scholarly journals Genetic architecture of human plasma lipidome and its link to cardiovascular disease

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Rubina Tabassum ◽  
◽  
Joel T. Rämö ◽  
Pietari Ripatti ◽  
Jukka T. Koskela ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Genetic Regulation ◽  
Genetic Correlations ◽  
Cvd Risk ◽  
Association Analyses ◽  
False Discovery ◽  
Medium Length ◽  
Genome Wide ◽  
Lipid Species ◽  
Polyunsaturated Lipids

Abstract Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10−8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.

scholarly journals The Genetic Architecture of Amygdala Nuclei

2021 ◽  
Author(s):  
Mary S Mufford ◽  
Dennis van der Meer ◽  
Tobias Kaufmann ◽  
Oleksandr Frei ◽  
Raj Ramesar ◽  
...  
Keyword(s):  
Statistical Power ◽  
Genetic Architecture ◽  
Brain Mri ◽  
Genetic Correlations ◽  
Complex Trait ◽  
Central Nucleus ◽  
Association Analyses ◽  
Genome Wide ◽  
Amygdala Volume ◽  
Shared Genetic

Background: Whereas a number of genetic variants influencing total amygdala volume have been identified in previous research, genetic architecture of its distinct nuclei have yet to be thoroughly explored. We aimed to investigate whether increased phenotypic specificity through segmentation of the nuclei aids genetic discoverability and sheds light on the extent of shared genetic architecture and biological pathways between the nuclei and disorders associated with the amygdala. Methods: T1-weighted brain MRI scans (n=36,352, mean age= 64.26 years, 52% female) of trans-ancestry individuals from the UK Biobank were segmented into nine amygdala nuclei with FreeSurfer v6.1, and genome-wide association analyses were performed on the full sample and a European-only subset (n=31,690). We estimated heritability using Genome-wide Complex Trait Analysis, derived estimates of polygenicity, discoverability and power using MiXer, and identified genetic correlations and shared loci with psychiatric disorders using Linkage Disequilibrium Score Regression and conjunctional FDR, followed by functional annotation using FUMA. Results: The SNP-based heritability of the nuclei ranged between 0.17-0.33, and the central nucleus had the greatest statistical power for discovery. Across the whole amygdala and the nuclei volumes, 38 novel significant (p < 5x10-9) loci were identified, with most loci mapped to the central nucleus. The mapped genes and associated pathways revealed both unique and shared effects across the nuclei, and immune-related pathways were particularly enriched across several nuclei. Conclusions: These findings indicate that the amygdala nuclei volumes have significant genetic heritability, increased power for discovery compared to whole amygdala volume, may have unique and shared genetic architectures, and a significant immune component to their aetiology.

scholarly journals Genetic heterogeneity in self-reported depressive symptoms identified through genetic analyses of the PHQ-9

2019 ◽  
Vol 50 (14) ◽  
pp. 2385-2396 ◽  
Author(s):  
Jackson G. Thorp ◽  
Andries T. Marees ◽  
Jue-Sheng Ong ◽  
Jiyuan An ◽  
Stuart MacGregor ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Genetic Heterogeneity ◽  
Structural Equation ◽  
Large Scale ◽  
Clinical Symptoms ◽  
Factor Model ◽  
Genetic Correlations ◽  
Genetic Covariance ◽  
Association Analyses ◽  
Genome Wide

AbstractBackgroundDepression is a clinically heterogeneous disorder. Previous large-scale genetic studies of depression have explored genetic risk factors of depression case–control status or aggregated sums of depressive symptoms, ignoring possible clinical or genetic heterogeneity.MethodsWe analyse data from 148 752 subjects of white British ancestry in the UK Biobank who completed nine items of a self-rated measure of current depressive symptoms: the Patient Health Questionnaire (PHQ-9). Genome-Wide Association analyses were conducted for nine symptoms and two composite measures. LD Score Regression was used to calculate SNP-based heritability (h2SNP) and genetic correlations (rg) across symptoms and to investigate genetic correlations with 25 external phenotypes. Genomic structural equation modelling was used to test the genetic factor structure across the nine symptoms.ResultsWe identified nine genome-wide significant genomic loci (8 novel), with no overlap in loci across symptoms. h2SNP ranged from 6% (concentration problems) to 9% (appetite changes). Genetic correlations ranged from 0.54 to 0.96 (all p < 1.39 × 10−3) with 30 of 36 correlations being significantly smaller than one. A two-factor model provided the best fit to the genetic covariance matrix, with factors representing ‘psychological’ and ‘somatic’ symptoms. The genetic correlations with external phenotypes showed large variation across the nine symptoms.ConclusionsPatterns of SNP associations and genetic correlations differ across the nine symptoms, suggesting that current depressive symptoms are genetically heterogeneous. Our study highlights the value of symptom-level analyses in understanding the genetic architecture of a psychiatric trait. Future studies should investigate whether genetic heterogeneity is recapitulated in clinical symptoms of major depression.

scholarly journals Unraveling the genetic architecture for carbon and nitrogen related traits and leaf hydraulic conductance in soybean using genome-wide association analyses

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Clinton J. Steketee ◽  
Thomas R. Sinclair ◽  
Mandeep K. Riar ◽  
William T. Schapaugh ◽  
Zenglu Li
Keyword(s):  
Drought Stress ◽  
Genetic Architecture ◽  
Isotope Composition ◽  
Nitrogen Concentration ◽  
Hydraulic Conductance ◽  
Genome Wide Association ◽  
Association Analyses ◽  
Carbon And Nitrogen ◽  
Leaf Hydraulic Conductance ◽  
Genome Wide

Abstract Background Drought stress is a major limiting factor of soybean [Glycine max (L.) Merr.] production around the world. Soybean plants can ameliorate this stress with improved water-saving, sustained N2 fixation during water deficits, and/or limited leaf hydraulic conductance. In this study, carbon isotope composition (δ13C), which can relate to variation in water-saving capability, was measured. Additionally, nitrogen isotope composition (δ15N) and nitrogen concentration that relate to nitrogen fixation were evaluated. Decrease in transpiration rate (DTR) of de-rooted soybean shoots in a silver nitrate (AgNO3) solution compared to deionized water under high vapor pressure deficit (VPD) conditions was used as a surrogate measurement for limited leaf hydraulic conductance. A panel of over 200 genetically diverse soybean accessions genotyped with the SoySNP50K iSelect BeadChips was evaluated for the carbon and nitrogen related traits in two field environments (Athens, GA in 2015 and 2016) and for transpiration response to AgNO3 in a growth chamber. A multiple loci linear mixed model was implemented in FarmCPU to perform genome-wide association analyses for these traits. Results Thirty two, 23, 26, and nine loci for δ13C, δ15N, nitrogen concentration, and transpiration response to AgNO3, respectively, were significantly associated with these traits. Candidate genes that relate to drought stress tolerance enhancement or response were identified near certain loci that could be targets for improving and understanding these traits. Soybean accessions with favorable breeding values were also identified. Low correlations were observed between many of the traits and the genetic loci associated with each trait were largely unique, indicating that these drought tolerance related traits are governed by different genetic loci. Conclusions The genomic regions and germplasm identified in this study can be used by breeders to understand the genetic architecture for these traits and to improve soybean drought tolerance. Phenotyping resources needed, trait heritability, and relationship to the target environment should be considered before deciding which of these traits to ultimately employ in a specific breeding program. Potential marker-assisted selection efforts could focus on loci which explain the greatest amount of phenotypic variation for each trait, but may be challenging due to the quantitative nature of these traits.

scholarly journals Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Kathryn S. Burch ◽  
Ruth Johnson ◽  
Malika K. Freund ◽  
Gleb Kichaev ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Empirical Bayes ◽  
Genetic Correlations ◽  
Causal Snps ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Snps ◽  
Genome Wide ◽  
Summary Data

AbstractDespite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

scholarly journals Examining sex-differentiated genetic effects across neuropsychiatric and behavioral traits

2020 ◽  
Author(s):  
Joanna Martin ◽  
Ekaterina A. Khramtsova ◽  
Slavina B. Goleva ◽  
Gabriëlla A M. Blokland ◽  
Michela Traglia ◽  
...  
Keyword(s):  
Sex Differences ◽  
Risk Taking ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Related Gene ◽  
Behavioral Traits ◽  
Gene Sets ◽  
Genome Wide ◽  
Z Scores ◽  
Differentiated Effects

AbstractBackgroundThe origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations across these traits, we tested for a sex-differentiated genetic architecture within and between traits.MethodsUsing genome-wide association study (GWAS) summary statistics for 20 neuropsychiatric and behavioral traits, we tested for differences in SNP-based heritability (h2) and genetic correlation (rg<1) between sexes. For each trait, we computed z-scores from sex-stratified GWAS regression coefficients and identified genes with sex-differentiated effects. We calculated Pearson correlation coefficients between z-scores for each trait pair, to assess whether specific pairs share variants with sex-differentiated effects. Finally, we tested for sex differences in between-trait genetic correlations.ResultsWith current sample sizes (and power), we found no significant, consistent sex differences in SNP-based h2. Between-sex, within-trait genetic correlations were consistently high, although significantly less than 1 for educational attainment and risk-taking behavior. We identified genome-wide significant genes with sex-differentiated effects for eight traits. Several trait pairs shared sex-differentiated effects. The top 0.1% of genes with sex-differentiated effects across traits overlapped with neuron- and synapse-related gene sets. Most between-trait genetic correlation estimates were similar across sex, with several exceptions (e.g. educational attainment & risk-taking behavior).ConclusionsSex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic, requiring large sample sizes. Genes with sex-differentiated effects are enriched for neuron-related gene sets. This work motivates further investigation of genetic, as well as environmental, influences on sex differences.

scholarly journals A polygenic p factor for major psychiatric disorders

2018 ◽  
Author(s):  
Saskia Selzam ◽  
Jonathan R. I. Coleman ◽  
Avshalom Caspi ◽  
Terrie E. Moffitt ◽  
Robert Plomin
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Genetic Research ◽  
Strong Support ◽  
Principal Component ◽  
Complex Trait ◽  
Genome Wide ◽  
P Factor ◽  
First Time

AbstractIt has recently been proposed that a single dimension, called the p factor, can capture a person’s liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods – family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression – and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

scholarly journals Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Stroke ◽  
2020 ◽  
Vol 51 (7) ◽  
pp. 2111-2121 ◽  
Author(s):  
Nicola J. Armstrong ◽  
Karen A. Mather ◽  
Muralidharan Sargurupremraj ◽  
Maria J. Knol ◽  
Rainer Malik ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
White Matter Hyperintensities ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Imaging Genetics ◽  
Genome Wide Association ◽  
Association Analyses ◽  
New Genes ◽  
Genome Wide

Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

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