scholarly journals Reconstructing the history of polygenic scores using coalescent trees

2018 ◽  
Author(s):  
Michael D. Edge ◽  
Graham Coop
Keyword(s):  
Natural Selection ◽  
Time Course ◽  
Association Studies ◽  
Present Theory ◽  
Frequency Change ◽  
Genome Wide Association Studies ◽  
Coalescent Theory ◽  
Polygenic Scores ◽  
History Of ◽  
Time Courses

1AbstractGenome-wide association studies (GWAS) have revealed that many traits are highly polygenic, in that their within-population variance is governed in part by small-effect variants at many genetic loci. Standard population-genetic methods for inferring evolutionary history are ill-suited for polygenic traits—when there are many variants of small effect, signatures of natural selection are spread across the genome and subtle at any one locus. In the last several years, several methods have emerged for detecting the action of natural selection on polygenic scores, sums of genotypes weighted by GWAS effect sizes. However, most existing methods do not reveal the timing or strength of selection. Here, we present a set of methods for estimating the historical time course of a population-mean polygenic score using local coalescent trees at GWAS loci. These time courses are estimated by using coalescent theory to relate the branch lengths of trees to allele-frequency change. The resulting time course can be tested for evidence of natural selection. We present theory and simulations supporting our procedures, as well as estimated time courses of polygenic scores for human height. Because of its grounding in coalescent theory, the framework presented here can be extended to a variety of demographic scenarios, and its usefulness will increase as both GWAS and ancestral recombination graph (ARG) inference continue to progress.

scholarly journals The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

scholarly journals A method for identifying haplotypes carrying the causative allele in positive natural selection and genome-wide association studies

2011 ◽  
Vol 27 (6) ◽  
pp. 822-828 ◽  
Author(s):  
R. T.-H. Ong ◽  
X. Liu ◽  
W.-T. Poh ◽  
X. Sim ◽  
K.-S. Chia ◽  
...  
Keyword(s):  
Natural Selection ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from Gwas Hits: A Replication of Previous Findings Using Recent Data

Psych ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Monte Carlo ◽  
Educational Attainment ◽  
Spatial Autocorrelation ◽  
Association Studies ◽  
Random Noise ◽  
Genome Wide Association Studies ◽  
Socioeconomic Variables ◽  
1000 Genomes ◽  
Polygenic Scores ◽  
Polygenic Selection

Genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment (EA) were used to test a polygenic selection model. Weighted and unweighted polygenic scores (PGS) were calculated and compared across populations using data from the 1000 Genomes (n = 26), HGDP-CEPH (n = 52) and gnomAD (n = 8) datasets. The PGS from the largest EA GWAS was highly correlated to two previously published PGSs (r = 0.96–0.97, N = 26). These factors are both highly predictive of average population IQ (r = 0.9, N = 23) and Learning index (r = 0.8, N = 22) and are robust to tests of spatial autocorrelation. Monte Carlo simulations yielded highly significant p values. In the gnomAD samples, the correlation between PGS and IQ was almost perfect (r = 0.98, N = 8), and ANOVA showed significant population differences in allele frequencies with positive effect. Socioeconomic variables slightly improved the prediction accuracy of the model (from 78–80% to 85–89%), but the PGS explained twice as much of the variance in IQ compared to socioeconomic variables. In both 1000 Genomes and gnomAD, there was a weak trend for lower GWAS significance SNPs to be less predictive of population IQ. Additionally, a subset of SNPs were found in the HGDP-CEPH sample (N = 127). The analysis of this sample yielded a positive correlation with latitude and a low negative correlation with distance from East Africa. This study provides robust results after accounting for spatial autocorrelation with Fst distances and random noise via an empirical Monte Carlo simulation using null SNPs.

scholarly journals Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

2019 ◽  
Vol 28 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Daniel W. Belsky ◽  
K. Paige Harden
Keyword(s):  
Educational Attainment ◽  
Association Studies ◽  
Science Research ◽  
Well Being ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Research Activities ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

scholarly journals Identification and characterization of SEC24D as a susceptibility gene for hepatitis B virus infection

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xianzhong Jiang ◽  
Bin Zhang ◽  
Junsheng Zhao ◽  
Yi Xu ◽  
Haijun Han ◽  
...  
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Time Course ◽  
Expression Profiles ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Hbv Infection ◽  
Genome Wide Association Studies ◽  
B Virus

Abstract Single nucleotide polymorphisms (SNPs) and genes associated with susceptibility to hepatitis B virus (HBV) infection that have been identified by genome-wide association studies explain only a limited portion of the known heritability, indicating more genetic variants remain to be discovered. In this study, we adopted a new research strategy to identify more susceptibility genes and variants for HBV infection. We first performed genetic association analysis of 300 sib-pairs and 3,087 case-control samples, which revealed that 36 SNPs located in 31 genes showed nominal associations with HBV infection in both samples. Of these genes, we selected SEC24D for further molecular analysis according to the following two main lines of evidence. First, a time course analysis of the expression profiles from HBV-infected primary human hepatocytes (PHH) demonstrated that SEC24D expression increased markedly as time passed after HBV infection (P = 4.0 × 10−4). Second, SNP rs76459466 in SEC24D was adversely associated with HBV risk (ORmeta = 0.82; Pmeta = 0.002), which again indicated that SEC24D represents a novel susceptibility gene for HBV infection. Moreover, SEC24D appeared to be protective against HBV infection in vitro. Consistently, we found that SEC24D expression was significantly enhanced in non-infected liver tissues (P = 0.002). We conclude that SEC24D is a novel candidate gene linked to susceptibility to HBV infection.

scholarly journals Quantitative Proteomics of Human Heart Samples Collected In Vivo Reveal the Remodeled Protein Landscape of Dilated Left Atrium Without Atrial Fibrillation

2020 ◽  
Vol 19 (7) ◽  
pp. 1132-1144
Author(s):  
Nora Linscheid ◽  
Pi Camilla Poulsen ◽  
Ida Dalgaard Pedersen ◽  
Emilie Gregers ◽  
Jesper Hastrup Svendsen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Mitral Valve ◽  
Protein Expression ◽  
High Resolution Mass Spectrometry ◽  
Association Studies ◽  
Genomic Research ◽  
Genome Wide Association Studies ◽  
History Of ◽  
The Impact

Genetic and genomic research has greatly advanced our understanding of heart disease. Yet, comprehensive, in-depth, quantitative maps of protein expression in hearts of living humans are still lacking. Using samples obtained during valve replacement surgery in patients with mitral valve prolapse (MVP), we set out to define inter-chamber differences, the intersect of proteomic data with genetic or genomic datasets, and the impact of left atrial dilation on the proteome of patients with no history of atrial fibrillation (AF).We collected biopsies from right atria (RA), left atria (LA) and left ventricle (LV) of seven male patients with mitral valve regurgitation with dilated LA but no history of AF. Biopsy samples were analyzed by high-resolution mass spectrometry (MS), where peptides were pre-fractionated by reverse phase high-pressure liquid chromatography prior to MS measurement on a Q-Exactive-HF Orbitrap instrument. We identified 7,314 proteins based on 130,728 peptides. Results were confirmed in an independent set of biopsies collected from three additional individuals. Comparative analysis against data from post-mortem samples showed enhanced quantitative power and confidence level in samples collected from living hearts. Our analysis, combined with data from genome wide association studies suggested candidate gene associations to MVP, identified higher abundance in ventricle for proteins associated with cardiomyopathies and revealed the dilated LA proteome, demonstrating differential representation of molecules previously associated with AF, in non-AF hearts.This is the largest dataset of cardiac protein expression from human samples collected in vivo. It provides a comprehensive resource that allows insight into molecular fingerprints of MVP and facilitates novel inferences between genomic data and disease mechanisms. We propose that over-representation of proteins in ventricle is consequent not to redundancy but to functional need, and conclude that changes in abundance of proteins known to associate with AF are not sufficient for arrhythmogenesis.

Sociology, Genetics, and the Coming of Age of Sociogenomics

2020 ◽  
Vol 46 (1) ◽  
pp. 553-581 ◽  
Author(s):  
Melinda C. Mills ◽  
Felix C. Tropf
Keyword(s):  
Coming Of Age ◽  
Association Studies ◽  
Molecular Genetic ◽  
Genetic Correlations ◽  
Genetic Research ◽  
Well Being ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Molecular Genetic Data ◽  
Polygenic Scores

Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital. We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline. We conclude with a critical reflection on the lack of diversity, nonrepresentative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.

Alzheimer's disease genetics: lessons to improve disease modelling

2011 ◽  
Vol 39 (4) ◽  
pp. 910-916 ◽  
Author(s):  
Rita J. Guerreiro ◽  
John Hardy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Complete Understanding ◽  
Genome Wide ◽  
History Of ◽  
Ad Alzheimer’S Disease

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

scholarly journals One size does not fit all. Genomics differentiates among binge-eating disorder, bulimia nervosa, and anorexia nervosa

2020 ◽  
Author(s):  
Christopher Hübel ◽  
Mohamed Abdulkadir ◽  
Moritz Herle ◽  
Ruth J.F. Loos ◽  
Gerome Breen ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Anorexia Nervosa ◽  
Eating Disorder ◽  
Bulimia Nervosa ◽  
Binge Eating ◽  
Binge Eating Disorder ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Scores

AbstractObjectiveGenome-wide association studies have identified multiple genomic regions associated with anorexia nervosa. Relatively few or no genome-wide studies of other eating disorders, such as bulimia nervosa and binge-eating disorder, have been performed, despite their substantial heritability. Exploratively, we aimed to identify traits that are genetically associated with binge-type eating disorders.MethodWe calculated genome-wide polygenic scores for 269 trait and disease outcomes using PRSice v2.2 and their association with anorexia nervosa, bulimia nervosa, and binge-eating disorder in up to 640 cases and 17,050 controls from the UK Biobank. Significant associations were tested for replication in the Avon Longitudinal Study of Parents and Children (up to 217 cases and 3018 controls).ResultsIndividuals with binge-type eating disorders had higher polygenic scores than controls for other psychiatric disorders, including depression, schizophrenia, and attention deficit hyperactivity disorder, and higher polygenic scores for body mass index.DiscussionOur findings replicate some of the known comorbidities of eating disorders on a genomic level and motivate a deeper investigation of shared and unique genomic factors across the three primary eating disorders.

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