study of disability in a sample of 24,068 subjects from five studies with 12,550 disabled individuals. We identified 30 promising disability-associated polymorphisms in 19 loci at p < 10^-4; four of them attained suggestive significance, p < 10^-5. In contrast, polygenic risk scores aggregating effects of minor alleles of independent SNPs that were adversely or beneficially associated with disability showed highly significant associations in meta-analysis, p = 3.13 × 10^-45 and p = 5.60 × 10^-23, respectively, and were replicated in each study. The analysis of genetic pathways, related diseases, and biological functions supported the connections of genes for the identified SNPs with disabling and age-related conditions primarily through oxidative/nitrosative stress, inflammatory response, and ciliary signaling. We identified musculoskeletal system development, maintenance, and regeneration as important components of gene functions. The beneficial and adverse gene sets may be differently implicated in the development of musculoskeletal-related disability with the beneficial set characterized, e.g., by regulation of chondrocyte proliferation and bone formation, and the adverse set by inflammation and bone loss.

PleioNet: a web-based visualization tool for exploring pleiotropy across complex traits

SUMMARY

Pleiotropy plays an important role in furthering our understanding of the shared genetic architecture of different human diseases and traits. However, exploring and visualizing pleiotropic information with currently publicly available tools is limiting and challenging. To aid researchers in constructing and digesting pleiotropic networks, we present PleioNet, a web-based visualization tool for exploring this information across human diseases and traits. This program provides an intuitive and interactive web interface that seamlessly integrates large database queries with visualizations that enable users to quickly explore complex high-dimensional pleiotropic information. PleioNet works on all modern computer and mobile web browsers, making pleiotropic information readily available to a broad range of researchers and clinicians with diverse technical backgrounds. We expect that PleioNet will be an important tool for studying the underlying pleiotropic connections among human diseases and traits.

AVAILABILITY AND IMPLEMENTATION

PleioNet is hosted on Google cloud and freely available at http://www.pleionet.com/.

A regression framework to uncover pleiotropy in large-scale electronic health record data

OBJECTIVE

Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype-phenotype relationship. Although individual genotype-phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources.

MATERIALS AND METHODS

In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework.

RESULTS

Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods.

CONCLUSION

The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large-scale genome-wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease-related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the "single trait" association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well-characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome-phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual-level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P-value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta-analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta-analysis. All the proposed methods are implemented in a publicly available R package.

A decade of genome-wide association studies for coronary artery disease: the challenges ahead

In this review, we summarize current knowledge on the genetics of coronary artery disease, based on 10 years of genome-wide association studies. The discoveries began with individual studies using 200K single nucleotide polymorphism arrays and progressed to large-scale collaborative efforts, involving more than a 100 000 people and up to 40 Mio genetic variants. We discuss the challenges ahead, including those involved in identifying causal genes and deciphering the links between risk variants and disease pathology. We also describe novel insights into disease biology based on the findings of genome-wide association studies. Moreover, we discuss the potential for discovery of novel treatment targets through the integration of different layers of 'omics' data and the application of systems genetics approaches. Finally, we provide a brief outlook on the potential for precision medicine to be enhanced by genome-wide association study findings in the cardiovascular field.