A comparative study of three methods for detecting association of quantitative traits in samples of related subjects

We used Genetic Analysis Workshop 16 Problem 3 Framingham Heart Study simulated data set to compare methods for association analysis of quantitative traits in related individuals. More specifically, we investigated type I error and relative power of three approaches: the measured genotype, the quantitative transmission-disequilibrium test (QTDT), and the quantitative trait linkage-disequilibrium (QTLD) tests. We studied high-density lipoprotein and triglyceride (TG) lipid variables, as measured at Visit 1. Knowing the answers, we selected three true major genes for high-density lipoprotein and/or TG. Empirical distributions of the three association models were derived from the first 100 replicates. In these data, all three models were similar in error rates. Across the three association models, the power was the lowest for the functional SNP with smallest size effects (i.e., α2), and for the less heritable trait (i.e., TG). Our results showed that measured genotype outperformed the two orthogonal-based association models (QTLD, QTDT), even after accounting for population stratification. QTDT had the lowest power rates. This is consistent with the amount of marker and trait data used by each association model. While the effective sample sizes varied little across our tested variants, we observed some large power drops and marked differences in performances of the models. We found that the performances contrasted the most for the tightly linked, but not associated, functional variants

Efecto de la varianza genética aditiva generacional sobre las componentes de la respuesta a la selección en una población con generaciones superpuestas

p.221-230El objetivo de esta investigación fue comparar la respuesta a la selección usando el Modelo Animal-BLUP con grupos genéticos, utilizando la variancia genética aditiva de cada generación (s²A(C), con aquel que utiliza la variancia aditiva en la población base (s²a), mediante simulación estocastica de una población animal con generaciones superpuestas. A diferencia de otros estudios, el modelo de generación de datos incluyó efectos fijos como el sexo (variable clasificatoria) y la edad del animal a la medición del carácter (covariable), con el objeto de asemejarse a los modelos de evaluación en poblaciones reales. Los resultados corresponden a 20 años de selección, tomando el promedio de 100 réplicas. La h² original en la población fue 0,4. La pérdida de información consistió en omitir al azar relaciones de parentesco, afín de incorporar los grupos al modelo de evaluación animal. El 25 por ciento de los animales poseían ambos padres desconocidos, 25 por ciento poseían la madre desconocida, 25 por ciento el padre y el 25 por ciento restante poseían ambos padres conocidos. En las condiciones simuladas no se observaron diferencias significativas (pmayor a 0,05), en las variables estudiadas: respuesta a la selección, variancia aditiva, exactitud, intensidad de selección, consanguinidad e intervalo generacional, para los casos de información completa e incompleta con la inclusión de grupos, según se consideró las s²a ó la s²a(g

Weighting familiar and individual information in animal models and BLUP: 1. Genetic group models, 2. Uncertain paternity models

El objetivo de este trabajo fue mostrar analíticamente como el modelo animal y BLUP "funcionan", ponderando de forma diferente la información familiar e individual. Para ello se consideraron dos situaciones clásicas de desconocimiento de la genealogía: el modelo con grupos genéticos y con paternidad incierta. La importancia relativa de la información individual y familiar se analizó para diversos valores de heredabiiidad y de consanguinidad.The goal of this research was to show analytically how thc animal model and BLUP "work", weighting differently individual and family information (by the data of parents and sibs). Two classic situations of unknown genealogy were considered: model with genetic groups and with uncertain paternity. The weights of individual and family information were analyzed for different values of heritability and of inbreeding.Fil: Vitezica, Zulma G.. Institut National de la Recherche Agronomique; Francia. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; ArgentinaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; Argentin

SNP-based mate allocation strategies to maximize total genetic value in pigs

International audienceAbstractBackgroundMate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored.ResultsGenetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE.ConclusionsGenomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain

Linkage analysis of high myopia susceptibility locus in 26 families

Purpose: We conducted a linkage analysis in high myopia families to replicate suggestive results from chromosome 7q36 using a model of autosomal dominant inheritance and genetic heterogeneity. We also performed a genome-wide scan to identify novel loci. Methods: Twenty-six families, with at least two high-myopic subjects (ie. refractive value in the less affected eye of -5 diopters) in each family, were included. Phenotypic examination included standard autorefractometry, ultrasonographic eye length measurement, and clinical confirmation of the non-syndromic character of the refractive disorder. Nine families were collected de novo including 136 available members of whom 34 were highly myopic subjects. Twenty new subjects were added in 5 of the 17 remaining families. A total of 233 subjects were submitted to a genome scan using ABI linkage mapping set LMSv2-MD-10, additional markers in all regions where preliminary LOD scores were greater than 1.5 were used. Multipoint parametric and non-parametric analyses were conducted with the software packages Genehunter 2.0 and Merlin 1.0.1. Two autosomal recessive, two autosomal dominant, and four autosomal additive models were used in the parametric linkage analyses. Results: No linkage was found using the subset of nine newly collected families. Study of the entire population of 26 families with a parametric model did not yield a significant LOD score (>3), even for the previously suggestive locus on 7q36. A non-parametric model demonstrated significant linkage to chromosome 7p15 in the entire population (Z-NPL=4.07, p=0.00002). The interval is 7.81 centiMorgans (cM) between markers D7S2458 and D7S2515. Conclusions: The significant interval reported here needs confirmation in other cohorts. Among possible susceptibility genes in the interval, certain candidates are likely to be involved in eye growth and development

Quantitative trait loci linked to PRNP gene controlling health and production traits in INRA 401 sheep

In this study, the potential association of PrP genotypes with health and productive traits was investigated. Data were recorded on animals of the INRA 401 breed from the Bourges-La Sapinière INRA experimental farm. The population consisted of 30 rams and 852 ewes, which produced 1310 lambs. The animals were categorized into three PrP genotype classes: ARR homozygous, ARR heterozygous, and animals without any ARR allele. Two analyses differing in the approach considered were carried out. Firstly, the potential association of the PrP genotype with disease (Salmonella resistance) and production (wool and carcass) traits was studied. The data used included 1042, 1043 and 1013 genotyped animals for the Salmonella resistance, wool and carcass traits, respectively. The different traits were analyzed using an animal model, where the PrP genotype effect was included as a fixed effect. Association analyses do not indicate any evidence of an effect of PrP genotypes on traits studied in this breed. Secondly, a quantitative trait loci (QTL) detection approach using the PRNP gene as a marker was applied on ovine chromosome 13. Interval mapping was used. Evidence for one QTL affecting mean fiber diameter was found at 25 cM from the PRNP gene. However, a linkage between PRNP and this QTL does not imply unfavorable linkage disequilibrium for PRNP selection purposes