A Genetic Programming Model for Association Studies to Detect Epistasis in Low Heritability Data

The genome-wide associations studies (GWAS) aims to identify the most influential markers in relation to the phenotype values. One of the substantial challenges is to find a non-linear mapping between genotype and phenotype, also known as epistasis, that usually becomes the process of searching and identifying functional SNPs more complex. Some diseases such as cervical cancer, leukemia and type 2 diabetes have low heritability. The heritability of the sample is directly related to the explanation defined by the genotype, so the lower the heritability the greater the influence of the environmental factors and the less the genotypic explanation. In this work, an algorithm capable of identifying epistatic associations at different levels of heritability is proposed. The developing model is a aplication of genetic programming with a specialized initialization for the initial population consisting of a random forest strategy. The initialization process aims to rank the most important SNPs increasing the probability of their insertion in the initial population of the genetic programming model. The expected behavior of the presented model for the obtainment of the causal markers intends to be robust in relation to the heritability level. The simulated experiments are case-control type with heritability level of 0.4, 0.3, 0.2 and 0.1 considering scenarios with 100 and 1000 markers. Our approach was compared with the GPAS software and a genetic programming algorithm without the initialization step. The results show that the use of an efficient population initialization method based on ranking strategy is very promising compared to other models

Population structure of Girolando breed

O objetivo neste estudo foi avaliar a estrutura genética da população de bovinos da raça Girolando no Brasil. Analisou-se o arquivo de pedigree de 26.969 animais, composto de 3.031 machos e 23.938 fêmeas. O nível de conteúdo de informação do pedigree na geração atual foi 61%, mostrando ser de qualidade moderada. O coefi ciente de endogamia médio e o coefi ciente de relação médio da população Girolando foram 0,11 e 0,13%, respectivamente. O tamanho efetivo da população, considerando a geração completa traçada, foi 188, acima do nível crítico. Do total de 9.457 ancestrais que contribuíram para a população de referência, 457 explicaram 50% da variabilidade genética da população. O número efetivo de fundadores foi 551 e o de ancestrais 393. O intervalo médio de geração foi de 5,26 anos, sendo ligeiramente maior nas trilhas gaméticas mãe-fi lho e pai-fi lha. A partir dos coefi cientes estimados, pode-se concluir que a endogamia nos rebanhos da raça Girolando foi de pequena magnitude e que as práticas de acasalamento foram adequadas durante o período avaliado. No entanto, é importante continuar com o monitoramento desses coefi cientes a fi m de prevenir perda de variabilidade genéticaThe aim of this study was to evaluate the population structure of Girolando cattle in Brazil. The pedigree fi le contained 26,969 individuals, from which 3,031 were males and 23,938 were females. The average level of completeness of the pedigree in the current generation was of reasonable quality (61%). Inbreeding and average relatedness coeffi cients were low: 0.11 and 0.13%, respectively. Estimates of effective population size considering the full generations traced was 188, which is above the critical level range. The number of ancestors that contributed to the reference population was 9,457 animals, from which 457 explained 50% of the genetic variability of the population. The effective number of founders and the effective number of ancestors in this population were, respectively, 551 and 393. The average generation interval was 5.26 years, slightly higher in genetic pathways dam-son and sire-daughter. The inbreeding in the Girolando breed was of small magnitude, indicating that the current practices of mating were adequate during the study period. However, it is important to continue monitoring these coeffi cients in order to prevent loss of genetic variabilit

Genetic parameters for milk yield and lactation persistency using random regression models in Girolando cattle

A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre’s polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre’s polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from −0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from −0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits

Estrutura populacional da raça Girolando

O objetivo neste estudo foi avaliar a estrutura genética da população de bovinos da raça Girolando no Brasil. Analisou-se o arquivo de pedigree de 26.969 animais, composto de 3.031 machos e 23.938 fêmeas. O nível de conteúdo de informação do pedigree na geração atual foi 61%, mostrando ser de qualidade moderada. O coeficiente de endogamia médio e o coeficiente de relação médio da população Girolando foram 0,11 e 0,13%, respectivamente. O tamanho efetivo da população, considerando a geração completa traçada, foi 188, acima do nível crítico. Do total de 9.457 ancestrais que contribuíram para a população de referência, 457 explicaram 50% da variabilidade genética da população. O número efetivo de fundadores foi 551 e o de ancestrais 393. O intervalo médio de geração foi de 5,26 anos, sendo ligeiramente maior nas trilhas gaméticas mãe-filho e pai-filha. A partir dos coeficientes estimados, pode-se concluir que a endogamia nos rebanhos da raça Girolando foi de pequena magnitude e que as práticas de acasalamento foram adequadas durante o período avaliado. No entanto, é importante continuar com o monitoramento desses coeficientes a fim de prevenir perda de variabilidade genétic

Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied

Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

ABSTRACT: Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied