Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs

Abstract Background Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. Methods We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Results Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Conclusions Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

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Effects in bacon pigs of differing levels of nutrition to 20 kg. body weight

Animal Science ◽

10.1017/s0003356100022091 ◽

1964 ◽

Vol 6 (3) ◽

pp. 301-308 ◽

Cited By ~ 20

Author(s):

Henning E. Nielsen

Keyword(s):

Body Weight ◽

Average Daily Gain ◽

Live Weight ◽

Conversion Ratio ◽

Feed Conversion Ratio ◽

Feed Conversion ◽

Eye Muscle ◽

Group 4 ◽

Second Period ◽

Daily Gain

Sixteen blocks of eight littermate pigs were weaned at 3 weeks of age and each divided into four groups. Various levels of nutrition were applied to the pigs so that the four groups reached 20 kg. at 59, 68, 80 and 91 days of age, respectively.Only small differences were observed between groups in feed conversion ratio to 20 kg.; however, Group 4 required more feed per kg. gain than the three other groups.During the second period (20–90 kg.) the pigs in each group were divided into two sub-groups, which were on a high and a low plane of nutrition respectively. In both sub-groups the type of feeding before 20 kg. influenced the average daily gain and feed conversion ratio. The older the pigs were at 20 kg. the higher the daily gain and the lower the feed conversion ratio in the following period. There was an increase in daily feed intake with increased age at 20 kg. for pigs fed ad lib. during the second period.An increase in age at 20 kg. caused a significant decrease in thickness of backfat, a greater area of eye muscle and a smaller area of fat overlying muscle. For pigs fed ad lib. the area of eye muscle was significantly increased by a higher age at 20 kg. live-weight.

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A review of deep learning applications for genomic selection

BMC Genomics ◽

10.1186/s12864-020-07319-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Osval Antonio Montesinos-López ◽

Abelardo Montesinos-López ◽

Paulino Pérez-Rodríguez ◽

José Alberto Barrón-López ◽

Johannes W. R. Martini ◽

...

Keyword(s):

Deep Learning ◽

Plant Breeding ◽

Genomic Selection ◽

Genomic Prediction ◽

Mixed Model ◽

Prediction Models ◽

Genetic Effect ◽

Training Data ◽

Additive Genetic Effect ◽

Main Body

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

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Effect of boars own performance on progeny fattening and carcass traits in two different environments

Biotechnology in Animal Husbandry ◽

10.2298/bah0404055b ◽

2004 ◽

Vol 20 (3-4) ◽

pp. 55-63 ◽

Cited By ~ 4

Author(s):

I. Bahelka ◽

P. Fľak ◽

Anna Lukácová

Keyword(s):

Performance Test ◽

Average Daily Gain ◽

Carcass Traits ◽

Live Weight ◽

Feed Consumption ◽

Muscle Area ◽

Eye Muscle ◽

Carcass Parameters ◽

Performance Traits ◽

Progeny Performance

The effect of own performance traits of meat breed boars on fattening and carcass parameters of progeny in two different test stations (Bucany and Nitra) was evaluated. Own performance traits of boars were average daily gain (ADG) from birth to 100 kg live weight, backfat thickness (BF) and lean meat content (LMC) in field conditions. Progeny of boars was housed in pairs (gilt and barrow) and fed standardized feed mixture semi ad libitum. Progeny test lasted from 30 to 100 kg live weight. There were evaluated following parameters: ADG and feed consumption/kg gain (FC) at test from 30 to 100 kg live weight, slaughter weight (SW), proportion of meaty cuts (PMC) proportion of ham (PHAM), eye muscle area (EMA), and BF. At Bucany and Nitra was found the effect of genotype of boars on progeny BF and/or FC respectively. Better tested boars from own performance test individually as well as a group achieved in progeny better fattening and carcass traits than worse tested boars at Bucany (+33 g ADG, -0.21 cm BF, +2.34 % PMC). Progeny performance from better evaluated boars at Nitra did not exceed the progeny performance from worse tested boars. There was found significant effect of dams on progeny performance.

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Growth performance and fattening ability in castrated male and female East Balkan pigs reared organically in Strandzha mountain

Archiva Zootechnica ◽

10.2478/azibna-2020-0005 ◽

2020 ◽

Vol 23 (1) ◽

pp. 44-54

Author(s):

Nadezhda Palova ◽

Jivko Nakev ◽

Teodora Popova ◽

Maya Ignatova

Keyword(s):

Growth Performance ◽

Average Daily Gain ◽

Live Weight ◽

Conversion Ratio ◽

Feed Conversion Ratio ◽

Daily Weight Gain ◽

Feed Conversion ◽

Average Daily Weight Gain ◽

The Difference ◽

Daily Gain

Abstract(An experiment with two groups of East Balkan pigs – castrated males (n=10) and females (n=10), was carried out to study the growth performance and fattening abilities of the animals from the weaning until slaughter. The pigs were included in the trial at 60 day age. The average live weight of the castrated males was 8. 65±1.08 kg and for the females, 8.5±0.62 kg.The pigs were reared using traditional technology, grazing on natural pastures in the Strandzha mountain, Bulgaria. In autumn, acorns were naturally present in their diet. According to the category, the animals additionally received ground organic feed (50% barley and 50% wheat). The trial lasted 304 days (from February to November, 2019). During this period the final live weight of the male castrated pigs reached 88.00 kg while that of the females was 84.4 kg. The difference, however, was not significant. Furthermore, no significant differences in the growth performance characteristics that could be attributed to the sex of the animals were observed. The average daily gain of both sexes tended to be lower in summer and higher in autumn. The feed conversion ratio was higher in summer. The animals showed high average daily weight gain at pasture when their live weight was over 50 kg.

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Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods

10.1101/595397 ◽

2019 ◽

Author(s):

Daniel Runcie ◽

Hao Cheng

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Cross Validation ◽

Prediction Models ◽

Selection Index ◽

Parametric Method ◽

Multiple Traits ◽

Gold Standard Method ◽

Secondary Traits ◽

Validation Strategy

ABSTRACTIncorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.

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Natural variation and genomic prediction of growth, physiological traits, and nitrogen-use efficiency in perennial ryegrass under low-nitrogen stress

Journal of Experimental Botany ◽

10.1093/jxb/eraa388 ◽

2020 ◽

Vol 71 (20) ◽

pp. 6670-6683

Author(s):

Xiongwei Zhao ◽

Gang Nie ◽

Yanyu Yao ◽

Zhongjie Ji ◽

Jianhua Gao ◽

...

Keyword(s):

Perennial Ryegrass ◽

Nitrogen Use Efficiency ◽

Ridge Regression ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Prediction Models ◽

Physiological Traits ◽

Grass Species ◽

Nitrogen Use ◽

Use Efficiency

Abstract Genomic prediction of nitrogen-use efficiency (NUE) has not previously been studied in perennial grass species exposed to low-N stress. Here, we conducted a genomic prediction of physiological traits and NUE in 184 global accessions of perennial ryegrass (Lolium perenne) in response to a normal (7.5 mM) and low (0.75 mM) supply of N. After 21 d of treatment under greenhouse conditions, significant variations in plant height increment (ΔHT), leaf fresh weight (LFW), leaf dry weight (LDW), chlorophyll index (Chl), chlorophyll fluorescence, leaf N and carbon (C) contents, C/N ratio, and NUE were observed in accessions , but to a greater extent under low-N stress. Six genomic prediction models were applied to the data, namely the Bayesian method Bayes C, Bayesian LASSO, Bayesian Ridge Regression, Ridge Regression-Best Linear Unbiased Prediction, Reproducing Kernel Hilbert Spaces, and randomForest. These models produced similar prediction accuracy of traits within the normal or low-N treatments, but the accuracy differed between the two treatments. ΔHT, LFW, LDW, and C were predicted slightly better under normal N with a mean Pearson r-value of 0.26, compared with r=0.22 under low N, while the prediction accuracies for Chl, N, C/N, and NUE were significantly improved under low-N stress with a mean r=0.45, compared with r=0.26 under normal N. The population panel contained three population structures, which generally had no effect on prediction accuracy. The moderate prediction accuracies obtained for N, C, and NUE under low-N stress are promising, and suggest a feasible means by which germplasm might be initially assessed for further detailed studies in breeding programs.

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Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize

Genetics ◽

10.1534/genetics.120.303278 ◽

2020 ◽

Vol 216 (1) ◽

pp. 27-41

Author(s):

Simon Rio ◽

Laurence Moreau ◽

Alain Charcosset ◽

Tristan Mary-Huard

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Prediction Models ◽

Best Linear Unbiased Prediction ◽

Linear Unbiased Prediction ◽

Modeling Group ◽

A Genome ◽

Specific Allele ◽

Best Linear Unbiased ◽

Unbiased Prediction

Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.

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Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

Plants ◽

10.3390/plants9060719 ◽

2020 ◽

Vol 9 (6) ◽

pp. 719

Author(s):

Mulusew Fikere ◽

Denise M. Barbulescu ◽

M. Michelle Malmberg ◽

Pankaj Maharjan ◽

Phillip A. Salisbury ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Seed Quality ◽

Agronomic Traits ◽

Genetic Correlations ◽

Quality Traits ◽

Blackleg Disease ◽

Genetic Progress ◽

Seed Quality Traits

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

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Effect of including canola meal in diets of slaughter ostriches (Struthio camelus var. domesticus)

South African Journal of Animal Science ◽

10.4314/sajas.v50i4.6 ◽

2020 ◽

Vol 50 (4) ◽

pp. 537-551

Author(s):

T.S. Brand ◽

J. Van der Merwe ◽

L.C. Hoffman

Keyword(s):

Dry Matter ◽

Average Daily Gain ◽

Live Weight ◽

Conversion Ratio ◽

Feed Conversion Ratio ◽

Dry Matter Intake ◽

Canola Meal ◽

Production Traits ◽

Feed Conversion ◽

Daily Gain

Canola meal (CM) is a locally produced protein source that may be less expensive than soybean meal (SBM). This study evaluated the effects of replacing 0%, 25%, 50%, 75%, and 100% SBM with CM in diets for slaughter ostriches. The CM was added at the expense of SBM and other concentrates, with minor changes in other ingredients. Birds (n = 15 per treatment) were reared from 77 to 337 days old on the trial diets, which were supplied ad libitum for starter, grower, and finisher phases. Bodyweights and feed intake were measured during these phases. No differences (P >0.05) were found between treatments for live weight at the end of each phase, dry matter intake (DMI), average daily gain (ADG) and feed conversion ratio (FCR) over all the growth phases. Although no differences were observed in live weight at the end of each phase, the birds reared on the diet with 50% CM were heaviest at slaughter, and birds reared with 100% CM were lightest (P <0.05). Differences (P <0.05) between diets were observed for the weight at slaughter, weights of the liver and thyroid glands and the pH of the cold carcass. However, no differences (P >0.05) were observed between diets for fat pad weight, dressing percentage, and weights of thighs and Muscularis gastrocnemius. The results indicate that CM could replace SBM in the diets of slaughter ostriches without affecting production traits and slaughter yields.Keywords: alternative protein, average daily gain, canola, dry matter intake, feed conversion ratio, growth, ostrich nutrition, production

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How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

Frontiers in Plant Science ◽

10.3389/fpls.2020.592977 ◽

2020 ◽

Vol 11 ◽

Author(s):

Christian R. Werner ◽

R. Chris Gaynor ◽

Gregor Gorjanc ◽

John M. Hickey ◽

Tobias Kox ◽

...

Keyword(s):

Family Structure ◽

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Cross Validation ◽

Careful Analysis ◽

Critical Approach ◽

Crop Species ◽

Breeding Programs ◽

Mendelian Sampling

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

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