HCLC-FC: A novel statistical method for phenome-wide association studies

Abstract

The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

“…Based on a widely used simulation procedure 17 , 27 , we generate scores from a multivariate normal distribution . We consider two different correlation matrix structures: (1) is the sample correlation matrix of 70 related musculoskeletal system and connective tissue phenotypes in the UK Biobank (details of the 70 phenotypes are described in the Application to UK Biobank summary statistics); and (2) is generated based on the Autoregressive model (AR(1) model) 28 for 40 phenotypes, where , a block diagonal matrix, with and . We use in the simulation studies.…”

“…Sequenced genotypes for 488,377 participants with 784,256 variants in autosomal chromosomes were extracted by UK Biobank dataset 34 . Similar to Liang et al 28 , we first perform quality controls (QCs) on genotypes and individuals by using PLINK 1.9 35 . We remove SNPs with missing rates larger than 5%, p-values from Hardy–Weinberg equilibrium exact test less than , and minor allele frequency (MAF) less than 5%.…”

“…Since our proposed method is a population-based method and cannot be applied to a mixed population due to population stratification, we analyze 409,672 individuals with the white British ancestry. Similar to Liang et al 28 , we also exclude individuals who are marked as outliers for heterozygosity, and have been identified to have more than ten third-degree relatives or closer, etc. The final dataset includes individuals with common variants across phenotypes for analyses.…”

Joint analysis of multiple phenotypes for extremely unbalanced case‐control association studies

“…The phenotypes are defined by using the International Classification of Diseases, 10th Revision (ICD-10) codes (Denny et al, 2016). Following the phenotype preprocess introduced in Liang et al (2022), we consider 92 level-3 phenotypes in category IX (diseases of the circulatory system) in this study (http://biobank.ndph.ox.ac.uk/ showcase/field.cgi?id=41202). We follow the same quality control (QC) pressures as in Liang et al (2022) for both genetic variants and individuals using PLINK 1.9 (Chang et al, 2015) (https://www.cog-genomics.org/ plink/1.9/).…”

“…Following the phenotype preprocess introduced in Liang et al (2022), we consider 92 level-3 phenotypes in category IX (diseases of the circulatory system) in this study (http://biobank.ndph.ox.ac.uk/ showcase/field.cgi?id=41202). We follow the same quality control (QC) pressures as in Liang et al (2022) for both genetic variants and individuals using PLINK 1.9 (Chang et al, 2015) (https://www.cog-genomics.org/ plink/1.9/). After preprocess of phenotype data and QC of genotype data, 322,607 individuals remained with 288,647 common variants and 92 phenotypes.…”