scholarly journals Genome-Wide Detection of Allele Frequency Change and Quantitative Trait Loci for Respiratory Disease and Immune-Related Traits in Landrace Pigs Selected for Mycoplasmal Pneumonia of Swine Resistance

2021 ◽  
Author(s):  
Yoshinobu Uemoto ◽  
Kasumi Ichinoseki ◽  
Toshimi Matsumoto ◽  
Nozomi Oka ◽  
Hironori Takamori ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Allele Frequency ◽  
Respiratory Disease ◽  
Quantitative Trait ◽  
Association Studies ◽  
Frequency Change ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mycoplasmal Pneumonia ◽  
Frequency Changes

Abstract Background: The genetic improvement of disease resistance in pig has been well-received. Identification of a quantitative trait locus (QTL) related to a chronic respiratory disease such as Mycoplasmal pneumonia of swine (MPS) and immune-related traits is important for understanding the genomic background of disease resistance and to apply marker-assisted selection. The objective of this study was to understand the influence of genomic factors on respiratory disease and immune-related traits in MPS-selected pigs.Results: A total of 874 Landrace purebred pigs, which were selected based on MPS resistance, were genotyped using the Illumina PorcineSNP60 BeadChip, and were then used for genomic analyses. First, we performed genome-wide association studies (GWAS) to detect a novel QTL for a total of 22 performance, respiratory disease, and immune-related traits using additive and nonadditive genetic effects. Second, we evaluated the changes in allele frequency due to selection for MPS resistance and compared the putative selected regions with the detected QTL. GWAS detected a total of 11 genome-wide significant single nucleotide polymorphisms (SNPs) with an additive effect in five traits and a total of three significant SNPs with a nonadditive effect in three traits. Most of these detected QTL regions were novel regions with some candidate genes located in them. With regard to a pleiotropic region among traits, only five of these detected QTL regions overlapped among traits. Changes in allele frequencies at the many putative selected regions were spread across the whole genome and overlapped with the detected QTL. Some of these selected regions were the ones that contained the detected QTL for MPS score and other traits.Conclusion: These results suggest that a closed-line breeding population is a useful target population to refine and confirm QTL regions by integrating the results of GWAS and allele frequency changes. The study provides new insights into the genomic factors that affect respiratory disease and immune-related traits in pigs.

scholarly journals Metabolome-scale genome-wide association studies reveal chemical diversity and genetic control of maize specialized metabolites

2018 ◽  
Author(s):  
Zhou Shaoqun ◽  
Karl A. Kremling ◽  
Bandillo Nonoy ◽  
Richter Annett ◽  
Ying K. Zhang ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Citrate Synthase ◽  
Association Studies ◽  
Inbred Lines ◽  
Chemical Diversity ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Linkage ◽  
Genome Wide ◽  
Maize Varieties

One Sentence SummaryHPLC-MS metabolite profiling of maize seedlings, in combination with genome-wide association studies, identifies numerous quantitative trait loci that influence the accumulation of foliar metabolites.AbstractCultivated maize (Zea mays) retains much of the genetic and metabolic diversity of its wild ancestors. Non-targeted HPLC-MS metabolomics using a diverse panel of 264 maize inbred lines identified a bimodal distribution in the prevalence of foliar metabolites. Although 15% of the detected mass features were present in >90% of the inbred lines, the majority were found in <50% of the samples. Whereas leaf bases and tips were differentiated primarily by flavonoid abundance, maize varieties (stiff-stalk, non-stiff-stalk, tropical, sweet corn, and popcorn) were differentiated predominantly by benzoxazinoid metabolites. Genome-wide association studies (GWAS), performed for 3,991 mass features from the leaf tips and leaf bases, showed that 90% have multiple significantly associated loci scattered across the genome. Several quantitative trait locus hotspots in the maize genome regulate the abundance of multiple, often metabolically related mass features. The utility of maize metabolite GWAS was demonstrated by confirming known benzoxazinoid biosynthesis genes, as well as by mapping isomeric variation in the accumulation of phenylpropanoid hydroxycitric acid esters to a single linkage block in a citrate synthase-like gene. Similar to gene expression databases, this metabolomic GWAS dataset constitutes an important public resource for linking maize metabolites with biosynthetic and regulatory genes.

scholarly journals Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants

2020 ◽  
Vol 24 ◽  
pp. 100145 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Alencar Xavier ◽  
Travis Beckett ◽  
Savannah Beyer ◽  
Liyang Chen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

scholarly journals Genome-wide Identification of Powdery Mildew Resistance in Common Bean

2020 ◽  
Author(s):  
Papias Hongera Binagwa ◽  
Sy M. Traore ◽  
Marceline Egnin ◽  
Gregory C. Bernard ◽  
Inocent Ritte ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Common Bean ◽  
Resistance Genes ◽  
Agronomic Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Resistance Protein ◽  
Resistance Protein

Abstract Background: Genome-wide association studies (GWAS) was utilized to detect genetic variations related to the powdery mildew (PM) resistance and several agronomic traits in common bean. However, its application in common bean and the PM interactions to identify genes and their location in the common bean genome has not been fully addressed. Results: Genome-wide association studies (GWAS) through marker-trait association are useful molecular tools for the identification of disease resistance and other agronomic traits. SNP genotyping with a BeadChip containing 5398 SNPs was used to detect genetic variations related to resistance to PM disease in a panel of 206 genotypes grown under field conditions for two consecutive years. Significant SNPs identified on chromosomes 4 and 10 (Pv04 and Pv10) were repeatable, confirming the reliability of the phenotypic data scored from the genotypes grown in two locations within two years. A cluster of resistance genes was revealed on the chromosome 4 of common bean genome among which CNL and TNL like resistance genes were identified. Furthermore, two resistance genes Phavu_010G1320001g and Phavu_010G136800g were also identified on Pv10; further sequence analysis showed that these genes were homologs to the Arabidopsis disease resistance protein (RLM1A-like) and the putative disease resistance protein (At4g11170.1), respectively. Two LRR receptor-like kinases (RLK) were also identified on Pv11 in samples collected in 2018 only. Many genes encoding auxin-responsive protein, TIFY10A protein, growth-regulating factor 5-like, ubiquitin-like protein, cell wall protein RBR3-like protein related to PM resistance were identified nearby significant SNPs. These results suggested that the resistance to PM pathogen involves a network of many genes constitutively co-expressed and may generate several layers of defense barriers or inducible reactions.Conclusion: Our results provide new insights into common bean and PM interactions, and revealed putative resistance genes as well as their location on common bean genome that could be used for marker-assisted selection, functional genomic study approaches to confirm the role of these putative genes; hence, developing common bean resistance lines to the PM disease.

scholarly journals The promise and deceit of genomic selection component analyses

2021 ◽  
Vol 288 (1961) ◽  
Author(s):  
John K. Kelly
Keyword(s):  
Allele Frequency ◽  
Statistical Power ◽  
Field Experiments ◽  
Whole Genome Sequence ◽  
Frequency Change ◽  
Genome Wide ◽  
Pooled Sequencing ◽  
Frequency Changes ◽  
Selection Intensities ◽  
Combine Information

Selection component analyses (SCA) relate individual genotype to fitness components such as viability, fecundity and mating success. SCA are based on population genetic models and yield selection estimates directly in terms of predicted allele frequency change. This paper explores the statistical properties of gSCA: experiments that apply SCA to genome-wide scoring of SNPs in field sampled individuals. Computer simulations indicate that gSCA involving a few thousand genotyped samples can detect allele frequency changes of the magnitude that has been documented in field experiments on diverse taxa. To detect selection, imprecise genotyping from low-level sequencing of large samples of individuals provides much greater power than precise genotyping of smaller samples. The simulations also demonstrate the efficacy of ‘haplotype matching’, a method to combine information from a limited collection of whole genome sequence (the reference panel) with the much larger sample of field individuals that are measured for fitness. Pooled sequencing is demonstrated as another way to increase statistical power. Finally, I discuss the interpretation of selection estimates in relation to the Beavis effect, the overestimation of selection intensities at significant loci.

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