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An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns.

Authors :
Jin B
Capra JA
Benchek P
Wheeler N
Naj AC
Hamilton-Nelson KL
Farrell JJ
Leung YY
Kunkle B
Vadarajan B
Schellenberg GD
Mayeux R
Wang LS
Farrer LA
Pericak-Vance MA
Martin ER
Haines JL
Crawford DC
Bush WS
Source :
Genome research [Genome Res] 2022 Apr; Vol. 32 (4), pp. 778-790. Date of Electronic Publication: 2022 Feb 24.
Publication Year :
2022

Abstract

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure-based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes ( TREM2 , SORL1 , and EXOC3L4 ) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1 , two known Alzheimer's disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.<br /> (© 2022 Jin et al.; Published by Cold Spring Harbor Laboratory Press.)

Details

Language :
English
ISSN :
1549-5469
Volume :
32
Issue :
4
Database :
MEDLINE
Journal :
Genome research
Publication Type :
Academic Journal
Accession number :
35210353
Full Text :
https://doi.org/10.1101/gr.276069.121