Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

Abstract

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

“…Previous studies have estimated continuity in the genetic correlation of BMI measured at different ages 78 , which is theorised to emerge by two possible mechanisms 79 : (1) common genetic (or environmental) factors are associated with the rates of change in BMI over time, which we test in this study, and (2) that these correlations are induced by time-specific genetic (or environmental) factors in an autoregressive manner, i.e., BMI genetics at time-point t −1 causally affect BMI at time t . Studies testing the latter hypothesis have arrived at opposing conclusions: Gillespie et al 80 find that on a genome-wide scale, age-specific genetic effects in an autoregressive framework do not explain differences in BMI heritability across ages 40–73 years, while Winkler et al 79 did identify 15 genetic loci with differential effects on BMI in younger adults (age <50 years) and older adults (age >50 years). Both studies were pseudo-longitudinal, i.e., the same individuals were not monitored over a period of time, but rather cross-sectional individual data was grouped into age bins.…”

Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information

“…The OQFE version of the Plink formatted exome files was then downloaded and utilized for all the analyses described in this study (field 23155). Samples were initially filtered to retain only unrelated subjects of British ancestry (n = 359,980) as in previous analyses[33]. This yielded 147,376 participants with exome data from the 200k exome release of whom 51,357 had AUDIT directly measured.…”