TwinEQTL: ultrafast and powerful association analysis for eQTL and GWAS in twin studies.

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model for twin genome-wide association study data. Instead of analyzing all twin samples together with linear mixed-effects model, TwinEQTL first splits twin samples into 2 independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the 2 nonindependent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between 2 dependent test statistics at each single-nucleotide polymorphism is independent of its minor allele frequency. Thus, the correlation is constant across all single-nucleotide polymorphisms. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared with the gold-standard linear mixed-effects models. To accommodate expression quantitative loci analysis with twin subjects, we further implement TwinEQTL into an R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for genome-wide association study and expression quantitative loci analysis with twin samples.

Using machine learning to understand age and gender classification based on infant temperament.

Age and gender differences are prominent in the temperament literature, with the former particularly salient in infancy and the latter noted as early as the first year of life. This study represents a meta-analysis utilizing Infant Behavior Questionnaire-Revised (IBQ-R) data collected across multiple laboratories (N = 4438) to overcome limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. Algorithmic modeling techniques were leveraged to discern the extent to which the 14 IBQ-R subscale scores accurately classified participating children as boys (n = 2,298) and girls (n = 2,093), and into three age groups: youngest (< 24 weeks; n = 1,102), mid-range (24 to 48 weeks; n = 2,557), and oldest (> 48 weeks; n = 779). Additionally, simultaneous classification into age and gender categories was performed, providing an opportunity to consider the extent to which gender differences in temperament are informed by infant age. Results indicated that overall age group classification was more accurate than child gender models, suggesting that age-related changes are more salient than gender differences in early childhood with respect to temperament attributes. However, gender-based classification was superior in the oldest age group, suggesting temperament differences between boys and girls are accentuated with development. Fear emerged as the subscale contributing to accurate classifications most notably overall. This study leads infancy research and meta-analytic investigations more broadly in a new direction as a methodological demonstration, and also provides most optimal comparative data for the IBQ-R based on the largest and most representative dataset to date.

Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706).

Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 × 10-05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 × 10-12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value < 4.5 × 10-10, adjusted for testing multiple phenotypes). Gene-based association study prioritized 112 significant genes, most of which are novel. More importantly, association lookups found that many of the novel SNPs and genes of DTI parameters have previously been implicated with cognitive and mental health traits. In conclusion, the present study identifies many new genetic variants at SNP, locus and gene levels for integrity of brain WM tracts and provides the overview of pleiotropy with cognitive and mental health traits.