Inexpensive Computation of the Inverse of the Genomic Relationship Matrix in Populations with Small Effective Population Size

Conservation of genetic resources is of great concern globally to maintain genetic diversity for sustainable food security. Comprehensive identification of the breed composition, estimation of inbreeding and effective population size are essential for the effective management of farm animal genetic resources and to prevent the animals from genetic erosion. The Zhongwei male (ZWM), Arbas Cashmere male (ACM) and Jining Grey male (JGM) goats are conserved in three different state goat farms in China but their family information, level of inbreeding and effective population size are unknown. We investigated the genomic relationship, inbreeding coefficient and effective population size in these three breeds from three state goat farms using the Illumina goat SNP50 BeadChip. Genomic relationships and phylogenetic analysis revealed that the breeds are clearly separated and formed separate clusters based on their genetic relationship. We obtained a high proportion of informative SNPs, ranging from 91.8% in the Arbas Cashmere male to 96.2% in the Jining Grey male goat breeds with an average mean of 96.8%. Inbreeding, as measured by FROH, ranged from 1.79% in ZWM to 8.62% in ACM goat populations. High FROH values, elevated genomic coverage of very long ROH (>30 Mb) and severe decline in effective population size were recorded in ACM goat farm. The existence of a high correlation between FHOM and FROH indicates that FROH can be used as an alternative to inbreeding estimates in the absence of pedigree records. The Ne estimates 13 generations ago were 166, 69 and 79 for ZWM, ACM and JGM goat farm, respectively indicating that these goat breeds were strongly affected by selection pressure or genetic drift. This study provides insight into the genomic relationship, levels of inbreeding and effective population size in the studied goat populations conserved in the state goat farms which will be valuable in prioritizing populations for conservation and for developing suitable management practices for further genetic improvement of these Chinese male goats.

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Temporal genetic samples indicate small effective population size of the endangered yellow-eyed penguin

Conservation Genetics ◽

10.1007/s10592-009-9988-8 ◽

2009 ◽

Vol 11 (2) ◽

pp. 539-546 ◽

Cited By ~ 12

Author(s):

Sanne Boessenkool ◽

Bastiaan Star ◽

Philip J. Seddon ◽

Jonathan M. Waters

Keyword(s):

Population Size ◽

Effective Population Size ◽

Effective Population ◽

Small Effective Population Size

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335 Genomic predictions with a multi-breed genomic relationship matrix

Journal of Animal Science ◽

10.1093/jas/skz258.099 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 49-50

Author(s):

Yvette Steyn ◽

Daniela Lourenco ◽

Ignacy Misztal

Keyword(s):

Prediction Accuracy ◽

Negative Impact ◽

Reference Population ◽

Single Step ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Genomic Relationship ◽

Effective Population ◽

Specific Allele ◽

Missing Genotypes

Abstract Multi-breed evaluations have the advantage of increasing the size of the reference population for genomic evaluations and are quite simple; however, combining breeds usually have a negative impact on prediction accuracy. The aim of this study was to evaluate the use of a multi-breed genomic relationship matrix (G), where SNP for each breed are non-shared. The multi-breed G is set assuming known genotypes for one breed and missing genotypes for the remaining breeds. This setup may avoid spurious IBS relationships between breeds and considers breed-specific allele frequencies. This scenario was contrasted to multi-breed evaluations where all SNP are shared, i.e., the same SNP, and to single-breed evaluations. Different SNP densities, namely 9k and 45k, and different effective population sizes (Ne) were tested. Five breeds mimicking recent beef cattle populations that diverged from the same historical population were simulated using different selection criteria. It was assumed that QTL effects were the same over all breeds. For the recent population, generations 1 to 9 had approximately half of the animals genotyped, whereas all 1200 animals were genotyped in generation 10. Genotyped animals in generation 10 were set as validation; therefore, each breed had a validation set. Analysis were performed using single-step GBLUP (ssGBLUP). Prediction accuracy was calculated as correlation between true (T) and genomic estimated (GE) BV. Accuracies of GEBV were lower for the larger Ne and low SNP density. All three scenarios using 45K resulted in similar accuracies, suggesting that the marker density is high enough to account for relationships and linkage disequilibrium with QTL. A shared multi-breed evaluation using 9K resulted in a decrease of accuracy of 0.08 for a smaller Ne and 0.11 for a larger Ne. This loss was mostly avoided when markers were treated as non-shared within the same genomic relationship matrix.

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Small effective population size in microrefugia?

Journal of Systematics and Evolution ◽

10.1111/jse.12128 ◽

2014 ◽

Vol 53 (2) ◽

pp. 163-165 ◽

Cited By ~ 2

Author(s):

Wei-Ning BAI ◽

Da-Yong ZHANG

Keyword(s):

Population Size ◽

Effective Population Size ◽

Effective Population ◽

Small Effective Population Size

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Increased accuracy of artificial selection by using the realized relationship matrix

Genetics Research ◽

10.1017/s0016672308009981 ◽

2009 ◽

Vol 91 (1) ◽

pp. 47-60 ◽

Cited By ~ 374

Author(s):

B. J. HAYES ◽

P. M. VISSCHER ◽

M. E. GODDARD

Keyword(s):

Population Size ◽

Effective Population Size ◽

Family Relationships ◽

Random Mating ◽

Family Relationship ◽

Relationship Matrix ◽

Effective Number ◽

Effective Population ◽

Breeding Values ◽

Selection Methodology

SummaryDense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0·5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0·82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0·3, and Ne=1000, approximately 5750 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0·7.

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Use of the uncertain relationship matrix to compute effective population size

Journal of Animal Breeding and Genetics ◽

10.1111/j.1439-0388.1995.tb00574.x ◽

1995 ◽

Vol 112 (1-6) ◽

pp. 327-332 ◽

Cited By ~ 19

Author(s):

M. Pérez-Enciso

Keyword(s):

Population Size ◽

Effective Population Size ◽

Relationship Matrix ◽

Effective Population

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Population Data for the Transferrin Variants in the Australian Merino

Australian Journal of Biological Sciences ◽

10.1071/bi9670959 ◽

1967 ◽

Vol 20 (5) ◽

pp. 959 ◽

Cited By ~ 3

Author(s):

DW Cooper ◽

LF Bailey ◽

O Mayo

Keyword(s):

Population Size ◽

Effective Population Size ◽

Population Data ◽

The South ◽

Effective Population ◽

Parent Population ◽

Gene Frequencies ◽

The Relationship ◽

Small Effective Population Size ◽

Australian Merino

Population data for the transferrin varil;mts in the South Australian and Camden Park strains of the Australian Merino are reported. In all, five variants designated A, B, C, D, and E were distinguished. The relationship between these variants and those reported in previous investigations of the Merino and other breeds has been determined. In two out of the six samples there were significant departures from Hardy-Weinberg expectations. It was observed that closed flocks with small effective population size, Camden Park and one South Australian (Roseworthy) flock had fewer than five variants, the number generally found in all strains of the Australian Merino so far examined. For the Roseworthy material it was possible to demonstrate that the parent population, Anama, had the five variants. Further, the two Roseworthy flocks derived from the Anama stock had significantly different gene frequencies from that flock.

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