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      The value of genomic relationship matrices to estimate levels of inbreeding

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          Abstract

          Background

          Genomic relationship matrices are used to obtain genomic inbreeding coefficients. However, there are several methodologies to compute these matrices and there is still an unresolved debate on which one provides the best estimate of inbreeding. In this study, we investigated measures of inbreeding obtained from five genomic matrices, including the Nejati-Javaremi allelic relationship matrix ( F NEJ ), the Li and Horvitz matrix based on excess of homozygosity ( F L&H ), and the VanRaden (methods 1, F VR1 , and 2, F VR2 ) and Yang ( F YAN ) genomic relationship matrices. We derived expectations for each inbreeding coefficient, assuming a single locus model, and used these expectations to explain the patterns of the coefficients that were computed from thousands of single nucleotide polymorphism genotypes in a population of Iberian pigs.

          Results

          Except for F NEJ , the evaluated measures of inbreeding do not match with the original definitions of inbreeding coefficient of Wright (correlation) or Malécot (probability). When inbreeding coefficients are interpreted as indicators of variability (heterozygosity) that was gained or lost relative to a base population, both F NEJ and F L&H led to sensible results but this was not the case for F VR1 , F VR2 and F YAN . When variability has increased relative to the base, F VR1 , F VR2 and F YAN can indicate that it decreased. In fact, based on F YAN , variability is not expected to increase. When variability has decreased, F VR1 and F VR2 can indicate that it has increased. Finally, these three coefficients can indicate that more variability than that present in the base population can be lost, which is also unreasonable. The patterns for these coefficients observed in the pig population were very different, following the derived expectations. As a consequence, the rate of inbreeding depression estimated based on these inbreeding coefficients differed not only in magnitude but also in sign.

          Conclusions

          Genomic inbreeding coefficients obtained from the diagonal elements of genomic matrices can lead to inconsistent results in terms of gain and loss of genetic variability and inbreeding depression estimates, and thus to misleading interpretations. Although these matrices have proven to be very efficient in increasing the accuracy of genomic predictions, they do not always provide a useful measure of inbreeding.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12711-021-00635-0.

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          Most cited references66

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            GCTA: a tool for genome-wide complex trait analysis.

            For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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              Efficient methods to compute genomic predictions.

              P VanRaden (2008)
              Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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                Author and article information

                Contributors
                villanueva.beatriz@inia.es
                afedez@inia.es
                saura.maria@inia.es
                armando@uvigo.es
                jmj@inia.es
                morales.elisabeth@inia.es
                miguel.toro@upm.es
                ricardo.pong-wong@roslin.ed.ac.uk
                Journal
                Genet Sel Evol
                Genet Sel Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                1 May 2021
                1 May 2021
                2021
                : 53
                : 42
                Affiliations
                [1 ]Departamento de Mejora Genética Animal, INIA, Ctra. de La Coruña, km 7.5, 28040 Madrid, Spain
                [2 ]Centro de Investigación Mariña, Universidade de Vigo, Departamento de Bioquímica, Genética E Inmunología, Campus de Vigo, 36310 Vigo, Spain
                [3 ]Departamento de Producción Agraria, ETSI Agrónomos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
                [4 ]Genetics and Genomics, The Roslin Institute and the R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
                Article
                635
                10.1186/s12711-021-00635-0
                8088726
                33933002
                a2258620-3038-44d7-87d4-403407445f50
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 July 2020
                : 19 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100014440, Ministerio de Ciencia, Innovación y Universidades;
                Award ID: CGL2016-75904-C2
                Award Recipient :
                Funded by: European Commission Horizon 2020 (H2020) Framework Programme
                Award ID: 727315
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010801, Xunta de Galicia;
                Award ID: ED431C 2020-05
                Award Recipient :
                Funded by: Fondo Europeo de Desarrollo Regional (FEDER)
                Funded by: FundRef http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 772787
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BBS/E/D/30002275
                Award Recipient :
                Funded by: Ministerio de Ciencia e Innovación
                Award ID: PID2020-114426GB-C22
                Award Recipient :
                Categories
                Research Article
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                © The Author(s) 2021

                Genetics
                Genetics

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