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Leveraging multi-ancestry and whole genome sequence data to improve our understanding of the genetic architecture of neurological traits – applications from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program

Submitted by
Sarnowski, Chloé
Authors
C Sarnowski, PhD; LMP Shade, BS; M Fornage, PhD; JB Meigs, MD; S Seshadri, MD; AC Morrison, PhD, on behalf of the TOPMed Neurocognitive & Diabetes working groups.
Name and Date of Professional Meeting
International Stoke Genetics Consortium (ISGC), 21-23 Sept 2022
Associated paper proposal(s)
Evaluation of the causal association between insulin resistance and Alzheimer’s disease using Mendelian Randomization techniques and whole genome sequence data from TOPMed Whole Genome sequence analysis of brain MRI measures
Working Group(s)
Diabetes Neurocognitive
Abstract Text
Objective:
To better characterize genetic variations underlying neurological traits by leveraging whole-genome sequencing (WGS) data in participants of diverse ancestry from the Trans-Omics for Precision Medicine (TOPMed) program.

Background:
Genome-wide association studies (GWAS), conducted mainly in European (EA) participants, have identified common genetic variants with modest effect sizes for brain volumes, accepted endophenotypes of vascular brain injury, and few genetic loci for insulin resistance (IR), a major risk factor for stroke and dementia.

Design/Methods:
1. We performed WGS association analyses of hippocampal (HV), total brain, lateral ventricular (LVV), and intracranial (ICV) volumes in ~8k participants (62% EA, 21% African-American (AA), 16% Hispanic/Latino (HA)).
2. We derived three ancestry-specific polygenic scores (PSEA, PSAA, PSHA) based on UK Biobank reference panels and fasting insulin GWAS summary statistics, adjusted for body mass index. We generated a multi-ancestry PS (Multi-PS) by fitting a linear combination of the standardized PSEA, PSAA, and PSHA that most accurately predicted IR in a validation set of ~17k participants without diabetes (34% EA, 28% AA, 38% HA). We evaluated the association of the PSs with neurological traits in a testing set of ~14k participants (66% EA, 22% AA, 11% HA).
Mixed-effect models were used for all analyses, adjusted for age, sex, study, and principal components, and accounting for relatedness and trait variance variability. Brain volume analyses were adjusted for ICV and excluded participants with dementia or stroke.

Results:
We identified novel significant hits (P<5×10-8) at 2q22 & 5q14 for LVV and at 1q32 & 13q14 for HV. The top 13q14 variant was common in AA (13%), less frequent in HA (1.4%), and rare in EA (0.1%), and had a consistent effect size across population groups (-0.27 to -0.34). The Multi-PS was strongly associated with IR (proportion of variance explained: 12%). The PSEA was significantly (P<0.002) associated with ICV (P=7×10-7), and suggestively with LVV (P=0.004).

Conclusions:
By leveraging TOPMed multi-ancestry and WGS data, we identified new loci underlying brain volumes, including ancestry-specific associations, and confirmed the association of IR with brain volumes.

Disclosure and Study Support:
None.
NINDS F30NS124136, NIA T32AG57461, K99AG066849, U01AG058589, NHLBI R01s HL131136, HL105756