Whole genome approaches to quantitative genetics

Visscher, Peter M.

doi:10.1007/s10709-008-9301-7

Cited by 39 publications

(54 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameterizations of these paired models were identical, except for the origin of the relationship matrices (pedigree or marker based). The limited capacity of pedigree-based models to partition these components is not surprising, as all relationship matrices are derived from the pedigree additive relationship matrix (Mrode 2005) and, therefore, are strongly correlated (Visscher 2009). The models M_A#A, M_D#D, and M_A#D had the lowest correlation between additive and nonadditive, showing a partition substantially better than that of pedigree-based models (Table S3).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, clonal populations are an alternative to explore the full genetic architecture in these species (Foster and Shaw 1988). Several studies aimed at partitioning genetic variance into its various components detected small dominance and negligible epistatic effects (Foster and Shaw 1988;Mullin et al 1992;Wu 1996;Isik et al 2003Isik et al , 2005Costa E Silva et al 2004, 2009Baltunis et al 2007Baltunis et al , 2008Baltunis et al , 2009Araujo et al 2012). These results do not necessarily imply that such effects are not important.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices

et al. 2014

View full text Add to dashboard Cite

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive-and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree-or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices

et al. 2014

View full text Add to dashboard Cite

show abstract

“…In line with this interpretation, analytical models yield larger s.d. values in IBD sharing between relatives when they use a localized distribution of crossovers instead of an infinitesimal model (Risch and Lange, 1979;Suarez et al, 1979;Visscher, 2009). …”

Section: Mendelian Noise In Gwibd and A Comparison With Analytical Rementioning

confidence: 99%

“…For any given inbreeding constellation, the more segments in a genome segregate independently, the lower the variation in GWIBD between individuals of the same inbreeding constellation will be (law of large numbers; Rasmuson, 1993;Visscher, 2009). Consequently, because chromosomes get inherited as independent units in meiosis, the Mendelian noise for a given inbreeding constellation will be smaller in species with more chromosomes (Hill and Weir, 2011).…”

Section: Introductionmentioning

confidence: 99%

Meiotic recombination shapes precision of pedigree- and marker-based estimates of inbreeding

2016

View full text Add to dashboard Cite

The proportion of an individual's genome that is identical by descent (GWIBD) can be estimated from pedigrees (inbreeding coefficient 'Pedigree F') or molecular markers ('Marker F'), but both estimators come with error. Assuming unrelated pedigree founders, Pedigree F is the expected proportion of GWIBD given a specific inbreeding constellation. Meiotic recombination introduces variation around that expectation (Mendelian noise) and related pedigree founders systematically bias Pedigree F downward. Marker F is an estimate of the actual proportion of GWIBD but it suffers from the sampling error of markers plus the error that occurs when a marker is homozygous without reflecting common ancestry (identical by state). We here show via simulation of a zebra finch and a human linkage map that three aspects of meiotic recombination (independent assortment of chromosomes, number of crossovers and their distribution along chromosomes) contribute to variation in GWIBD and thus the precision of Pedigree and Marker F. In zebra finches, where the genome contains large blocks that are rarely broken up by recombination, the Mendelian noise was large (nearly twofold larger s.d. values compared with humans) and Pedigree F thus less precise than in humans, where crossovers are distributed more uniformly along chromosomes. Effects of meiotic recombination on Marker F were reversed, such that the same number of molecular markers yielded more precise estimates of GWIBD in zebra finches than in humans. As a consequence, in species inheriting large blocks that rarely recombine, even small numbers of microsatellite markers will often be more informative about inbreeding and fitness than large pedigrees.

show abstract

“…The availability of dense single nucleotide polymorphism arrays for research permits the evaluation of genomic relatedness among animals with records based on their marker genotypes. Modeling of genomic relatedness, rather than average relatedness based on pedigree, was considered an important opportunity for genetic prediction (Liu et al , 2002; Visscher et al , 2006; Visscher, 2009), due to the improvement of regression equations through refinement of the covariances in the coefficient matrix that are due to relatedness among animals. Inclusion of genomic relatedness in prediction equations improved estimates of variances, and hence heritability (Thomas, 2005; Visscher et al , 2006; Wolc et al , 2013) and improved accuracies of predicted genetic merit (Hayes and Goddard, 2008).…”

Section: Introductionmentioning

confidence: 99%

Alternative parameterizations of relatedness in whole genome association analysis of pre-weaning traits of Nelore-Angus calves

et al. 2014

View full text Add to dashboard Cite

Gestation length, birth weight, and weaning weight of F2 Nelore-Angus calves (n = 737) with designed extensive full-sibling and half-sibling relatedness were evaluated for association with 34,957 SNP markers. In analyses of birth weight, random relatedness was modeled three ways: 1) none, 2) random animal, pedigree-based relationship matrix, or 3) random animal, genomic relationship matrix. Detected birth weight-SNP associations were 1,200, 735, and 31 for those parameterizations respectively; each additional model refinement removed associations that apparently were a result of the built-in stratification by relatedness. Subsequent analyses of gestation length and weaning weight modeled genomic relatedness; there were 40 and 26 trait-marker associations detected for those traits, respectively. Birth weight associations were on BTA14 except for a single marker on BTA5. Gestation length associations included 37 SNP on BTA21, 2 on BTA27 and one on BTA3. Weaning weight associations were on BTA14 except for a single marker on BTA10. Twenty-one SNP markers on BTA14 were detected in both birth and weaning weight analyses.

show abstract

Whole genome approaches to quantitative genetics

Cited by 39 publications

References 23 publications

Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices

Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices

Meiotic recombination shapes precision of pedigree- and marker-based estimates of inbreeding

Alternative parameterizations of relatedness in whole genome association analysis of pre-weaning traits of Nelore-Angus calves

Contact Info

Product

Resources

About