Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.Fil: Gualdron Duarte, Jose Luis. Michigan State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, Ronald O.. Michigan State University; Estados UnidosFil: Ernst, Catherine W.. Michigan State University; Estados UnidosFil: Raney, Nancy E.. Michigan State University; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, Juan P.. Michigan State University; Estados Unido

Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations

Background: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. Results: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements. The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. Conclusions: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.Fil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, Ronald O.. Michigan State University; Estados UnidosFil: Ernst, Catherine W.. Michigan State University; Estados UnidosFil: Raney, Nancy E.. Michigan State University; Estados UnidosFil: Steibel, Juan P.. Michigan State University; Estados Unido

Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle.

BACKGROUND: Genomic selection has been successfully implemented in many livestock and crop species. The genomic best linear unbiased predictor (GBLUP) approach, assigning equal variance to all SNP effects, is one of the reference methods. When large-effect variants contribute to complex traits, it has been shown that genomic prediction methods that assign a higher variance to subsets of SNP effects can achieve higher prediction accuracy. We herein compared the efficiency of several such approaches, including the Adaptive MultiBLUP (AM-BLUP) that uses local genomic relationship matrices (GRM) to automatically identify and weight genomic regions with large effects, to predict genetic merit in Belgian Blue beef cattle. RESULTS: We used a population of approximately 10,000 genotyped cows and their phenotypes for 14 traits, mostly related to muscular development and body dimensions. According to the trait, we found that 4 to 25% of the genetic variance could be associated with 2 to 12 genomic regions harbouring large-effect variants. Noteworthy, three previously identified recessive deleterious variants presented heterozygote advantage and were among the most significant SNPs for several traits. The AM-BLUP resulted in increased reliability of genomic predictions compared to GBLUP (+ 2%), but Bayesian methods proved more efficient (+ 3%). Overall, the reliability gains remained thus limited although higher gains were observed for skin thickness, a trait affected by two genomic regions having particularly large effects. Higher accuracies than those from the original AM-BLUP were achieved when applying the Bayesian Sparse Linear Mixed Model to pre-select groups of SNPs with large effects and subsequently use their estimated variance to build a weighted GRM. Finally, the single-step GBLUP performed best and could be further improved (+ 3% prediction accuracy) by using these weighted GRM. CONCLUSIONS: The AM-BLUP is an attractive method to automatically identify and weight genomic regions with large effects on complex traits. However, the method was less accurate than Bayesian methods. Overall, weighted methods achieved modest accuracy gains compared to GBLUP. Nevertheless, the computational efficiency of the AM-BLUP might be valuable at higher marker density, including with whole-genome sequencing data. Furthermore, weighted GRM are particularly useful to account for large variance loci in the single-step GBLUP

Sequence-based multi-trait genome-wide association study for linear classification traits in Belgian Blue beef cattle

peer reviewe

A hypomorphic mutation in the ATP2A1 gene increases muscle mass yet compromises meat quality of Belgian Blue cattle

peer reviewe

Meta-analysis of genome-wide association from genomic prediction models

Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.Fil: Bernal Rubio, Yeni Liliana. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, R. O.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; ArgentinaFil: Ernst, C. W.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; ArgentinaFil: Nonneman, D.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Rohrer, G. A.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: King, A.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Shackelford, S. D.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Wheeler, T. L.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, J. P.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unido

Enrichment of causative variants in tissue-specific and shared ATAC-Seq peaks in cattle

peer reviewe

Selección Genómica

La evaluación genética animal tiene como objeto la predicción del mérito genético individual o valor de cría (en inglés, breeding value, BV). En el modelo clásico de la genética cuantitativa, los BVs de los caracteres de importancia económica son controlados por un número muy grande (más formalmente, tendiendo a infinito) de genes: el modelo infinitesimal. Estos genes actúan en forma aditiva sin ligamiento entre ellos, cada uno con un efecto pequeño sobre la expresión fenotípica del carácter (Bulmer, 1985). En consecuencia, y hasta hoy, la selección en animales se realizó asumiendo una distribución normal para el BV resultante de una suma que tiende a infinito de variables aleatorias cada una de valor muy pequeño. En la actualidad cada uno de los genes o fracciones del genoma que afectan los caracteres productivos bajo un modelo infinitesimal es conocido como QTL (en inglés Quantitative Trait Locus). Si bien grande, el máximo número de genes en un genoma como por ejemplo el humano, se estima en el rango entre 20.000 a 25.000 (International Human Consortium, 2004), ciertamente un número finito. Los avances en genética molecular han mostrado que las características cuantitativas están afectadas por unos pocos genes con gran impacto y muchos con escaso, de modo tal que la distribución del efecto de los QTLs es aproximadamente exponencial (Otto y Jones, 2000)Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal. Cátedra de Mejoramiento Genético Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; ArgentinaFil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; ArgentinaFil: Munilla Leguizamon, Sebastian. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal. Cátedra de Mejoramiento Genético Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; Argentin