Treatment of long-term stored DNA- Comparison between different methods to obtain high-quality materia

Long-term stored DNA can be sometimes the only source of genetic material of an organism that does not exist anymore, but a research interest still persists. However, there is a lack of information about useful methods to improve quality from such type of material. In this study, we compared four different protocols using DNA samples collected in 1998. Fresh DNA was also tested aiming to check the differences between these two material types. Sixteen samples of each DNA type treated with phenol-chloroform with PEG 5.0%, silica-gel membrane spin column, PEG 7.5%, and glass-fiber matrix spin column were submitted to spectrophotometer measurements, electrophoresis, PCR, and RFLP-PCR to assess the best method concerning yield, quality, and purity. Based on the results, purification with PEG 7.5% was considered the best method to treat aged DNA samples. In addition to the efficiency, this protocol has low cost. Analyzing the data, we also conclude that long-term stored DNA may be considered a reliable and potential resource for future molecular studies.Long-term stored DNA can be sometimes the only source of genetic material of an organism that does not exist anymore, but a research interest still persists. However, there is a lack of information about useful methods to improve quality from such type of material. In this study, we compared four different protocols using DNA samples collected in 1998. Fresh DNA was also tested aiming to check the differences between these two material types. Sixteen samples of each DNA type treated with phenol-chloroform with PEG 5.0%, silica-gel membrane spin column, PEG 7.5%, and glass-fiber matrix spin column were submitted to spectrophotometer measurements, electrophoresis, PCR, and RFLP-PCR to assess the best method concerning yield, quality, and purity. Based on the results, purification with PEG 7.5% was considered the best method to treat aged DNA samples. In addition to the efficiency, this protocol has low cost. Analyzing the data, we also conclude that long-term stored DNA may be considered a reliable and potential resource for future molecular studies

Genomic selection for boar taint compounds and carcass traits in a commercial pig population

AbstractThis study aimed to compare two different Genome-Wide Selection (GWS) methods (Ridge Regression BLUP − RR-BLUP and Bayesian LASSO − BL) to predict the genomic estimated breeding values (GEBV) of four phenotypes, including two boar taint compounds, i.e., the concentrations of androstenone (andro) and skatole (ska), and two carcass traits, i.e., backfat thickness (fat) and loin depth (loin), which were measured in a commercial male pig line. Six hundred twenty-two boars were genotyped for 2,500 previously selected single nucleotide polymorphisms (SNPs). The accuracies of the GEBV using both methods were estimated based on Jack-knife cross-validation. The BL showed the best performance for the andro, ska and loin traits, which had accuracy values of 0.65, 0.58 and 0.33, respectively; for the fat trait, the RR-BLUP accuracy of 0.61 outperformed the BL accuracy of 0.56. Considering that BL was more accurate for the majority of the traits, this method is the most favoured for GWS under the conditions of this study. The most relevant SNPs for each trait were located in the chromosome regions that were previously indicated as QTL regions in other studies, i.e., SSC6 for andro and ska, SSC2 for fat, and SSC11, SSC15 and SSC17 for loin

Toll-Like Receptor 6 differential expression in two pig genetic groups vaccinated against Mycoplasma hyopneumoniae

<p>Abstract</p> <p>Background</p> <p><it>Mycoplasma hyopneumoniae</it> is the etiologic agent of enzootic pneumonia, which causes important economic losses to swine industry. The Toll-like receptors (TLRs) are pattern-recognition receptors which detect microbial presence and initiate the innate as well as the adaptative immune defense. Toll-like receptor 6 is a type I transmembrane protein that recognizes bacterial components. The aim of this study was to compare mRNA expression pattern of TLR6 gene in two genetically distinct groups of pigs vaccinated against <it>Mycoplasma hyopneumoniae.</it></p> <p>Methods</p> <p>For each genetic group, peripheral blood was collected just before and 10 days after vaccination from 10 Naturalized Brazilian Piau breed and 10 Commercial White Line serum-negative female piglets. RNA was extracted from peripheral blood mononuclear cells (PBMCs), reverse transcripted and the qRT-PCR performed using SYBR green fluorescence system, using GAPDH gene as endogenous control. Analyses were performed by UNIVARIATE (Shapiro-Wilk test) and MIXED procedures of SAS software (version 9.0).</p> <p>Results</p> <p>It was observed significant interaction between breed and vaccination, being the TLR6 mRNA expression higher in the Commercial White line than in the Piau breed after vaccination. Furthermore, there was differential expression before and after vaccination in the Commercial White line.</p> <p>Conclusions</p> <p>Analysis of in TLR6 gene expression showed difference between the two distinct genetic groups, however, other TLRs gene expression must be evaluated for a better understanding of innate resistance in the pig concerning <it>Mycoplasma hyopneumoniae</it> infection.</p

Alternative measures to evaluate the accuracy and bias of genomic predictions with censored records

This study aimed to propose and compare metrics of accuracy and bias of genomic prediction of breeding values for traits with censored data. Genotypic and censored-phenotypic information were simulated for four traits with QTL heritability and polygenic heritability, respectively: C1: 0.07-0.07, C2: 0.07-0.00, C3: 0.27-0.27, and C4: 0.27-0.00. Genomic breeding values were predicted using the Mixed Cox and Truncated Normal models. The accuracy of the models was estimated based on the Pearson (PC), maximal (MC), and Pearson correlation for censored data (PCC) while the genomic bias was calculated via simple linear regression (SLR) and Tobit (TB). MC and PCC were statistically superior to PC for the trait C3 with 10 and 40% censored information, for 70% censorship, PCC yielded better results than MC and PC. For the other traits, the proposed measures were superior or statistically equal to the PC. The coefficients associated with the marginal effects (TB) presented estimates close to those obtained for the SLR method, while the coefficient related to the latent variable showed almost unchanged pattern with the increase in censorship in most cases. From a statistical point of view, the use of methodologies for censored data should be prioritized, even for low censoring percentages

Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models.

peer reviewedThe aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from -0.22 to -0.59), between milk yield and milk FA (rg from -0.22 to -0.62), and between protein yield and milk FA (rg from -0.11 to -0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle

Desequilíbrio de ligação e blocos de haplótipo em seis linhas comerciais de suíno

O sucesso de estudos de associação e, consequentemente, a seleção genômica dependem da densidade de marcadores utilizados nas análises, a qual, por sua vez, é determinada pela extensão do desequilíbrio de ligação (LD) ao longo do genoma. O LD é organizado em blocos de haplótipos, separados por hot spots de recombinação. Essa organização do LD permite a seleção de um conjunto de SNPs que caracterizam o bloco, o que constitui uma forma adequada de escolher SNPs. O objetivo deste estudo foi estimar a extensão do desequilíbrio de ligação e o tamanho dos blocos de haplótipos de seis linhas comerciais de suínos. Foram genotipados 2050 animais com o SNP chip de 60K para suínos da Illumina. Os marcadores foram filtrados com base na MAF (>0,05) e Equilíbrio de Hardy-Weinberg (p valor > 0,001), o que resultou na utilização de, em média, 34021 SNPs para análises subsequentes. O programa Haploview foi usado no cáculo do LD de todos os pares de SNPs sintênicos, como também na construção dos blocos de haplótipo. O tamanho dos blocos de haplótipo das diferentes linhas foi comparado, utilizando-se o procedimento PROC MIXED do software SAS. Marcadores entre 105 &#8211; 175 Kb de distância apresentaram r2 (correlação entre frequências gênicas) médio acima de 0,3 para todas as linhas, o qual é considerado um bom limiar para estudos de associação. Assim, mapas com um SNP, a cada 105 Kb, seriam adequados a esse tipo de análise. Teoricamente, o LD decresce com o aumento da distância entre os SNPs, entretanto, alguns cromossomos (1, 4, 5, 7, 9, 11, 12, 13, 14, 15 e 16) apresentaram r2 elevado entre SNPs distantes em todas as linhas estudadas, o que poderia ser resultado de erros na distância e na posição dos marcadores no mapa utilizado. Em alguns cromossomos (2 e 18) alto r², entre SNPs distantes, foi observado apenas em algumas linhas, o que poderia ter sido causado por uma série de fatores que influenciam o LD. Entretanto, por tratar-se de linhas diferentes, provavelmente elas possuem histórico, endogamia e cruzamentos distintos. Dessa maneira, pode-se pressupor que esse efeito teria sido causado pela seleção, uma vez que existem características de importância econômica que com certeza, em algum momento, foram selecionadas em mais de uma linha. O tamanho médio dos blocos de haplótipos foi de 287,81 Kb, com predominância de blocos pequenos com menos de 50 Kb. Nenhuma linha apresentou blocos maiores ou menores que as demais, em todos os cromossomos, não existindo, portanto, um padrão que possa discriminar as diferentes linhas. De acordo com a extensão do LD observado neste estudo, seriam necessários 22915 SNPs informativos (MAF > 0,05) para estudos de associação que abrangerem todo o genoma. O elevado desequilíbrio de ligação, observado entre pares de SNPs distantes, pode ter sido causado por erros no mapa e, em alguns casos, por seleção, entretanto para confirmação dessa última hipótese, seria necessário um estudo mais aprofundado das regiões onde esses SNPs se encontram.Conselho Nacional de Desenvolvimento Científico e TecnológicoThe success of association studies and genomic selection depends on marker density, which is determined by the linkage disequilibrium extended across the genome. The LD is organized into haplotypes blocks separated by recombination hot spots and this organization allows the selection of a set of SNPs that label the blocks. The objective of the present study was to estimate the linkage disequilibrium extent and haplotype block size of six commercial pig lines. Two thousand and fifty animals were genotyped using Illumina Porcine SNP60K. The MAF and Hardy-Weinberg equilibrium were used to filter the SNPs, which resulted, on average, in the use of 34021 markers for the subsequent analysis. The data were submitted to Haploview to calculate the LD for all SNP pairs and the haplotype blocks construction. The haplotype block size for all six lines was compared using the PROC MIXED procedure of SAS in a model with the number of SNPs per block as covariate. In markers distant 105 - 175 Kb the average r2 was above 0.3 for all lines, which is considered a usable threshold for association studies; therefore maps with one SNP every 105 Kb would be suitable for this type of analysis. Following the theory, the LD decreases when the distance between SNPs increases, but high r2 was observed between distant SNPs for some chromosomes (1, 4, 5, 7, 9, 11, 12, 13, 14, 15 and 16) in all lines that could be produced by errors in the marker distance and position of the map used. In some chromosomes (2 and 18) high r² between distant SNPs was observed only for some lines, which could be a result of a number of factors that influence the LD. However, the studied lines probably have different history and inbreeding. It could be argued that this is a selection effect, as these lines at a certain moment were certainly selected for traits of economic importance. Although the overall average haplotype block size was 287.81 Kb, a predominance of blocks with less than 50 Kb was observed for all lines. There is not a line that presents smaller or bigger blocks than the others in every chromosome; therefore there was no pattern that could be used to discriminate the lines.. According to the LD extent observed in this study, 22915 informative SNPs (MAF > 0.05) would be necessary for whole genome association studies for the six lines analysed. The high linkage disequilibrium observed between distant SNPs may have been caused by map errors and in other cases by selection. Nevertheless, to confirm the last hypothesis a detailed study would be necessary of the regions where these SNPs are found

Desequilíbrio de ligação e seleção genômica em suínos

Genomic selection and genomic wide association studies (GWAS) are widely used methods that aim to exploit the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). Securing a sufficiently large set of genotypes and phenotypes can be a limiting factor when implementing genomic selection that may be overcome by combining data from multiple populations or using crossbred information. The overall objective of this thesis was to characterize LD patterns in different pig populations and to evaluate whether the differences in LD determine the accuracy of genomic predictions when using different reference sets (within-, across- and multi- population) and methodologies. In this thesis I used data from pure lines and crossbred pig populations genotyped with PorcineSNP60 BeadChip. Loess regression provided a better fit to the real LD data, and more accurate LD predictions could be made, compared to nonlinear regression. It was also shown that Loess regression can be used to statistically compare the LD decay of different populations. The persistence of LD phase between crosses and the parental pig lines was found to be high, from which it was hypothesized that similar marker-QTL associations would be found in a cross and in their purebred parent populations and therefore accuracies of genomic prediction across these populations should be high. Between the pure lines the persistence of phase was low, thus higher density panels should be used to have the same marker-QTL associations across these lines. Accuracies obtained from across- and multi-population genomic prediction and from using crossbred data did however not follow the expectations based on LD. Having the same LD phase may therefore not be as important for genomic prediction accuracy as previously thought but rather the interplay between LD, genetic architecture and allele frequencies also plays a major role. Differences in allele frequencies between lines and information from GWAS on the genetic architecture of traits for the different lines were taken into account in analyses developed in the later chapters. The use of weights, based on GWAS results, was expected to lead the GBLUP model towards the real genetic architecture of the traits. This strategy was shown to have some benefit for the genomic predictions with single- and multi-population data sets. Weights obtained from GWAS in different data sets (within and combining populations) did not always lead to increased accuracies of prediction, depending on which lines the weights are applied to. Using weights from GWAS in a combined population was the best approach, resulting in higher accuracy of GBLUP predictions within single- as well as in multi-population analysis. Understanding and evaluating how the accuracy of within-, across- and multi-population genomic prediction is affected by differences in LD, in genetic architecture and in allele frequencies is key to optimize the accuracy of genomic prediction in pig breeding.A seleção genômica (SG) e associação genômica ampla (GWAS) são métodos que exploram o desequilíbrio de ligação (LD) entre marcadores e loci de características quantitativas (QTL). Um dos fatores limitantes para a implementação da SG é a necessidade de um grande número de animais genotipados e fenotipados para obtenção de valores genéticos com alta acurácia. Essa limitação pode ser superada combinando dados de múltiplas populações ou utilizando dados de animais cruzados. O objetivo geral desta tese foi caracterizar os padrões de LD de diferentes populações de suínos. Além disso, avaliar em que medida as diferenças de LD se refletem na acurácia da seleção genômica quando utilizadas diferentes metodologias e arranjos para população de referência e validação. Os arranjos testados foram: utilização de subconjuntos da mesma população como referência e validação (within), populações diferentes nos conjuntos de referência e validação (across) e combinação de duas populações na referência (multi). Nessa tese foram utilizados dados de suínos de linhas puras e de animais cruzados, genotipados com o PorcineSNP60 BeadChip. A regressão Loess proporcionou melhor ajuste aos dados de LD, bem como em predições mais acuradas em comparação a regressão não linear. Mostrou-se também, que a regressão Loess pode ser utilizada para realizar uma comparação estatística do LD decay de diferentes populações. A persistência de fase do LD entre animais cruzados e as linhas puras parentais foi alta, o que nos leva a hipotetizar que associações marcador-QTL similares poderiam ser encontradas em animais cruzados e as linhas parentais e, portanto, esperava-se encontrar altas acurácias de predição genômica entre essas populações. Entre as linhas puras a persistência de fase foi baixa, logo painéis de SNPs de maior densidade deveriam ser utilizados para manter a mesma associação marcador-QTL entre essas linhas. Acurácias obtidas na predição genômica utilizando animais cruzados assim como os arranjos across e multi, não seguiram as expectativas baseadas em LD. Portanto, a consistência de fase de ligação entre populações pode não ser tão importante para a acurácia da seleção genômica como se pensava, mas sim a ação combinada de LD, arquitetura genética e frequências alélicas. Portanto, foi desenvolvida uma metodologia que leva em consideração differenças nas frequências alélicas, bem como informações dos GWAS para comtemplar a arquitetura genética da característica. Esta estratégia trouxe alguns benefícios para a predição genônima para os arranjos within e multi. Ponderações obtidas por meio de GWAS em diferentes conjuntos de dados (uma única população e combinando múltiplas populações) nem sempre resultou em aumento da acurácia, sendo dependente da linha que estava sob seleção. O uso de pesos advindos do GWAS ao se utilizar uma população combinada resultou nas melhores acurácias tanto para os arranjos within quanto multi. A avaliação e o entendimento de como diferenças de LD, frequências alélicas e arquitetura genética afetam a acurácia da predição genômica é fundamental para otimizar a inserção da seleção genômica no melhoramento de suínos.Coordenação de Aperfeiçoamento de Pessoal de Nível Superio