Rare genetic variants in LDLR, APOB and PCSK9 are known causes of familial hypercholesterolaemia and it is expected that rare variants in other genes will also have effects on hyperlipidaemia risk although such genes remain to be identified. The UK Biobank consists of a sample of 500,000 volunteers and exome sequence data is available for 50,000 of them. 11,490 of these were classified as hyperlipidaemia cases on the basis of having a relevant diagnosis recorded and/or taking lipid-lowering medication while the remaining 38,463 were treated as controls. Variants in each gene were assigned weights according to rarity and predicted impact and overall weighted burden scores were compared between cases and controls, including population principal components as covariates. One biologically plausible gene, HUWE1, produced statistically significant evidence for association after correction for testing 22,028 genes with a signed log10 p value (SLP) of -6.15, suggesting a protective effect of variants in this gene. Other genes with uncorrected p < .001 are arguably also of interest, including LDLR (SLP = 3.67), RBP2 (SLP = 3.14), NPFFR1 (SLP = 3.02) and ACOT9 (SLP = -3.19). Gene set analysis indicated that rare variants in genes involved in metabolism and energy can influence hyperlipidaemia risk. Overall, the results provide some leads which might be followed up with functional studies and which could be tested in additional data sets as these become available. This research has been conducted using the UK Biobank Resource. NB Corrigendem published 03/2021 (doi - 10.1016/j.ymgme.2021.01.012)
Study of effects of common and rare genetic variants on health-related phenotypes
Both common and rare genetic variants contribute to the heritability of complex traits. However, usually they are analysed separately using different analytical techniques, such as single variant regression versus burden methods. This fails to fully utilise the information contained in genomes. Here, we aim to apply computational methods we have developed which combine information from common and rare variants and which can utilise information about the predicted effects of the variants to identify genes influencing complex traits. We will use our results to assess how natural selection has shaped the genetic architecture of complex traits in modern humans. We will particularly focus on non-communicable diseases, such as cardiovascular disease, neurological disorders and cancer, and disease-related traits, such as obesity and cholesterol levels, as well as the susceptibility to infectious diseases. Differences in environments, natural selection and drift may have distinctly shaped the genetic architectures in some populations. Therefore, we will compare results for different ancestry groups.
|Lead investigator:||David Curtis|
|Lead institution:||University College London|