Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine

Abstract Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

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Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

10.1101/2020.10.26.355420 ◽

2020 ◽

Author(s):

Charlotte Brault ◽

Agnès Doligez ◽

Loïc le Cunff ◽

Aude Coupel-Ledru ◽

Thierry Simonneau ◽

...

Keyword(s):

Climate Change ◽

Candidate Genes ◽

Genomic Prediction ◽

Penalized Regression ◽

Interval Mapping ◽

Qtl Detection ◽

Wine Quality ◽

Positional Candidate ◽

Regression Methods ◽

Marker Selection

ABSTRACTViticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

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A strategy for QTL detection in half-sib populations

Animal Science ◽

10.1017/s1357729800010018 ◽

1998 ◽

Vol 67 (2) ◽

pp. 257-268 ◽

Cited By ~ 18

Author(s):

D. J. de Koning ◽

P. M. Visscher ◽

S. A. Knott ◽

C. S. Haley

Keyword(s):

Interval Mapping ◽

Rapid Identification ◽

Published Data ◽

Production Traits ◽

Qtl Detection ◽

Marker Interval ◽

Regression Methods ◽

Likelihood Analysis ◽

Significance Levels ◽

Analysis Strategy

AbstractA statistical analysis strategy for the detection of quantitative trait loci (QTLs) in half-sib populations is outlined. The initial exploratory analysis is a multiple regression of the trait score on a subset of markers to allow a rapid identification of possible chromosomal regions of interest. This is followed by multiple marker interval mapping with regression methods within and across families fitting one or two QTLs. Empirical thresholds are determined by experiment-wise permutation tests for different significance levels and empirical confidence intervals for the QTLs' positions are obtained by bootstrapping methods. For traits with evidence for a significant single-QTL effect, an approximate maximum likelihood analysis is performed to obtain estimates of QTL effect and the probability of the QTL genotype for each parent of a group of half-sibs. The strategy is demonstrated in an analysis of previously published data on chromosome 6 and five production traits from a granddaughter design in dairy cattle. The results confirm and extend evidence for QTLs affecting protein percentage. Informativeness of markers limited the possibility of mapping more than one QTL on the same linkage group.

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The role of gene polymorphisms in the pathogenesis of chronic obstructive pulmonary disease

Biologia ◽

10.2478/s11756-008-0020-4 ◽

2008 ◽

Vol 63 (1) ◽

Author(s):

Eva Slabá ◽

Pavol Joppa ◽

Ján Šalagovič ◽

Ružena Tkáčová

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Inflammatory Response ◽

Candidate Genes ◽

Pulmonary Disease ◽

Gene Polymorphisms ◽

Association Studies ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Positional Candidate ◽

Linkage Analyses

AbstractChronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. Irreversible airflow limitation, both progressive and associated with an inflammatory response of the lungs to noxious particles or gases, is a hallmark of the disease. Cigarette smoking is the most important environmental risk factor for COPD, nevertheless, only approximately 20–30% of smokers develop symptomatic disease. Epidemiological studies, case-control studies in relatives of patients with COPD, and twin studies suggest that COPD is a genetically complex disease with environmental factors and many involved genes interacting together. Two major strategies have been employed to identify the genes and the polymorphisms that likely contribute to the development of complex diseases: association studies and linkage analyses. Biologically plausible pathogenetic mechanisms are prerequisites to focus the search for genes of known function in association studies. Protease-antiprotease imbalance, generation of oxidative stress, and chronic inflammation are recognized as the principal mechanisms leading to irreversible airflow obstruction and parenchymal destruction in the lung. Therefore, genes which have been implicated in the pathogenesis of COPD are involved in antiproteolysis, antioxidant barrier and metabolism of xenobiotic substances, inflammatory response to cigarette smoke, airway hyperresponsiveness, and pulmonary vascular remodelling. Significant associations with COPD-related phenotypes have been reported for polymorphisms in genes coding for matrix metalloproteinases, microsomal epoxide hydrolase, glutathione-S-transferases, heme oxygenase, tumor necrosis factor, interleukines 1, 8, and 13, vitamin D-binding protein and β-2-adrenergic receptor (ADRB2), whereas adequately powered replication studies failed to confirm most of the previously observed associations. Genome-wide linkage analyses provide us with a novel tool to identify the general locations of COPD susceptibility genes, and should be followed by association analyses of positional candidate genes from COPD pathophysiology, positional candidate genes selected from gene expression studies, or dense single nucleotide polymorphism panels across regions of linkage. Haplotype analyses of genes with multiple polymorphic sites in linkage disequilibrium, such as the ADRB2 gene, provide another promising field that has yet to be explored in patients with COPD. In the present article we review the current knowledge about gene polymorphisms that have been recently linked to the risk of developing COPD and/or may account for variations in the disease course.

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Penalized Regression Methods in the Source Analysis of Face Recognition

Advances in Cognitive Neurodynamics ICCN 2007 ◽

10.1007/978-1-4020-8387-7_107 ◽

2008 ◽

pp. 621-624

Author(s):

Mayrim Vega-Hernández ◽

Eduardo Martínez-Montes ◽

Jhoanna Pérez-Hidalgo-Gato ◽

José M. Sánchez-Bornot ◽

Pedro Valdés-Sosa

Keyword(s):

Face Recognition ◽

Penalized Regression ◽

Source Analysis ◽

Regression Methods

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Refinement of the locus for hereditary congenital facial palsy on chromosome 3q21 in two unrelated families and screening of positional candidate genes

European Journal of Human Genetics ◽

10.1038/sj.ejhg.5201706 ◽

2006 ◽

Vol 14 (12) ◽

pp. 1306-1312 ◽

Cited By ~ 11

Author(s):

Caroline B Michielse ◽

Meena Bhat ◽

Angela Brady ◽

Hussain Jafrid ◽

José A J M van den Hurk ◽

...

Keyword(s):

Candidate Genes ◽

Facial Palsy ◽

Positional Candidate

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Meta-analysis of grain iron and zinc associated QTLs identified hotspot chromosomal regions and positional candidate genes for breeding biofortified rice

Plant Science ◽

10.1016/j.plantsci.2019.110214 ◽

2019 ◽

Vol 288 ◽

pp. 110214 ◽

Cited By ~ 5

Author(s):

Qasim Raza ◽

Awais Riaz ◽

Muhammad Sabar ◽

Rana Muhammad Atif ◽

Khurram Bashir

Keyword(s):

Candidate Genes ◽

Meta Analysis ◽

Positional Candidate ◽

Iron And Zinc

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Comparison of deterministic and stochastic methods of calculating identity-by-descent matrices using multiple markers

Proceedings of the British Society of Animal Science ◽

10.1017/s1752756200007122 ◽

2002 ◽

Vol 2002 ◽

pp. 56-56

Author(s):

A.C. Sørensen ◽

R. Pong-Wong ◽

J.J. Windig ◽

J.A. Woolliams

Keyword(s):

Monte Carlo ◽

Marker Assisted Selection ◽

Interval Mapping ◽

Stochastic Methods ◽

Qtl Detection ◽

Identity By Descent ◽

Deterministic Method ◽

Practical Applications ◽

Breeding Schemes ◽

Computationally Expensive

Identity-by-descent (IBD) matrices are used for a number of practical applications, e.g. QTL-detection, marker assisted selection in breeding schemes (MAS), refining of covariances among relatives, and MAS for maintaining genetic variation. The calculation of IBD matrices can be made using Markov Chain Monte Carlo (MCMC). However, this is a computationally expensive method. Therefore, a simple deterministic method (Det) has been developed (Pong-Wong et al., 2001). The objective of this study is to evaluate this deterministic method relative to MCMC for the precision of the matrices and their performance in interval mapping and MAS.

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