Genome-Wide Association Study of the Frailty Index - Atkins et al. 2020

Genome-wide summary statistics from the GWAS analysis of the Frailty Index in UK Biobank participants of European descent aged 60 to 70 years.<br>Preprint available https://doi.org/10.1101/19007559<br>Abstract<br>Frailty is a common geriatric syndrome, strongly associated with disability, mortality and hospitalisation. Frailty is commonly measured using the frailty index (FI), based on the accumulation of a number of health deficits during the life course. The mechanisms underlying the FI are multifactorial and not well understood, but a genetic basis has been suggested with heritability estimates between 30 and 45%. Understanding the genetic determinants and biological mechanisms underpinning the FI may help to delay or even prevent frailty. We performed a genome-wide association study (GWAS) of a frailty index in European descent participants from UK Biobank (n=164,610, aged 60-70 years). FI calculation was based on 49 self-reported items on symptoms, disabilities and diagnosed diseases. Following meta-analysis with the Swedish TwinGene study (n=10,616), 10 loci were associated with the FI (p&lt;5*10-8). We identified 9 loci in UK Biobank only that require further follow-up. Many FI-associated loci have previously been identified with traits such as body mass index, cardiovascular disease, smoking, HLA proteins, depression and neuroticism; however, three appear to be novel. The estimated single nucleotide polymorphism (SNP) heritability of the FI was 14% (0.14, SE 0.006). In pathway analysis, genes associated with synapse function were significantly enriched (p&lt;3*10-6). We also used Mendelian randomization to identify modifiable traits and exposures that may affect the risk of frailty, with a higher educational attainment genetic risk score being associated with a lower risk of frailty. Risk of frailty is influenced by many genetic factors, including well-known disease risk factors and mental health, with particular emphasis on synapse maintenance pathways.<br><br>Results are from UK Biobank genetic data release version 3 (including imputation from HRC and the combined UK10K and 1000 Genomes panels) including 16.4million genetic variants that met the following criteria: minor allele frequency (MAF) &gt;0.1%, Hardy-Weinberg p-value &gt;1x10-9, and imputation quality &gt;0.3. We used the BOLT-LMM (v2.3.2) software used for the GWAS itself (Loh et al. 2015), which uses linear mixed-effects modelling to account for genetic relatedness and confounding by ancestry. Models included age, sex, assessment centre (22 categories), and genotyping array (two categories: Axiom or BiLEVE) as covariates. <br><br>If used please cite paper Atkins et al. 2020 "A Genome-Wide Association Study of the Frailty Index Highlights Synaptic Pathways in Healthy Aging"<br>Included are two files: the full UK Biobank results, and an extended version of Supplementary Table 1 (known variant-trait associations from GWAS catalog, overlap with FI).<br><br>Fields in GWAS summary stats file are as follows:<br> SNP: dbSNP name of genetic marker, if available<br> CHR: chromosome<br> BP: base-pair position on CHR (hg19 / b37)<br> ALLELE1: effect allele<br> ALLELE0: non-effect allele<br> A1FREQ: frequency of ALLELE1INFO: Imputation quality<br>BETA: effect size from BOLT-LMM approximation to infinitesimal mixed model with respect to ALLELE1<br> SE: standard error of effect size<br> P_BOLT_LMM: non-infinitesimal mixed model association test p-value