Multi-trait multi-locus SEM model discriminates SNPs of different effects

Anna A Igolkina,Georgy Meshcheryakov,Sergey V Nuzhdin,Maria V Gretsova,Maria G Samsonova

doi:10.1186/s12864-020-06833-2

Abstract

BackgroundThere is a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes.ResultsWe propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it possible to discriminate pleiotropic and single-trait SNPs of direct and indirect effect. We also propose an automatic procedure to construct the model using factor analysis and the maximum likelihood method. For estimating a large number of parameters in the model, we performed Bayesian inference and implemented Gibbs sampling. An important feature of the model is that it correctly copes with non-normally distributed variables, such as some traits and variants.ConclusionsWe applied the model to Vavilov’s collection of 404 chickpea (Cicer arietinum L.) accessions with 20-fold cross-validation. We analyzed 16 phenotypic traits which we organized into five groups and found around 230 SNPs associated with traits, 60 of which were of pleiotropic effect. The model demonstrated high accuracy in predicting trait values.

Highlights

There is a plethora of methods for genome-wide association studies
Application of multi-trait multi-locus SEM (mtmlSEM) model to chickpea dataset To test whether the relations between latent factors in the model are reasonable and to evaluate impacts of different types of single nucleotide polymorphisms (SNPs), we compared four types of models (Fig. 1)
The structure of the model is automatically constructed, such that correlated traits are joined into latent factors and explanatory SNPs are introduced to latent factors and phenotypic traits directly

Summary

Introduction

There is a plethora of methods for genome-wide association studies. only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes. Single-locus approaches may lead to biased estimates due to multiple testing correction, and they are not suitable in the common case of genetically correlated traits To alleviate the latter challenge, multi-trait models have been proposed [1, 2]. These constructs play the role of new traits and can be obtained with a standard principal component analysis of traits (PCA), various principal components of heritability (PCH) [5,6,7] or pseudo-principal components [8]; the biological interpretation of these artificial traits is not clear These methods do not distinguish trait-specific and pleiotropic variants.

Methods

Results

Discussion

Conclusion