scholarly journals Screening human embryos for polygenic traits has limited utility

2019 ◽  
Author(s):  
Ehud Karavani ◽  
Or Zuk ◽  
Danny Zeevi ◽  
Gil Atzmon ◽  
Nir Barzilai ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Embryo Selection ◽  
Human Embryos ◽  
Genome Wide Association Studies ◽  
Preimplantation Genetic Testing ◽  
Genome Wide ◽  
Polygenic Traits

AbstractGenome-wide association studies have led to the development of polygenic score (PS) predictors that explain increasing proportions of the variance in human complex traits. In parallel, progress in preimplantation genetic testing now allows genome-wide genotyping of embryos generated viain vitrofertilization (IVF). Jointly, these developments suggest the possibility of screening embryos for polygenic traits such as height or cognitive function. There are clear ethical, legal, and societal concerns regarding such a procedure, but these cannot be properly discussed in the absence of data on the expected outcomes of screening. Here, we use theory, simulations, and real data to evaluate the potential gain of PS-based embryo selection, defined as the expected difference in trait value between the top-scoring embryo and an average, unselected embryo. We observe that the gain increases very slowly with the number of embryos, but more rapidly with increased variance explained by the PS. Given currently available polygenic predictors and typical IVF yields, the average gain due to selection would be ≈2.5cm if selecting for height, and ≈2.5 IQ (intelligence quotient) points if selecting for cognitive function. These mean values are accompanied by wide confidence intervals; in real data drawn from nuclear families with up to 20 offspring each, we observe that the offspring with the highest PS for height was the tallest only in 25% of the families. We discuss prospects and limitations of PS-based embryo selection for the foreseeable future.

scholarly journals On prs for complex polygenic trait prediction

2018 ◽  
Author(s):  
Bingxin Zhao ◽  
Fei Zou
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Association Studies ◽  
Prediction Method ◽  
Causal Snps ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Traits ◽  
Low Performance

Polygenic risk score (PRS) is the state-of-art prediction method for complex traits using summary level data from discovery genome-wide association studies (GWAS). The PRS, as its name suggests, is designed for polygenic traits by aggregating small genetic effects from a large number of causal SNPs and thus is viewed as a powerful method for predicting complex polygenic traits by the genetics community. However, one concern is that the prediction accuracy of PRS in practice remains low with little clinical utility, even for highly heritable traits. Another practical concern is whether genome-wide SNPs should be used in constructing PRS or not. To address the two concerns, we investigate PRS both empirically and theoretically. We show how the performance of PRS is influenced by the triplet (n, p, m), where n, p, m are the sample size, the number of SNPs studied, and the number of true causal SNPs, respectively. For a given heritability, we find that i) when PRS is constructed with all p SNPs (referred as GWAS-PRS), its prediction accuracy is controlled by the p/n ratio; while ii) when PRS is built with a set of top-ranked SNPs that pass a pre-specified threshold (referred as threshold-PRS), its accuracy varies depending on how sparse the true genetic signals are. Only when m is magnitude smaller than n, or genetic signals are sparse, can threshold-PRS perform well and outperform GWAS-PRS. Our results demystify the low performance of PRS in predicting highly polygenic traits, which will greatly increase researchers’ aware-ness of the power and limitations of PRS, and clear up some confusion on the clinical application of PRS.

scholarly journals Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Declan Bennett ◽  
Donal O’Shea ◽  
John Ferguson ◽  
Derek Morris ◽  
Cathal Seoighe
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Data ◽  
Phenotype Prediction ◽  
Genome Wide ◽  
Polygenic Scores

AbstractOngoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

scholarly journals Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

2017 ◽  
Vol 60 (3) ◽  
pp. 335-346 ◽  
Author(s):  
Markus Schmid ◽  
Jörn Bennewitz
Keyword(s):  
Statistical Models ◽  
Complex Traits ◽  
Quantitative Traits ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Livestock Breeding ◽  
Genome Wide

Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.

scholarly journals Joint Genotype- and Ancestry-based Genome-wide Association Studies in Admixed Populations

2016 ◽  
Author(s):  
Piotr Szulc ◽  
Malgorzata Bogdan ◽  
Florian Frommlet ◽  
Hua Tang
Keyword(s):  
Linkage Disequilibrium ◽  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Marker Analysis ◽  
Marker Analysis ◽  
Genome Wide ◽  
Single Marker

AbstractIn Genome-Wide Association Studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand Admixture Mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry).Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here we extend this approach for population-based GWAS in the direction of multi marker models. A modified version of the Bayesian Information Criterion is developed for building a multi-locus model, which accounts for the differential correlation structure due to linkage disequilibrium and admixture linkage disequilibrium. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis and modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/.

scholarly journals Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

2020 ◽  
Author(s):  
Ying Wang ◽  
Jing Guo ◽  
Guiyan Ni ◽  
Jian Yang ◽  
Peter M. Visscher ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
African Ancestry ◽  
Real Data ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Causal Variants ◽  
The Uk

AbstractPolygenic scores (PGS) have been widely used to predict complex traits and risk of diseases using variants identified from genome-wide association studies (GWASs). To date, most GWASs have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European populations. Here, we develop a new theory to predict the relative accuracy (RA, relative to the accuracy in populations of the same ancestry as the discovery population) of PGS across ancestries. We used simulations and real data from the UK Biobank to evaluate our results. We found across various simulation scenarios that the RA of PGS based on trait-associated SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of SNP effect sizes and heritability. Altogether, we find that LD and MAF differences between ancestries explain alone up to ~70% of the loss of RA using European-based PGS in African ancestry for traits like body mass index and height. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWASs are mostly shared across continents.

scholarly journals Controlling for Background Genetic Effects Using Polygenic Scores Improves the Power of Genome-wide Association Studies

2021 ◽  
Author(s):  
Declan Bennett ◽  
Dónal O'Shea ◽  
John Ferguson ◽  
Derek Morris ◽  
Cathal Seoighe
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Real Data ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Data ◽  
Phenotype Prediction ◽  
Genome Wide ◽  
Polygenic Scores

Abstract Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

scholarly journals Controlling for off-target genetic effects using polygenic scores improves the power of genome-wide association studies

2020 ◽  
Author(s):  
Declan Bennett ◽  
Derek Morris ◽  
Cathal Seoighe
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Diseases ◽  
Real Data ◽  
Genetic Effects ◽  
Genome Wide Association Studies ◽  
Genotype Data ◽  
Phenotype Prediction ◽  
Genome Wide ◽  
Polygenic Scores

ABSTRACTOngoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data have brought forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Optimal realization of these objectives requires ongoing methodological developments, designed to identify true trait-associated variants and accurately predict phenotype from genotype. These methods must be computationally efficient, in order to remain tractable in the context of high variant densities and very large sample sizes. Here we show that the power of linear mixed models that are in widespread use for GWAS can be increased significantly by modeling off-target genetic effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of off-target genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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