scholarly journals Genetic prediction of complex traits: integrating infinitesimal and marked genetic effects

Genetica ◽  
2013 ◽  
Vol 141 (4-6) ◽  
pp. 239-246 ◽  
Author(s):  
Clément Carré ◽  
Fabrice Gamboa ◽  
David Cros ◽  
John Michael Hickey ◽  
Gregor Gorjanc ◽  
...  
Genetics ◽  
2001 ◽  
Vol 159 (3) ◽  
pp. 1319-1323
Author(s):  
Hong-Wen Deng

Abstract Association studies using random population samples are increasingly being applied in the identification and inference of genetic effects of genes underlying complex traits. It is well recognized that population admixture may yield false-positive identification of genetic effects for complex traits. However, it is less well appreciated that population admixture can appear to mask, change, or reverse true genetic effects for genes underlying complex traits. By employing a simple population genetics model, we explore the effects and the conditions of population admixture in masking, changing, or even reversing true genetic effects of genes underlying complex traits.


Genetics ◽  
2003 ◽  
Vol 165 (2) ◽  
pp. 867-883 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu ◽  
David B Allison

AbstractMost complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.


Science ◽  
2020 ◽  
Vol 369 (6509) ◽  
pp. 1318-1330 ◽  
Author(s):  

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.


2018 ◽  
Author(s):  
Jenna C. Carlson ◽  
Deepti Anand ◽  
Azeez Butali ◽  
Carmen J. Buxo ◽  
Kaare Christensen ◽  
...  

AbstractPhenotypic heterogeneity is a hallmark of complex traits, and genetic studies of such traits may focus on them as a single diagnostic entity or by analyzing specific components. For example, in orofacial clefting (OFC), three subtypes – cleft lip (CL), cleft lip and palate (CLP), and cleft palate (CP) have been studied separately and in combination. To further dissect the genetic architecture of OFCs and how a given associated locus may be contributing to distinct subtypes of a trait we developed a framework for quantifying and interpreting evidence of subtype-specific or shared genetic effects in complex traits. We applied this technique to create a “cleft map” of the association of 30 genetic loci with three OFC subtypes. In addition to new associations, we found loci with subtype-specific effects (e.g., GRHL3 (CP), WNT5A (CLP)), as well as loci associated with two or all three subtypes. We cross-referenced these results with mouse craniofacial gene expression datasets, which identified additional promising candidate genes. However, we found no strong correlation between OFC subtypes and expression patterns. In aggregate, the cleft map revealed that neither subtype-specific nor shared genetic effects operate in isolation in OFC architecture. Our approach can be easily applied to any complex trait with distinct phenotypic subgroups.


2019 ◽  
Author(s):  
François Aguet ◽  
Alvaro N Barbeira ◽  
Rodrigo Bonazzola ◽  
Andrew Brown ◽  
Stephane E Castel ◽  
...  

AbstractThe Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing incisandtrans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.


2020 ◽  
Author(s):  
Meng Luo ◽  
Shiliang Gu

AbstractAlthough genome-wide association studies have successfully identified thousands of markers associated with various complex traits and diseases, our ability to predict such phenotypes remains limited. A perhaps ignored explanation lies in the limitations of the genetic models and statistical techniques commonly used in association studies. However, using genotype data for individuals to perform accurate genetic prediction of complex traits can promote genomic selection in animal and plant breeding and can lead to the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling genetic variants together via polygenic methods. Here, we also utilize our proposed polygenic methods, which refer to as the iterative screen regression model (ISR) for genome prediction. We compared ISR with several commonly used prediction methods with simulations. We further applied ISR to predicting 15 traits, including the five species of cattle, rice, wheat, maize, and mice. The results of the study indicate that the ISR method performs well than several commonly used polygenic methods and stability.


2020 ◽  
Vol 29 (7) ◽  
pp. 1218-1228 ◽  
Author(s):  
Wan-Yu Lin ◽  
Chang-Chuan Chan ◽  
Yu-Li Liu ◽  
Albert C Yang ◽  
Shih-Jen Tsai ◽  
...  

Abstract Previous studies have shown that men and women have different genetic architectures across many traits. However, except waist-to-hip ratio (WHR) and waist circumference (WC), it remains unknown whether the genetic effects of a certain trait are weaker or stronger on men/women. With ~18 000 Taiwan Biobank subjects, we comprehensively investigate sexual heterogeneity in autosomal genetic effects, for traits regarding cardiovascular health, diabetes, kidney, liver, anthropometric profiles, blood, etc. ‘Gene-by-sex interactions’ (G $\times$ S) were detected in 18 out of 26 traits, each with an interaction P-value (${{P}}_{{INT}}$) less than $0.05/104={0.00048}$, where 104 is the number of tests conducted in this study. The most significant evidence of G $\times$ S was found in WHR (${{P}}_{{INT}}$ = 3.2 $\times{{10}}^{-{55}}$) and WC (${{P}}_{{INT}}$ = 2.3$\times{{10}}^{-{41}}$). As a novel G$\times$S investigation for other traits, we here find that the autosomal genetic effects are weaker on women than on men, for low-density lipoprotein cholesterol (LDL-C), uric acid (UA) and diabetes-related traits such as fasting glucose and glycated hemoglobin. For LDL-C and UA, the evidence of G$\times$S is especially notable in subjects aged less than 50 years, where estrogen can play a role in attenuating the autosomal genetic effects of these two traits. Men and women have systematically distinct environmental contexts caused by hormonal milieu and their specific society roles, which may trigger diverse gene expressions despite the same DNA materials. As many environmental exposures are difficult to collect and quantify, sex can serve as a good surrogate for these factors.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Declan Bennett ◽  
Donal O’Shea ◽  
John Ferguson ◽  
Derek Morris ◽  
Cathal Seoighe

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.


2021 ◽  
Vol 118 (25) ◽  
pp. e2023184118
Author(s):  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yunong Lin ◽  
Zijie Zhao ◽  
Jiawen Chen ◽  
...  

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.


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