Adam Taiti Harth Utsunomiya

Resumo Escores visuais são atribuídos sem considerar uma referência absoluta. Nesse caso, as referências de avaliação são diferentes para cada grupo avaliado, dependendo apenas da estrutura do grupo. Assim, fatores que alteram a estrutura dos grupos de avaliação, como o tamanho do grupo, podem afetar a atribuição do escore visual, bem como a estimação e predição de parâmetros genéticos e valores genéticos aditivos, respectivamente. O objetivo deste estudo é determinar as consequências da atribuição de escores com base em uma referência relativa na estimativa de parâmetros genéticos e valores genéticos aditivos, considerando diferentes tamanhos de grupo de avaliação. Foi realizado um estudo de simulação considerando 6 cenários formados pela combinação de duas populações (selecionadas e não selecionadas) e 3 tamanhos de grupos (10, 40 e 100 animais) para avaliação. Parâmetros e valores genéticos foram estimados usando dois modelos de limiar que diferiram em sua correção para efeitos ambientais. As estimativas de herdabilidade para as duas populações foram próximas quando o modelo corrigiu os efeitos ambientais (0,407 - 0,418). No entanto, os níveis de herdabilidade caíram ao longo das gerações quando o modelo não considerou os efeitos ambientais (0,172 - 0,247). O tamanho do grupo de avaliação afetou principalmente a precisão das predições do valor genético, uma vez que maior precisão estava relacionada a tamanhos de grupo de avaliação maiores (0,576 - 0,715). Concluímos que a forma como os escores visuais são atribuídos não afeta a estimativa de parâmetros e valores genéticos. No entanto, grupos de avaliação de mais de 40 animais devem ser utilizados para obter maior precisão e progresso genético.

Visual scores are assigned with no basis on an absolute reference. In this case, evaluation references are different for each group, depending only on the structure of the group. Thus, factors that change the structure of evaluation groups, such as group size, can affect visual score assignment, as well as estimation of genetic parameters and prediction of breeding values, respectively. The objective of this study to determine the consequences of assigning scores based on a relative reference on the estimation of genetic parameters and breeding values considering different evaluation group sizes. A simulation study was conducted considering 6 scenarios formed by the combination of two populations (selected and unselected) and 3 group sizes (10, 40, and 100 animals) for evaluation. Genetic parameters and breeding values were estimated using two threshold models that differed in their correction for environmental effects. Heritability estimates for the two populations were close when the model corrected for environmental effects (0.407 - 0.418). However, heritability levels fell over generations when the model did not consider environmental effects (0.172 - 0.247). The size of the evaluation group mainly affected the accuracy of breeding values prediction, since higher accuracy was related to larger evaluation group sizes (0.576 - 0.715). We concluded that the way in which visual scores are assigned does not affect the estimation of genetic parameters and values. However, evaluation groups of more than 40 animals should be used to obtain greater accuracy and genetic progress.

Introduction Gir is a Bos indicus breed originally from India, first imported to Brazil in 1850. High-performance Dairy Gir has been systematically selected in Brazil from its arrival. Since the major phenotypic difference between Gir in India and Brazil is a higher milk production in the former, it is speculated that Brazilian Gir was strategically crossed with Holstein or another Bos taurus to improve milk yield. This study evaluated the purity of Brazilian Gir breed stocks from BASA Farms in Brazil, trying to identify possible admixture events with other cattle breeds based on DNA analysis. Materials and methods The population included 1061 pure registered individuals genotyped using two commercial platforms with 37 k and 25 k SNPs. Admixture analysis was performed individually to estimate levels of genomic composition derived from six different reference populations, three indicine and three taurine breeds. Results A Gir ancestry of 99% or higher was found for 94.2% of the population, while the remaining showed levels of non-Gir ancestry up to 6.8%. Only five individuals were identified with possible taurine ancestry, all of them exhibiting levels lower than 2%. The remaining non-Gir ancestry identified was derived from indicine breeds. The levels of admixture observed in the population were from low to non-detectable. No consistent patterns of admixture were observed indicating sustained introgression of taurine lines as means of genetic improvement. Conclusion According to these results, genetic improvement achieved by Brazilian Gir breeders is the result of within-breed selection methods applied intensively over the past five decades, rather than the result from sustained introgression.

Background: Nellore cattle ( Bos indicus ) are well-known for their adaptation to warm and humid environments. Hair length and coat color may impact heat tolerance. The Nellore breed has been strongly selected for white coat, but bulls generally exhibit darker hair ranging from light grey to black on the head, neck, hump, and knees. Given the potential contribution of coat color variation to the adaptation of cattle populations to tropical and sub-tropical environments, our aim was to map positional and functional candidate genetic variants associated with darkness of hair coat (DHC) in Nellore bulls. Results: We performed a genome-wide association study (GWAS) for DHC using data from 432 Nellore bulls that were genotyped for more than 777 k single nucleotide polymorphism (SNP) markers. A single major association signal was detected in the vicinity of the agouti signaling protein gene ( ASIP ). The analysis of whole-genome sequence (WGS) data from 21 bulls revealed functional variants that are associated with DHC, including a structural rearrangement involving ASIP ( ASIP -SV1). We further characterized this structural variant using Oxford Nanopore sequencing data from 13 Australian Brahman heifers, which share ancestry with Nellore cattle; we found that this variant originates from a 1155-bp deletion followed by an insertion of a transposable element of more than 150 bp that may impact the recruitment of ASIP non-coding exons. Conclusions: Our results indicate that the variant ASIP sequence causes darker coat pigmentation on specific parts of the body, most likely through a decreased expression of ASIP and consequently an increased production of eumelanin.

Background: Ending the COVID-19 pandemic is arguably one of the most prominent challenges in recent human history. Following closely the growth dynamics of the disease is one of the pillars towards achieving that goal. Objective: We aimed at developing a simple framework to facilitate the analysis of the growth rate (cases/day) and growth acceleration (cases/day ² ) of COVID-19 cases in real-time. Methods: The framework was built using the Moving Regression (MR) technique and a Hidden Markov Model (HMM). The dynamics of the pandemic was initially modeled via combinations of four different growth stages: lagging (beginning of the outbreak), exponential (rapid growth), deceleration (growth decay) and stationary (near zero growth). A fifth growth behavior, namely linear growth (constant growth above zero), was further introduced to add more flexibility to the framework. An R Shiny application was developed, which can be accessed at http://www.theguarani.com.br/covid-19 or downloaded from https://github.com/adamtaiti/SARS-CoV-2 . The framework was applied to data from the European Center for Disease Prevention and Control (ECDC), which comprised 853,200 cases reported worldwide as of April 2 ^nd 2020. Results: We found that the impact of public health measures on the prevalence of COVID-19 could be perceived in seemingly real-time by monitoring growth acceleration curves. Restriction to human mobility produced detectable decline in growth acceleration within few days, deceleration within ∼2 weeks and near-stationary growth within ∼6 weeks. Countries exhibiting different permutations of the five growth stages indicated that the evolution of COVID-19 prevalence is more complex and dynamic than previously appreciated. Conclusions: These results corroborate that mass social isolation is a highly effective measure against the dissemination of SARS-CoV-2, as previously suggested. Apart from the analysis of prevalence partitioned by country, the proposed framework is easily applicable to city, state, region and arbitrary territory data, serving as an asset to monitor the local behavior of COVID-19 cases.

Genomic selection may accelerate genetic progress in breeding programs of indicine breeds when compared with traditional selection methods. We present results of genomic predictions in Gyr (Bos indicus) dairy cattle of Brazil for milk yield (MY), fat yield (FY), protein yield (PY), and age at first calving using information from bulls and cows. Four different single nucleotide polymorphism (SNP) chips were studied. Additionally, the effect of the use of imputed data on genomic prediction accuracy was studied. A total of 474 bulls and 1,688 cows were genotyped with the Illumina BovineHD (HD; San Diego, CA) and BovineSNP50 (50K) chip, respectively. Genotypes of cows were imputed to HD using FImpute v2.2. After quality check of data, 496,606 markers remained. The HD markers present on the GeneSeek SGGP-20Ki (15,727; Lincoln, NE), 50K (22,152), and GeneSeek GGP-75Ki (65,018) were subset and used to assess the effect of lower SNP density on accuracy of prediction. Deregressed breeding values were used as pseudophenotypes for model training. Data were split into reference and validation to mimic a forward prediction scheme. The reference population consisted of animals whose birth year was ≤2004 and consisted of either only bulls (TR1) or a combination of bulls and dams (TR2), whereas the validation set consisted of younger bulls (born after 2004). Genomic BLUP was used to estimate genomic breeding values (GEBV) and reliability of GEBV (R2PEV) was based on the prediction error variance approach. Reliability of GEBV ranged from ∼0.46 (FY and PY) to 0.56 (MY) with TR1 and from 0.51 (PY) to 0.65 (MY) with TR2. When averaged across all traits, R2PEV were substantially higher (R2PEV of TR1 = 0.50 and TR2 = 0.57) compared with reliabilities of parent averages (0.35) computed from pedigree data and based on diagonals of the coefficient matrix (prediction error variance approach). Reliability was similar for all the 4 marker panels using either TR1 or TR2, except that imputed HD cow data set led to an inflation of reliability. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information. A reduced panel of ∼15K markers resulted in reliabilities similar to using HD markers. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information.

Misassembly signatures, created by shuffling the order of sequences while assembling a genome, can be detected by the unexpected behavior of marker linkage disequilibrium (LD) decay. We developed a heuristic process to identify misassembly signatures, applied it to the bovine reference genome assembly (UMDv3.1) and presented the consequences of misassemblies in two case studies. We identified 2,906 single nucleotide polymorphism (SNP) markers presenting unexpected LD decay behavior in 626 putative misassembled contigs, which comprised less than 1 % of the whole genome. Although this represents a small fraction of the reference sequence, these poorly assembled segments can lead to severe implications to local genome context. For instance, we showed that one of the misassembled regions mapped to the POLL locus, which affected the annotation of positional candidate genes in a GWAS case study for polledness in Nellore (Bos indicus beef cattle). Additionally, we found that poorly performing markers in imputation mapped to putative misassembled regions, and that correction of marker positions based on LD was capable to recover imputation accuracy. This heuristic approach can be useful to cross validate reference assemblies and to filter out markers located at low confidence genomic regions before conducting downstream analyses.

The GHap R package was designed to call haplotypes from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. Availability and implementation: https://cran.r-project.org/package=GHap Contact: ytutsunomiya@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

O objetivo do trabalho foi estimar os parâmetros genéticos das características de crescimento da raça Suffolk, a fim de fornecer subsídio para a definição de estratégias de seleção para programas de melhoramento genético. Os dados analisados, coletados entre os anos de 2007 a 2009, são referentes a ovinos da raça Suffolk oriundos de uma propriedade localizada no Estado de São Paulo, Brasil, participante do programa de melhoramento genético Ovigol, conduzido pela empresa Aries Reprodução e Melhoramento Genético Ovino Ltda. em parceria com a empresa AbacusBio Limited, da Nova Zelândia. As características avaliadas foram: peso ao nascer (PN), ganho de peso pré-desmame (GPP) e peso ao desmame (PD), com número de registro de 1.039, 636 e 649 animais, respectivamente. Para análise estatística, utilizou-se o procedimento GLM do programa computacional SAS; para a consistência do pedigree e para as estimativas dos parâmetros genéticos, os programas computacionais utilizados foram Relax2: pedigree analysis program e o WOMBAT, respectivamente. O modelo linear geral incluiu sexo, grupo de contemporâneos (tipo de parto e ano de nascimento), covariável idade ao desmame (GPP e PD) com efeito quadrático, efeito genético aditivo direto, efeito de ambiente permanente materno e efeito residual. As herdabilidades para PN, GPP e PD foram 0,06, 0,42 e 0,37, respectivamente. As correlações genéticas entre PN e GPP, PN e PD e GPP e PD foram -0,10, -0,03 e 0,97, respectivamente. Na população estudada, as características GPP e PD, apresentam altas respostas à seleção, ao contrário do PN. As características PN e GPP são suficientes para compor um índice de seleção, sendo aconselhável o monitoramento do PN na fase inicial do programa.

Genotype imputation is widely used as a cost-effective strategy in genomic evaluation of cattle. Key determinants of imputation accuracies, such as linkage disequilibrium patterns, marker densities, and ascertainment bias, differ between Bos indicus and Bos taurus breeds. Consequently, there is a need to investigate effectiveness of genotype imputation in indicine breeds. Thus, the objective of the study was to investigate strategies and factors affecting the accuracy of genotype imputation in Gyr (Bos indicus) dairy cattle. Four imputation scenarios were studied using 471 sires and 1,644 dams genotyped on Illumina BovineHD (HD-777K; San Diego, CA) and BovineSNP50 (50K) chips, respectively. Scenarios were based on which reference high-density single nucleotide polymorphism (SNP) panel (HDP) should be adopted [HD-777K, 50K, and GeneSeek GGP-75Ki (Lincoln, NE)]. Depending on the scenario, validation animals had their genotypes masked for one of the lower-density panels: Illumina (3K, 7K, and 50K) and GeneSeek (SGGP-20Ki and GGP-75Ki). We randomly selected 171 sires as reference and 300 as validation for all the scenarios. Additionally, all sires were used as reference and the 1,644 dams were imputed for validation. Genotypes of 98 individuals with 4 and more offspring were completely masked and imputed. Imputation algorithms FImpute and Beagle v3.3 and v4 were used. Imputation accuracies were measured using the correlation and allelic correct rate. FImpute resulted in highest accuracies, whereas Beagle 3.3 gave the least-accurate imputations. Accuracies evaluated as correlation (allelic correct rate) ranged from 0.910 (0.942) to 0.961 (0.974) using 50K as HDP and with 3K (7K) as low-density panels. With GGP-75Ki as HDP, accuracies were moderate for 3K, 7K, and 50K, but high for SGGP-20Ki. The use of HD-777K as HDP resulted in accuracies of 0.888 (3K), 0.941 (7K), 0.980 (SGGP-20Ki), 0.982 (50K), and 0.993 (GGP-75Ki). Ungenotyped individuals were imputed with an average accuracy of 0.970. The average top 5 kinship coefficients between reference and imputed individuals was a strong predictor of imputation accuracy. FImpute was faster and used less memory than Beagle v4. Beagle v4 outperformed Beagle v3.3 in accuracy and speed of computation. A genotyping strategy that uses the HD-777K SNP chip as a reference panel and SGGP-20Ki as the lower-density SNP panel should be adopted as accuracy was high and similar to that of the 50K. However, the effect of using imputed HD-777K genotypes from the SGGP-20Ki on genomic evaluation is yet to be studied.