Autism spectrum disorder (ASD) is diagnosed three to four times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. ...However, sex differences in common inherited genetic risk for ASD are less studied, particularly within families. Leveraging the Danish iPSYCH resource, we found siblings of female ASD cases (n = 1,707) had higher rates of ASD than siblings of male ASD cases (n = 6,270; p < 1.0 × 10−10). In the Simons Simplex and SPARK collections, mothers of ASD cases (n = 7,436) carried more polygenic risk for ASD than fathers of ASD cases (n = 5,926; 0.08 polygenic risk score PRS SD; p = 7.0 × 10−7). Further, male unaffected siblings under-inherited polygenic risk (n = 1,519; p = 0.03). Using both epidemiologic and genetic approaches, our findings strongly support an FPE against ASD’s common inherited influences.
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•Evidence of female protective effect against ASD from common, inherited variation•Evidence of FPE in both affected and unaffected members of ASD-impacted families•Mothers of children with ASD carry more genetic risk for ASDs than fathers
Wigdor et al. find evidence supporting a female protective effect against autism spectrum disorder (ASD): (1) siblings of female ASD probands are more likely to be diagnosed with ASD than siblings of male ASD probands and (2) mothers carry more common, inherited genetic risk for ASD than fathers. Taken together, these results emphasize the breadth of the role of sex in ASD risk and could impact the design and interpretation of genetic and neurobiological studies of ASD.
Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide
. GDM is related to an increased lifetime risk of type 2 diabetes ...(T2D)
, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition
, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only
, and the three prior genome-wide association studies of GDM
have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.
Response to survey questionnaires is vital for social and behavioural research, and most analyses assume full and accurate response by participants. However, nonresponse is common and impedes proper ...interpretation and generalizability of results. We examined item nonresponse behaviour across 109 questionnaire items in the UK Biobank (N = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, 'Prefer not to answer' (PNA) and 'I don't know' (IDK), each predicted participant nonresponse in follow-up surveys (incremental pseudo-R
= 0.056), even when controlling for education and self-reported health (incremental pseudo-R
= 0.046). After performing genome-wide association studies of our factors, PNA and IDK were highly genetically correlated with one another (r
= 0.73 (s.e. = 0.03)) and with education (r
= -0.51 (s.e. = 0.03); r
= -0.38 (s.e. = 0.02)), health (r
= 0.51 (s.e. = 0.03); r
= 0.49 (s.e. = 0.02)) and income (r
= -0.57 (s.e. = 0.04); r
= -0.46 (s.e. = 0.02)), with additional unique genetic associations observed for both PNA and IDK (P < 5 × 10
). We discuss how these associations may bias studies of traits correlated with item nonresponse and demonstrate how this bias may substantially affect genome-wide association studies. While the UK Biobank data are deidentified, we further protected participant privacy by avoiding exploring non-response behaviour to single questions, assuring that no information can be used to associate results with any particular respondents.
Preschool internalizing problems (INT) are highly heritable and moderately genetically stable from childhood into adulthood. Gene-finding studies are scarce. In this study, the influence of ...genome-wide measured single nucleotide polymorphisms (SNPs) was investigated in 3 cohorts (total N = 4,596 children) in which INT was assessed with the same instrument, the Child Behavior Checklist (CBCL).
First, genome-wide association (GWA) results were used for density estimation and genome-wide complex trait analysis (GCTA) to calculate the variance explained by all SNPs. Next, a fixed-effect inverse variance meta-analysis of the 3 GWA analyses was carried out. Finally, the overlap in results with prior GWA studies of childhood and adulthood psychiatric disorders and treatment responses was tested by examining whether SNPs associated with these traits jointly showed a significant signal for INT.
Genome-wide SNPs explained 13% to 43% of the total variance. This indicates that the genetic architecture of INT mirrors the polygenic model underlying adult psychiatric traits. The meta-analysis did not yield a genome-wide significant signal but was suggestive for the PCSK2 gene located on chromosome 20p12.1. SNPs associated with other psychiatric disorders appeared to be enriched for signals with INT (λ = 1.26, p < .03).
Our study provides evidence that INT is influenced by many common genetic variants, each with a very small effect, and that, even as early as age 3, genetic variants influencing INT overlap with variants that play a role in childhood and adulthood psychiatric disorders.
Abstract
Summary
Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an ...open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.
Availability and implementation
RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home
Supplementary information
Supplementary data are available at Bioinformatics online.
Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that ...models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.
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