Abstract
Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63–0.76, p < 10−10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.
Highlights
Big data research initiatives - including the Human Connectome Project (HCP) and UK Biobank (UKBB) - collect comprehensive multimodal neuroimaging datasets and allow researchers to quantify genetic and environmental risk and protective factors that affect human brain in health and illness (Glasser et al, 2013; Van Essen et al, 2013)
We showed a good agreement in heritability estimates measured in UKBB and these reported by large meta-and-mega analyses performed by Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) studies (Jahanshad et al, 2013; Kochunov et al, 2014 )
The scatter plots of the heritability estimates showed an excellent agreement between the h2 values estimated from Fast and Powerful Heritability Inference (FPHI) and Genome-wide Complex Trait Analysis (GCTA) in both the HCP and UKBB samples (Fig. 2A and B, Table S1; see supplement)
Summary
Big data research initiatives - including the Human Connectome Project (HCP) and UK Biobank (UKBB) - collect comprehensive multimodal neuroimaging datasets and allow researchers to quantify genetic and environmental risk and protective factors that affect human brain in health and illness (Glasser et al, 2013; Van Essen et al, 2013). CR can be calculated empirically from high-throughput genome-wide single nucleotide polymorphism (SNP) data, in which case the heritability measures the proportion of the observed variation explained by common SNPs (SNP-h2) (Kochunov et al, 2019a; Ramstetter et al, 2017; Speed et al, 2017; Toro et al, 2014; Wood et al, 2014; Yang et al., 2010). The SNP-h2 can be calculated in samples of unrelated individuals based on the phenotypic variance explained by small amounts of genetic similarity shared among participants (Yang et al, 2010). We performed two sets of analyses: We first evaluated a novel Fast and Powerful Heritability Inference (FPHI) approach that accelerates classical variance component models using algorithmic and hardware approaches and compared the measurements to that of a commonly used SNP-h2 approach implemented in the Genome-wide Complex Trait Analysis (GCTA) software (https://cnsgenomics.com/software/gcta/). We showed a good agreement in heritability estimates measured in UKBB and these reported by large meta-and-mega analyses performed by Enhancing Neuro Imaging Genetics through Meta-
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
