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

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Emilie Delpuech ◽  
Amir Aliakbari ◽  
Yann Labrune ◽  
Katia Fève ◽  
Yvon Billon ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Agronomic Traits ◽  
Association Studies ◽  
Pedigree Information ◽  
Strong Impact ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Allelic Frequencies ◽  
Genomic Regions

Abstract Background Feed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations (LRFI, low RFI; HRFI, high RFI). Results We built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 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). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines. Conclusions In addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.

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.

scholarly journals Regional genomic heritability mapping for agronomic traits in sugarcane

2020 ◽  
Author(s):  
Pedro Marcus Pereira Vidigal ◽  
Mehdi Momen ◽  
Paulo Mafra de Almeida Costa ◽  
Márcio Henrique Pereira Barbosa ◽  
Gota Morota ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Association Studies ◽  
Variant Calling ◽  
Breeding Population ◽  
Primary Concern ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genomic Heritability ◽  
Genomic Regions ◽  
Sugarcane Genome

AbstractBackgroundThe identification of genomic regions involved in agronomic traits is the primary concern for sugarcane breeders. Genome-wide association studies (GWAS) leverage the sequence variations to bridge phenotypes and genotypes. However, their effectiveness is limited in species with high ploidy and large genomes, such as sugarcane. As an alternative, a regional heritability mapping (RHM) method can be used to capture genetic signals that may be missed by GWAS by combining genetic variance from neighboring regions. We used RHM to screen the sugarcane genome aiming to identify regions with higher heritability associated with agronomic traits. We considered percentage of fiber in sugarcane bagasse (FB), apparent percentage of sugarcane sucrose (PC), tonnes of pol per hectare (TPH), and tonnes of stalks per hectare (TSH).MethodsSequence-capture data of 508 sugarcane (Saccharum spp.) clones from a breeding population under selection were processed for variant calling analysis using the sugarcane genome cultivar R570 as a reference. A set of 375,195 single nucleotide polymorphisms were selected after quality control. RHM was conducted by splitting the sugarcane genome into windows of 2 Mb length.ResultsWe selected the windows explaining > 20% of the total genomic heritability for TPH (64 windows - 5,654 genes) and TSH (72 windows - 6,050 genes), and > 15% for PC (16 windows - 1,517 genes) and FB (17 windows - 1,615 genes). The top five windows that explained the highest genomic heritability ranged from 20.8 to 24.6% for FB (629 genes), 18.0 to 22.0% for PC (452 genes), 53.8 to 66.0% for TPH (705 genes), and 59.5 to 67.4% for TSH (413 genes). The functional annotation of genes included in those top five windows revealed a set of genes that encode enzymes that integrate carbon metabolism, starch and sucrose metabolism, and phenylpropanoid biosynthesis pathways.ConclusionsThe selection of windows that explained the large proportions of genomic heritability allowed us to identify genomic regions containing a set of genes that are related to the agronomic traits in sugarcane. These windows spanned a region of 58.38Mb, which corresponds to 14.28% of the reference assembly in the sugarcane genome. We contend that RHM can be used as an alternative method for sugarcane breeders to reduce the complexity of the sugarcane genome.

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 Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Thierry Tribout ◽  
Pascal Croiseau ◽  
Rachel Lefebvre ◽  
Anne Barbat ◽  
Mekki Boussaha ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Candidate Genes ◽  
Milk Production ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Milk Production Traits ◽  
Udder Health ◽  
Gwas Approach

Abstract Background Over the last years, genome-wide association studies (GWAS) based on imputed whole-genome sequences (WGS) have been used to detect quantitative trait loci (QTL) and highlight candidate genes for important traits. However, in general this approach does not allow to validate the effects of candidate mutations or determine if they are truly causative for the trait(s) in question. To address these questions, we applied a two-step, within-breed GWAS approach on 15 traits (5 linked with milk production, 2 with udder health, and 8 with udder morphology) in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) cattle. We detected the most-promising candidate variants (CV) using imputed WGS of 2515 MON, 2203 NOR, and 6321 HOL bulls, and validated their effects in three younger populations of 23,926 MON, 9400 NOR, and 51,977 HOL cows. Results Bull sequence-based GWAS detected 84 QTL: 13, 10, and 30 for milk production traits; 3, 0, and 2 for somatic cell score (SCS); and 8, 2 and 16 for udder morphology traits, in MON, NOR, and HOL respectively. Five genomic regions with effects on milk production traits were shared among the three breeds whereas six (2 for production and 4 for udder morphology and health traits) had effects in two breeds. In 80 of these QTL, 855 CV were highlighted based on the significance of their effects and functional annotation. The subsequent GWAS on MON, NOR, and HOL cows validated 8, 9, and 23 QTL for production traits; 0, 0, and 1 for SCS; and 4, 1, and 8 for udder morphology traits, respectively. In 47 of the 54 confirmed QTL, the CV identified in bulls had more significant effects than single nucleotide polymorphisms (SNPs) from the standard 50K chip. The best CV for each validated QTL was located in a gene that was functionally related to production (36 QTL) or udder (9 QTL) traits. Conclusions Using this two-step GWAS approach, we identified and validated 54 QTL that included CV mostly located within functional candidate genes and explained up to 6.3% (udder traits) and 37% (production traits) of the genetic variance of economically important dairy traits. These CV are now included in the chip used to evaluate French dairy cattle and can be integrated into routine genomic evaluation.

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.

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.

scholarly journals Genome-wide associations for immune traits in two maternal pig lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christina M. Dauben ◽  
Maren J. Pröll-Cornelissen ◽  
Esther M. Heuß ◽  
Anne K. Appel ◽  
Hubert Henne ◽  
...  
Keyword(s):  
Immune System ◽  
Candidate Genes ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Biological Relevance ◽  
Genome Wide ◽  
Major Interest ◽  
Immune Traits ◽  
Genomic Regions

Abstract Background In recent years, animal welfare and health has become more and more important in pig breeding. So far, numerous parameters have been considered as important biomarkers, especially in the immune reaction and inflammation. Previous studies have shown moderate to high heritabilities in most of these traits. However, the genetic background of health and robustness of pigs needs to be extensively clarified. The objective of this study was to identify genomic regions with a biological relevance for the immunocompetence of piglets. Genome-wide Association Studies (GWAS) in 535 Landrace (LR) and 461 Large White (LW) piglets were performed, investigating 20 immune relevant traits. Besides the health indicators of the complete and differential blood count, eight different cytokines and haptoglobin were recorded in all piglets and their biological dams to capture mediating processes and acute phase reactions. Additionally, all animals were genotyped using the Illumina PorcineSNP60v2 BeadChip. Results In summary, GWAS detected 25 genome-wide and 452 chromosome-wide significant SNPs associated with 17 immune relevant traits in the two maternal pig lines LR and LW. Only small differences were observed considering the maternal immune records as covariate within the statistical model. Furthermore, the study identified across- and within-breed differences as well as relevant candidate genes. In LR more significant associations and related candidate genes were detected, compared with LW. The results detected in LR and LW are partly in accordance with previously identified quantitative trait loci (QTL) regions. In addition, promising novel genomic regions were identified which might be of interest for further detailed analysis. Especially putative pleiotropic regions on SSC5, SSC12, SSC15, SSC16 and SSC17 are of major interest with regard to the interacting structure of the immune system. The comparison with already identified QTL gives indications on interactions with traits affecting piglet survival and also production traits. Conclusion In conclusion, results suggest a polygenic and breed-specific background of immune relevant traits. The current study provides knowledge about regions with biological relevance for health and immune traits. Identified markers and putative pleiotropic regions provide first indications in the context of balancing a breeding-based modification of the porcine immune system.

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 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 Exploration of CYP21A2 and CYP17A1 polymorphisms and preeclampsia risk among Chinese Han population: a large-scale case-control study based on 5021 subjects

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Bo Hou ◽  
Xuewen Jia ◽  
Ziwen Deng ◽  
Xin Liu ◽  
Huitang Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Chinese Han ◽  
Single Nucleotide ◽  
Allelic Frequencies ◽  
Genome Wide ◽  
And Control ◽  
The Relationship

Abstract Background Several genome-wide association studies have identified single-nucleotide polymorphisms (SNPs), such as rs4409766, rs1004467, and rs3824755 in CYP17A1 and rs2021783 in CYP21A2, as new hypertension susceptibility genetic variants in the Chinese population. This study aimed to look into the relationship between preeclampsia (PE) and these SNPs in Chinese Han women. Methods Overall, 5021 unrelated pregnant women were recruited, including 2002 patients with PE and 3019 normal healthy controls. The real-time PCR (TaqMan) method was applied to genotype these four polymorphisms. Results A statistically obvious difference in the allelic frequencies was observed in CYP21A2 rs2021783 between cases and controls (χ2 = 7.201, Pc = 0.028 by allele), and the T allele was associated with the occurrence and development of PE (OR = 1.151, 95% CI 1.039–1.275). We also found a significant association between rs2021783 and the development of early-onset PE (Pc = 0.008 by genotype, Pc = 0.004 by allele). For rs1004467 and rs3824755, the distribution of allelic frequencies differed markedly between mild PE and control groups (χ2 = 6.843, Pc = 0.036; χ2 = 6.869, Pc = 0.036), and patients with the TT genotype of rs1004467 were less easy to develop mild PE than were those carrying the CT or CC genotype (χ2 = 7.002, Pc = 0.032, OR = 1.306, 95% CI 1.071–1.593). The GG genotype of rs3824755 appeared to a protective effect on the occurrence of mild PE (OR = 0.766, 95% CI 0.629–0.934). Conclusions CYP21A2 rs2021783 appears to be closely related to PE susceptibility, and CYP17A1 rs1004467 and rs3824755 seem to be closely associated with mild PE in Han women.

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