Explore the vast range of titles available.

Editorial summary Endophenotypes, quantitative neurobehavioral traits that index genetic susceptibility for a psychiatric disorder, have been examined in thousands of studies. Nevertheless, they have underexploited potential to provide etiological insights into prognosis, how psychopathology develops, the etiology of comorbidity, and the mechanisms of gene function.

Danielle Posthuma and colleagues perform a large meta-analysis for intelligence and determine genetic overlap with several neuropsychiatric and metabolic traits. They find 15 new significant loci and implicate 40 new genes, most of which are predominantly expressed in the brain. Intelligence is associated with important economic and health-related life outcomes 1 . Despite intelligence having substantial heritability 2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered 3 , 4 , 5 . Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10 −8 ) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10 −6 ), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10 −6 ). Despite the well-known difference in twin-based heritability 2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation ( r g = 0.89, LD score regression P = 5.4 × 10 −29 ). These findings provide new insight into the genetic architecture of intelligence.

Educational success is associated with greater quality of life and depends, in part, on heritable cognitive and non-cognitive traits. We used polygenic scores (PGS) for smoking and educational attainment to examine different genetic influences on facets of academic adjustment in adolescence and educational attainment in adulthood. PGSs were calculated for participants of the Minnesota Twin Family Study ( N = 3225) and included as predictors of grades, academic motivation, and discipline problems at ages 11, 14, and 17 years-old, cigarettes per day from ages 14 to 24 years old, and educational attainment in adulthood (mean age 29.4 years). Smoking and educational attainment PGSs had significant incremental associations with each academic variable and cigarettes per day. About half of the adjusted effects of the smoking and education PGSs on educational attainment in adulthood were mediated by the academic variables in adolescence. Cigarettes per day from ages 14 to 24 years old did not account for the effect of the smoking PGS on educational attainment, suggesting the smoking PGS indexes genetic influences related to general behavioral disinhibition. In sum, distinct genetic influences measured by the smoking and educational attainment PGSs contribute to academic adjustment in adolescence and educational attainment in adulthood.

We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus three other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens. We conducted the GWAS across ∼ 2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric bootstrapping, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA. The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score. Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect. We place our study in the context of the literature-both contemporary and historical-and provide accessible explication of our statistical methods.

We report results from a genome wide association study (GWAS) of five quantitative indicators of behavioral disinhibition: nicotine, alcohol consumption, alcohol dependence, illicit drugs, and non-substance related behavioral disinhibition. The sample, consisting of 7,188 Caucasian individuals clustered in 2,300 nuclear families, was genotyped on over 520,000 SNP markers from Illumina’s Human 660W-Quad Array. Analysis of individual SNP associations revealed only one marker-component phenotype association, between rs1868152 and illicit drugs, with a p value below the standard genome-wide threshold of 5 × 10 −8 . Because we had analyzed five separate phenotypes, we do not consider this single association to be significant. However, we report 13 SNPs that were associated at p  < 10 −5 for one phenotype and p  < 10 −3 for at least two other phenotypes, which are potential candidates for future investigations of variants associated with general behavioral disinhibition. Biometric analysis of the twin and family data yielded estimates of additive heritability for the component phenotypes ranging from 49 to 70 %, GCTA estimates of heritability for the same phenotypes ranged from 8 to 37 %. Consequently, even though the common variants genotyped on the GWAS array appear in aggregate to account for a sizable proportion of heritable effects in multiple indicators of behavioral disinhibition, our data suggest that most of the additive heritability remains “missing”.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

Christian Fuchsberger, Gonçalo Abecasis and colleagues describe a new web-based imputation service that enables rapid imputation of large numbers of samples and allows convenient access to large reference panels of sequenced individuals. Their state space reduction provides a computationally efficient solution for genotype imputation with no loss in imputation accuracy. Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.

The present study attempted to determine the direction and etiology of the robust relationship between childhood externalizing (EXT) symptoms and parent–child conflict using a genetically informative longitudinal model and data from the ongoing Minnesota Twin Family Study. Participants consisted of 1,506 same-sex twins assessed at ages 11 and 14, and their parents. The relationship between EXT and parent–child conflict from ages 11 to 14 was examined within a biometrical cross-lagged design. The results revealed three primary findings: first, the stability of conflict and externalizing over time is largely, although not solely, a result of genetic factors. Second, there appears to be a bidirectional relationship between conflict and EXT over time, such that both conflict and EXT at 11 independently predict the other 3 years later. Finally, the results are consistent with the notion that parent–child conflict partially results from parental responses to their child's heritable externalizing behavior, while simultaneously contributing to child externalizing via environmental mechanisms. These results suggest a “downward spiral” of interplay between parent–child conflict and EXT, and offer confirmation of a (partially) environmentally mediated effect of parenting on child behavior.This research was funded in part by USPHS grants (DA05147, DA13240, AA09367, AA00175, MH 65137), a National Institutes of Mental Health training grant (MH17069 to S.A.B.), and a Doctoral Dissertation Fellowship (S.A.B.). We also thank Hennepin County Medical Center in Minneapolis for the research time that enabled the primary author to conduct this study.

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infraslow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting state ( = 928, 473 females), we quantified the heritability of multivariate (multistate) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60–500 ms. Temporal features were heritable, particularly Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of the phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for the heritability of dynamic spatial features, specifically states’ Modularity and connectivity pattern. We conclude that genetic effects shape individuals’ connectome dynamics at rapid timescales, specifically states’ overall occurrence and sequencing. In this study, we investigate the genetic influence on rapid electrophysiological connectome dynamics. Using hidden Markov model on source-localized EEG data at rest, we obtained measures describing temporal trajectories and time-varying spatial characteristics of connectome states. Applying two heritability assessment methods to these multivariate, time-varying connectome dynamics features, we discovered that the duration (Fractional Occupancy) and frequency of state switches (Transition Probability) were heritable, particularly in theta, alpha, beta, and gamma bands. However, no genetic influence was observed on spatial features.

As part of the Genes, Environment and Development Initiative, the Minnesota Center for Twin and Family Research (MCTFR) undertook a genome-wide association study, which we describe here. A total of 8,405 research participants, clustered in four-member families, have been successfully genotyped on 527,829 single nucleotide polymorphism (SNP) markers using Illumina's Human660W-Quad array. Quality control screening of samples and markers as well as SNP imputation procedures are described. We also describe methods for ancestry control and how the familial clustering of the MCTFR sample can be accounted for in the analysis using a Rapid Feasible Generalized Least Squares algorithm. The rich longitudinal MCTFR assessments provide numerous opportunities for collaboration.