Beyond genomic selection: the animal model strikes back (one generation)!

Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.Instituto de Genética Veterinari

Beyond genomic selection: the animal model strikes back (one generation)!

Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.Instituto de Genética Veterinari

Efectos de la incorporación de las relaciones de parentesco sobre la estimación REML de la heredabilidad para rendimiento en soja ["Glycine max" (L.) Merr.]

El objetivo de este trabajo es estudiar los efectos de incluir la matriz de relaciones aditivas A en la evaluación de progenitores de variedades de soja empleando modelos lineales mixtos. Estos efectos se manifiestan en: i) las estimaciones REML (máxima verosimilitud restringida) de los componentes de varianza, y ii) la varianza asintótica de dichas estimaciones. Los datos utilizados provinieron de la red nacional de ensayos, donde se evaluaron 39 genotipos, durante cuatro años, en 15 localidades. La estimación de la heredabilidad (h2) mediante REML fue 0,15 0,0241 al incorporar A y 0,09 0,0185 sin dicha matriz. Asimismo, el coeficiente de variación de la estimación REML de la varianza aditiva al incluir A fue de 18, siendo igual a 22 al no considerar las relaciones aditivas. La inclusión de A en el modelo redujo la magnitud de la correlación entre los estimadores REML de la varianza aditiva y del error, siendo ¿0,07 y ¿0,10 incluyendo y excluyendo A, respectivamente. Por último, la aplicación de modelos mixtos aumentó la magnitud de la correlación de rangos de Spearman entre los órdenes de mérito de los cruzamientos predichos con los órdenes de mérito obtenidos experimentalmente: r = 0,51 (P < 0,001) para BLUP vs. r = 0,42 (P < 0,001) para el valor medio de los padres

10th WCGALP in beautiful Vancouver

The 10th World Congress was inaugurated by organizers Filippo Miglior and John Pollak in Vancouver at 8 pm on Sunday 17 Aug, preceded by a cocktail to warm up attendees' epigenomes. We return to these congresses each time in higher numbers, now over 1500 participants. The arrangements were very good and the weather cherished us all week, including the boat trip out to open sea among the small hydroplanes whirling up and down around us on the water. The new technology was adopted in presenting the posters (of rather dated outlay though) and the talks could now be easily found by author names and also re-listened to at the congress web site. It is not easy to itemise separate themes or avoid overlaps in reviewing the congress, where the sessions were thoroughly filled or hollowed by our extensive genome-wide studies.Peer reviewe

Quality control of genotypes using heritability estimates of gene content.

ABSTRACT.Quality control filtering of single nucleotide polymorphisms (SNP) is a key step when analyzing genomic data. Here, we present a practical method to identify poorly genotyped SNPs by detecting those for which gene content deviates from the pedigree-based expectation. This can be achieved by estimating the heritability of gene content at each marker, defined as the number of copies of a particular reference allele in a genotype of an animal (0, 1 or 2). The method uses Restricted Maximum Likelihood (REML) to estimate heritability of gene content at each SNP and also builds a likelihood ratio test statistic to test for zero error variance in genotyping. The proposed method is illustrated with a real dataset with genotypes from Illumina PorcineSNP60 chip

Componentes de (co)variância e parâmetros genéticos de características de crescimento da raça Simental no Brasil Variance components and genetic parameters estimates for growth traits of Simmental cattle in Brazil

Informações de genealogia e produção, cedidas pela Associação Brasileira de Criadores da Raça Simental (ABCRS), relativas aos pesos desde o nascimento até um ano de idade, foram utilizadas para estimar, sob modelos alternativos, os componentes de variância e os parâmetros genéticos em animais da raça Simental no Brasil. A matriz de parentesco incluiu 25.812 animais dos quais 7587 com dados de produção. O modelo 1 contém, além do erro, o efeito genético direto. Os modelos seguintes contêm os componentes do modelo 1, mais o efeito permanente de ambiente materno (modelo 2), ou o componente genético materno (modelo 3), ambos os componentes (modelo 5), os componentes do modelo 3 mais a covariância entre os efeitos genéticos direto e materno (modelo 4) e todos os componentes citados (modelo 6). Os modelos foram comparados pelo teste de razão de verossimilhança pelo chi² (P<0,01). Os componentes de variância e os valores de herdabilidades, estimados para os efeitos direto e materno, foram decrescentes, desde o modelo 1 até o modelo 6, na razão direta em que o modelo incorpora mais efeitos aleatórios. Para a fase de aleitamento foi encontrada variância genética nula, entretanto, alto valor para a variância de ambiente permanente. Os efeitos maternos, genético e de ambiente permanente são importantes para a raça Simental no Brasil e devem ser considerados em programas de seleção. Entretanto, os valores mais elevados de herdabilidade materna, encontrados com modelos sem efeito de ambiente permanente, sugerem que o método utilizado não discrimina apropriadamente esses efeitos, oriundos de mesma fonte de variação.<br>Birth, 100-day, weaning and yearling weights of 7587 Simmental cattle, and 25,812 pedigree data were used to estimate genetic parameters using different animal models. The simplest model (model l) included additive genetic and residual random effects. Models 2 and 3 were the same as model 1, but included, respectively, maternal permanent and maternal genetic effects. Model 4 did not include permanent effect. The most complete model (model 6) also included maternal additive and permanent effects, assuming covariance between them. Model 5 was the same as model 6, but did not included direct maternal covariance. Contemporary groups considered animals born in the same herd, year and season, from the same sex and raised under the same nutritional system. The models were compared using likelihood ratio tests. The (co)variance components and the genetic parameters decreased from the most simple (model 1) to the most complete model (model 6). One-hundred-day weight showed no (.00±.00) maternal genetic variance but moderate maternal environmental permanent effect (.17±.07). The maternal effects were important (P<.01) from birth to yearling. Maternal effects were important for weight traits of Simmental cattle and should be considered in genetic evaluations. However, comparing the values of maternal effects from models with and without permanent effects suggest confounding between these effects