Regional heritability mapping identifies several novel loci (STAT4, ULK4, and KCNH5) for primary biliary cholangitis in the Japanese population

Olivier Gervais,Kazuko Ueno,Katsushi Tokunaga,Minoru Nakamura,Yuki Hitomi,Yosuke Kawai,Yoshihiro Aiba,Mayumi Ueta,Masao Nagasaki

doi:10.1038/s41431-021-00854-5

Abstract

While the advent of GWAS more than a decade ago has ushered in remarkable advances in our understanding of complex traits, the limitations of single-SNP analysis have also led to the development of several other approaches. Simulation studies have shown that the regional heritability mapping (RHM) method, which makes use of multiple adjacent SNPs jointly to estimate the genetic effect of a given region of the genome, generally has higher detection power than single-SNP GWAS. However, thus far its use has been mostly limited to agricultural settings, and its potential for the discovery of new genes in human diseases is yet to be fully exploited. In this study, by applying the RHM method to primary biliary cholangitis (PBC) in the Japanese population, we identified three novel loci (STAT4, ULK4, and KCNH5) at the genome-wide significance level, two of which (ULK4 and KCNH5) have not been found associated with PBC in any population previously. Notably, these genes could not be detected by using conventional single-SNP GWAS, highlighting the potential of the RHM method for the detection of new susceptibility loci in human diseases. These findings thereby provide strong empirical evidence that RHM is an effective and practical complementary approach to GWAS in this context. Also, liver tissue mRNA microarray analysis revealed higher gene expression levels in ULK4 in PBC patients (P < 0.01). Lastly, we estimated the common SNP heritability of PBC in the Japanese population (0.210 ± 0.026).

Highlights

It has been demonstrated that the regional heritability mapping (RHM) method [9, 10], which consists in estimating the genetic effect of “windows” composed of multiple adjacent SNPs, possesses in a number of cases higher statistical power for the detection of causal loci compared with conventional single-SNP GWAS [10, 11], albeit at the expense of computational power
This can be explained in large part by the fact that (1) historically, most major advances regarding the application of general mixed model methods to genetics have taken place in the field of animal breeding for the purpose of estimating random genetic effects [14], and their application to human genetics is relatively new, and (2) the substantial discoveries that have resulted from the application of GWAS to newly established large-scale genomic cohorts in recent years have overshadowed the benefits and slowed down the spread of other methods, including RHM, which are generally more complex and computationally demanding than single-SNP mapping methods
All patients for whom liver biopsy specimens were available were included in this analysis; patients with primary biliary cholangitis (PBC) (36 individuals), Table 1 P value and likelihood ratio test (LRT) thresholds for the RHM analysis

Summary

Introduction

In spite of the many successes of GWAS, this development has created a need for innovative analytical methods and statistical models to better make sense of this newly available data, for instance by accounting for population structure and relatedness, reducing error rates in unbalanced case/control traits, improving the detection power of rare variants, or analyzing complex immunemediated diseases [1] These aspects are especially important given that the heterogeneous nature of the genetic architecture of complex traits suggests that increasing the sample size and/or the number of phenotypes analyzed does not always produce the anticipated gains in terms of novel loci discovery [2]. This can be explained in large part by the fact that (1) historically, most major advances regarding the application of general mixed model methods to genetics have taken place in the field of animal breeding for the purpose of estimating random genetic effects [14], and their application to human genetics is relatively new, and (2) the substantial discoveries that have resulted from the application of GWAS to newly established large-scale genomic cohorts in recent years have overshadowed the benefits and slowed down the spread of other methods, including RHM, which are generally more complex and computationally demanding than single-SNP mapping methods

Methods

Results

Discussion

Conclusion