Joint Analysis Of Summary Statistics Research Articles

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

BackgroundApproximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores.MethodsHere, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)).ResultsWe detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria.ConclusionsMulti-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

Molecular genetics of trauma exposure and PTSD

ABSTRACT Background: The notion that genetic factors play a role in response to exposure to traumatic events was made over 75 years ago. Great progress has been made since this early inception in understanding the role of genetic liability on both trauma exposure, as well as posttraumatic stress disorder (PTSD). The field has recently undergone tremendous growth in the area of molecular genetic investigations utilizing agnostic genome wide association study (GWAS) designs of traumatic stress related outcomes, largely due to team science efforts such as the Psychiatric Genomics Consortia (PGC). Objective: The objective of this presentation is to provide an overview of the PGC-PTSD workgroup and samples, and present the most up-to-date analyses of GWAS analyses on trauma exposure and PTSD, using quantitative phenotypes from both the PGC-PTSD broader literature, including the Million Veterans Program (MVP). Methods: The PGC PTSD workgroup sample size in Freeze 2.0 was ~200,000, consisting of ~30,000 PTSD cases and focused on PTSD diagnostic status (Nievergelt et al., 2019). An overview of recently published analyses (e.g., recent GWAS analyses of childhood maltreatment (Dalvie et al., 2020)), as well as recent analyses examining PTSD as a quantitative variable will be presented, including analyses jointly analysing trauma exposure and PTSD symptoms from MTAG, a novel method that allows for joint analysis of summary statistics from GWAS of different traits. Recent findings from MVP (Gelernter et al., 2019) will also be reviewed, ending with a meta-analysis of PTSD as a quantitative trait in the PGC and re-experiencing symptoms from MVP. Results: Childhood maltreatment, defined as a quantitative variable, was found to have a significant molecular heritability, and to be genetically correlated with depression (Dalvie et al., 2020). Further, trauma exposure defined as a quantitative variable, was found to have molecular influence and to be genetically correlated with PTSD. Analyses of PTSD as a quantitative variable yielded significant loci, with some replicating in MVP. MTAG analyses yielded additional hits, and again, some with replication in MVP. Lastly, the meta-analysis of Freeze 2.0 with MVP, resulting in ~339,000 subjects, yielded >20 significant hits, including novel loci. Conclusions: Quantitative analyses of childhood maltreatment, trauma exposure, and PTSD prove to increase statistical power across analyses conducted in the PGC-PTSD as well as MVP. Additionally, the joint analysis using MTAG revealed novel GWAS hits. With more samples and sophisticated analytic techniques the genetic architecture of PTSD will become elucidated, bringing with it critical treatment implications.

JASS: command line and web interface for the joint analysis of GWAS results.

Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits.

Genetic correlations estimated from GWAS reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores, and identify loci that cause divergence between traits. We demonstrate several applications of Genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent SNPs not previously identified in the contributing univariate GWASs. Polygenic scores from Genomic SEM consistently outperform those from univariate GWAS. Genomic SEM is flexible, open ended, and allows for continuous innovation in multivariate genetic analysis.

Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies.

In genome-wide association studies (GWASs) of common diseases/traits, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is difficult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze datasets from multiple GWASs. In this paper, we propose a novel summary-statistics-based joint analysis method based on controlling the joint local false discovery rate (Jlfdr). We prove that our method is the most powerful summary-statistics-based joint analysis method when controlling the false discovery rate at a certain level. In particular, the Jlfdr-based method achieves higher power than commonly used meta-analysis methods when analyzing heterogeneous datasets from multiple GWASs. Simulation experiments demonstrate the superior power of our method over meta-analysis methods. Also, our method discovers more associations than meta-analysis methods from empirical datasets of four phenotypes. The R-package is available at: http://bioinformatics.ust.hk/Jlfdr.html . eeyu@ust.hk. Supplementary data are available at Bioinformatics online.