A novel genomic selection method combining GBLUP and LASSO

Li, H. D.; Wang, Jingwei; Bao, Zhenmin

doi:10.1007/s10709-015-9826-5

Cited by 10 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our results showed no advantage in combining MAS and GP for predicting LNTI, a low-heritability highly polygenic trait [18], of tropical maize hybrids. That corroborates the findings of Bernardo [3], Li et al [34], and Spindel et al [10], which On the usefulness of parental lines GWAS for predicting hybrids in tropical maize suggest that differentially modeling significant markers improve prediction performance only when the trait is highly heritable and the markers explain a fair proportion of the genetic variance. In our case, none of these prerequisites were met.…”

Section: Genome-wide Association Analysis Based On Hybrids Performancesupporting

confidence: 88%

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

et al. 2020

View full text Add to dashboard Cite

Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding. OPEN ACCESSCitation: Galli G, Alves FC, Morosini JS, Fritsche-Neto R (2020) On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids. PLoS ONE 15(2): e0228724. https://doi.org/10.1371/journal.

show abstract

Section: Genome-wide Association Analysis Based On Hybrids Performancesupporting

confidence: 88%

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Since then, empirical studies have validated the methodology ( Rutkoski et al 2014 ; Spindel et al 2016 ; Rice and Lipka 2019 ). In contrast, others have shown little-to-no improvement over GP ( Li et al 2015 ; Galli et al 2020 ), suggesting that modeling significant markers can improve prediction accuracy only when markers explain a substantial portion of both genetic and phenotypic variance ( Galli et al 2020 ). With the high densities of genome-wide markers commonly assayed in gene finding studies, investigators often identify DNA markers tightly linked to a candidate or known causal genes as exemplified by diverse real-world examples ( Hayes and Goddard 2001 ; Hayes et al 2010 ; Jensen et al 2012 ; Visscher et al 2012 , 2017 ; Li et al 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

Feldmann

Covarrubias‐Pazaran

Piepho

2023

G3: Genes, Genomes, Genetics

View full text Add to dashboard Cite

Large-effect loci—those statistically significant loci discovered by genome-wide association studies or linkage mapping—associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model (LMM) analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction (MAP) and its successor, genomic prediction (GP), have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

show abstract

“…2014; Rice and Lipka 2019; Spindel et al . 2016) while others have shown little-to-no improvement over GP (Li et al . 2015; Galli et al .…”

Section: Resultsmentioning

confidence: 99%

Complex Traits and Candidate Genes: Estimation of Genetic Variance Components Across Modes of Inheritance

Feldmann

Covarrubias‐Pazaran²,

Piepho

2022

Preprint

View full text Add to dashboard Cite

Large-effect loci—those discovered by genome-wide association studies or linkage mapping—associated with key traits segregate amidst a background of minor, often undetectable genetic effects in both wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model (LMM) analysis is important for both selecting superior progeny and parents in plant and animal breeding, but also for gene therapy and medical genetics in humans. Marker-assisted prediction (MAP) and its successor, genomic prediction (GP), have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to simultaneously study the modes of inheritance of complex traits. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms, simultaneously, and yields accurate estimates of the variance explained for all relevant terms. Our previous research focused on large-effect loci and polygenic variance exclusively, and in this work we want to synthesize and expand the average semivariance framework to a multitude of different genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

show abstract

A novel genomic selection method combining GBLUP and LASSO

Cited by 10 publications

References 20 publications

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

Complex Traits and Candidate Genes: Estimation of Genetic Variance Components Across Modes of Inheritance

Contact Info

Product

Resources

About