Genome-Wide Association Study between Single Nucleotide Polymorphisms and Flight Speed in Nellore Cattle

Valente, Tiago da Silva; Baldi, Fernando; Sant’Anna, Aline Cristina; Albuquerque, Lúcia Galvão de; Costa, Mateus José Rodrigues Paranhos da

doi:10.1371/journal.pone.0156956

Cited by 41 publications

(38 citation statements)

References 145 publications

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“…This region and adjacent regions (regions 2.2-2.11) are located within QTL found in Limousin associated with carcass fat and carcass conformation (Purfield et al 2019). Furthermore, region 2.16 (ZHp = À3.17) contains NCKAP5, a gene associated in Nellore with flight speed, a temperament trait (Valente et al 2016). This gene has been associated with human attention deficit hyperactivity disorders (Lasky-Su et al 2008), schizophrenia and bipolar disorders (Wang et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Detection of selection signatures in Limousin cattle using whole‐genome resequencing

et al. 2020

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Summary Limousin, a renowned beef breed originating from central France, has been selectively bred over the last 100 years to improve economically important traits. We used whole‐genome sequencing data from 10 unrelated Limousin bull calves to detect polymorphisms and identify regions under selection. A total of 13 943 766 variants were identified. Moreover, 311 852 bi‐allelic SNPs and 92 229 indels located on autosomes were fixed for the alternative allele in all sequenced animals, including the previously reported missense deleterious F94L mutation in MSTN. We performed a whole‐genome screen to discover genomic regions with excess homozygosity, using the pooled heterozygosity score and identified 171 different candidate selective sweeps. In total, 68 candidate genes were found in only 57 of these regions, indicating that a large fraction of the genome under selection might lie in non‐coding regions and suggesting that a majority of adaptive mutations might be regulatory in nature. Many QTL were found within candidate selective sweep regions, including QTL associated with shear force or carcass weight. Among the putative selective sweeps, we located genes (MSTN, NCKAP5, RUNX2) that potentially contribute to important phenotypes in Limousin. Several candidate regions and genes under selection were also found in previous genome‐wide selection scans performed in Limousin. In addition, we were able to pinpoint candidate causative regulatory polymorphisms in GRIK3 and RUNX2 that might have been under selection. Our results will contribute to improved understanding of the mechanisms and targets of artificial selection and will facilitate the interpretation of GWASs performed in Limousin.

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Section: Discussionmentioning

confidence: 99%

Detection of selection signatures in Limousin cattle using whole‐genome resequencing

et al. 2020

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“…Consecutive 20 SNPs, which explained 1% or more of the total genetic variance, based on the WssGWAS, were considered as a genomic window associated with the studied traits [ 37 , 38 , 39 ]. The Map Viewer tool for the bovine genome was used to identify positional candidate genes based on the starting and ending coordinates of each window using the UMD 3.1.1 assembly as the reference map ( ).…”

Section: Methodsmentioning

confidence: 99%

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Naserkheil

Bahrami

Lee

et al. 2020

Animals

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In recent years, studies on the biological mechanisms underlying complex traits have been facilitated by innovations in high-throughput genotyping technology. We conducted a weighted single-step genome-wide association study (WssGWAS) to evaluate backfat thickness, carcass weight, eye muscle area, marbling score, and yearling weight in a cohort of 1540 Hanwoo beef cattle using BovineSNP50 BeadChip. The WssGWAS uncovered thirty-three genomic regions that explained more than 1% of the additive genetic variance, mostly located on chromosomes 6 and 14. Among the identified window regions, seven quantitative trait loci (QTL) had pleiotropic effects and twenty-six QTL were trait-specific. Significant pathways implicated in the measured traits through Gene Ontology (GO) term enrichment analysis included the following: lipid biosynthetic process, regulation of lipid metabolic process, transport or localization of lipid, regulation of growth, developmental growth, and multicellular organism growth. Integration of GWAS results of the studied traits with pathway and network analyses facilitated the exploration of the respective candidate genes involved in several biological functions, particularly lipid and growth metabolism. This study provides novel insight into the genetic bases underlying complex traits and could be useful in developing breeding schemes aimed at improving growth and carcass traits in Hanwoo beef cattle.

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“…Furthermore, the availability of genotype information on only a limited proportion of animals in some developing countries has promoted the implementation of the single-step methodology (ssGBLUP) (Misztal et al, 2009). In this model, pedigree and genomic relationships are combined into an H matrix to predict the genetic merit of the animal, which results in higher accuracy due to the utilization of all the available data (Misztal et al, 2009;Cardoso et al, 2015;Valente et al, 2016). Aguilar et al (2010) showed that a single-step methodology can be simple, fast, and accurate.…”

Section: Single-step Genomic Best Linear Unbiased Prediction (Ssgblup)mentioning

confidence: 99%

A review of traditional and machine learning methods applied to animal breeding

Nayeri

Sargolzaei

Tulpan

2019

Anim. Health. Res. Rev.

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The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

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Genome-Wide Association Study between Single Nucleotide Polymorphisms and Flight Speed in Nellore Cattle

Cited by 41 publications

References 145 publications

Detection of selection signatures in Limousin cattle using whole‐genome resequencing

Detection of selection signatures in Limousin cattle using whole‐genome resequencing

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

A review of traditional and machine learning methods applied to animal breeding

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