Linear models for joint association and linkage QTL mapping

Legarra, Andrés; Fernando, Rohan L.

doi:10.1186/1297-9686-41-43

Cited by 32 publications

(55 citation statements)

References 47 publications

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“…Relatedness estimates measured in this way result in approximate marginal probabilities in the relatedness matrix instead of a joint distribution. The occurrence of negative-definite matrices is common in genetics (48)(49)(50)(51). Genetic covariance matrices are well known for being non-negative-definite, as are identity-by-descent matrices (50,51).…”

Section: Identification Of Close Relatives Formentioning

confidence: 99%

“…The occurrence of negative-definite matrices is common in genetics (48)(49)(50)(51). Genetic covariance matrices are well known for being non-negative-definite, as are identity-by-descent matrices (50,51). Bending procedures (48,49), are commonly used to correct non-negative-definiteness in genetic matrices (52), as they bend matrices until their lowest eigenvalues exceed a preset limit (e.g., zero to achieve non-negative-definiteness).…”

Section: Identification Of Close Relatives Formentioning

confidence: 99%

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Social and genetic interactions drive fitness variation in a free-living dolphin population

Frère

Krützen

Mann

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

206

161

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The evolutionary forces that drive fitness variation in species are of considerable interest. Despite this, the relative importance and interactions of genetic and social factors involved in the evolution of fitness traits in wild mammalian populations are largely unknown. To date, a few studies have demonstrated that fitness might be influenced by either social factors or genes in natural populations, but none have explored how the combined effect of social and genetic parameters might interact to influence fitness. Drawing from a long-term study of wild bottlenose dolphins in the eastern gulf of Shark Bay, Western Australia, we present a unique approach to understanding these interactions. Our study shows that female calving success depends on both genetic inheritance and social bonds. Moreover, we demonstrate that interactions between social and genetic factors also influence female fitness. Therefore, our study represents a major methodological advance, and provides critical insights into the interplay of genetic and social parameters of fitness.animal model | gene-culture coevolution | social learning | reproductive success | relatedness

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Section: Identification Of Close Relatives Formentioning

confidence: 99%

Section: Identification Of Close Relatives Formentioning

confidence: 99%

Social and genetic interactions drive fitness variation in a free-living dolphin population

Frère

Krützen

Mann

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

206

161

View full text Add to dashboard Cite

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“…A statistical method that explicitly models both LD and cosegregation was proposed for genomic selection (Calus et al 2008), but it did not outperform a Bayesian method similar to BayesA (Meuwissen et al 2001). The question remains, how much cosegregation is captured implicitly by genomic BLUP compared to methods that model LD and cosegregation explicitly (e.g., Meuwissen et al 2002;Fernando 2003;Pérez-Enciso 2003;Legarra and Fernando 2009)? This article has two objectives: (1) to present concepts that allow us to disentangle LD, cosegregation, and additivegenetic relationships and (2) to study the contributions of these parameters to accuracy of GEBVs depending on SNP density, training data size, and extent of LD. Dairy cattle and corn breeding scenarios were simulated to evaluate accuracy of GEBVs both within and across families obtained by different types of information, discrepancy between accuracy of GEBVs due to additive-genetic relationships and accuracy of traditional pedigree-based selection indexes, persistence of accuracy due to LD and due to cosegregation from one generation to the next without retraining, and the effect of each type of information on the correlation of GEBVs within families.…”

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confidence: 99%

Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

2013

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Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors. To understand better how genomic relationships capture relationships at QTL, we propose to apply concepts of pedigree analyses that define founders in a recent past generation. Based on these concepts, we show that coefficients of the genomic relationship matrix do not explain genetic covariances between individuals at QTL unless either there is linkage disequilibrium (LD) between QTL and SNPs measured in founders or selection candidates are related by pedigree to the training individuals. The latter results in cosegregation of alleles at QTL and SNPs that are

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“…In any case, the screening of high density SNP distributed along the sheep genome will, for sure, facilitate the identification of allelic variants directly associated with milk production traits and other traits of interest in dairy sheep. The whole genome association study (GWAS) approach, which directly exploit linkage disequilibrium between markers and causative mutations, together with scans based on the LDLA methodology (Legarra & Fernando, 2009;Druet et al, 2008), are currently under way in some of the resource populations previously used to map classical QTL detection. Several of these genome screenings are included in the framework of the European funded project Sustainable Solutions for Small Ruminants (3SR) where the genetic basis of resistance to mastitis, parasite infections and paratuberculosis in different European populations are under dissecting using the last genomic advances.…”

Section: Short Future Research and Expectationsmentioning

confidence: 99%