Dataset Information

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.

ABSTRACT: There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.

SUBMITTER: Tang ZZ

PROVIDER: S-EPMC4571037 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.

Tang Zheng-Zheng ZZ Lin Dan-Yu DY

American journal of human genetics 20150618 1

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant sum ...[more]

PMID: 26094574

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Project description:BackgroundBipolar disorder (BD) affects both sexes, but important sex differences exist with respect to its symptoms and comorbidities. For example, rapid cycling (RC) is more prevalent in females, and alcohol use disorder (AUD) is more prevalent in males. We hypothesize that X chromosome variants may be associated with sex-specific characteristics of BD. Few studies have explored the role of the X chromosome in BD, which is complicated by X chromosome inactivation (XCI). This process achieves "dosage compensation" for many X chromosome genes by silencing one of the two copies in females, and most statistical methods either ignore that XCI occurs or falsely assume that one copy is inactivated at all loci. We introduce new statistical methods that do not make these assumptions.MethodsWe investigated this hypothesis in 1001 BD patients from the Genetic Association Information Network (GAIN) and 957 BD patients from the Mayo Clinic Bipolar Disorder Biobank. We examined the association of over 14,000 X chromosome single nucleotide polymorphisms (SNPs) with sex-associated BD traits using two statistical approaches that account for whether a SNP may be undergoing or escaping XCI. In the "XCI-informed approach," we fit a sex-adjusted logistic regression model assuming additive genetic effects where we coded the SNP either assuming one copy is expressed or two copies are expressed based on prior knowledge about which regions are inactivated. In the "XCI-robust approach," we fit a logistic regression model with sex, SNP, and SNP-sex interaction effects that is flexible to whether the region is inactivated or escaping XCI.ResultsUsing the "XCI-informed approach," which considers only the main effect of SNP and does not allow the SNP effect to differ by sex, no significant associations were identified for any of the phenotypes. Using the "XCI-robust approach," intergenic SNP rs5932307 was associated with BD (P = 8.3 × 10-8), with a stronger effect in females (odds ratio in males (ORM) = 1.13, odds ratio in females for a change of two allele copies (ORW2) = 3.86).ConclusionX chromosome association studies should employ methods which account for its unique biology. Future work is needed to validate the identified associations with BD, to formally assess the performance of both approaches under different true genetic architectures, and to apply these approaches to study sex differences in other conditions.

Dataset Information

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.

Publications

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.

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