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Biomedical subjects

Harold Snieder

Publications and source records attributed to Harold Snieder.

6 recordsLinked to original sources

Robust inference and correlates from genetic associations with personality.

Personality traits describe stable differences in how people think, feel and behave, and how they interact with and experience their social and physical environments1,2. Many questions remain unanswered about associations between DNA and personality traits, such as their robustness, their generalizability and the biological and social pathways through which they act. Here we meta-analyse data across 46 cohorts comprising 611,037 to 1.14 million participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism and openness to experience), and data from up to 50,725 participants for within-family GWAS. We identify 1,260 lead genetic variants associated with personality, including 824 novel variants3. Common genetic variants explain a moderate 4.8-9.3% of the variance in measures of each trait, and 9.3-13.3% among instruments with typical measurement reliability. Genetic associations with personality are highly consistent but not identical across geography, reporter (self versus close other), age group and measurement instrument, and we find minimal spousal assortment for personality in recent history. In contrast to many other social and behavioural traits4,5, within-family GWAS and polygenic index analyses indicate that genetic associations with personality are minimally confounded by the shared family environment. Polygenic prediction, genetic correlation and Mendelian randomization analyses indicate that personality traits have widespread, potentially causal associations with consequential behaviours and life outcomes. Overall, we find that the genetic architecture of personality is robustly generalizable, minimally confounded and widely relevant to human experience.

Journal Article

Genetic determinants of childhood blood pressure and heart rate in relation to adult health outcomes: the consortium of childhood blood pressure.

BACKGROUND AND AIMS: To elucidate the genetic architecture of blood pressure (BP) and heart rate (HR) during early life and assess their potential relevance to adult health outcomes. METHODS: The largest genome-wide association study (GWAS) meta-analyses to date of childhood systolic BP, diastolic BP, pulse pressure, and mean arterial pressure (n = 28 425) and HR (n = 22 565) were conducted in children of European ancestry aged 4-17 years. Follow-up analyses included comparisons with adult GWAS results, polygenic risk score (PRS) analyses in independent cohorts of diverse ancestries, and a phenome-wide association study in the UK Biobank. RESULTS: Eight genome-wide significant loci were identified for childhood BP (KIAA2013, CACNB2, PLCE1, PAX2, COL4A2, RP11-236L14.1, CFDP1, TPX2) and three loci for childhood HR (CCDC141, ACHE, MYH6); all novel in children but previously reported in adults. Childhood PRSs explained up to 1.6% of BP variance and 5.2% of HR variance among children of European ancestry. Genetic correlations between childhood and adulthood BP traits were moderate (rg = 0.4-0.7), suggesting age-specific genetic effects on BP. In the UK Biobank, higher childhood BP PRS levels were significantly associated with a broad range of adult health outcomes, particularly cardiometabolic outcomes such as hypertension, angina, myocardial infarction, and cardiovascular disease-related mortality. CONCLUSIONS: These findings advance the understanding of the genetic architecture of childhood BP and HR and provide compelling genetic evidence linking childhood BP to a broad spectrum of adult health outcomes-particularly cardiometabolic conditions-which may inform targeted prevention strategies from a young age.

Humans

SNPannotator: automated functional annotation of genetic variants and linked proxies.

SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Software

Psychiatric and neurological predictors of early ADHD medication discontinuation across the lifespan: a multinational study.

BACKGROUND: Early discontinuation of attention-deficit/hyperactivity disorder (ADHD) medication is common and linked to worse outcomes. Identifying clinical predictors could aid personalised treatment yet evidence is inconsistent across ages and countries/regions. OBJECTIVE: Investigate psychiatric and neurological comorbidity as predictors of early ADHD medication discontinuation in new ADHD medication users across age groups, sex and countries/regions. METHODS: Using health records from eight countries/regions, we identified 1 000 411 (44% female) new ADHD medication users (2011-2020). Discontinuation was defined as a ≥180 day gap between dispensations. We examined 23 indicators of psychiatric or neurological comorbidity, severity and psychotropic medication use. Associations were estimated using Cox regression, pooled with random-effects meta-analyses and stratified by age-at-initiation and sex. FINDINGS: Discontinuation rates varied widely (children 19%-61%, adolescents 37%-68%, young adults 52-67%, adults 38%-68%). In pooled analyses, earlier discontinuation in children was predicted by intellectual disability, autism and use of psychotropic medications (HR range 1.32-1.51), while conduct/oppositional defiant disorder (CD/ODD) was protective (HR 0.83, 95% CI 0.73 to 0.94). In adolescents, no indicators remained statistically significant after multiple-testing control. In young adults, CD/ODD (HR 1.42, 95% CI 1.30 to 1.55), and in adults, schizophrenia (HR 1.25, 95% CI 1.09 to 1.44) and tic disorders (HR 1.27, 95% CI 1.11 to 1.46) predicted earlier discontinuation. Statistical heterogeneity was substantial, largely driven by US estimates. In meta-analyses excluding the USA, additional associations emerged. For example, in children, OCD and anxiety disorders predicted earlier discontinuation, while eating disorders and antidepressants/anxiolytics were protective in adults. Associations with schizophrenia, tic disorders and CD/ODD were no longer significant. Country-specific analyses showed similar association patterns, except in the USA, Hong Kong and the UK. Sex differences were limited. CONCLUSIONS: Children with neuropsychiatric comorbidity and related comedication are more likely to discontinue ADHD medication early, whereas few consistent predictors were seen from adolescence onwards. Marked cross-country variation, particularly in the USA, points to system-level influences on treatment patterns. CLINICAL IMPLICATIONS: Improving ADHD medication persistence will require consideration of healthcare context and age-specific strategies, including close monitoring for children with complex neuropsychiatric profiles, and consideration of broader factors in adolescents and adults, where clinical predictors were limited.

Humans

Drug targets for lipid modification and risk of type 2 diabetes: a cis-Mendelian randomization study.

BACKGROUND AND AIMS: Reducing plasma levels of low-density lipoprotein cholesterol (LDL-C) is the cornerstone in the prevention of coronary artery disease (CAD) but may also increase risk of type 2 diabetes (T2D). A comprehensive examination of the genetic evidence of T2D related side-effects of all current lipid-modifying drugs, including those in development, has not yet been performed. METHODS: This cis-Mendelian randomization study used individual level data from the UK Biobank, Lifelines, and publicly available genome-wide association data. We identified loci that are either targeted directly with drugs, or alternatively, targeting their gene products (mRNA and/or protein). Included are, in alphabetical order, the loci ACLY, ANGPTL3, ANGPTL4, APOB, APOC3, CETP, HMGCR, LDLR, LIPG, LPA, MTTP, NPC1L1, and PCSK9. We used cis-genetic instruments weighted for LDL-C, HDL-C, triglycerides, and apolipoproteins as downstream proxies for the drug targets. Main outcomes were prevalent and incident T2D, with CAD as a contrast outcome. RESULTS: Lipid modification through HMGCR is predicted to reduce CAD risk and increase T2D risk. Modification through targeting APOC3, LDLR, LPA, MTTP, NPC1L1, and PCSK9 is predicted to reduce CAD risk without a change in T2D risk. Modification through ANGPTL4 and CETP is predicted to reduce risk of both CAD and T2D. For ACLY, ANGPTL3, APOB, and LIPG, we found evidence for neither CAD nor T2D. CONCLUSIONS: This study provides genetic evidence for variation in diabetes-related side-effects of different lipid-modifying drugs, with potential relevance for future clinical trials and individual treatment decisions.

Humans

Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.

BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87&#xa0;% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20&#xa0;% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels. RESULTS: The one-degree of freedom variant-sleep interaction test identified 10 novel loci (Pint<5.0e-9), and we additionally identify 7 loci within the two-degree of freedom analyses (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature. CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

Humans