| Title: | Super-variants identification for brain connectivity |
| Journal: | Human Brain Mapping |
| Published: | 24 Nov 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33236465/ |
| DOI: | https://doi.org/10.1002/hbm.25294 |
| URL: | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.25294 |
Publication 5786
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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.</p>
4 Authors
- Ting Li
- Jianchang Hu
- Shiying Wang
- Heping Zhang
Enabling scientific discoveries that improve human health