scholarly journals Detection of Genomic Regions with Pleiotropic Effects for Growth and Carcass Quality Traits in the Rubia Gallega Cattle Breed

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1682
Author(s):  
Maria Martinez-Castillero ◽  
Carlos Then ◽  
Juan Altarriba ◽  
Houssemeddine Srihi ◽  
David López-Carbonell ◽  
...  
Keyword(s):  
Association Studies ◽  
Cattle Breed ◽  
Snp Markers ◽  
Single Step ◽  
Pleiotropic Effects ◽  
Carcass Quality ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Nucleotide Polymorphisms ◽  
Genomic Regions

The breeding scheme in the Rubia Gallega cattle population is based upon traits measured in farms and slaughterhouses. In recent years, genomic evaluation has been implemented by using a ssGBLUP (single-step Genomic Best Linear Unbiased Prediction). This procedure can reparameterized to perform ssGWAS (single-step Genome Wide Association Studies) by backsolving the SNP (single nucleotide polymorphisms) effects. Therefore, the objective of this study was to identify genomic regions associated with the genetic variability in growth and carcass quality traits. We implemented a ssGBLUP by using a database that included records for Birth Weight (BW-327,350 records-), Weaning Weight (WW-83,818-), Cold Carcass Weight (CCW-91,621-), Fatness (FAT-91,475-) and Conformation (CON-91,609-). The pedigree included 464,373 individuals, 2449 of which were genotyped. After a process of filtering, we ended up using 43,211 SNP markers. We used the GBLUP and SNPBLUP model equivalences to obtain the effects of the SNPs and then calculated the percentage of variance explained by the regions of the genome between 1 Mb. We identified 7 regions of the genome for CCW; 8 regions for BW, WW, FAT and 9 regions for CON, which explained the percentage of variance above 0.5%. Furthermore, a number of the genome regions had pleiotropic effects, located at: BTA1 (131–132 Mb), BTA2 (1–11 Mb), BTA3 (32–33 Mb), BTA6 (36–38 Mb), BTA16 (24–26 Mb), and BTA 21 (56–57 Mb). These regions contain, amongst others, the following candidate genes: NCK1, MSTN, KCNA3, LCORL, NCAPG, and RIN3.

scholarly journals Genomic Approaches Reveal Pleiotropic Effects in Crossbred Beef Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Fernanda M. Rezende ◽  
Eduardo Rodriguez ◽  
Joel D. Leal-Gutiérrez ◽  
Mauricio A. Elzo ◽  
Dwain D. Johnson ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Selection ◽  
Meat Quality ◽  
Additive Genetic Variance ◽  
Cytoskeletal Proteins ◽  
Pleiotropic Effects ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Meat Quality Traits ◽  
Genomic Regions

Carcass and meat quality are two important attributes for the beef industry because they drive profitability and consumer demand. These traits are of even greater importance in crossbred cattle used in subtropical and tropical regions for their superior adaptability because they tend to underperform compared to their purebred counterparts. Many of these traits are challenging and expensive to measure and unavailable until late in life or after the animal is harvested, hence unrealistic to improve through traditional phenotypic selection, but perfect candidates for genomic selection. Before genomic selection can be implemented in crossbred populations, it is important to explore if pleiotropic effects exist between carcass and meat quality traits. Therefore, the objective of this study was to identify genomic regions with pleiotropic effects on carcass and meat quality traits in a multibreed Angus–Brahman population that included purebred and crossbred animals. Data included phenotypes for 10 carcass and meat quality traits from 2,384 steers, of which 1,038 were genotyped with the GGP Bovine F-250. Single-trait genome-wide association studies were first used to investigate the relevance of direct additive genetic effects on each carcass, sensory and visual meat quality traits. A second analysis for each trait included all other phenotypes as covariates to correct for direct causal effects from identified genomic regions with pure direct effects on the trait under analysis. Five genomic windows on chromosomes BTA5, BTA7, BTA18, and BTA29 explained more than 1% of additive genetic variance of two or more traits. Moreover, three suggestive pleiotropic regions were identified on BTA10 and BTA19. The 317 genes uncovered in pleiotropic regions included anchoring and cytoskeletal proteins, key players in cell growth, muscle development, lipid metabolism and fat deposition, and important factors in muscle proteolysis. A functional analysis of these genes revealed GO terms directly related to carcass quality, meat quality, and tenderness in beef cattle, including calcium-related processes, cell signaling, and modulation of cell–cell adhesion. These results contribute with novel information about the complex genetic architecture and pleiotropic effects of carcass and meat quality traits in crossbred beef cattle.

scholarly journals Longitudinal Phenotypes Improve Genotype Association for Hyperketonemia in Dairy Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1059 ◽  
Author(s):  
Francisco A. Leal Yepes ◽  
Daryl V. Nydam ◽  
Sabine Mann ◽  
Luciano Caixeta ◽  
Jessica A. A. McArt ◽  
...  
Keyword(s):  
Linear Models ◽  
Association Studies ◽  
Genetic Selection ◽  
Area Under The Curve ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Mixed Linear Models ◽  
Biological Insight ◽  
Group 2 ◽  
Genomic Regions

The objective of our study was to identify genomic regions associated with varying concentrations of non-esterified fatty acid (NEFA), β-hydroxybutyrate (BHB), and the development of hyperketonemia (HYK) in longitudinally sampled Holstein dairy cows. Our study population consisted of 147 multiparous cows intensively characterized by serial NEFA and BHB concentrations. To identify individuals with contrasting combinations in longitudinal BHB and NEFA concentrations, phenotypes were established using incremental area under the curve (AUC) and categorized as follows: Group (1) high NEFA and high BHB, group (2) low NEFA and high BHB), group (3) low NEFA and low BHB, and group (4) high NEFA and low BHB. Cows were genotyped on the Illumina Bovine High-density (777 K) beadchip. Genome-wide association studies using mixed linear models with the least-related animals were performed to establish a genetic association with HYK, BHB-AUC, NEFA-AUC, and the comparisons of the 4 AUC phenotypic groups using Golden Helix software. Nine single-nucleotide polymorphisms were associated with high longitudinal concentrations of BHB and further investigated. Five candidate genes related to energy metabolism and homeostasis were identified. These results provide biological insight and help identify susceptible animals thus improving genetic selection criteria thereby decreasing the incidence of HYK.

scholarly journals Exome resequencing and GWAS for growth, ecophysiology, and chemical and metabolomic composition of wood of Populus trichocarpa

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fernando P. Guerra ◽  
Haktan Suren ◽  
Jason Holliday ◽  
James H. Richards ◽  
Oliver Fiehn ◽  
...  
Keyword(s):  
Biomass Production ◽  
Complex Traits ◽  
Association Studies ◽  
Populus Trichocarpa ◽  
Significant Snps ◽  
Snp Markers ◽  
Exome Capture ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Improvement Programs

Abstract Background Populus trichocarpa is an important forest tree species for the generation of lignocellulosic ethanol. Understanding the genomic basis of biomass production and chemical composition of wood is fundamental in supporting genetic improvement programs. Considerable variation has been observed in this species for complex traits related to growth, phenology, ecophysiology and wood chemistry. Those traits are influenced by both polygenic control and environmental effects, and their genome architecture and regulation are only partially understood. Genome wide association studies (GWAS) represent an approach to advance that aim using thousands of single nucleotide polymorphisms (SNPs). Genotyping using exome capture methodologies represent an efficient approach to identify specific functional regions of genomes underlying phenotypic variation. Results We identified 813 K SNPs, which were utilized for genotyping 461 P. trichocarpa clones, representing 101 provenances collected from Oregon and Washington, and established in California. A GWAS performed on 20 traits, considering single SNP-marker tests identified a variable number of significant SNPs (p-value < 6.1479E-8) in association with diameter, height, leaf carbon and nitrogen contents, and δ15N. The number of significant SNPs ranged from 2 to 220 per trait. Additionally, multiple-marker analyses by sliding-windows tests detected between 6 and 192 significant windows for the analyzed traits. The significant SNPs resided within genes that encode proteins belonging to different functional classes as such protein synthesis, energy/metabolism and DNA/RNA metabolism, among others. Conclusions SNP-markers within genes associated with traits of importance for biomass production were detected. They contribute to characterize the genomic architecture of P. trichocarpa biomass required to support the development and application of marker breeding technologies.

Data Mining in Genome Wide Association Studies

2011 ◽  
pp. 465-471
Author(s):  
Tom Burr
Keyword(s):  
Data Mining ◽  
Genetic Basis ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Common Diseases ◽  
Genome Wide ◽  
Genomic Regions

The genetic basis for some human diseases, in which one or a few genome regions increase the probability of acquiring the disease, is fairly well understood. For example, the risk for cystic fibrosis is linked to particular genomic regions. Identifying the genetic basis of more common diseases such as diabetes has proven to be more difficult, because many genome regions apparently are involved, and genetic effects are thought to depend in unknown ways on other factors, called covariates, such as diet and other environmental factors (Goldstein and Cavalleri, 2005). Genome-wide association studies (GWAS) aim to discover the genetic basis for a given disease. The main goal in a GWAS is to identify genetic variants, single nucleotide polymorphisms (SNPs) in particular, that show association with the phenotype, such as “disease present” or “disease absent” either because they are causal, or more likely, because they are statistically correlated with an unobserved causal variant (Goldstein and Cavalleri, 2005). A GWAS can analyze “by DNA site” or “by multiple DNA sites. ” In either case, data mining tools (Tachmazidou, Verzilli, and De Lorio, 2007) are proving to be quite useful for understanding the genetic causes for common diseases.

Use of single-step genome-wide association studies for prospecting genomic regions related to milk production and milk quality of buffalo

2018 ◽  
Vol 85 (4) ◽  
pp. 402-406 ◽  
Author(s):  
Camila da Costa Barros ◽  
Daniel Jordan de Abreu Santos ◽  
Rusbel Raul Aspilcueta-Borquis ◽  
Gregório Miguel Ferreira de Camargo ◽  
Francisco Ribeiro de Araújo Neto ◽  
...  
Keyword(s):  
Milk Production ◽  
Milk Fat ◽  
Association Studies ◽  
Single Step ◽  
Milk Quality ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Genomic Regions

The aim of this research communication was to identify chromosome regions and genes that could be related to milk yield (MY), milk fat (%F) and protein percentage (%P) in Brazilian buffalo cows using information from genotyped and non-genotyped animals. We used the 90 K Axiom® Buffalo Genotyping array. A repeatability model was used. An iterative process was performed to calculate the weights of markers as a function of the squared effects of Single Nucleotide Polymorphism (SNP) and allele frequencies. The 10 SNPs with the largest effects for MY, %F and %P were studied and they explained 7·48, 9·94 and 6·56% of the genetic variance, respectively. These regions harbor genes with biological functions that could be related to the traits analyzed. The identification of such regions and genes will contribute to a better understanding of their influence on milk production and milk quality traits of buffaloes.

scholarly journals Single-step genome-wide association studies (GWAS) and post-GWAS analyses to identify genomic regions and candidate genes for milk yield in Brazilian Girolando cattle

2020 ◽  
Vol 103 (11) ◽  
pp. 10347-10360
Author(s):  
Pamela I. Otto ◽  
Simone E.F. Guimarães ◽  
Mario P.L. Calus ◽  
Jeremie Vandenplas ◽  
Marco A. Machado ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Milk Yield ◽  
Association Studies ◽  
Single Step ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genomic Regions

scholarly journals Finding genetic variants in plants without complete genomes

2019 ◽  
Author(s):  
Yoav Voichek ◽  
Detlef Weigel
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Structural Variants ◽  
Sequencing Data ◽  
New Associations ◽  
Maize Populations ◽  
Genome Wide ◽  
Genomic Regions

AbstractStructural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the genetic variants detected in GWAS to include major deletions, insertions, and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we re-analyzed 2,000 traits measured in Arabidopsis thaliana, tomato, and maize populations. Associations identified with k-mers recapitulate those found with single-nucleotide polymorphisms (SNPs), however, with stronger statistical support. Moreover, we identified new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allow detection of a wider range of genetic variants responsible for phenotypic variation.

scholarly journals Genome-wide association studies of grain quality traits in maize

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunxiao Zheng ◽  
Fan Yuan ◽  
Yaqun Huang ◽  
Yongfeng Zhao ◽  
Xiaoyan Jia ◽  
...  
Keyword(s):  
Grain Quality ◽  
Association Studies ◽  
Inbred Lines ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Nucleotide Polymorphisms ◽  
High Quality ◽  
Essential Information ◽  
Genome Wide

AbstractHigh quality is the main goal of today’s maize breeding and the investigation of grain quality traits would help to breed high-quality varieties in maize. In this study, genome-wide association studies in a set of 248 diverse inbred lines were performed with 83,057 single nucleotide polymorphisms (SNPs), and five grain quality traits were investigated in diverse environments for two years. The results showed that maize inbred lines showed substantial natural variations of grain quality and these traits showed high broad-sense heritability. A total of 49 SNPs were found to be significantly associated with grain quality traits. Among these SNPs, four co-localized sites were commonly detected by multiple traits. The candidate genes which were searched for can be classified into 11 biological processes, 13 cellular components, and 6 molecular functions. Finally, we found 29 grain quality-related genes. These genes and the SNPs identified in the study would offer essential information for high-quality varieties breeding programs in maize.

scholarly journals Predicting novel genomic regions linked to genetic disorders using GWAS and chromosome conformation data – a case study of schizophrenia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Daniel S. Buxton ◽  
Declan J. Batten ◽  
Jonathan J. Crofts ◽  
Nadia Chuzhanova
Keyword(s):  
Human Genome ◽  
Association Studies ◽  
Genetic Disorders ◽  
Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Novel Genes ◽  
Chromosome Conformation ◽  
Genomic Regions

AbstractGenome-wide association studies identified numerous loci harbouring single nucleotide polymorphisms (SNPs) associated with various human diseases, although the causal role of many of them remains unknown. In this paper, we postulate that co-location and shared biological function of novel genes with genes known to associate with a specific phenotype make them potential candidates linked to the same phenotype (“guilt-by-proxy”). We propose a novel network-based approach for predicting candidate genes/genomic regions utilising the knowledge of the 3D architecture of the human genome and GWAS data. As a case study we used a well-studied polygenic disorder ‒ schizophrenia ‒ for which we compiled a comprehensive dataset of SNPs. Our approach revealed 634 novel regions covering ~398 Mb of the human genome and harbouring ~9000 genes. Using various network measures and enrichment analysis, we identified subsets of genes and investigated the plausibility of these genes/regions having an association with schizophrenia using literature search and bioinformatics resources. We identified several genes/regions with previously reported associations with schizophrenia, thus providing proof-of-concept, as well as novel candidates with no prior known associations. This approach has the potential to identify novel genes/genomic regions linked to other polygenic disorders and provide means of aggregating genes/SNPs for further investigation.

scholarly journals Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency

2020 ◽  
Author(s):  
Emilie Delpuech ◽  
Amir Aliakbari ◽  
Yann Labrune ◽  
Katia Fève ◽  
Yvon Billon ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Production Systems ◽  
Agronomic Traits ◽  
Association Studies ◽  
Pedigree Information ◽  
Strong Impact ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Genomic Regions

AbstractBackgroundFeed efficiency is a major driver of the sustainability of pig production systems. Understanding biological mechanisms underlying these agronomic traits is an important issue whether for environment and farms economy. This study aimed at identifying genomic regions affecting residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during 9 generations (LRFI, low RFI; HRFI, high RFI).ResultsWe built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2,426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (Global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). A total of 54 chromosomic regions were detected with the Global-GWAS, whereas 37 and 61 regions were detected in LRFI-GWAS and HRFI-GWAS, respectively. Among those, only 15 regions were shared between at least two analyses, and only one was common between the three GWAS but affecting different traits. Among the 12 QTL detected for RFI, some were close to QTL detected for meat quality traits and 9 pinpointed novel genomic regions for some harbored candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or lipid metabolism-related signaling pathways. Detection of mostly different QTL regions between the three designs suggests the strong impact of the dataset on the detection power, which could be due to the changes of allelic frequencies during the line selection.ConclusionsBesides efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted the identification of chromosomic regions under selection that affect various production traits.

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