Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

The regulation of body weight and composition is complex, simultaneously affected by genetic architecture, the environment, and their interactions. We sought to analyze the complex phenotypic relationships between voluntary exercise, food consumption, and changes in body weight and composition and simultaneously localize quantitative trait loci (QTL) controlling these traits. A large ( n = 815) murine advanced intercross line (G4) was created from a reciprocal cross between a high-running line and the inbred strain C57BL/6J. Body weight and composition (% fat, % lean) were measured at 4, 6, and 8 wk of age. After measurements at 8 wk of age, mice were given access to running wheels, during which food consumption was quantified and after which body weight and composition were assessed to evaluate exercise-induced changes. Phenotypic correlations indicated that the relationship between exercise and overall change in weight and adiposity depended on body composition before the initiation of exercise. Interval mapping revealed QTL for body weight, % fat, and % lean at 4, 6, and 8 wk of age. Furthermore, QTL were observed for food consumption and changes in weight, % fat, and % lean in response to short-term exercise. Here we provide some clarity for the relationship between weight loss, reduction in adiposity, food consumption, and exercise. Simultaneously, we reinforce the genetic basis for body weight and composition with some independent loci controlling growth at different ages. Finally, we present unique QTL providing insight regarding variation in weight loss and reduction in adiposity in response to exercise.

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Estimating breeding values for juvenile body weight using trait associated SNP assisted BLUP in turbot (Scophthalmus maximus)

Aquaculture ◽

10.1016/j.aquaculture.2019.04.075 ◽

2019 ◽

Vol 508 ◽

pp. 46-51 ◽

Cited By ~ 2

Author(s):

Ding Lyu ◽

Yulong Hu ◽

Huanhuan Wu ◽

Song Sun ◽

Weiji Wang

Keyword(s):

Body Weight ◽

Scophthalmus Maximus ◽

Breeding Values ◽

Turbot Scophthalmus Maximus

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Short communication: Estimation of genetic trends for body weight traits in Markhoz goat at different ages

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2019171-13608 ◽

2019 ◽

Vol 17 (1) ◽

pp. e04SC01 ◽

Cited By ~ 1

Author(s):

Meysam Latifi ◽

Mohammad Razmkabir

Keyword(s):

Body Weight ◽

Fixed Effects ◽

Growth Traits ◽

Genetic Selection ◽

Information Criteria ◽

Pedigree Information ◽

Genetic Trend ◽

Breeding Values ◽

Different Ages ◽

Genetic Trends

The objective of the present study was to estimate genetic trends for body weight at different ages in Markhoz goat, including birth weight (BW, n = 4758), weaning weight (WW, n= 3685), 6-month weight (6MW, n = 3420), 9-month weight (9MW, n = 3032) and 12-month weight (12MW, n = 2697). Data and pedigree information were collected from 1992 until 2014 at the Breeding Center of Markhoz goat, Sanandaj, Iran. The GLM procedure of SAS was used for selecting the variables and identifying significant fixed effects in the equation of model. Various animal models were applied for genetic analysis and the best model was determined based on Akaike information criteria (AIC). Breeding values of animals were predicted using Wombat program. Genetic trends were obtained by regressing the average predicted breeding values on birth year for each trait. Based on the best model, direct estimated genetic trends were positive and significance for WW, 6MW, 9MW and 12 MW were 15.51, 26.28, 58.36 and 76.70 g/year, respectively (p<0.001). Maternal genetic trend for BW and WW were 0.61 and 5.47 g/year, respectively (p<0.01). The low and moderate generic trends obtained in the present study, indicated the possibility of growth traits improvements through genetic selection at all ages in Markhoz goat.

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Genetic changes generated within the Katanning Merino Resource flocks

Australian Journal of Experimental Agriculture ◽

10.1071/ea06007 ◽

2006 ◽

Vol 46 (7) ◽

pp. 803 ◽

Cited By ~ 7

Author(s):

J. C. Greeff ◽

G. Cox

Keyword(s):

Body Weight ◽

Phase 1 ◽

Fibre Diameter ◽

Linear Unbiased Prediction ◽

Genetic Changes ◽

Breeding Values ◽

Best Linear Unbiased ◽

Fleece Weight ◽

Estimated Breeding Values ◽

Genetic Trends

Genetic changes for clean fleece weight, fibre diameter and hogget body weight were determined in the Katanning Merino Resource flocks from 1982 to 2004. From 1982 to 1992 genetic trends are presented for individual studs that used mainly subjective classing selection methods (Phase 1) and the genetic trends from 1997 to 2004 demonstrate the genetic changes that can be achieved from using estimated breeding values calculated from best linear unbiased prediction (BLUP) mixed methodology (Phase 2). The results during the first phase show that very few genetic changes occurred in most studs, except for the 4 studs of the Performance Sheep Breeding strain which showed genetic increases in hogget body weight. The genetic trends show that some studs generated change towards their breeding objective, while others show no changes or changes in the opposite direction. In contrast, the use of BLUP estimated breeding values resulted in positive changes in clean fleece weight, fibre diameter and body weight in accordance with the defined breeding objectives.

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High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

Frontiers in Plant Science ◽

10.3389/fpls.2021.715983 ◽

2021 ◽

Vol 12 ◽

Author(s):

Fabiana Freitas Moreira ◽

Hinayah Rojas de Oliveira ◽

Miguel Angel Lopez ◽

Bilal Jamal Abughali ◽

Guilherme Gomes ◽

...

Keyword(s):

High Throughput ◽

Regression Models ◽

Genetic Architecture ◽

Crop Improvement ◽

Random Regression ◽

Genome Wide Association Studies ◽

Breeding Values ◽

High Throughput Phenotyping ◽

General Response ◽

Over Time

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

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Using genomic prediction to detect microevolutionary change of a quantitative trait

10.1101/2021.01.06.425564 ◽

2021 ◽

Author(s):

Darren C Hunter ◽

Bilal Ashraf ◽

Camillo Bérénos ◽

Susan E Johnston ◽

Alastair J Wilson ◽

...

Keyword(s):

Body Weight ◽

Genomic Prediction ◽

Ovis Aries ◽

Phenotypic Change ◽

Pedigree Data ◽

Breeding Values ◽

Adult Body ◽

Microevolutionary Change ◽

Adult Body Weight ◽

Prediction Approach

Detecting microevolutionary responses to natural selection by observing temporal changes in individual breeding values is challenging. The collection of suitable datasets can take many years and disentangling the contributions of the environment and genetics to phenotypic change is not trivial. Furthermore, pedigree-based methods of obtaining individual breeding values have known biases. Here, we apply a genomic prediction approach to estimate breeding values of adult weight in a 35-year dataset of Soay sheep (Ovis aries). During the study period adult body weight decreased, but the underlying genetic component of body weight increased, at a rate that is unlikely to be attributable to genetic drift. Thus cryptic microevolution of greater adult body weight has probably occurred. Using genomic prediction to study microevolution in wild populations can remove the requirement for pedigree data, potentially opening up new study systems for similar research.

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Accuracy of breeding values for production traits in turkeys (Meleagris gallopavo) using recursive models with or without genomics

Genetics Selection Evolution ◽

10.1186/s12711-021-00611-8 ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Emhimad A. Abdalla ◽

Benjamin J. Wood ◽

Christine F. Baes

Keyword(s):

Body Weight ◽

Feed Intake ◽

Structural Equation ◽

Structural Equation Models ◽

Residual Feed Intake ◽

Equation Model ◽

Feed Conversion ◽

Multiple Trait ◽

Breeding Values ◽

Causal Structures

Abstract Background Knowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values. Methods Phenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models. Results In contrast to the two unconditioned traits (i.e., feed conversion ratio and breast meat yield) in the causal structures, the three conditioned traits (i.e., residual feed intake, body weight, and walking score) showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits. Conclusions Our results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs.

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Age and diet shape the genetic architecture of body weight in Diversity Outbred mice

10.1101/2020.11.04.364398 ◽

2020 ◽

Author(s):

Kevin M. Wright ◽

Andrew Deighan ◽

Andrea Di Francesco ◽

Adam Freund ◽

Vladimir Jojic ◽

...

Keyword(s):

Body Weight ◽

Genetic Architecture ◽

Mixed Model ◽

Linear Mixed Model ◽

Environment Interaction ◽

Dependent Manner ◽

Dietary Interventions ◽

Functional Variants ◽

Diversity Outbred ◽

Age Dependent

AbstractUnderstanding how genetic variation shapes an age-dependent complex trait relies on accurate quantification of both the additive genetic effects and genotype-environment interaction effects in an age-dependent manner. We used a generalization of the linear mixed model to quantify diet-dependent genetic contributions to body weight and growth rate measured from early development through adulthood of 960 Diversity Outbred female mice subjected to five dietary interventions. We observed that heritability of body weight remained substantially high (h2 ≈ 0.8) throughout adulthood under the 40% calorie restriction diet, while heritability, although still appreciably high, declined with age under all other dietary regimes. We identified 14 loci significantly associated with body weight in an age-dependent manner and 19 loci that contribute to body weight in an age- and diet-dependent manner. We found the effect of body weight alleles to be dynamic with respect to genomic background, age, and diet, identifying the scope of pleiotropy and several instances of allelic heterogeneity. In many cases, we fine-mapped these loci to narrow genomic intervals containing a few genes and impute putative functional variants from the genome sequence of the DO founders. Of the loci associated with body weight in a diet-dependent manner, many have been previously linked to neurological function and behavior in mice or humans. These results enable us to more fully understand the dynamics of the genetic architecture of body weight with age and in response to different dietary interventions, and to predict the effectiveness of dietary intervention on overall health in distinct genetic backgrounds.

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Genome-Wide Detection of Genetic Loci and Candidate Genes for Body Conformation Traits in Duroc × Landrace × Yorkshire Crossbred Pigs

Frontiers in Genetics ◽

10.3389/fgene.2021.664343 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hui Zhang ◽

Zhanwei Zhuang ◽

Ming Yang ◽

Rongrong Ding ◽

Jianping Quan ◽

...

Keyword(s):

Body Weight ◽

Candidate Genes ◽

Genetic Architecture ◽

Genome Wide Association Study ◽

Body Height ◽

Gene Set Enrichment Analysis ◽

Phenotypic Data ◽

Body Conformation ◽

Genome Wide ◽

A Genome

The Duroc × (Landrace × Yorkshire) hybrid pigs (DLY) are the most popular commercial pigs, providing consumers with the largest source of pork. In order to gain more insights into the genetic architecture of economically important traits in pigs, we performed a genome-wide association study (GWAS) using the GeneSeek Porcine 50 K SNP Chip to map the genetic markers and genes associated with body conformation traits (BCT) in 311 DLY pigs. The quantitative traits analyzed included body weight (BW), carcass length (CL), body length (BL), body height (BH), and body mass index (BMI). BMI was defined as BMICL, BMIBL, and BMIBH, respectively, based on CL, BL, and BH phenotypic data. We identified 82 SNPs for the seven traits by GEMMA-based and FarmCPU-based GWASs. Both methods detected two quantitative trait loci (QTL) on SSC8 and SSC17 for body conformation traits. Several candidate genes (such as TNFAIP3, KDM4C, HSPG2, BMP2, PLCB4, and GRM5) were found to be associated with body weight and body conformation traits in pigs. Notably, the BMP2 gene had pleiotropic effects on CL, BL, BH, BMICL, and BMIBL and is proposed as a strong candidate gene for body size due to its involvement in growth and bone development. Furthermore, gene set enrichment analysis indicated that most of the pathway terms are associated with regulation of cell growth, negative regulation of cell population proliferation, and chondrocyte differentiation. We anticipate that these results further advance our understanding of the genetic architecture of body conformation traits in the popular commercial DLY pigs and provide new insights into the genetic architecture of BMI in pigs.

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