scholarly journals A novel method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data

2021 ◽  
Author(s):  
Md. Moksedul Momin ◽  
Jisu Shin ◽  
Soohyun Lee ◽  
Buu Truong ◽  
Beben Benyamin ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Trait ◽  
European Ancestry ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Individual Level ◽  
Level Data ◽  
Specific Allele ◽  
Novel Method ◽  
The Uk

AbstractCross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups for a complex trait. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific causal effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to six anthropometric traits from the UK Biobank data across 5 ancestry groups. One of our findings is that for obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestry groups.

scholarly journals Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ronald de Vlaming ◽  
Eric A. W. Slob ◽  
Philip R. Jansen ◽  
Alain Dagher ◽  
Philipp D. Koellinger ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Gray Matter Volume ◽  
Genetic Correlations ◽  
Brain Morphology ◽  
Computationally Efficient ◽  
Individual Level ◽  
Level Data ◽  
The Uk ◽  
And Behavior ◽  
The Brain

AbstractHuman variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.

Phenotypic and Genotypic Associations Between Migraine and Lipoprotein Subtractions

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012919
Author(s):  
Yanjun Guo ◽  
Iyas Daghlas ◽  
Padhraig Gormley ◽  
Franco Giulianini ◽  
Paul M Ridker ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
European Ancestry ◽  
Health Study ◽  
Lipoprotein Subfractions ◽  
Genetic Components ◽  
The Uk ◽  
Summary Data

Background and Objective:To evaluate phenotypic and genetic relationships between migraine and lipoprotein subfractions.Methods:We evaluated phenotypic associations between migraine and 19 lipoprotein subfractions measures in the Women’s Genome Health Study (WGHS, N=22,788). We then investigated genetic relationships between these traits using summary statistics from the International Headache Genetics Consortium (IHGC) for migraine (Ncase=54,552, Ncontrol=297,970) and combined summary data for lipoprotein subfractions (N up to 47,713).Results:There was a significant phenotypic association (odds ratio=1.27 [95% confidence interval:1.12-1.44]) and a significant genetic correlation at 0.18 (P=0.001) between migraine and triglyceride-rich lipoproteins (TRLP) concentration but not for LDL or HDL subfractions. Mendelian randomization (MR) estimates were largely null implying that pleiotropy rather than causality underlies the genetic correlation between migraine and lipoprotein subfractions. Pleiotropy was further supported in cross-trait meta-analysis revealing significant shared signals at four loci (chr2p21 harboring THADA, chr5q13.3 harboring HMGCR, chr6q22.31 harboring HEY2, and chr7q11.23 harboring MLXIPL) between migraine and lipoprotein subfractions. Three of these loci were replicated for migraine (P<0.05) in a smaller sample from the UK Biobank. The shared signal at chr5q13.3 colocalized with expression of HMGCR, ANKDD1B, and COL4A3BP in multiple tissues.Conclusions:The current study supports the association between certain lipoprotein subfractions, especially for TRLP, and migraine in populations of European ancestry. The corresponding shared genetic components may be help identify potential targets for future migraine therapeutics.Classification of Evidence:This study provides Class I evidence that migraine is significantly associated with some lipoprotein subfractions.

scholarly journals Estimating genetic correlation jointly using individual-level and summary-level GWAS data

2021 ◽  
Author(s):  
Yiliang Zhang ◽  
Youshu Cheng ◽  
Yixuan Ye ◽  
Wei Jiang ◽  
Qiongshi Lu ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Real Data ◽  
Efficient Estimation ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Correlation Estimation ◽  
Level Data ◽  
Summary Data

AbstractWith the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.

Shared genetic influences on depression and menopause symptoms

2021 ◽  
pp. 1-11
Author(s):  
Joeri J. Meijsen ◽  
Hanyang Shen ◽  
Mytilee Vemuri ◽  
Natalie L. Rasgon ◽  
Karestan C. Koenen ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Hot Flashes ◽  
Medication Use ◽  
European Ancestry ◽  
Genetic Influences ◽  
Uk Biobank ◽  
Genetic Analyses ◽  
Correlation Analyses ◽  
The Uk ◽  
Shared Genetic

Abstract Background Women experience major depression and post-traumatic stress disorder (PTSD) approximately twice as often as men. Estrogen is thought to contribute to sex differences in these disorders, and reduced estrogen is also known to be a key driver of menopause symptoms such as hot flashes. Moreover, estrogen is used to treat menopause symptoms. In order to test for potential shared genetic influences between menopause symptoms and psychiatric disorders, we conducted a genome-wide association study (GWAS) of estrogen medication use (as a proxy for menopause symptoms) in the UK Biobank. Methods The analysis included 232 993 women aged 39–71 in the UK Biobank. The outcome variable for genetic analyses was estrogen medication use, excluding women using hormonal contraceptives. Trans-ancestry GWAS meta-analyses were conducted along with genetic correlation analyses on the European ancestry GWAS results. Hormone usage was also tested for association with depression and PTSD. Results GWAS of estrogen medication use (compared to non-use) identified a locus in the TACR3 gene, which was previously linked to hot flashes in menopause [top rs77322567, odds ratio (OR) = 0.78, p = 7.7 × 10−15]. Genetic correlation analyses revealed shared genetic influences on menopause symptoms and depression (rg = 0.231, s.e.= 0.055, p = 2.8 × 10−5). Non-genetic analyses revealed higher psychiatric symptoms scores among women using estrogen medications. Conclusions These results suggest that menopause symptoms have a complex genetic etiology which is partially shared with genetic influences on depression. Moreover, the TACR3 gene identified here has direct clinical relevance; antagonists for the neurokinin 3 receptor (coded for by TACR3) are effective treatments for hot flashes.

scholarly journals 335 Genomic predictions with a multi-breed genomic relationship matrix

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 49-50
Author(s):  
Yvette Steyn ◽  
Daniela Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Prediction Accuracy ◽  
Negative Impact ◽  
Reference Population ◽  
Single Step ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Effective Population ◽  
Specific Allele ◽  
Missing Genotypes

Abstract Multi-breed evaluations have the advantage of increasing the size of the reference population for genomic evaluations and are quite simple; however, combining breeds usually have a negative impact on prediction accuracy. The aim of this study was to evaluate the use of a multi-breed genomic relationship matrix (G), where SNP for each breed are non-shared. The multi-breed G is set assuming known genotypes for one breed and missing genotypes for the remaining breeds. This setup may avoid spurious IBS relationships between breeds and considers breed-specific allele frequencies. This scenario was contrasted to multi-breed evaluations where all SNP are shared, i.e., the same SNP, and to single-breed evaluations. Different SNP densities, namely 9k and 45k, and different effective population sizes (Ne) were tested. Five breeds mimicking recent beef cattle populations that diverged from the same historical population were simulated using different selection criteria. It was assumed that QTL effects were the same over all breeds. For the recent population, generations 1 to 9 had approximately half of the animals genotyped, whereas all 1200 animals were genotyped in generation 10. Genotyped animals in generation 10 were set as validation; therefore, each breed had a validation set. Analysis were performed using single-step GBLUP (ssGBLUP). Prediction accuracy was calculated as correlation between true (T) and genomic estimated (GE) BV. Accuracies of GEBV were lower for the larger Ne and low SNP density. All three scenarios using 45K resulted in similar accuracies, suggesting that the marker density is high enough to account for relationships and linkage disequilibrium with QTL. A shared multi-breed evaluation using 9K resulted in a decrease of accuracy of 0.08 for a smaller Ne and 0.11 for a larger Ne. This loss was mostly avoided when markers were treated as non-shared within the same genomic relationship matrix.

scholarly journals Improved polygenic prediction by Bayesian multiple regression on summary statistics

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Luke R. Lloyd-Jones ◽  
Jian Zeng ◽  
Julia Sidorenko ◽  
Loïc Yengo ◽  
Gerhard Moser ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Association Studies ◽  
Meta Analysis ◽  
Multiple Regression Model ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Individual Level ◽  
Level Data ◽  
The Uk

Abstract Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

Good Deal, Bad Deal? Job Satisfaction in Occupations

2003 ◽  
Vol 17 (3) ◽  
pp. 503-530 ◽  
Author(s):  
Michael Rose
Keyword(s):  
Job Satisfaction ◽  
Life Quality ◽  
Good Deal ◽  
Individual Level ◽  
Work Related ◽  
Level Data ◽  
Self Actualization ◽  
Related Stress ◽  
The Uk ◽  
Rational Evaluation

Sharply varying levels of job satisfaction in occupations in the UK are documented and explained primarily by reference to individual level data for a large sample of current employees collected in 1999-2000. An accompanying critique of the approach to job satisfaction in some applied and organizational psychology makes two points. First, the terms job and work need to be more carefully distinguished when examining satisfaction data, giving more attention to the terms of the employment contract, skill data, and the mobility implications of jobs, and relatively less weight to employee involvement, empowerment and self-actualization. Second, job satisfaction data supply evidence of the competent rational evaluation of utility on the part of employees, though individual affectivity undoubtedly conditions such assessments. The findings support a re-balancing in explanation between extrinsic and intrinsic sources of job satisfaction, while showing that work-related stress and excessive hours may in practice comprise a more urgent practical problem for management than socio-technical aspects of work-life quality.

Gender and the “Laws of Migration”

2012 ◽  
Vol 36 (2) ◽  
pp. 223-241
Author(s):  
J. Trent Alexander ◽  
Annemarie Steidl
Keyword(s):  
Late Nineteenth Century ◽  
Individual Level ◽  
The North ◽  
Level Data ◽  
Internal Migrants ◽  
Data Files ◽  
The Difference ◽  
Feminization Of Migration ◽  
The Uk

Ernest George Ravenstein’s influential “laws of migration” argued that short-distance and within-country moves were typically dominated by women. We use census microdata to take a fresh look at the relationship between gender and internal migration in late nineteenth-century Europe and North America. We argue that there was a significant flaw in Ravenstein’s key finding on gender and that this flaw has implications for more recent scholarship of the long-term “feminization of migration.” The apparent overrepresentation of women among internal migrants was due not to their higher propensity to move but to the much higher rate at which male migrants left the population, through either death or emigration. Men were just as likely to make internal moves as women were; the difference was that men did not remain in the population to be counted when the decennial census was conducted. Like Ravenstein’s “laws of migration,” this article relies primarily on data from the 1881 census of England and Wales. Whereas Ravenstein’s work was constrained by the contents of tables published by the UK Census Office in the 1880s, we are able to ask new questions by analyzing individual-level data files recently made available by the North Atlantic Population Project.

scholarly journals Interplay between the genetics of personality traits, severe psychiatric disorders and COVID-19 host genetics in the susceptibility to SARS-CoV-2 infection

BJPsych Open ◽  
2021 ◽  
Vol 7 (6) ◽  
Author(s):  
Urs Heilbronner ◽  
Fabian Streit ◽  
Thomas Vogl ◽  
Fanny Senner ◽  
Sabrina K. Schaupp ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Personality Traits ◽  
Psychiatric Disorders ◽  
Genetic Correlation ◽  
Genetic Association ◽  
Genetic Risk ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Level Data

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus. Aims We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection. Method Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data. Results We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies. Conclusions We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

scholarly journals Genomic predictions in purebreds with a multibreed genomic relationship matrix1

2019 ◽  
Vol 97 (11) ◽  
pp. 4418-4427 ◽  
Author(s):  
Yvette Steyn ◽  
Daniela A L Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Prediction Accuracy ◽  
Negative Impact ◽  
Single Step ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Limited Information ◽  
Genomic Relationship ◽  
Effective Population ◽  
Specific Allele ◽  
Trait Locus

Abstract Combining breeds in a multibreed evaluation can have a negative impact on prediction accuracy, especially if single nucleotide polymorphism (SNP) effects differ among breeds. The aim of this study was to evaluate the use of a multibreed genomic relationship matrix (G), where SNP effects are considered to be unique to each breed, that is, nonshared. This multibreed G was created by treating SNP of different breeds as if they were on nonoverlapping positions on the chromosome, although, in reality, they were not. This simple setup may avoid spurious Identity by state (IBS) relationships between breeds and automatically considers breed-specific allele frequencies. This scenario was contrasted to a regular multibreed evaluation where all SNPs were shared, that is, the same position, and to single-breed evaluations. Different SNP densities (9k and 45k) and different effective population sizes (Ne) were tested. Five breeds mimicking recent beef cattle populations that diverged from the same historical population were simulated using different selection criteria. It was assumed that quantitative trait locus (QTL) effects were the same over all breeds. For the recent population, generations 1–9 had approximately half of the animals genotyped, whereas all animals in generation 10 were genotyped. Generation 10 animals were set for validation; therefore, each breed had a validation group. Analyses were performed using single-step genomic best linear unbiased prediction. Prediction accuracy was calculated as the correlation between true (T) and genomic estimated breeding values (GEBV). Accuracies of GEBV were lower for the larger Ne and low SNP density. All three evaluation scenarios using 45k resulted in similar accuracies, suggesting that the marker density is high enough to account for relationships and linkage disequilibrium with QTL. A shared multibreed evaluation using 9k resulted in a decrease of accuracy of 0.08 for a smaller Ne and 0.12 for a larger Ne. This loss was mostly avoided when markers were treated as nonshared within the same G matrix. A G matrix with nonshared SNP enables multibreed evaluations without considerably changing accuracy, especially with limited information per breed.

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