Genetically independent phenotype analysis identifies LPA and VCAM1 as drug targets for human ageing

Abstract

The length and quality of life is important to us all, yet identification of promising drug targets for human ageing using genetics has had limited success. Here, we combine six large European-ancestry genome-wide association studies (GWAS) of human ageing traits - healthspan, father and mother lifespan, exceptional longevity, frailty index, and self-rated health - in a principal component framework that maximises their shared genetic architecture. The first principal component (GIP1) is more heritable than the original studies and shows strong genetic correlations with length of life as well as multiple indices of mental and physical wellbeing. We identify 27 genomic regions associated with GIP1, and provide additional, independent evidence for an effect on human ageing for loci near HTT and MAML3 using a study of Finnish and Japanese subject survival. Across the genome, GIP1 associations are enriched in genes involved in haem metabolism and pathways related to transcription, neurogenesis, homeostasis, proteolysis, intracellular signalling, immunity, and the muscle system. Finally, using proteome-wide two-sample Mendelian randomisation and colocalisation, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) (LPA) and vascular cell adhesion molecule 1 (VCAM1) on GIP1. Together, our results demonstrate that combining multiple ageing traits using genetic principal components enhances power to detect biological targets for human ageing.

A novel framework for analysis of the shared genetic background of correlated traits

“…One of them is making a summary-level adjustment of traits by other traits using the same scheme as was used for obtaining the UGIT GWAS statistics. This can be helpful, for example, for ridding the studied trait’s genetic component of the genetic component that was caused by the confounding or unaccounted effects of assortative mating or family effects, which is quite a problem in GWAS at the biobank scale 15, 18 . Another task is a GWAS for the trait that appears as a linear combination of the original traits.…”

“…Thirdly, although the set of SNPs identified by the SGIT GWAS is enriched for the SGF, each SNP should be interpreted with caution for whether it is shared or not, because SHAHER has some level of unspecificity. Finally, if any confounding effects were included in the GWAS of the original traits, these effects are amplified in the SGIT 15 . The confounding effects can be controlled easily using special methods like LD score regression 20 , although this method fails to distinguish a polygenic component if the trait was measured in the sample with the assortative mating or family effects.…”

“…This method is based on the calculation of the principal components using genetic rather than phenotypic correlations. We applied this method to genetically correlated pain phenotypes and aging related phenotypes and showed that the first GIP component, GIP1, that explains the largest proportion of the genetic variance probably could be interpreted as SGI 2, 15 . This makes GIP promising for identification of loci attributed to SGF.…”

Decomposing the genetic background of chronic back pain

“…We assessed genetic correlations of SGIT and UGIT with 730 complex human traits from the GWAS-Map database using LD Score regression tool embedded in the platform. The detailed protocol of selection of 730 complex traits included in the analysis and the full list of them were provided in a recent study by Timmers et al [63]. For further interpretation we restricted our analysis to 322 complex traits which had statistically significant genetic correlations with SGIT and/or UGIT with magnitude greater than 0.25 (p-value < 3.42e-5 = 0.05/(730*2), where 730 represents the total number of complex traits and 2 refers to the number of traits).…”