# An evaluation of age-varying genetic effects underlying body-mass index and blood pressure in the UK Biobank

## TL;DR

This study explores how genetic effects on BMI and blood pressure change with age, showing that these effects vary depending on the trait and age group.

## Contribution

The study introduces an age-stratified GWAS approach to identify age-varying genetic effects and applies it to BMI and blood pressure traits.

## Key findings

- Age-interaction effects were more common for pulsatile pressure (44.7%) than for BMI (10.3%).
- Genetically predicted increases in pulsatile pressure with age were strongly associated with higher PAD risk.
- The study provides a resource of age-stratified GWAS summary data for further research.

## Abstract

Genome-wide association studies (GWAS) are conventionally conducted in cohorts spanning a wide age-range. These studies typically assume that genetic associations are constant across different ages. Some traits, however, may have age-varying genetic associations. This has implications for the interpretation of genetic effects derived in downstream applications, such as Mendelian randomization (MR) analyses. In this study we conducted a series of age-stratified GWAS on individuals aged 40–69 years in the UK Biobank, for body-mass index (BMI) and three blood pressure traits (systolic, diastolic and pulsatile pressure (PP)) in 2-year age strata (N up to 26,330). We used a meta-regression approach to systematically identify single nucleotide polymorphisms (SNPs) with evidence for age interaction effects among trait-associated GWAS signals and additional loci genome-wide. Within an MR framework, we examine the relationship between BMI and blood pressure traits on cardiovascular and cardiometabolic outcomes (type-2 diabetes (T2D), stroke, peripheral artery disease (PAD), heart failure, coronary heart disease and atrial fibrillation). Next, we describe the effect of the SNP*Age interaction on those relationships in a modified inverse-variance weighted (ivw) analysis. We identified differential enrichment of age-interaction effects, which was trait dependent. For example, 10.3% of BMI discovery SNPs had evidence for an age-interaction in our data compared to 44.7% for PP (at P < 0.05). Our downstream MR and modified ivw analyses highlight the influence of age on the genetically predicted relationship between PP and adverse cardiovascular outcomes. For example, our results indicated that an increased rate of change in genetically predicted PP across the age period is associated with higher susceptibility to PAD (interaction odds ratio = 2.71; P = 1.82x10-13; 95%-CI: 2.08-3.53). The data generated in this project provides a valuable resource for further exploration of mechanisms relevant to the genetic architecture of complex traits and all summary data has been made accessible to the research community.

Genetic variants which reliably predict variation in a trait are a valuable tool within genetic epidemiology studies, offering a means to estimate whether an exposure-outcome relationship is likely to be causal using a method called Mendelian randomization (MR). Typically, MR results are interpreted as the cumulative lifetime effect of the exposure on the outcome. However, there is growing evidence which suggests that the influence of genetic effects on trait variation detected in cross-sectional population studies may be age dependent in some scenarios. In this work we aimed to conduct a thorough investigation on whether and to what extent the influence of genetics on population-level trait variation changes across adulthood. We investigated this question within a methodological framework which used age-stratified summary level data, demonstrating that this approach may have wide applicability to the research community where individual level cohort data are not publicly available. We demonstrate that age interacts with genetic influences across adulthood in a trait dependent manner, where genetics may have a stronger influence on variation in body-mass index measured earlier in life, and on pulsatile pressure later in life. We take advantage of the MR and ivw frameworks to further illustrate how the variation in the exposure explained by genetics varies with increasing age. This exploratory work helps provide insight on the extent that distinct genetic effects are detectable across adulthood, helping us to understand how more precise lifecourse effects may be genetically proxied within an MR setting.