Genetic Mapping Reveals Complex Architecture and Candidate Genes Involved in Common Bean Response to Meloidogyne Incognita Infection

Abstract Root-knot nematodes (RKN), particularly Meloidogyne incognita, are among the most damaging and prevalent agricultural pathogens due to their ability to infect roots of almost all crop species, including common bean. The best strategy for their control is through the use of resistant cultivars. However, laborious phenotyping procedures make it difficult to assess nematode resistance in breeding programs. For common bean, this task is especially challenging since little has been done to discover resistance genes or find markers to assist selection. In this study, we performed genome-wide association studies and QTL mapping to explore the genetic architecture and genomic regions underlying the resistance to M. incognita and to identify candidate resistance genes. Phenotypic data were collected by a high-throughput assay, and the number of egg masses and root-galling index were evaluated 30 days after inoculation. Complex genetic architecture and independent genomic regions were associated with each trait according to the Fixed and random model Circulating Probability Unification. SNPs located on chromosomes Pv06, Pv07, Pv08 and Pv11 were associated with the number of egg masses, and on Pv01, Pv02, Pv05 and Pv10 with root-galling. A total of 215 candidate genes were identified, including 14 resistance gene analogs and five differentially expressed in a previous RNA-seq analysis. The histochemical analysis indicated that the reactive oxygen species might play a role in the resistance response. Our findings open new perspectives to improve selection efficiency for RKN resistance in common bean, and the candidate genes are valuable targets for functional investigation and gene editing approaches.

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Genome-Wide Association Studies (GWAS) for Traits Related to Fodder Quality and Biofuel in Sorghum: Progress and Prospects

Protein and Peptide Letters ◽

10.2174/0929866528666210127153103 ◽

2021 ◽

Vol 28 ◽

Author(s):

Vinutha Kanuganahalli Somegowda ◽

Laavanya Rayaprolu ◽

Abhishek Rathore ◽

Santosh Pandurang Deshpande ◽

Rajeev Gupta

Keyword(s):

Genetic Architecture ◽

Association Studies ◽

Current Status ◽

Genome Wide Association Studies ◽

Potential Candidate ◽

Crop Species ◽

Fodder Quality ◽

Genome Wide ◽

Genomic Regions ◽

Model Crop

: The main focus of this review is to discuss the current status of the use of GWAS for fodder quality and biofuel owing to its similarity of traits. Sorghum is a potential multipurpose crop, popularly cultivated for various uses as food, feed fodder, and biomass for ethanol. Production of a huge quantity of biomass and genetic variation for complex sugars are the main motivation not only to use sorghum as fodder for livestock nutritionists but also a potential candidate for biofuel generation. Few studies have been reported on the knowledge transfer that can be used from the development of biofuel technologies to complement improved fodder quality and vice versa. With recent advances in genotyping technologies, GWAS became one of the primary tools used to identify the genes/genomic regions associated with the phenotype. These modern tools and technologies accelerate the genomic assisted breeding process to enhance the rate of genetic gains. Hence, this mini-review focuses on GWAS studies on genetic architecture and dissection of traits underpinning fodder quality and biofuel traits and their limited comparison with other related model crop species.

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A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations

BMC Genomics ◽

10.1186/s12864-020-07288-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shenping Zhou ◽

Rongrong Ding ◽

Fanming Meng ◽

Xingwang Wang ◽

Zhanwei Zhuang ◽

...

Keyword(s):

Candidate Genes ◽

Growth And Development ◽

Genetic Architecture ◽

Association Studies ◽

Meta Analysis ◽

Average Daily Gain ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Daily Gain

Abstract Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

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Dissecting the Genetic Architecture of Seed Protein and Oil Content in Soybean from the Yangtze and Huaihe River Valleys Using Multi-Locus Genome-Wide Association Studies

International Journal of Molecular Sciences ◽

10.3390/ijms20123041 ◽

2019 ◽

Vol 20 (12) ◽

pp. 3041 ◽

Cited By ~ 4

Author(s):

Li ◽

Xu ◽

Yang ◽

Zhao

Keyword(s):

Candidate Genes ◽

Genetic Architecture ◽

Oil Content ◽

Seed Protein ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Huaihe River ◽

Genome Wide ◽

River Valleys

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.

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Genome-wide association studies reveal genomic regions and positional candidate genes for fat deposition in chickens

10.11606/t.11.2018.tde-17072018-191146 ◽

2018 ◽

Author(s):

Gabriel Costa Monteiro Moreira

Keyword(s):

Candidate Genes ◽

Association Studies ◽

Fat Deposition ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Positional Candidate ◽

Genome Wide ◽

Genomic Regions

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Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis

10.1101/500579 ◽

2018 ◽

Author(s):

Satish K Nandakumar ◽

Sean K McFarland ◽

Laura Marlene Mateyka ◽

Caleb A Lareau ◽

Jacob C Ulirsch ◽

...

Keyword(s):

Candidate Genes ◽

Target Genes ◽

Association Studies ◽

Human Diseases ◽

Genome Wide Association Studies ◽

Loss Of Function ◽

Genome Wide ◽

Causal Variants ◽

Genomic Regions

Genome-wide association studies (GWAS) have identified thousands of variants associated with human diseases and traits. However, the majority of GWAS-implicated variants are in non-coding genomic regions and require in depth follow-up to identify target genes and decipher biological mechanisms. Here, rather than focusing on causal variants, we have undertaken a pooled loss-of-function screen in primary hematopoietic cells to interrogate 389 candidate genes contained in 75 loci associated with red blood cell traits. Using this approach, we identify 77 genes at 38 GWAS loci, with most loci harboring 1-2 candidate genes. Importantly, the hit set was strongly enriched for genes validated through orthogonal genetic approaches. Genes identified by this approach are enriched in relevant biological pathways, allowing regulators of human erythropoiesis and blood disease modifiers to be defined. More generally, this functional screen provides a paradigm for gene-centric follow up of GWAS for a variety of human diseases and traits.

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Genome-wide Identification of Powdery Mildew Resistance in Common Bean

10.21203/rs.3.rs-27731/v2 ◽

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.

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Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Scientific Reports ◽

10.1038/s41598-021-03864-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fabricio Almeida-Silva ◽

Thiago M. Venancio

Keyword(s):

Candidate Genes ◽

Resistance Genes ◽

Association Studies ◽

Fungal Species ◽

Genome Wide Association ◽

Economic Losses ◽

Genome Wide Association Studies ◽

Link Type ◽

Physical Defense ◽

Genome Wide

AbstractSoybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application (https://soyfungigcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyFungiGCN) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

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Genetic Parameters and Genome-Wide Association Studies for Anti-Müllerian Hormone Levels and Antral Follicle Populations Measured After Estrus Synchronization in Nellore Cattle

Animals ◽

10.3390/ani10071185 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1185

Author(s):

Laís Grigoletto ◽

Miguel Henrique Almeida Santana ◽

Fabiana Fernandes Bressan ◽

Joanir Pereira Eler ◽

Marcelo Fábio Gouveia Nogueira ◽

...

Keyword(s):

Candidate Genes ◽

Genetic Parameters ◽

Association Studies ◽

Antral Follicle ◽

Genome Wide Association Studies ◽

Hormone Levels ◽

Estrous Synchronization ◽

Nellore Cattle ◽

Genomic Regions ◽

Indicator Traits

Reproductive efficiency plays a major role in the long-term sustainability of livestock industries and can be improved through genetic and genomic selection. This study aimed to estimate genetic parameters (heritability and genetic correlation) and identify genomic regions and candidate genes associated with anti-Müllerian hormone levels (AMH) and antral follicle populations measured after estrous synchronization (AFP) in Nellore cattle. The datasets included phenotypic records for 1099 and 289 Nellore females for AFP and AMH, respectively, high-density single nucleotide polymorphism (SNP) genotypes for 944 animals, and 4129 individuals in the pedigree. The heritability estimates for AMH and AFP were 0.28 ± 0.07 and 0.30 ± 0.09, and the traits were highly and positively genetically correlated (rG = 0.81 ± 0.02). These findings indicated that these traits can be improved through selective breeding, and substantial indirect genetic gains are expected by selecting for only one of the two traits. A total of 31 genomic regions were shown to be associated with AMH or AFP, and two genomic regions located on BTA1 (64.9–65.0 Mb and 109.1–109.2 Mb) overlapped between the traits. Various candidate genes were identified to be potentially linked to important biological processes such as ovulation, tissue remodeling, and the immune system. Our findings support the use of AMH and AFP as indicator traits to genetically improve fertility rates in Nellore cattle and identify better oocyte donors.

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Identification of Loci Controlling the Dwarfism Trait in the White Sailfin Molly (Poecilia latipinna) Using Genome-Wide Association Studies Based on Genotyping-By-Sequencing

Genes ◽

10.3390/genes10060418 ◽

2019 ◽

Vol 10 (6) ◽

pp. 418

Author(s):

Fan Shao ◽

Jing Liu ◽

Mengyuan Ren ◽

Junying Li ◽

Haigang Bao ◽

...

Keyword(s):

Candidate Genes ◽

Association Studies ◽

Genotyping By Sequencing ◽

Genome Wide Association ◽

Expression Level ◽

Genome Wide Association Studies ◽

Sailfin Molly ◽

Genome Wide ◽

Genomic Regions ◽

Sailfin Mollies

Dwarfism is a condition defined by low harvest weight in fish, but also results in strange body figures which may have potential for the selective breeding of new ornamental fish strains. The objectives of this study are to reveal the physiological causes of dwarfism and identify the genetic loci controlling this trait in the white sailfin molly. Skeletons of dwarf and normal sailfin mollies were observed by X-ray radioscopy and skeletal staining. Genome-wide association studies based on genotyping-by-sequencing (n = 184) were used to map candidate genomic regions associated with the dwarfism trait. Quantitative real-time PCR was performed to determine the expression level of candidate genes in normal (n = 8) and dwarf (n = 8) sailfin mollies. We found that the dwarf sailfin molly has a short and dysplastic spine in comparison to the normal fish. Two regions, located at NW_015112742.1 and NW_015113621.1, were significantly associated with the dwarfism trait. The expression level of three candidate genes, ADAMTS like 1, Larp7 and PPP3CA, were significantly different between the dwarf and normal sailfin mollies in the hepatopancreas, with PPP3CA also showing significant differences in the vertebrae and Larp7 showing significant differences in the muscle. This study identified genomic regions and candidate genes associated with the dwarfism trait in the white sailfin molly and would provide a reference to determine dwarf-causing variations.

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Genetic Associations in Four Decades of Multienvironment Trials Reveal Agronomic Trait Evolution in Common Bean

Genetics ◽

10.1534/genetics.120.303038 ◽

2020 ◽

Vol 215 (1) ◽

pp. 267-284 ◽

Cited By ~ 8

Author(s):

Alice H. MacQueen ◽

Jeffrey W. White ◽

Rian Lee ◽

Juan M. Osorno ◽

Jeremy Schmutz ◽

...

Keyword(s):

Common Bean ◽

Association Studies ◽

Crop Improvement ◽

Genetic Research ◽

The United States ◽

Genetic Effects ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Phenotypic Data ◽

Genomic Regions

Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for > 70 years in the United States and Canada, consisting of 20–50 entries each year at 10–20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.

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