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Whole genome sequencing association analysis of general cognitive function in a multi-ethnic sample from the Trans-Omics for Precision Medicine (TOPMed) Program

Authors
Jennifer A. Smith, Minjung Kho, Jan Bressler, Chloé Sarnowski, Wei Zhao, Dima Chaar, Yi Zhe Wang, Farah Ammous, Joshua C. Bis, Paul Nyquist, Susan R. Heckbert, Claudia Satizabal, Qiong Yang, Beverly Snively, Amanda Rodrigue, Elizabeth Litkowski, David Glahn, Kathleen M. Hayden, Annette Fitzpatrick, Bruce Psaty, Sharon L.R. Kardia, Myriam Fornage, Sudha Seshadri, on behalf of the TOPMed Neurocognitive Working Group
Name and Date of Professional Meeting
American Society of Human Genetics, October 18-22, 2021
Associated paper proposal(s)
Working Group(s)
Abstract Text
Background: General cognitive function is an index of performance in multiple cognitive domains. GWAS of general cognitive function, conducted mostly in European ancestry (EA) populations, have identified hundreds of genetic variants. However, investigations of rare (MAF<1%) variants have been limited, especially in non-EA populations.

Methods: We performed whole-genome sequence (WGS) association analysis for general cognitive function in N=12,615 EA, N=4,419 African ancestry (AA), and N=1,140 Hispanic/Latino ethnicity (HIS) participants from the NHLBI TOPMed Program, after exclusion for dementia, clinical stroke, or age <45 years. Linear mixed effects models were used to test for single variant association (MAC>=10), and SKAT-O was used for rare variant aggregation tests (MAF<1%, >5 alternate alleles). Using SKAT-O, we tested coding variants within genes (high-confidence LoF, predicted deleterious missense and protein-altering) and both coding and non-coding variants within genes and their regulatory elements (enhancers and promoters). Associations were evaluated within and across ancestry/ethnicity with adjustment for age, sex, study, and genetic principal components, before and after adjustment for educational attainment.

Results: Single-variant association analysis identified three genome-wide significant loci for general cognitive function: intronic in AMPH (rs17500486, MAF=3.2%, P=3.4x10-8) in EA, proximal to FTLP5 and ATCAY (rs570203641, MAF=0.1%, P=2.7x10-8) in EA, and upstream of RN7SL300P (rs113037723, MAF=8.0%, P=8.8x10-9) in AA. After adjustment for educational attainment, an additional two loci were identified: intronic in SYNPO2 (rs146805942, MAF=2.1%, P=3.4x10-8) in AA and intronic in BRINP3 (rs72729138, MAF=6.6%, P=3.0x10-8) in HIS. Rare variant aggregation tests identified two suggestive associations using coding variants (SH3GL3 in EA (P=3.2x10-6) and P2RY11 in AA (P=9.0x10-6)) and one association using coding and noncoding variants (AC113410.3 in EA, P=9.06x10-6). Of the nine genes identified, all except RN7SL300P and AC113410.3 had previous evidence of association with neuronal development or activity, neurological/cognitive disorders, or cognitive function. No variants or genes were associated with general cognitive function at the genome-wide threshold in the pooled analysis of all ancestry/ethnic groups.

Conclusions: In this WGS association analysis, we identified several new variants associated with general cognitive function, many of which lie in genes that play a role in brain function. Identified loci tended to be specific to ancestry/ethnicity.
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