Changes in genomic predictions when new information is added

Hidalgo, Jorge; Lourenço, Daniela; Tsuruta, S.; Masuda, Yutaka; Miller, Stephen; Bermann, Matias; Garcia, André; Misztal, I.

doi:10.1093/jas/skab004

Cited by 9 publications

(10 citation statements)

References 20 publications

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“…Optimising expansion of the APY core subset with the arrival of new genotype data has not been addressed in the literature although several empirical studies have been done [36, 37, 38]. When new genotype data arrives, we can either construct the core subset anew or expand the existing core subset.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Hidalgo et al . [38] examined the impact of adding new data with or without updating the core subset. They found that changes in GEBVs only slightly increased when the core was updated, compared to using the fixed core in the same period of time.…”

Section: Discussionmentioning

confidence: 99%

“…[19], Hidalgo et al . [38] recommend usage of the same core subset for a longer period of time (for example, one year), and update it when a significant amount of new data is generated. This update can be optimised with the conditional algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…They found that changes in GEBVs only slightly increased when the core was updated, compared to using the fixed core in the same period of time. Therefore, in line with the recommendations in Misztal et al [19], Hidalgo et al [38] recommend usage of the same core subset for a longer period of time (for example, one year), and update it when a significant amount of new data is generated. This update can be optimised with the conditional algorithm.…”

Section: Expanding the Core Subset With New Genotype Datamentioning

confidence: 91%

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Optimisation of the core subset for the APY approximation of genomic relationships

Pocrnić

Lindgren

Tolhurst

et al. 2022

Preprint

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Background: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have have been proposed to address this challenge, like the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of genomic relationship matrix. The partitioning into subsets is often done at random. While APY is a good approximation of the full model, the random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. Methods: We derived a novel algorithm for optimising the core subset based on the conditional genomic relationship matrix or the conditional SNP genotype matrix. We compared accuracy of genomic predictions with different core subsets on simulated and real pig data. The core subsets were constructed (1) at random, (2) based on the diagonal of genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we have visualised population structure of genotyped animals with the linear Principal Component Analysis and the non-linear Uniform Manifold Approximation and Projection. Results: All core subset constructions performed equally well when the number of core animals captured most of variation in genomic relationships, both in simulated and real data. When the number of core animals was not optimal, there was substantial variability in results with the random construction and no variability with the conditional construction. Visualisation of population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. Conclusions: Our results confirm that the size of the core subset in APY is critical. The results further show that the core subset can be optimised with the conditional algorithm that achieves a good and repeatable spread of core animals across the domain of genotyped animals.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Expanding the Core Subset With New Genotype Datamentioning

confidence: 91%

See 2 more Smart Citations

Optimisation of the core subset for the APY approximation of genomic relationships

Pocrnić

Lindgren

Tolhurst

et al. 2022

Preprint

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“…Other factors can also reduce the frequency of retraining. Hidalgo et al (2021) showed that the decay in predictive ability was less when the number of genotyped individuals with phenotypes was greater than the number of independent chromosome segments (ICS). The ICS was defined as four times the effective population size (Ne) and the length of the genome in Morgans (Stam, 1980), which can be approached by the number of largest eigenvalues explaining 98% of the variance in the genomic relationship matrix (Pocrnic et al,TABLE 2 | Rates of concordance between marker-based allele calls using flanking markers and the observed phenotype for five loci in the SoyNAM population.…”

Section: Discussionmentioning

confidence: 99%

PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species

2021

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The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present “Parental Allele Tracing, Recombination Identification, and Optimal predicTion” (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity-by-descent relationships, PATRIOT showed an improvement in GP accuracy by 16.6% relative to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

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Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Junqueira

Lourenço

Masuda

et al. 2022

Journal of Animal Science

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Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H -1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h 2 = 0.3) were available for all animals in generations 1-9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G -1 and GAPY-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.

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Changes in genomic predictions when new information is added

Cited by 9 publications

References 20 publications

Optimisation of the core subset for the APY approximation of genomic relationships

Optimisation of the core subset for the APY approximation of genomic relationships

PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

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