Fast and powerful heritability inference for family-based neuroimaging studies

Abstract

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wiseP-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods.In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero etal., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-basedP-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study.

Cingulo-opercular Network Efficiency Mediates the Association Between Psychotic-like Experiences and Cognitive Ability in the General Population

“…Mediation analyses were performed using the PROCESS macro in SPSS (45), with bias-corrected 95% confidence intervals using 1000 bootstrap samples. The analogous correlation analyses were also performed after approximately adjusting for family structure in a Mixed Generalized Linear Model, with “family ID” as the highest level and “subject” as the second level (46), in order to assess whether significant findings were due to increased correlations between genetically related subjects. Finally, analyses were performed excluding individuals who tested positive for drugs on the day of the testing (N=63), as well as when excluding a single subject with a psychosis score of six (all other scores were <6).…”

Heritability Estimation of Reliable Connectomic Features

“…In this project, we estimated the heritability of brain connectomoic features extracted from twin subjects in the HCP cohort without using any genetic data. SOLAR-Eclipse software tool is chosen over traditional ACE modal due to its capability in evaluating the covariate effects, significance of heritability and standard error for each trait [9, 4]. It requires three inputs: phenotype traits, covariates measures and a kinship matrix indicating the pairwise relationship between twin individuals.…”

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

“…Using a few summary variables of the brain features is the most popular approach in the literature (Joyner et al, 2009; Potkin et al, 2009; Vounou et al, 2010); voxel-wise and genome-wide association approaches offer a more holistic perspective and are used in exploratory studies (Hibar et al, 2011; Vounou et al, 2012); multivariate analyses have also been used to capture the epistatic and pleiotropic interactions, therefore boosting the overall sensitivity (Hardoon et al, 2009; Ge at al., 2015a,b). Apart from the population studies, family-based studies offer additional insights on the genetic heritability (Ganjgahi et al, 2015). Recently, a few probabilistic approaches have been proposed to jointly model the interactions between genetic factors, brain endophenotypes and behavior phenotypes (Batmanghelich et al, 2013, Stingo et al, 2013), and some Bayesian methods originally developed for eQTL studies can also be applied to imaging-genetic problems (Zhang and Liu, 2007; Jiang and Liu, 2015).…”