A sequence of methodological changes due to sequencing

Exome-wide rare variant analyses of two bone mineral density phenotypes: the challenges of analyzing rare genetic variation

Performance of a recently developed test for association between multivariate phenotypes and sets of genetic variants (MURAT) is demonstrated using measures of bone mineral density (BMD). By combining individual-level whole genome sequenced data from the UK10K study, and imputed genome-wide genetic data on individuals from the Study of Osteoporotic Fractures (SOF) and the Osteoporotic Fractures in Men Study (MrOS), a data set of 8810 individuals was assembled; tests of association were performed between autosomal gene-sets of genetic variants and BMD measured at lumbar spine and femoral neck. Distributions of p-values obtained from analyses of a single BMD phenotype are compared to those from the multivariate tests, across several region definitions and variant weightings. There is evidence of increased power with the multivariate test, although no new loci for BMD were identified. Among 17 genes highlighted either because there were significant p-values in region-based association tests or because they were in well-known BMD genes, 4 windows in 2 genes as well as 6 single SNPs in one of these genes showed association at genome-wide significant thresholds with the multivariate phenotype test but not with the single-phenotype test, Sequence Kernel Association Test (SKAT).

A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects

For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.

Increased prevalence of MEFV exon 10 variants in Japanese patients with adult-onset Still's disease

Autoinflammatory diseases include a large spectrum of monogenic diseases, e.g. familial Mediterranean fever (FMF), as well as complex genetic trait diseases, e.g. adult-onset Still's disease (AOSD). In populations where FMF is common, an increased MEFV mutation rate is found in patients with rheumatic diseases. The aim of this study was to examine MEFV mutations in Japanese patients with AOSD. Genomic DNA was isolated from 49 AOSD patients and 105 healthy controls, and exons 1, 2, 3 and 10 of the MEFV gene genotyped by direct sequencing. MEFV mutation frequencies in AOSD patients were compared with controls. We found no significant difference in overall allele frequencies of MEFV variants between AOSD patients and controls. However, MEFV exon 10 variants (M694I and G632S) were significantly higher in AOSD patients than controls (6.1 versus 0%). In addition, there was no significant difference between MEFV variant carriers and non-carriers with clinical manifestations, but the monocyclic clinical course of the AOSD disease phenotype was observed less frequently in patients without MEFV variants. AOSD patients had significantly higher frequencies of MEFV exon 10 mutations, suggesting that low-frequency variants of MEFV gene may be one of the susceptibility factors of AOSD.

Exploring the potential benefits of stratified false discovery rates for region-based testing of association with rare genetic variation

When analyzing the data that arises from exome or whole-genome sequencing studies, window-based tests, (i.e., tests that jointly analyze all genetic data in a small genomic region), are very popular. However, power is known to be quite low for finding associations with phenotypes using these tests, and therefore a variety of analytic strategies may be employed to potentially improve power. Using sequencing data of all of chromosome 3 from an interim release of data on 2432 individuals from the UK10K project, we simulated phenotypes associated with rare genetic variation, and used the results to explore the window-based test power. We asked two specific questions: firstly, whether there could be substantial benefits associated with incorporating information from external annotation on the genetic variants, and secondly whether the false discovery rate (FDRs) would be a useful metric for assessing significance. Although, as expected, there are benefits to using additional information (such as annotation) when it is associated with causality, we confirmed the general pattern of low sensitivity and power for window-based tests. For our chosen example, even when power is high to detect some of the associations, many of the regions containing causal variants are not detectable, despite using lax significance thresholds and optimal analytic methods. Furthermore, our estimated FDR values tended to be much smaller than the true FDRs. Long-range correlations between variants—due to linkage disequilibrium—likely explain some of this bias. A more sophisticated approach to using the annotation information may improve power, however, many causal variants of realistic effect sizes may simply be undetectable, at least with this sample size. Perhaps annotation information could assist in distinguishing windows containing causal variants from windows that are merely correlated with causal variants.