Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

Understanding the genetic basis of dynamic plant phenotypes has largely been limited due to lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to non-destructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g. genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits, and provide a robust framework for modeling traits trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice using 33,674 SNPs. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over a conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent, as well as time-specific, transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.

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Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits

The Plant Genome ◽

10.3835/plantgenome2018.10.0075 ◽

2019 ◽

Vol 12 (2) ◽

pp. 180075 ◽

Cited By ~ 9

Author(s):

Malachy Campbell ◽

Mehdi Momen ◽

Harkamal Walia ◽

Gota Morota

Keyword(s):

Regression Models ◽

Random Regression ◽

Breeding Values ◽

Genetic Inference

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GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽

10.3390/ani11072050 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2050

Author(s):

Beatriz Castro Dias Cuyabano ◽

Gabriel Rovere ◽

Dajeong Lim ◽

Tae Hun Kim ◽

Hak Kyo Lee ◽

...

Keyword(s):

Environmental Factors ◽

Genomic Prediction ◽

Prediction Models ◽

Phenotypic Expression ◽

Genetic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

Korean Cattle ◽

Evaluation Programs ◽

The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

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Hierarchical mixed-model expedites genome-wide longitudinal association analysis

Briefings in Bioinformatics ◽

10.1093/bib/bbab096 ◽

2021 ◽

Author(s):

Ying Zhang ◽

Yuxin Song ◽

Jin Gao ◽

Hengyu Zhang ◽

Ning Yang ◽

...

Keyword(s):

Association Analysis ◽

Mixed Model ◽

Repeated Measurements ◽

Random Regression ◽

Association Analyses ◽

Genome Wide ◽

Random Regression Model ◽

A Genome ◽

Longitudinal Association ◽

Poultry Data

AbstractA hierarchical random regression model (Hi-RRM) was extended into a genome-wide association analysis for longitudinal data, which significantly reduced the dimensionality of repeated measurements. The Hi-RRM first modeled the phenotypic trajectory of each individual using a RRM and then associated phenotypic regressions with genetic markers using a multivariate mixed model (mvLMM). By spectral decomposition of genomic relationship and regression covariance matrices, the mvLMM was transformed into a multiple linear regression, which improved computing efficiency while implementing mvLMM associations in efficient mixed-model association expedited (EMMAX). Compared with the existing RRM-based association analyses, the statistical utility of Hi-RRM was demonstrated by simulation experiments. The method proposed here was also applied to find the quantitative trait nucleotides controlling the growth pattern of egg weights in poultry data.

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Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models

Genetics and Molecular Research ◽

10.4238/2012.february.8.2 ◽

2012 ◽

Vol 11 (1) ◽

pp. 271-276 ◽

Cited By ~ 2

Author(s):

N.W. Selapa ◽

K.A. Nephawe ◽

A. Maiwashe ◽

D. Norris

Keyword(s):

South Africa ◽

Genetic Analysis ◽

Regression Models ◽

Random Regression ◽

Body Weights ◽

Beef Bulls

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Genetic analysis of production traits in turbot (Scophthalmus maximus) using random regression models based on molecular relatedness

Journal of Animal Breeding and Genetics ◽

10.1111/jbg.12337 ◽

2018 ◽

Vol 135 (4) ◽

pp. 275-285

Author(s):

Kristina Schlicht ◽

Nina Krattenmacher ◽

Vincent Lugert ◽

Carsten Schulz ◽

Georg Thaller ◽

...

Keyword(s):

Genetic Analysis ◽

Regression Models ◽

Scophthalmus Maximus ◽

Random Regression ◽

Production Traits ◽

Turbot Scophthalmus Maximus

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Dairy cattle breeding.

Genetic improvement of farmed animals ◽

10.1079/9781789241723.0234 ◽

2021 ◽

pp. 234-291

Author(s):

Geoff Simm ◽

Geoff Pollott ◽

Raphael Mrode ◽

Ross Houston ◽

Karen Marshall

Keyword(s):

Genetic Analysis ◽

Dairy Cattle ◽

Selection Response ◽

Animal Breeding ◽

Technology Selection ◽

Cattle Breeding ◽

Breeding Values ◽

Breeding Technology ◽

Dairy Cattle Breeding

Abstract This chapter discussed the effects of applying the different principles in animal breeding such genetic analysis, predicting breeding values, use of tools and breeding technology, selection response within breeds, and strategies for genetic improvements in dairy cattle.

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A study of Genomic Prediction across Generations of Two Korean Pig Populations

Animals ◽

10.3390/ani9090672 ◽

2019 ◽

Vol 9 (9) ◽

pp. 672 ◽

Cited By ~ 1

Author(s):

Beatriz Castro Dias Castro Dias Cuyabano ◽

Hanna Wackel ◽

Donghyun Shin ◽

Cedric Gondro

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Reference Population ◽

Relevant Information ◽

Production Traits ◽

Genomic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

The Relationship ◽

Dense Marker

Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.

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Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Animals ◽

10.3390/ani10050752 ◽

2020 ◽

Vol 10 (5) ◽

pp. 752 ◽

Cited By ~ 2

Author(s):

Jungjae Lee ◽

Yongmin Kim ◽

Eunseok Cho ◽

Kyuho Cho ◽

Soojin Sa ◽

...

Keyword(s):

Genomic Prediction ◽

Genome Wide Association Study ◽

Genomic Analysis ◽

Snp Markers ◽

Snp Genotyping ◽

Muscle Area ◽

Bayesian Approaches ◽

Breeding Values ◽

Estimated Breeding Values ◽

Response Variables

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

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Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic

Czech Journal of Animal Science ◽

10.17221/3976-cjas ◽

2011 ◽

Vol 50 (No. 1) ◽

pp. 7-13 ◽

Cited By ~ 3

Author(s):

L. Zavadilová ◽

E. Němcová ◽

J. Přibyl ◽

J. Wolf

Keyword(s):

Animal Model ◽

Milk Production ◽

Legendre Polynomials ◽

Environmental Effect ◽

Random Regression ◽

Breeding Values ◽

Regression Test ◽

Days Open ◽

Calving Season ◽

Definition Of

The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

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