Super‐variants identification for brain connectivity

Abstract

Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. Similar to but different from the classic concept of gene, a super‐variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super‐variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super‐variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super‐variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super‐variants and its capability of unifying existing results as well as discovering novel and replicable results.

Deep learning identified genetic variants associated with COVID-19 related mortality

“…In contrast to a gene, which refers to a physically connected region of a chromosome, the loci contributing to a super variant can locate in any part of the genome. Since the super variant aggregates the strength of distinct signals, it has been shown that it is both powerful and stable in association studies [17, 18]. Furthermore, super variants take into consideration the potential complex interactions among loci.…”

“…In contrast to a gene, which is a physically connected region on a chromosome, the loci contributing to a super variant might be located anywhere in the genome. Recent studies have demonstrated the usefulness of super variants in detecting genetic associations with complex diseases [15, 17, 18]. To incorporate potentially complex interactions more effectively among SNPs or genes, we propose a novel approach, namely deep learning-based ranking and aggregation method for identifying genetic variants (DRAG).…”

“…It is important to examine interactions between SNPs or genes on COVID-19 mortality. Several works utilizing tree-based method have been proposed to overcome drawbacks of traditional GWAS [15][16][17][18]. For example, Hu et al combined a local ranking and aggregation approach with random forest to identify genetic variants associated with risk of COVID-19 related mortality [17].…”

Super-taxon in human microbiome are identified to be associated with colorectal cancer

“…To overcome the issues mentioned above, we borrow the idea of super-variant which has been adopted in genome-wide association studies and brain imaging genetics [ 14 – 16 ]. In this study, we propose a new concept called super-taxon for microbiota-disease association studies.…”

“…The super-taxon is expected to reflect both the marginal and joint effects of contributing OTUs on diseases. It has been shown that the super-variants in genetic studies are easy to detect, and the aggregated signals are stable in their associations with the responses [ 14 – 16 ]. By exploiting the proposed methods, not only can we find the association for a microbiome community, but also report the top contributing individual OTUs.…”