Bivariate Genome-Wide Association Analysis of the Growth and Intake Components of Feed Efficiency

Serão, Nick V. L.; González-Peña, Dianelys; Beever, Jonathan E.; Bollero, Germán A.; Southey, Bruce R.; Faulkner, Daniel B; Rodriguez-Zas, Sandra L.

doi:10.1371/journal.pone.0078530

Cited by 28 publications

(22 citation statements)

References 61 publications

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“…Furthermore, these authors suggested that the multivariate methods might be able to identify genetic variants that are currently not identifiable by standard univariate analysis. For this reason, recent relevant applied GWAS in beef cattle populations[ 12 , 14 ]have considered this multi-trait analysis.…”

Section: Discussionmentioning

confidence: 99%

“…In this context, a multi-trait GWAS might help to explain these correlations. Multi-trait methods have already been successfully used to identify QTL sustaining genetic correlations in beef cattle, such as growth and intake components of feed efficiency[ 12 ]as well as stature, fatness, and reproduction[ 13 , 14 ]. However, studies still carried the above mentioned limitation: phenotypes were original measurements at a given age or condition.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

et al. 2015

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Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to growth processes in beef cattle. The Brody model presented the best goodness of fit, and the heritability values for the parameter estimates for mature weight (A) and maturity rate (K) were 0.23 and 0.32, respectively, proving that these traits can be a feasible alternative when the objective is to change the shape of growth curves within genetic improvement programs. The genetic correlation between A and K was -0.84, indicating that animals with lower mature body weights reached that weight at younger ages. One hundred and sixty seven (167) and two hundred and sixty two (262) significant SNPs were associated with A and K, respectively. The annotated genes closest to the most significant SNPs for A had direct biological functions related to muscle development (RAB28), myogenic induction (BTG1), fetal growth (IL2), and body weights (APEX2); K genes were functionally associated with body weight, body height, average daily gain (TMEM18), and skeletal muscle development (SMN1). Candidate genes emerging from this GWAS may inform the search for causative mutations that could underpin genomic breeding for improved growth rates.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

et al. 2015

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“…Multivariate models are advantageous when the response variables are correlated through the signal or noise components of the model. There is a compromise between the gains in precision to detect the relationship between the indicators and explanatory variables and the additional parameters in the multivariate relative to the univariate models (Stearns et al, 2005; Serão et al, 2013). In a multivariate analysis, the test statistics available to assess the association between BCG-treatment group and behavioral indicators are equivalent when comparing two groups.…”

Section: Methodsmentioning

confidence: 99%

Advancing the understanding of behaviors associated with Bacille Calmette Guérin infection using multivariate analysis

Rodriguez-Zas

Nixon

Lawson

et al. 2015

Brain, Behavior, and Immunity

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Behavioral indicators in the murine Bacille Calmette Guérin (BCG) model of inflammation have been studied individually; however, the variability of the behaviors across BCG levels and the mouse-to-mouse variation within BCG-treatment group are only partially understood. The objectives of this study were: 1) to gain a comprehensive understanding of sickness and depression-like behaviors in a BCG model of inflammation using multivariate approaches, and 2) to explore behavioral differences between BCG-treatment groups and among mice within group. Adult mice were challenged with either 0mg (saline), 5mg or 10mg of BCG (BCG-treatment groups: BCG0, BCG5, or BCG10, respectively) at Day 0 of the experiment. Sickness indicators included body weight changes between Day 0 and Day 2 and between Day 2 and Day 5, and horizontal locomotor activity and vertical activity (rearing) measured at Day 6. Depression-like indicators included duration of immobility in the forced swim test and in the tail suspension test at Day 6 and sucrose consumption in the sucrose preference test at Day 7. The simultaneous consideration of complementary sickness and depression-like indicators enabled a more precise characterization of behavioral changes associated with BCG-treatment and of mouse-to-mouse variation, relative to the analysis of indicators individually. Univariate and multivariate analyses confirmed differences between BCG-treatment groups in weight change early on the trial. Significant differences between BCG-treatment groups in depression-like behaviors were still measurable after Day 5. The potential for multivariate models to account for the correlation between behavioral indicators and to augment the analytical precision relative to univariate models was demonstrated both for sickness and for depression-like indicators. Unsupervised learning approaches revealed the complementary information provided by the sickness and depression-like indicators considered. Supervised learning approaches using cross-validation confirmed subtle differences between BCG-treatment groups and among mice within group identified by the consideration of sickness and depression-like indicators. These findings support the recommendation for multivariate and multidimensional analyses of sickness and depression-like indicators to augment the systemic understanding of the behavioral changes associated with infection.

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“…The glycosaminoglycan pathway, involving the CHST4 (chr 6) gene, was also significant in the HFE group. This pathway was identified in a GWAS study of beef cattle for growth and intake components for feed efficiency (Serão et al, 2013).…”

Section: Mtor and Mapk: Key Signaling Pathways In Durocmentioning

confidence: 99%

Genome-Wide Epistatic Interaction Networks Affecting Feed Efficiency in Duroc and Landrace Pigs

2020

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Interactions among genomic loci have often been overlooked in genome-wide association studies, revealing the combinatorial effects of variants on phenotype or disease manifestation. Unexplained genetic variance, interactions among causal genes of small effects, and biological pathways could be identified using a network biology approach. The main objective of this study was to determine the genome-wide epistatic variants affecting feed efficiency traits [feed conversion ratio (FCR) and residual feed intake (RFI)] based on weighted interaction SNP hub (WISH-R) method. Herein, we detected highly interconnected epistatic SNP modules, pathways, and potential biomarkers for the FCR and RFI in Duroc and Landrace purebreds considering the whole population, and separately for low and high feed efficient groups. Highly interacting SNP modules in Duroc (1,247 SNPs) and Landrace (1,215 SNPs) across the population and for low feed efficient (Duroc-80 SNPs, Landrace-146 SNPs) and high feed efficient group (Duroc-198 SNPs, Landrace-232 SNPs) for FCR and RFI were identified. Gene and pathway analyses identified ABL1, MAP3K4, MAP3K5, SEMA6A, KITLG, and KAT2B from chromosomes 1, 2, 5, and 13 underlying ErbB, Ras, Rap1, thyroid hormone, axon guidance pathways in Duroc. GABBR2, GNA12, and PRKCG genes from chromosomes 1, 3, and 6 pointed towards thyroid hormone, cGMP-PKG and cAMP pathways in Landrace. From Duroc low feed efficient group, the TPK1 gene was found involved with thiamine metabolism, whereas PARD6G, DLG2, CRB1 were involved with the hippo signaling pathway in high feed efficient group. PLOD1 and SETD7 genes were involved with lysine degradation in low feed efficient group in Landrace, while high feed efficient group pointed to genes underpinning valine, leucine, isoleucine degradation, and fatty acid elongation. Some SNPs and genes identified are known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction, increasing the accuracy of estimated genetic merit for FE and helping the pig breeding industry.

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Bivariate Genome-Wide Association Analysis of the Growth and Intake Components of Feed Efficiency

Cited by 28 publications

References 61 publications

Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

Advancing the understanding of behaviors associated with Bacille Calmette Guérin infection using multivariate analysis

Genome-Wide Epistatic Interaction Networks Affecting Feed Efficiency in Duroc and Landrace Pigs

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