Genome-enabled prediction through quantile random forest for complex traits / Predição genômica por meio do random forest quantílitico para características complexas

ABSTRACT: Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

RESUMO: Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.

Genome-enabled prediction through quantile random forest for complex traits / Predição genômica por meio do random forest quantílitico para características complexas

Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.

Overview of the use of genomic data in animal breeding / Panorama do uso de dados genômicos no melhoramento genético animal

ABSTRACT: The use of molecular information in breeding programs contributed to important advances in the improvement of traits of economic interest in livestock production. The advent of single nucleotide polymorphism (SNP) panels applied to genome-wide selection (GWS) and genome-wide association studies (GWAS), along with computational advances (e.g., use of powerful software and robust analyses) allowed a better understanding of the genetic architecture of farm animals and increased the selection efficiency. In this context, the statistic method single-step GBLUP has been frequently used to perform GWS, and more recently GWAS analyses, providing accurate predictions and QTL detection, respectively. Nevertheless, in developing countries, species such as sheep and goats, whose genomic data are more difficult to be obtained, the use of data simulation has been efficient in the study of the major factors involved in the selection process, such as size of training population, density of SNP chips, and genotyping strategies. The effects of these factors are directly associated with the prediction accuracy of genomic breeding values. In this review we showed important aspects of the use of genomics in the genetic improvement of production traits of animals, the main methods currently used for prediction and estimation of molecular marker effects, the importance of data simulation for validation of those methods, as well as the advantages, challenges and limitations of the use of GWS and GWAS in the current scenario of livestock production.

RESUMO: Em programas de melhoramento genético, o uso de informações moleculares garantiu importantes avanços para a melhoria de características de interesse econômico, no âmbito da produção animal. O advento da tecnologia de painéis de SNPs aplicados à seleção genômica ampla (GWS) e associação genômica ampla (GWAS), aliado ao avanço computacional, com o uso de softwares e análises robustas, permitiram melhor compreensão sobre a arquitetura genética dos animais de produção e, consequentemente, maior eficiência na seleção. Nesse contexto, o método estatístico single-step GBLUP tem sido utilizado, frequentemente, na execução da GWS e, mais recentemente, em GWAS, possibilitando predições acuradas e detecção de QTLs, respectivamente. No entanto, em países em desenvolvimento e, em espécies como os ovinos e caprinos, que existe maior dificuldade para a aquisição de dados genômicos, o uso da simulação de dados tem se mostrado eficiente para estudar os principais fatores envolvidos no processo de seleção, como o tamanho da população de treinamento, densidade de chipde SNPs e estratégias de genotipagem, cujos efeitos estão diretamente associados à acurácia da predição de valores genéticos genômicos. Nesta revisão, serão abordados pontos importantes sobre o uso da genômica no melhoramento genético de características produtivas em animais, principais métodos de predição e estimação de efeitos de marcadores moleculares na atualidade, a importância da simulação de dados para a validação desses métodos, bem como as vantagens, os desafios e as limitações no cenário atual da produção animal com o uso da seleção e associação genômica ampla.

Overview of the use of genomic data in animal breeding / Panorama do uso de dados genômicos no melhoramento genético animal

The use of molecular information in breeding programs contributed to important advances in the improvement of traits of economic interest in livestock production. The advent of single nucleotide polymorphism (SNP) panels applied to genome-wide selection (GWS) and genome-wide association studies (GWAS), along with computational advances (e.g., use of powerful software and robust analyses) allowed a better understanding of the genetic architecture of farm animals and increased the selection efficiency. In this context, the statistic method single-step GBLUP has been frequently used to perform GWS, and more recently GWAS analyses, providing accurate predictions and QTL detection, respectively. Nevertheless, in developing countries, species such as sheep and goats, whose genomic data are more difficult to be obtained, the use of data simulation has been efficient in the study of the major factors involved in the selection process, such as size of training population, density of SNP chips, and genotyping strategies. The effects of these factors are directly associated with the prediction accuracy of genomic breeding values. In this review we showed important aspects of the use of genomics in the genetic improvement of production traits of animals, the main methods currently used for prediction and estimation of molecular marker effects, the importance of data simulation for validation of those methods, as well as the advantages, challenges and limitations of the use of GWS and GWAS in the current scenario of livestock production.

Em programas de melhoramento genético, o uso de informações moleculares garantiu importantes avanços para a melhoria de características de interesse econômico, no âmbito da produção animal. O advento da tecnologia de painéis de SNPs aplicados à seleção genômica ampla (GWS) e associação genômica ampla (GWAS), aliado ao avanço computacional, com o uso de softwares e análises robustas, permitiram melhor compreensão sobre a arquitetura genética dos animais de produção e, consequentemente, maior eficiência na seleção. Nesse contexto, o método estatístico single-step GBLUP tem sido utilizado, frequentemente, na execução da GWS e, mais recentemente, em GWAS, possibilitando predições acuradas e detecção de QTLs, respectivamente. No entanto, em países em desenvolvimento e, em espécies como os ovinos e caprinos, que existe maior dificuldade para a aquisição de dados genômicos, o uso da simulação de dados tem se mostrado eficiente para estudar os principais fatores envolvidos no processo de seleção, como o tamanho da população de treinamento, densidade de chipde SNPs e estratégias de genotipagem, cujos efeitos estão diretamente associados à acurácia da predição de valores genéticos genômicos. Nesta revisão, serão abordados pontos importantes sobre o uso da genômica no melhoramento genético de características produtivas em animais, principais métodos de predição e estimação de efeitos de marcadores moleculares na atualidade, a importância da simulação de dados para a validação desses métodos, bem como as vantagens, os desafios e as limitações no cenário atual da produção animal com o uso da seleção e associação genômica ampla.

Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data

The development of efficient methods for genome-wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single-marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non-overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single-marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.(AU)

Genome-wide association mapping for heading date in oats under subtropical environments

Selection for heading date has been a decisive factor to increase areas cropped with oats in Brazil. Although important to oat breeders, genomic regions controlling heading date have not been completely identified. The objective of this study was to identify genomic regions controlling oat heading date in subtropical environments. A set of 412 oat genotypes, developed from 1974 to 2015, was assessed for heading date in contrasting environments and genotyped using genotyping-by-sequencing (GBS). Phenotypic and genotypic data were used in single and multi-environment association models. Quantitative trait loci (QTL) associated to heading date were identified on oat consensus groups Mrg02, Mrg05, Mrg06, Mrg12, and Mrg21. Some of the findings confirmed the association of genomic regions with heading date, while others emerge as new candidate regions associated to the trait. The genomic regions identified on Mrg02 and Mrg12 were associated to Vernalization 3 (Vrn3), while the genomic region identified on Mrg21 is associated with Vernalization 1 (Vrn1). The Vrn1 region was detected in Londrina, an environment with reduced vernalization condition, and in the multi-environment model. The results reveal that some genotypes of the panel are responsive to vernalization, increasing the days to heading without this environmental stimulus. Our results provide important contribution for a better understanding of heading date in subtropical environments and a strong basis for marker-assisted selection in oats.

Identification of patterns related to linkage groups or disequilibrium by factor analysis / Identificação de padrões relacionados a grupos de ligação ou de desequilíbrio por análise de fatores

Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.(AU)

Padrões empíricos de desequilíbrio de ligação (LD) podem ser utilizados para aumentar o poder estatístico do mapeamento genético. Este trabalho foi realizado com o objetivo de verificar a eficácia da análise de fatores (AF) aplicada a conjuntos de dados de marcadores moleculares do tipo SNP, visando identificar grupos de ligação e blocos de haplótipos. O conjunto de dados SNPs utilizado foi oriundo de um processo de simulação de uma população F2, contendo 2000 marcas com informações de 500 indivíduos. A estimação das cargas fatoriais (loadings) da AF foi feita de duas formas, considerando a matriz de distâncias entre os marcadores (A) e considerando a matriz de correlação (R). O número de fatores (k) a ser utilizado foi estabelecido com base no gráfico scree-plot e com base na proporção da variância total explicada. Os resultados indicam que as matrizes A e R conduzem a resultados similares. Com base no scree-plot considerou-se k igual a 10 e os fatores interpretados como sendo representativos dos grupos de ligação. O segundo critério conduziu a um número de fatores igual a 50, e os fatores interpretados como sendo representativos dos blocos de haplótipos. Isto mostra o potencial da técnica que permite obter resultados aplicáveis a qualquer tipo de população, corroborando a interpretação de estudos genômicos. O trabalho demonstrou que a AF foi capaz de identificar padrões de associação entre marcadores, identificando subgrupos de marcadores que refletem grupos de ligação fatorial e também grupos de desequilíbrio de ligação.(AU)

Association mapping of drought tolerance indices in wheat: QTL-rich regions on chromosome 4A

Drought is likely the main abiotic stress that affects wheat yield. The identification of drought-tolerant genotypes represents an effective way of dealing with the continuous decrease in water resources as well as the increase in world population. The aim of this study was to identify single nucleotide polymorphisms (SNP) associated with drought tolerance indices in wheat by using a genome-wide association study (GWAS) under fully irrigated and rain-fed conditions. The drought tolerance indices (i.e., Stress Susceptibility Index, Stress Tolerance Index, Tolerance Index and Yield Stability Index) were calculated based on grain yield, 1,000-kernel weight and kernels per spike. The association panel was genotyped using genotyping-by-sequencing (GBS). A total of 175 SNPs exhibited statistical evidence of association with at least one drought tolerance index, explaining up to 6 % of the phenotypic variation. Forty-five SNPs were associated with more than one tolerance index (up to 4 agronomic traits). Most associations were located on chromosome 4A, supporting the hypothesis that this chromosome has a key role in drought tolerance which should be exploited for wheat improvement. In addition, statistical analysis detected SNPs associated with tolerance indices in both growing seasons, providing information about genetic regions with stable effects under different environmental conditions. This GWAS experiment serves as one of the few studies on association mapping for drought tolerance indices in wheat, which could increase the efficiency of rain-fed and irrigated crop production.

Association mapping of drought tolerance indices in wheat: QTL-rich regions on chromosome 4A

Drought is likely the main abiotic stress that affects wheat yield. The identification of drought-tolerant genotypes represents an effective way of dealing with the continuous decrease in water resources as well as the increase in world population. The aim of this study was to identify single nucleotide polymorphisms (SNP) associated with drought tolerance indices in wheat by using a genome-wide association study (GWAS) under fully irrigated and rain-fed conditions. The drought tolerance indices (i.e., Stress Susceptibility Index, Stress Tolerance Index, Tolerance Index and Yield Stability Index) were calculated based on grain yield, 1,000-kernel weight and kernels per spike. The association panel was genotyped using genotyping-by-sequencing (GBS). A total of 175 SNPs exhibited statistical evidence of association with at least one drought tolerance index, explaining up to 6 % of the phenotypic variation. Forty-five SNPs were associated with more than one tolerance index (up to 4 agronomic traits). Most associations were located on chromosome 4A, supporting the hypothesis that this chromosome has a key role in drought tolerance which should be exploited for wheat improvement. In addition, statistical analysis detected SNPs associated with tolerance indices in both growing seasons, providing information about genetic regions with stable effects under different environmental conditions. This GWAS experiment serves as one of the few studies on association mapping for drought tolerance indices in wheat, which could increase the efficiency of rain-fed and irrigated crop production.(AU)

Introdução ao uso da seleção assistida por marcadores na aquicultura comercial

Com o crescimento nos estudos do melhoramento genético e análises de DNA, novas técnicas para a reprodução comercial foram inventadas. Com o advento dos marcadores moleculares, criou-se a possibilidade de selecionar genótipos superiores encontrando a associação do genótipo com o fenótipo desejado. Marcadores moleculares são ferramentas utilizadas para auxiliar na busca do loci de características quantitativas (QTL). O QTL são regiões cromossômicas no genoma animal, que definem características quantitativas. Características as quais, são de interesse comercial e difíceis de serem mensuradas. A localização do QTL é realizada por uma varredura no genoma animal, com o uso de diversos marcadores distribuídos por todo o genoma, para se localizar e avaliar associações com os fenótipos observados.  Com a localização do QTL no genoma de espécies da aquicultura, pode-se utilizar-se dessa informação para o uso da Seleção Assistida por Marcadores (SAM). A SAM consiste, através do uso de marcadores moleculares e a localização do QTL, selecionar características que são difíceis de mensurar e utilizar dessa informação na reprodução, para assim conseguir controlar características de interesse comercial como: tolerância a salinidade e temperatura, ganho de peso corporal e resistência a doenças. Através do uso da SAM na aquicultura comercial, possibilitou-se a criação de uma população de Paralic

QTLs for seedling traits under salinity stress in hexaploid wheat / Caracterização de QTLs por traços de sementes sob o estresse de salinidade em trigo Hexaploid

Soil salinity limits agricultural production and is a major obstacle for increasing crop yield. Common wheat is one of the most important crops with allohexaploid characteristic and a highly complex genome. QTL mapping is a useful way to identify genes for quantitative traits such as salinity tolerance in hexaploid wheat. In the present study, a hydroponic trial was carried out to identify quantitative trait loci (QTLs) associated with salinity tolerance of wheat under 150mM NaCl concentration using a recombinant inbred line population (Xiaoyan 54×Jing 411). Values of wheat seedling traits including maximum root length (MRL), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW) and the ratio of TDW of wheat plants between salt stress and control (TDWR) were evaluated or calculated. A total of 19QTLs for five traits were detected through composite interval mapping method by using QTL Cartographer version 2.5 under normal and salt stress conditions. These QTLs distributed on 12 chromosomes explained the percentage of phenotypic variation by individual QTL varying from 7.9% to 19.0%. Among them, 11 and six QTLs were detected under normal and salt stress conditions, respectively and two QTLs were detected for TDWR. Some salt tolerance related loci may be pleiotropic. Chromosome 1A, 3A and 7A may harbor crucial candidate genes associated with wheat salt tolerance. Our results would be helpful for the marker assisted selection to breed wheat varieties with improved salt tolerance.(AU)

A salinidade do solo limita a produção agrícola. O trigo mole é uma das culturas mais importantes com característica allohexaploid e genoma altamente complexo. O mapeamento QTL é uma maneira muito útil de identificar genes para traços quantitativos, como a tolerância à salinidade em trigo hexaplóide. No presente estudo realizou-se um ensaio hidropónico para identificar locos de traços quantitativos (QTLs) associados à tolerância à salinidade do trigo sob concentração de NaCl 150 mM, usando uma população de linhagem consanguíneo recombinante (Xiaoyan 54 × Jing 411). Os valores dos traços de mudas de trigo, incluindo comprimento máximo da raiz (MRL), peso seco da raiz (RDW), ponha o peso seco (SDW), peso seco total (TDW) e a proporção das plantas de trigo TDW entre o estresse salgado e o controle (TDWR), foram avaliados ou calculados. Um total de 19QTLs para cinco traços foram detectados através do método de mapeamento de intervalo composto usando a versão 2.5 do cartógrafo QTL sob condições normais e de estresse salino. Estes QTLs distribuídos em 12 cromossomos explicaram a porcentagem de variação fenotípica por QTL individual variando de 7,9% a 19,0%. Entre eles, foram detectados 11 e 6 QTLs em condições de estresse normal e sal, respectivamente, e dois QTLs foram detectados para TDWR. Cromossoma 1A, 3A e 7A podem conter genes que são candidatos cruciais associados à tolerância ao sal de trigo. Nossos resultados seriam úteis para a seleção assistida por marcadores para produzir variedades de trigo com tolerância salina melhorada.(AU)

QTLs for seedling traits under salinity stress in hexaploid wheat / Caracterização de QTLs por traços de sementes sob o estresse de salinidade em trigo Hexaploid

Soil salinity limits agricultural production and is a major obstacle for increasing crop yield. Common wheat is one of the most important crops with allohexaploid characteristic and a highly complex genome. QTL mapping is a useful way to identify genes for quantitative traits such as salinity tolerance in hexaploid wheat. In the present study, a hydroponic trial was carried out to identify quantitative trait loci (QTLs) associated with salinity tolerance of wheat under 150mM NaCl concentration using a recombinant inbred line population (Xiaoyan 54×Jing 411). Values of wheat seedling traits including maximum root length (MRL), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW) and the ratio of TDW of wheat plants between salt stress and control (TDWR) were evaluated or calculated. A total of 19QTLs for five traits were detected through composite interval mapping method by using QTL Cartographer version 2.5 under normal and salt stress conditions. These QTLs distributed on 12 chromosomes explained the percentage of phenotypic variation by individual QTL varying from 7.9% to 19.0%. Among them, 11 and six QTLs were detected under normal and salt stress conditions, respectively and two QTLs were detected for TDWR. Some salt tolerance related loci may be pleiotropic. Chromosome 1A, 3A and 7A may harbor crucial candidate genes associated with wheat salt tolerance. Our results would be helpful for the marker assisted selection to breed wheat varieties with improved salt tolerance.

A salinidade do solo limita a produção agrícola. O trigo mole é uma das culturas mais importantes com característica allohexaploid e genoma altamente complexo. O mapeamento QTL é uma maneira muito útil de identificar genes para traços quantitativos, como a tolerância à salinidade em trigo hexaplóide. No presente estudo realizou-se um ensaio hidropónico para identificar locos de traços quantitativos (QTLs) associados à tolerância à salinidade do trigo sob concentração de NaCl 150 mM, usando uma população de linhagem consanguíneo recombinante (Xiaoyan 54 × Jing 411). Os valores dos traços de mudas de trigo, incluindo comprimento máximo da raiz (MRL), peso seco da raiz (RDW), ponha o peso seco (SDW), peso seco total (TDW) e a proporção das plantas de trigo TDW entre o estresse salgado e o controle (TDWR), foram avaliados ou calculados. Um total de 19QTLs para cinco traços foram detectados através do método de mapeamento de intervalo composto usando a versão 2.5 do cartógrafo QTL sob condições normais e de estresse salino. Estes QTLs distribuídos em 12 cromossomos explicaram a porcentagem de variação fenotípica por QTL individual variando de 7,9% a 19,0%. Entre eles, foram detectados 11 e 6 QTLs em condições de estresse normal e sal, respectivamente, e dois QTLs foram detectados para TDWR. Cromossoma 1A, 3A e 7A podem conter genes que são candidatos cruciais associados à tolerância ao sal de trigo. Nossos resultados seriam úteis para a seleção assistida por marcadores para produzir variedades de trigo com tolerância salina melhorada.

Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methods / Avaliação de dados genômicos imputados em características distintas usando os métodos de Random Forest e de limiares Bayesianos

The objectives of this study were (1) to quantify imputation accuracy and to assess the factors affecting it; and (2) to evaluate the accuracy of threshold BayesA (TBA), Bayesian threshold LASSO (BTL) and random forest (RF) algorithms to analyze discrete traits. Genomic data were simulated to reflect variations in heritability (h2 = 0.30 and 0.10), number of QTL (QTL = 81 and 810), number of SNP (10 K and 50 K) and linkage disequilibrium (LD=low and high) for 27 chromosomes. For real condition simulating, we randomly masked markers with 90% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software. In imputed genotypes, a wide range of accuracy was observed for RF (0.164-0.512) compared to TBA (0.283-0.469) and BTL (0.272-0.504). Comparing to original genotypes, using imputed genotypes decreased the average accuracy of genomic prediction about 0.0273 (range of 0.024 to 0.036). Comparing to Bayesian threshold, using RF was improved rapidly accuracy of genomic prediction with increase in the marker density. Despite the higher accuracy of BTL and TBA at different levels of LD and heritability, the increase in accuracy was greater for RF. Furthermore, the best method for prediction of genomic accuracy depends on genomic architecture of population.(AU)

Os objetivos deste estudo foram (1) quantificar a precisão de imputação e acessar os fatores que as afetam; e (2) avaliar a precisão do princípio de BayesA (TBA), do modelo Bayesiano LASSO (BTL), e o algoritmo Random Forest para analisar as características distintas. Dados genômicos foram simulados para indicar variações na herdabilidade (h2 = 0.30 e 0.10), número de QTL (QTL = 81 e 810), número de SNP (10 k e 50 k) e desequilíbrio de ligação (LD = baixo e alto) para 27 cromossomos. Para uma simulação mais realista, nós cobrimos os marcadores aleatoriamente com 90% da taxa ausente para cada cenário, depois, os marcadores foram imputados usando o software FImpute. Nos genótipos imputados uma grande oscilação de precisão foi observada pelo modelo RF (0.164-0.512) comparado com TBA (0.283 - 0.469) e BTL (0.272 - 0.504). Comparando com os genótipos originais, os genótipos imputados decaíram a precisão média da predição genômica em cerca de 0.0273 (oscilação de 0.024 para 0.036). Comparando-se ao princípio Bayesiano, o uso de RF melhorou a precisão de predição com o aumento da densidade do marcador. Além disso, o melhor método para predição de precisão genômica depende da arquitetura genômica da sua população.(AU)

Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methods / Avaliação de dados genômicos imputados em características distintas usando os métodos de Random Forest e de limiares Bayesianos

The objectives of this study were (1) to quantify imputation accuracy and to assess the factors affecting it; and (2) to evaluate the accuracy of threshold BayesA (TBA), Bayesian threshold LASSO (BTL) and random forest (RF) algorithms to analyze discrete traits. Genomic data were simulated to reflect variations in heritability (h2 = 0.30 and 0.10), number of QTL (QTL = 81 and 810), number of SNP (10 K and 50 K) and linkage disequilibrium (LD=low and high) for 27 chromosomes. For real condition simulating, we randomly masked markers with 90% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software. In imputed genotypes, a wide range of accuracy was observed for RF (0.164-0.512) compared to TBA (0.283-0.469) and BTL (0.272-0.504). Comparing to original genotypes, using imputed genotypes decreased the average accuracy of genomic prediction about 0.0273 (range of 0.024 to 0.036). Comparing to Bayesian threshold, using RF was improved rapidly accuracy of genomic prediction with increase in the marker density. Despite the higher accuracy of BTL and TBA at different levels of LD and heritability, the increase in accuracy was greater for RF. Furthermore, the best method for prediction of genomic accuracy depends on genomic architecture of population.

Os objetivos deste estudo foram (1) quantificar a precisão de imputação e acessar os fatores que as afetam; e (2) avaliar a precisão do princípio de BayesA (TBA), do modelo Bayesiano LASSO (BTL), e o algoritmo Random Forest para analisar as características distintas. Dados genômicos foram simulados para indicar variações na herdabilidade (h2 = 0.30 e 0.10), número de QTL (QTL = 81 e 810), número de SNP (10 k e 50 k) e desequilíbrio de ligação (LD = baixo e alto) para 27 cromossomos. Para uma simulação mais realista, nós cobrimos os marcadores aleatoriamente com 90% da taxa ausente para cada cenário, depois, os marcadores foram imputados usando o software FImpute. Nos genótipos imputados uma grande oscilação de precisão foi observada pelo modelo RF (0.164-0.512) comparado com TBA (0.283 - 0.469) e BTL (0.272 - 0.504). Comparando com os genótipos originais, os genótipos imputados decaíram a precisão média da predição genômica em cerca de 0.0273 (oscilação de 0.024 para 0.036). Comparando-se ao princípio Bayesiano, o uso de RF melhorou a precisão de predição com o aumento da densidade do marcador. Além disso, o melhor método para predição de precisão genômica depende da arquitetura genômica da sua população.

Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations

Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy.

Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations

Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy.(AU)

Genome selection in fruit breeding: application to table grapes

Genomic selection (GS) has recently been proposed as a new selection strategy which represents an innovative paradigm in crop improvement, now widely adopted in animal breeding. Genomic selection relies on phenotyping and high-density genotyping of a sufficiently large and representative sample of the target breeding population, so that the majority of loci that regulate a quantitative trait are in linkage disequilibrium with one or more molecular markers and can thus be captured by selection. In this study we address genomic selection in a practical fruit breeding context applying it to a breeding population of table grape obtained from a cross between the hybrid genotype D8909-15 (Vitis rupestris × Vitis arizonica/girdiana), which is resistant to dagger nematode and Pierces disease (PD), and B90-116, a susceptible Vitis vinifera cultivar with desirable fruit characteristics. Our aim was to enhance the knowledge on the genomic variation of agronomical traits in table grape populations for future use in marker-assisted selection (MAS) and GS, by discovering a set of molecular markers associated with genomic regions involved in this variation. A number of Quantitative Trait Loci (QTL) were discovered but this method is inaccurate and the genetic architecture of the studied population was better captured by the BLasso method of genomic selection, which allowed for efficient inference about the genetic contribution of the various marker loci. The technology of genomic selection afforded greater efficiency than QTL analysis and can be very useful in speeding up the selection procedures for agronomic traits in table grapes.

Genome selection in fruit breeding: application to table grapes

Genomic selection (GS) has recently been proposed as a new selection strategy which represents an innovative paradigm in crop improvement, now widely adopted in animal breeding. Genomic selection relies on phenotyping and high-density genotyping of a sufficiently large and representative sample of the target breeding population, so that the majority of loci that regulate a quantitative trait are in linkage disequilibrium with one or more molecular markers and can thus be captured by selection. In this study we address genomic selection in a practical fruit breeding context applying it to a breeding population of table grape obtained from a cross between the hybrid genotype D8909-15 (Vitis rupestris × Vitis arizonica/girdiana), which is resistant to dagger nematode and Pierces disease (PD), and B90-116, a susceptible Vitis vinifera cultivar with desirable fruit characteristics. Our aim was to enhance the knowledge on the genomic variation of agronomical traits in table grape populations for future use in marker-assisted selection (MAS) and GS, by discovering a set of molecular markers associated with genomic regions involved in this variation. A number of Quantitative Trait Loci (QTL) were discovered but this method is inaccurate and the genetic architecture of the studied population was better captured by the BLasso method of genomic selection, which allowed for efficient inference about the genetic contribution of the various marker loci. The technology of genomic selection afforded greater efficiency than QTL analysis and can be very useful in speeding up the selection procedures for agronomic traits in table grapes.(AU)

Quantitative genetics theory for genomic selection and efficiency of breeding value prediction in open-pollinated populations

To date, the quantitative genetics theory for genomic selection has focused mainly on the relationship between marker and additive variances assuming one marker and one quantitative trait locus (QTL). This study extends the quantitative genetics theory to genomic selection in order to prove that prediction of breeding values based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance. We also assessed the efficiency of genomic selection in relation to phenotypic selection, assuming mass selection in an open-pollinated population, all QTLs of lower effect, and reduced sample size, based on simulated data. We show that the average effect of a SNP substitution is proportional to LD measure and to average effect of a gene substitution for each QTL that is in LD with the marker. Weighted (by SNP frequencies) and unweighted breeding value predictors have the same accuracy. Efficiency of genomic selection in relation to phenotypic selection is inversely proportional to heritability. Accuracy of breeding value prediction is not affected by the dominance degree and the method of analysis, however, it is influenced by LD extent and magnitude of additive variance. The increase in the number of markers asymptotically improved accuracy of breeding value prediction. The decrease in the sample size from 500 to 200 did not reduce considerably accuracy of breeding value prediction.

Quantitative genetics theory for genomic selection and efficiency of breeding value prediction in open-pollinated populations

To date, the quantitative genetics theory for genomic selection has focused mainly on the relationship between marker and additive variances assuming one marker and one quantitative trait locus (QTL). This study extends the quantitative genetics theory to genomic selection in order to prove that prediction of breeding values based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance. We also assessed the efficiency of genomic selection in relation to phenotypic selection, assuming mass selection in an open-pollinated population, all QTLs of lower effect, and reduced sample size, based on simulated data. We show that the average effect of a SNP substitution is proportional to LD measure and to average effect of a gene substitution for each QTL that is in LD with the marker. Weighted (by SNP frequencies) and unweighted breeding value predictors have the same accuracy. Efficiency of genomic selection in relation to phenotypic selection is inversely proportional to heritability. Accuracy of breeding value prediction is not affected by the dominance degree and the method of analysis, however, it is influenced by LD extent and magnitude of additive variance. The increase in the number of markers asymptotically improved accuracy of breeding value prediction. The decrease in the sample size from 500 to 200 did not reduce considerably accuracy of breeding value prediction.(AU)