A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

Abstract

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.

Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation

“…Foremost, DAWGSS are limited to describing statistics that have an additive effect across the SNVs. Therefore, rank-based approaches, 7 methods that allow for interactions, 6 methods with non-additive effects, 18 and methods that compare all subsets of SNVs 33 are outside of the DAWGSS framework. However, we believe these limitations have minimal practical implications.…”

“…[1][2][3][4][5] As these variants are rare, most studies will be inadequately powered to detect an association with any single variant. [6][7][8] When only a handful of minor alleles are observed for any single-nucleotide variant (SNV), obtaining statistical significance, especially at traditional genome wide levels of 10 À8 , can be near impossible. Therefore, instead of trying to identify associations with individual variants, the goal has been to identify associations with a group of rare variants in a shared region (eg, exons, genes) or pathway, effectively increasing power by pooling information across SNVs.…”

Rare DNA variants in the brain-derived neurotrophic factor gene increase risk for attention-deficit hyperactivity disorder: a next-generation sequencing study

“…This filtering process resulted in a set of 1288 rare putatively functional SNVs (hereafter referred to as rare functional SNVs). Gene level analyses were then conducted across our rare functional SNV set using the adaptive permutation version of the Kernel-Based Adaptive Cluster method (KBAC) 28 …”

Adaptive clustering and adaptive weighting methods to detect disease associated rare variants

“…However, empirical studies have shown that predictive errors of these tools are high and agreement among them is low. 17,25,30 Therefore, the usefulness of the bioinformatics tools is limited. As pointed by Liu and Leal, 30 even when functionality can be correctly inferred, whether the identified variants affect the phenotype of interest is still unknown.…”

“…17,25,30 Therefore, the usefulness of the bioinformatics tools is limited. As pointed by Liu and Leal, 30 even when functionality can be correctly inferred, whether the identified variants affect the phenotype of interest is still unknown. Thus, we expect that a large proportion of variants under study are neutral and the group-wise methods by collapsing or aggregating all variants in the group may not be optimal.…”