Genome-wide association study: Understanding the genetic basis of the gait type in Brazilian Mangalarga Marchador horses, a preliminary study

Bussiman, Fernando de Oliveira; Santos, Bruna Aparecida dos; Silva, Bárbara da Conceição Abreu; Mamani, G.; Grigoletto, Laís; Pereira, Guilherme Luís; Ferraz, Guilherme de Camargo; Ferraz, José Bento Sterman; Mattos, E. C.; Eler, Joanir Pereira; Ventura, Ricardo Vieira; Curi, Rogério Abdallah; Balieiro, J. C. C.

doi:10.1016/j.livsci.2019.103867

Cited by 4 publications

(6 citation statements)

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“…On the other hand, in the Campolina breed, animals expressing either the MB or MP gait types can carry the mutant allele in the heterozygotic or double-recessive form (Manso Filho et al, 2015). More recently, some studies have been pointing that the genetic mechanisms underlying those gait types might be polygenic (Fonseca et al, 2017;Bussiman et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Since gait phenotyping generally demands trained personnel, in Brazil, most of the research in gait genetics has been performed with a very limited sample size, which reduces the statistical power to detect important regions in genome-wide studies (Fonseca et al, 2017;Bussiman et al, 2019). Furthermore, the MB and MP gaits are generally analyzed jointly as a dichotomous trait, impairing a detailed prospection of the genetic mechanisms underlying each gait type.…”

Section: Discussionmentioning

confidence: 99%

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Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

et al. 2021

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This study focused on assessing the usefulness of using audio signal processing in the gaited horse industry. A total of 196 short-time audio files (4 s) were collected from video recordings of Brazilian gaited horses. These files were converted into waveform signals (196 samples by 80,000 columns) and divided into training (N = 164) and validation (N = 32) datasets. Twelve single-valued audio features were initially extracted to summarize the training data according to the gait patterns (Marcha Batida—MB and Marcha Picada—MP). After preliminary analyses, high-dimensional arrays of the Mel Frequency Cepstral Coefficients (MFCC), Onset Strength (OS), and Tempogram (TEMP) were extracted and used as input information in the classification algorithms. A principal component analysis (PCA) was performed using the 12 single-valued features set and each audio-feature dataset—AFD (MFCC, OS, and TEMP) for prior data visualization. Machine learning (random forest, RF; support vector machine, SVM) and deep learning (multilayer perceptron neural networks, MLP; convolution neural networks, CNN) algorithms were used to classify the gait types. A five-fold cross-validation scheme with 10 repetitions was employed for assessing the models' predictive performance. The classification performance across models and AFD was also validated with independent observations. The models and AFD were compared based on the classification accuracy (ACC), specificity (SPEC), sensitivity (SEN), and area under the curve (AUC). In the logistic regression analysis, five out of the 12 audio features extracted were significant (p < 0.05) between the gait types. ACC averages ranged from 0.806 to 0.932 for MFCC, from 0.758 to 0.948 for OS and, from 0.936 to 0.968 for TEMP. Overall, the TEMP dataset provided the best classification accuracies for all models. The most suitable method for audio-based horse gait pattern classification was CNN. Both cross and independent validation schemes confirmed that high values of ACC, SPEC, SEN, and AUC are expected for yet-to-be-observed labels, except for MFCC-based models, in which clear overfitting was observed. Using audio-generated data for describing gait phenotypes in Brazilian horses is a promising approach, as the two gait patterns were correctly distinguished. The highest classification performance was achieved by combining CNN and the rhythmic-descriptive AFD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

et al. 2021

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“…We did not find other QTNs for both trotting technique and racing performances. We did not find any effect of SNPs found by GWAS on gait traits in gaited horses (Amano et al., 2018; Bussiman et al., 2020; Fegraeus, Hirschberg, et al., 2017; Staiger et al., 2016) or those found in Standardbred to differentiate trotters from pacers (McCoy et al., 2019).…”

Section: Discussionmentioning

confidence: 73%

“…However, there has been an established relationship between genotype at DMRT3 and the ability to pace (Han & Peñagaricano, 2016). For alternative gaits such as marcha batida, marcha picada in Brazilian Mangalarga Marchador horses (Bussiman et al., 2019, 2020; Fonseca et al., 2017; Patterson et al., 2015), rack and slow gait in American Saddlebred horses (Regatieri et al., 2016), Tölt in Icelandic horse (Fegraeus, Hirschberg, et al., 2017; Jaderkvist et al., 2015; Kristjansson et al., 2014), and Trocha and Colombian trot in Colombian horses (Novoa‐Bravo et al., 2018), the relationship with DMRT3 genotype was not so obvious and many other markers were found. We had attempted to predict the DMRT3 genotype using only the knowledge of trotting technique and failed, even if a strong difference in genotype frequency was found between main trotting technique characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…More than 50% of the significant SNPs were not in the SNP list retained in our study, principally due to low MAF or low other quality controls. We also checked SNPs found in GWAS analysis in gaited horses (Amano et al., 2018; Bussiman et al., 2020; Fegraeus, Hirschberg, et al., 2017; Staiger et al., 2016) and those found in Standardbred to differentiate trotters from pacers (McCoy et al., 2019) with no success. Particular adaptations of each breed or population to the same racing performance depended on different genes according to its overall genetic background.…”

Section: Discussionmentioning

confidence: 99%

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Genomic analysis of gaits and racing performance of the French trotter

Duluard

2020

J Animal Breeding Genetics

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The aim was to disentangle gait characteristics from other qualities needed for racing performances with a genomic analysis of French trotters (FT). A sample of 1,390 horses were recruited, from which 46% were genotyped with Illumina chip of 54,602 SNPs, 49% with Affymetrix chip of 670,806 SNPs and 586 had a completed questionnaire on trotting technique. Racing performances cover the period 1996 to 2018. There were 252,368 FT‐born; 96,617 qualified and 83,962 which participated in a race. After quality control, 377,611 SNPs were retained and imputed. Questionnaire described trotting technique over 13 questions which were summarized, after principal component analysis in 3 traits: pacer, heavy trot/gallop and other defects. GWAS and genomic evaluation were performed using single‐step approach. We found 25 QTL for racing performances and 9 for trotting technique. Only DMRT3 mutation was significant for both traits. To tend to pace avoid the defect at gallop and lead to a better early career for earnings, less percentage of disqualified races at all ages and more harness than under saddle career. This is the portrait of AA genotype at DMRT3. We found 5 other QTL, not linked to gait traits, which might improve selection of genetically independent performance traits of earnings per races and percentage of finished races. For only earnings at different ages and in under saddle or harness races, genomic evaluation remains the best way to predict performances.

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Identification and Functional Annotation of Genes Related to Horses’ Performance: From GWAS to Post-GWAS

Littiere

Castro

Rodriguez

et al. 2020

Animals

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Integration of genomic data with gene network analysis can be a relevant strategy for unraveling genetic mechanisms. It can be used to explore shared biological processes between genes, as well as highlighting transcription factors (TFs) related to phenotypes of interest. Unlike other species, gene–TF network analyses have not yet been well applied to horse traits. We aimed to (1) identify candidate genes associated with horse performance via systematic review, and (2) build biological processes and gene–TF networks from the identified genes aiming to highlight the most candidate genes for horse performance. Our systematic review considered peer-reviewed articles using 20 combinations of keywords. Nine articles were selected and placed into groups for functional analysis via gene networks. A total of 669 candidate genes were identified. From that, gene networks of biological processes from each group were constructed, highlighting processes associated with horse performance (e.g., regulation of systemic arterial blood pressure by vasopressin and regulation of actin polymerization and depolymerization). Transcription factors associated with candidate genes were also identified. Based on their biological processes and evidence from the literature, we identified the main TFs related to horse performance traits, which allowed us to construct a gene–TF network highlighting TFs and the most candidate genes for horse performance.

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Genome-wide association study: Understanding the genetic basis of the gait type in Brazilian Mangalarga Marchador horses, a preliminary study

Cited by 4 publications

References 69 publications

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

Genomic analysis of gaits and racing performance of the French trotter

Identification and Functional Annotation of Genes Related to Horses’ Performance: From GWAS to Post-GWAS

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