scholarly journals Genome-Wide Association Study of Resistance to Bean Fly and Population Structure of Market Classes of Common Bean

2019 ◽  
Author(s):  
Pascal P. Okwiri Ojwang ◽  
Tilly Eldridge ◽  
Pilar Corredor-Moreno ◽  
Vincent Njung'e
Keyword(s):  
Population Structure ◽  
Common Bean ◽  
Southern Africa ◽  
Gene Pool ◽  
Association Studies ◽  
Genome Wide Association ◽  
Chromosome 1 ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Bean Fly

Common bean (Phaesolus vulgaris L.) distribution across eastern, central and southern Africa region is widely driven by choice of grain types, which is affecting the genetic composition and adaptation to target production environments for biotic and abiotic constraints. Two bean fly species, Ophiomyia spencerella and Ophiomyia phaseoli are harmful insect pests of beans causing significant yield losses. Our objectives were to assess the population structure of common bean germplasm of different market classes and to identify polymorphic loci associated with resistance to O. spencerella. The study was carried out on a diversity panel of 284 genotypes using 9040 SNP markers. The genotypes were differentiated in to 14 distinct clusters. The mean FST of 0.4849, revealed major differentiation among the populations. Andean gene pool was more diverse compared to Mesoamerica gene pool which could be attributed to preference for large seeded cultivars. Multi-dimensional scaling and structure analyses revealed admixture among seed types. From genome wide association studies (GWAS), major genomic regions associated with O. spencerella resistance were identified on chromosome 1 (Pv01). The most significant SNP on Pv01 was aligned to gene PHAVU_001G075500g that is related to the Interleukin-1 receptor-associated kinase (IRAK) pathway, critical in regulating inherent immune responses to disease infection and insect herbivore attack. The diversity uncovered on the basis of market classes of beans and the presence of QTL regions associated with resistance to bean fly could serve as a valuable genetic resource for improvement of beans of different seed types in eastern and southern Africa region.

scholarly journals Structure of genetic diversity and genome-wide association studies of bean fly (Ophiomyia spencerella) resistance in common bean

Euphytica ◽  
2021 ◽  
Vol 217 (12) ◽  
Author(s):  
Pascal P. Okwiri Ojwang ◽  
Tilly Eldridge ◽  
Pilar Corredor-Moreno ◽  
Vincent Njung’e
Keyword(s):  
Genetic Diversity ◽  
Common Bean ◽  
Association Studies ◽  
Sub Saharan Africa ◽  
Genome Wide Association ◽  
Eastern Africa ◽  
Genome Wide Association Studies ◽  
Ophiomyia Spencerella ◽  
Genome Wide ◽  
Bean Fly

AbstractEastern Africa is a significant region of common bean (Phaseolus vulgaris L.) production and genetic diversity. Insect pests are a major biotic constraint in subsistence crop production systems. Bean fly (Ophiomyia spencerella) is a serious pest of beans in eastern Africa highlands. Breeding efforts focus on combining adaptability traits with user preferred seed types. However, lack of information on molecular markers linked to genes modulating bean fly resistance has slowed breeding progress. The objectives were to: (i) characterize genetic diversity and uncover putative bean fly resistant genotypes within diverse seed types and market classes and (ii) identify genomic regions controlling bean fly resistance using genome-wide association analysis (GWAS). A set of 276 diverse genotypes comprising local landraces and varieties from Kenya alongside introductions from International Centre for Tropical Agriculture (CIAT), were assembled. The germplasm represented varied bean production ecologies and seed types. Genetic diversity conforming to Andean and Mesoamerican genepools was established. Out of 276 genotypes evaluated, 150 were Andean, 74 were Mesoamerican and 52 were admixed. Twenty-two genotypes were resistant to bean fly. Association mapping results for stem damage score and plant mortality identified six significant single-nucleotide polymorphisms (SNPs) on chromosomes Pv01 and Pv09. The most significant SNP marker was 12 kilobases downstream of Phvul.001G074900 gene with LOD score > 4.0 hence in linkage disequilibrium with the postulated gene. The identified candidate gene is pleiotropic and modulates both flowering time and plant responses to stress. These findings are a key step towards marker-enabled breeding in common bean for sub-Saharan Africa.

Genome-wide association studies of Ca and Mn in the seeds of the common bean (Phaseolus vulgaris L.)

Genomics ◽  
2020 ◽  
Vol 112 (6) ◽  
pp. 4536-4546
Author(s):  
Semih Erdogmus ◽  
Duygu Ates ◽  
Seda Nemli ◽  
Bulent Yagmur ◽  
Tansel Kaygisiz Asciogul ◽  
...  
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Phaseolus Vulgaris L ◽  
Genome Wide ◽  
The Common

scholarly journals Genome-Wide Association Studies of Anthracnose and Angular Leaf Spot Resistance in Common Bean (Phaseolus vulgaris L.)

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0150506 ◽  
Author(s):  
Juliana Morini Küpper Cardoso Perseguini ◽  
Paula Rodrigues Oblessuc ◽  
João Ricardo Bachega Feijó Rosa ◽  
Kleber Alves Gomes ◽  
Alisson Fernando Chiorato ◽  
...  
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Leaf Spot ◽  
Association Studies ◽  
Angular Leaf Spot ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Phaseolus Vulgaris L ◽  
Genome Wide

scholarly journals Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies

2017 ◽  
Author(s):  
Haohan Wang ◽  
Bryon Aragam ◽  
Eric P. Xing
Keyword(s):  
Population Structure ◽  
Variable Selection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Low Rank ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Genome Wide

AbstractA fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.

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