Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings

Visscher, Peter M.; Medland, Sarah E.; Ferreira, Manuel; Morley, Katherine I.; Zhu, Gu; Cornes, Belinda K.; Montgomery, Grant W.; Martin, Nicholas G.

doi:10.1371/journal.pgen.0020041

Cited by 555 publications

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“…00177)=70 . 6, close to the reported empirical value of 82 for human full sibs (Visscher et al, 2006). So if two gametes produced by the same sire (corresponding to two sibs) are considered, then a 35 M chromosome will experience approximately 70 crossovers (35 for each gamete).…”

Section: (I) An Equivalent Model For Genomic Selectionsupporting

confidence: 79%

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Increased accuracy of artificial selection by using the realized relationship matrix

Hayes¹,

2009

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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 N e =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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Section: (I) An Equivalent Model For Genomic Selectionsupporting

confidence: 79%

“…4 to 0 . 6 (Hill, 1993;Visscher et al, 2006). This variation in relationship comes about because sibs inherit large segments of chromosomes from their parents.…”

Section: (I) An Equivalent Model For Genomic Selectionmentioning

confidence: 99%

Increased accuracy of artificial selection by using the realized relationship matrix

Hayes¹,

2009

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“…The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.KEYWORDS marker-based estimation of heritability; GWAS; genomic prediction; Arabidopsis thaliana; one-vs. two-stage approaches N ARROW-SENSE heritability is an important parameter in quantitative genetics, determining the response to selection and representing the proportion of phenotypic variance that is due to additive genetic effects (Jacquard 1983;Ritland 1996;Visscher et al 2006Visscher et al , 2008Holland et al 2010;Sillanpaa 2011). This definition of heritability goes back to Fisher (1918) and Wright (1920) almost a century ago.…”

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Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within-and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.KEYWORDS marker-based estimation of heritability; GWAS; genomic prediction; Arabidopsis thaliana; one-vs. two-stage approaches N ARROW-SENSE heritability is an important parameter in quantitative genetics, determining the response to selection and representing the proportion of phenotypic variance that is due to additive genetic effects (Jacquard 1983;Ritland 1996;Visscher et al. 2006Visscher et al. , 2008Holland et al. 2010;Sillanpaa 2011). This definition of heritability goes back to Fisher (1918) and Wright (1920) almost a century ago. In plant species for which replicates of the same genotype are available (inbred lines, doubled haploids, clones), a different form of heritability, broadsense heritability, is traditionally estimated by the intraclass correlation coefficient for genotypic effects, using estimates for within-and between-genotype variance. Broad-sense heritability is also referred to as repeatability and gives the proportion of phenotypic variance explained by heritable (additive) and nonheritable (dominance, epistasis) genetic variance.With the arrival of high-density genotyping there is growing interest in marker-based estimation of narrow-sense heritability (WTCCC 2007;Yang et al. 2010Yang et al. , 2011Vatti...

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“…1 Additionally, other genetic questions can be well addressed by estimating IBD within a set of individuals. These include, detection of unknown or mistaken relationships, 2-6 estimation of heritability and genomic partitioning of genetic variance, 7,8 and mapping by the identification of shared segments. [9][10][11][12][13] IBD, however, is not directly observed but must be inferred from the available data.…”

Section: Introductionmentioning

confidence: 99%

Using identity by descent estimation with dense genotype data to detect positive selection

Han

Abney

2012

Eur J Hum Genet

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Identification of genomic loci and segments that are identical by descent (IBD) allows inference on problems such as relatedness detection, IBD disease mapping, heritability estimation and detection of recent or ongoing positive selection. Here, employing a novel statistical method, we use IBD to find signals of selection in the Maasai from Kinyawa, Kenya (MKK). In doing so, we demonstrate the advantage of statistical tools that can probabilistically estimate IBD sharing without having to thin genotype data because of linkage disequilibrium (LD), and that allow for both inbreeding and more than one allele to be shared IBD. We use our novel method, GIBDLD, to estimate IBD sharing between all pairs of individuals at all genotyped SNPs in the MKK, and, by looking for genomic regions showing excess IBD sharing in unrelated pairs, find loci that are known to have undergone recent selection (eg, the LCT gene and the HLA region) as well as many novel loci. Intriguingly, those loci that show the highest amount of excess IBD, with the exception of HLA, also show a substantial number of unrelated pairs sharing all four of their alleles IBD. In contrast to other IBD detection methods, GIBDLD provides accurate probabilistic estimates at each locus for all nine possible IBD sharing states between a pair of individuals, thus allowing for consanguinity, while also modeling LD, thus removing the need to thin SNPs. These characteristics will prove valuable for those doing genetic studies, and estimating IBD, in the wide variety of human populations. European Journal of Human Genetics (2013) 21, 205-211; doi:10.1038/ejhg.2012.148; published online 11 July 2012Keywords: identity by descent; natural selection; consanguinity; cryptic relatedness; relationship inference; SNPs INTRODUCTIONThe ability to discover recent positive selection in the human genome is one compelling reason to estimate identity by descent (IBD) in a cohort of largely unrelated individuals. In particular, IBD can be used to find selection on standing variation, a situation where many methods used for detecting selection may not perform well. 1 Additionally, other genetic questions can be well addressed by estimating IBD within a set of individuals. These include, detection of unknown or mistaken relationships, 2-6 estimation of heritability and genomic partitioning of genetic variance, 7,8 and mapping by the identification of shared segments. 9-13 IBD, however, is not directly observed but must be inferred from the available data. Traditionally, the combination of a pedigree with genotype data enabled the efficient computation of IBD using either 'peeling' 14 or hidden Markov models (HMMs). [15][16][17] More recently, however, the large amounts of information made available from high density SNP genotyping arrays has enabled estimation of IBD even for very distantly related pairs of individuals (ie, 410 generations) in the absence of pedigree information. This additional data, however, also presents the difficulty of accommodating the linkage disequilibrium (LD...

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Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings

Cited by 555 publications

References 44 publications

Increased accuracy of artificial selection by using the realized relationship matrix

Increased accuracy of artificial selection by using the realized relationship matrix

Marker-Based Estimation of Heritability in Immortal Populations

Using identity by descent estimation with dense genotype data to detect positive selection

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