Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models

TL;DR

This paper introduces a variable selection approach to improve heritability estimation in high-dimensional sparse linear mixed models, especially for neuroanatomical data, reducing standard errors and enhancing accuracy.

Contribution

It presents a novel three-step heritability estimation method combining variable selection, maximum likelihood, and bootstrap confidence intervals for sparse genetic effects.

Findings

Reduced standard errors in heritability estimates.

Effective in high-dimensional sparse genetic architectures.

Validated on synthetic and real neuroanatomical data.

Abstract

Motivated by applications in neuroanatomy, we propose a novel methodology for estimating the heritability which corresponds to the proportion of phenotypic variance which can be explained by genetic factors. Estimating this quantity for neuroanatomical features is a fundamental challenge in psychiatric disease research. Since the phenotypic variations may only be due to a small fraction of the available genetic information, we propose an estimator of the heritability that can be used in high dimensional sparse linear mixed models. Our method consists of three steps. Firstly, a variable selection stage is performed in order to recover the support of the genetic effects -- also called causal variants -- that is to find the genetic effects which really explain the phenotypic variations. Secondly, we propose a maximum likelihood strategy for estimating the heritability which only takes into account the causal genetic effects found in the first step. Thirdly, we compute the standard error and the 95% confidence interval associated to our heritability estimator thanks to a nonparametric bootsrap approach. Our main contribution consists in providing an estimation of the heritability with standard errors substantially smaller than methods without variable selection when the genetic effects are very sparse. Since the real genetic architecture is in general unknown in practice, we also propose an empirical criterion which allows the user to decide whether it is relevant to apply a variable selection based approach or not. We illustrate the performance of our methodology on synthetic and real neuroanatomic data coming from the Imagen project. We also show that our approach has a very low computational burden and is very efficient from a statistical point of view.