Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores

The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Background Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3). Conclusions Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replicatio

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

There is growing evidence of shared risk alleles between complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing between all individuals (whole-group pleiotropy), or a subset of individuals within a genetically heterogeneous cohort (subgroup heterogeneity). BUHMBOX is a well-powered statistic distinguishing between these two situations using genotype data. We observed a shared genetic basis between 11 autoimmune diseases and type 1 diabetes (T1D, p0.2, 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (p<10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (pBUHMBOX=0.008, 2,406 seronegative RA cases). We also observed a shared genetic basis between major depressive disorder (MDD) and schizophrenia (p<10−4) that was not explained by subgroup heterogeneity (pBUHMBOX=0.28 in 9,238 MDD cases)

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Background-Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co-occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results-We performed a mega-analysis of 1000 Genomes Project-imputed genome-wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA-, AAA-, and TAA-associated SNPs and tested these scores for association to case-control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium-score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single-nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]= 1.11; P=4.1 x 10(-5)) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1 x 10(-3)). Conclusions-Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.Peer reviewe

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Background: Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results: We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3). Conclusions: Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.Y

FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects

Motivation: In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level. Results: We identify and report a phenomenon that these methods for overlapping subjects can yield low power. For instance, in our simulation involving a meta-analysis of five studies that share 20% of individuals, whereas the traditional splitting method achieved 80% power, none of the new methods exceeded 32% power. We found that this low power resulted from the unaccounted differences between shared and unshared individuals in terms of their contributions towards the final statistic. Here, we propose an optimal summary-statistic-based method termed as FOLD that increases the power of meta-analysis involving studies with overlapping subjects.Y

An association mapping framework to account for potential sex difference in genetic architectures

Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.Y

Shared genetic risk factors of intracranial, abdominal, and thoracic aneurysms

CITATION: Van 't Hof, F. N. G. et al. 2016. Shared genetic risk factors of intracranial, abdominal, and thoracic aneurysms. Journal of the American Heart Association, 5:e002603, doi:10.1161/JAHA.115.002603.The original publication is available at http://jaha.ahajournals.orgBackground: Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results: We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3). Conclusions: Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.http://jaha.ahajournals.org/content/5/7/e002603Publisher's versio

An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures

Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping