Biology subjects

Rajagopal, V. M.

Publications and source records attributed to Rajagopal, V. M..

8 recordsLinked to original sources

bioRxiv2020

Association of educational attainment polygenic score with school grades in individuals with and without psychiatric disorders.

Suboptimal school performance is often seen in children with psychiatric disorders and is influenced by both genetics and the environment. Educational attainment polygenic score (EA-PGS) has been shown to significantly predict school performance in the general population. Here we analyze the association of EA-PGS with school performance in 18,495 children with and 12,487, without one or more of six psychiatric disorders and show that variance explained in the school performance by the EA-PGS is substantially lower in children with attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Accounting for parents socioeconomic status obliterated the variance difference between ADHD-but not ASD-and controls. Given that a large proportion of the prediction performance of EA-PGS originate from family environment, our findings hint that family environmental influences on school performance might differ between ADHD and controls; studying the same further will open new avenues to improve the school performance of children with ADHD.

genetics↗

bioRxiv2020

Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity

Individuals with psychiatric disorders perform differently in school compared to the general population. Genetic factors contribute substantially to such differences. It is however unclear if differential performance is seen across all cognitive domains such as math and language. Here we report a genome-wide association study (GWAS) of school grades in 30,982 individuals (18,495 with and 12,487 without one or more of six major psychiatric disorders) and a replication study in 4,547 individuals. GWAS of overall school performance yielded results that were highly similar to the results of a previous GWAS of educational attainment. Analyzing subject specific grades, we observed that math performance was severely affected whereas language performance (Danish and English) was relatively unaffected or enhanced in those with psychiatric disorders compared to controls. We found that the genetic variants associated with poor math performance, but better language performance were also associated with increased risk for multiple psychiatric disorders. The same variants were also associated with creativity, which we show through a polygenic score analysis of 2953 creative professionals and 164,622 controls. The results overall suggest that risk for psychiatric disorders, language ability and creativity might have overlapping genetic roots.

genomics↗

bioRxiv2020

Genetic analyses identify widespread sex-differential participation bias

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of this participation bias is challenging as it requires the genotypes of unseen individuals. However, we demonstrate that it is possible to estimate comparative biases by performing GWAS contrasting one subgroup versus another. For example, we show that sex exhibits autosomal heritability in the presence of sex-differential participation bias. By performing a GWAS of sex in ~3.3 million males and females, we identify over 158 autosomal loci significantly associated with sex and highlight complex traits underpinning differences in study participation between sexes. For example, the body mass index (BMI) increasing allele at the FTO locus was observed at higher frequency in males compared to females (OR 1.02 [1.02-1.03], P=4.4x10-36). Finally, we demonstrate how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

genetics↗

bioRxiv2019

Identification of risk variants and characterization of the polygenic architecture of disruptive behavior disorders in the context of ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood psychiatric disorder often comorbid with disruptive behavior disorders (DBDs). ADHD comorbid with DBDs (ADHD+DBDs) is a complex phenotype with a risk component that can be attributed to common genetic variants. Here we report a large GWAS meta-analysis of ADHD+DBDs based on seven cohorts in total including 3,802 cases and 31,305 controls. Three genome-wide significant loci were identified on chromosomes 1, 7, and 11. A GWAS meta-analysis including a Chinese cohort supported the locus on chromosome 11 to be a strong risk locus for ADHD+DBDs across European and Chinese ancestries (rs7118422, P=3.15x10-10, OR=1.17). This locus was not associated with ADHD without DBDs in a secondary GWAS of 13,583 ADHD cases and 22,314 controls, suggesting that the locus is a specific risk locus for the comorbid phenotype.\n\nWe found a higher SNP heritability for ADHD+DBDs (h2SNP =0.34) when compared to ADHD without DBDs (h2SNP =0.20). Genetic correlations of ADHD+DBDs with aggressive (rg =0.81) and anti-social behaviors (rg=0.82) were high, and polygenic risk score analyses revealed a significant increased burden of variants associated with ADHD and aggression in individuals with ADHD+DBDs compared to ADHD without DBDs. Our results suggests that ADHD+DBDs represent a more severe phenotype with respect to the genetic risk load than ADHD without DBDs, in line with previous studies, and that the risk load to some extent can be explained by variants associated with aggressive behavior.

genomics↗

bioRxiv2019

Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we improve the accuracy of transcriptome prediction and the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge to biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify known and novel compounds that mimic or reverse trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

genetics↗

bioRxiv2018

Exploring Genetic Variation That Influences Brain Methylation In Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder caused by an interplay of genetic and environmental factors. Epigenetics is crucial to lasting changes in gene expression in the brain. Recent studies suggest a role for DNA methylation in ADHD. We explored the contribution to ADHD of allele-specific methylation (ASM), an epigenetic mechanism that involves SNPs correlating with differential levels of DNA methylation at CpG sites. We selected 3,896 tagSNPs reported to influence methylation in human brain regions and performed a case-control association study using the summary statistics from the largest GWAS meta-analysis of ADHD, comprising 20,183 cases and 35,191 controls. We identified associations with eight tagSNPs that were significant at a 5% False Discovery Rate (FDR). These SNPs correlated with methylation of CpG sites lying in the promoter regions of six genes. Since methylation may affect gene expression, we inspected these ASM SNPs together with 52 ASM SNPs in high LD with them for eQTLs in brain tissues and observed that the expression of three of those genes was affected by them. ADHD risk alleles correlated with increased expression (and decreased methylation) of ARTN and PIDD1 and with a decreased expression (and increased methylation) of C2orf82. Furthermore, these three genes were predicted to have altered expression in ADHD, and genetic variants in C2orf82 correlated with brain volumes. In summary, we followed a systematic approach to identify risk variants for ADHD that correlated with differential cis-methylation, identifying three novel genes contributing to the disorder.

genetics↗

bioRxiv2017

Genome-wide association study implicates CHRNA2 in cannabis use disorder

Introductory paragraphCannabis is the most frequently used illicit psychoactive substance worldwide1. Life time use has been reported among 35-40% of adults in Denmark2 and the United States3. Cannabis use is increasing in the population4-6 and among users around 9% become dependent7. The genetic risk component is high with heritability estimates of 518-70%9. Here we report the first genome-wide significant risk locus for cannabis use disorder (CUD, P=9.31x10-12) that replicates in an independent population (Preplication=3.27x10-3, Pmetaanalysis=9.09x10-12). The finding is based on a genome-wide association study (GWAS) of 2,387 cases and 48,985 controls followed by replication in 5,501 cases and 301,041 controls. The index SNP (rs56372821) is a strong eQTL for CHRNA2 and analyses of the genetic regulated gene expressions identified significant association of CHRNA2 expression in cerebellum with CUD. This indicates a potential therapeutic use in CUD of compounds with agonistic effect on the neuronal acetylcholine receptor alpha-2 subunit encoded by CHRNA2. At the polygenic level analyses revealed a significant decrease in the risk of CUD with increased load of variants associated with cognitive performance.

genomics↗

bioRxiv2017

Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.

Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

genetics↗