Identification of genomic regions and candidate genes associated with growth ofEriocheir Sinensisby QTL mapping and marker annotation

2015 ◽  
Vol 48 (1) ◽  
pp. 246-258 ◽  
Author(s):  
Min Hui ◽  
Zhaoxia Cui ◽  
Yuan Liu ◽  
Chengwen Song ◽  
Yingdong Li ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Genomic Regions

scholarly journals GWAS Discovery Of Candidate Genes for Yield-Related Traits in Peanut and Support from Earlier QTL Mapping Studies

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 803 ◽  
Author(s):  
Wang ◽  
Yan ◽  
Li ◽  
Li ◽  
Zhao ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Genome Wide Association Study ◽  
Gene Annotation ◽  
Genotyping By Sequencing ◽  
Nucleotide Polymorphisms ◽  
Market Demand ◽  
Oil Crops ◽  
A Genome ◽  
Genomic Regions

Peanut (Arachis hypogaea L.) is one of the most important oil crops worldwide, and its yet increasing market demand may be met by genetic improvement of yield related traits, which may be facilitated by a good understanding of the underlying genetic base of these traits. Here, we have carried out a genome-wide association study (GWAS) with the aim to identify genomic regions and the candidate genes within these regions that may be involved in determining the phenotypic variation at seven yield-related traits in peanut. For the GWAS analyses, 195 peanut accessions were phenotyped and/or genotyped; the latter was done using a genotyping-by-sequencing approach, which produced a total of 13,435 high-quality single nucleotide polymorphisms (SNPs). Analyses of these SNPs show that the analyzed peanut accessions can be approximately grouped into two big groups that, to some extent, agree with the botanical classification of peanut at the subspecies level. By taking this genetic structure as well as the relationships between the analyzed accessions into consideration, our GWAS analyses have identified 93 non-overlapping peak SNPs that are significantly associated with four of the studied traits. Gene annotation of the genome regions surrounding these peak SNPs have found a total of 311 unique candidate genes. Among the 93 yield-related-trait-associated SNP peaks, 12 are found to be co-localized with the quantitative trait loci (QTLs) that were identified by earlier related QTL mapping studies, and these 12 SNP peaks are only related to three traits and are almost all located on chromosomes Arahy.05 and Arahy.16. Gene annotation of these 12 co-localized SNP peaks have found 36 candidates genes, and a close examination of these candidate genes found one very interesting gene (arahy.RI9HIF), the rice homolog of which produces a protein that has been shown to improve rice yield when over-expressed. Further tests of the arahy.RI9HIF gene, as well as other candidate genes especially those within the more confident co-localized genomic regions, may hold the potential for significantly improving peanut yield.

scholarly journals QTL mapping in outbred populations: successes and challenges

2014 ◽  
Vol 46 (3) ◽  
pp. 81-90 ◽  
Author(s):  
Leah C. Solberg Woods
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Complex Traits ◽  
Full Genome Sequence ◽  
Backcross Population ◽  
Animal Populations ◽  
Resolution Mapping ◽  
Trait Locus ◽  
Genomic Regions ◽  
Outbred Populations

Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been fine-mapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.

scholarly journals Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 456
Author(s):  
Hewa Bahithige Pavithra Chathurangi Ariyarathne ◽  
Martin Correa-Luna ◽  
Hugh Thomas Blair ◽  
Dorian John Garrick ◽  
Nicolas Lopez-Villalobos
Keyword(s):  
Gene Ontology ◽  
New Zealand ◽  
Dairy Cows ◽  
Candidate Genes ◽  
Milk Fat ◽  
Crude Protein ◽  
Single Locus ◽  
Protein Utilization ◽  
Grazing Dairy Cows ◽  
Genomic Regions

The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10−6 were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.

scholarly journals Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pablo Cáceres ◽  
Agustín Barría ◽  
Kris A. Christensen ◽  
Liane N. Bassini ◽  
Katharina Correa ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Atlantic Salmon ◽  
Candidate Genes ◽  
Protein Phosphatase ◽  
Sea Lice ◽  
Genome Wide Association Studies ◽  
Dual Specificity ◽  
Dual Specificity Protein Phosphatase ◽  
Genomic Regions ◽  
Specificity Protein

AbstractSea lice (Caligus rogercresseyi) is an ectoparasite which causes major production losses in the salmon aquaculture industry worldwide. Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) are two of the most susceptible salmonid species to sea lice infestation. The objectives of this study were to: (1) identify genomic regions associated with resistance to Caligus rogercresseyi in Atlantic salmon and rainbow trout by performing single-step Genome-Wide Association studies (ssGWAS), and (2) identify candidate genes related to trait variation based on exploring orthologous genes within the associated regions across species. A total of 2626 Atlantic salmon and 2643 rainbow trout were challenged and genotyped with 50 K and 57 K SNP panels, respectively. We ran two independent ssGWAS for sea lice resistance on each species and identified 7 and 13 regions explaining more than 1% of the genetic variance for the trait, with the most important regions explaining 3% and 2.7% for Atlantic salmon and rainbow trout, respectively. We identified genes associated with immune response, cytoskeleton function, and cell migration when focusing on important genomic regions for each species. Moreover, we found 15 common orthogroups which were present in more than one associated genomic region, within- or between-species; however, only one orthogroup showed a clear potential biological relevance in the response against sea lice. For instance, dual-specificity protein phosphatase 10-like (dusp10) and dual-specificity protein phosphatase 8 (dusp8) were found in genomic regions associated with lice density in Atlantic salmon and rainbow trout, respectively. Dusp10 and dusp8 are modulators of the MAPK pathway and might be involved in the differences of the inflammation response between lice resistant and susceptible fish from both species. Our results provide further knowledge on candidate genes related to sea lice resistance and may help establish better control for sea lice in fish populations.

scholarly journals Candidate Genes for the High-Altitude Adaptations of Two Mountain Pine Taxa

2021 ◽  
Vol 22 (7) ◽  
pp. 3477
Author(s):  
Julia Zaborowska ◽  
Bartosz Łabiszak ◽  
Annika Perry ◽  
Stephen Cavers ◽  
Witold Wachowiak
Keyword(s):  
Population Structure ◽  
Candidate Genes ◽  
Genetic Basis ◽  
Redox Homeostasis ◽  
Snp Markers ◽  
Regulation Of Transcription ◽  
Single Nucleotide ◽  
Homeostasis Regulation ◽  
Genomic Regions ◽  
Mountain Plants

Mountain plants, challenged by vegetation time contractions and dynamic changes in environmental conditions, developed adaptations that help them to balance their growth, reproduction, survival, and regeneration. However, knowledge regarding the genetic basis of species adaptation to higher altitudes remain scarce for most plant species. Here, we attempted to identify such corresponding genomic regions of high evolutionary importance in two closely related European pines, Pinus mugo and P. uncinata, contrasting them with a reference lowland relative—P. sylvestris. We genotyped 438 samples at thousands of single nucleotide polymorphism (SNP) markers, tested their genetic differentiation and population structure followed by outlier detection and gene ontology annotations. Markers clearly differentiated the species and uncovered patterns of population structure in two of them. In P. uncinata three Pyrenean sites were grouped together, while two outlying populations constituted a separate cluster. In P. sylvestris, Spanish population appeared distinct from the remaining four European sites. Between mountain pines and the reference species, 35 candidate genes for altitude-dependent selection were identified, including such encoding proteins responsible for photosynthesis, photorespiration and cell redox homeostasis, regulation of transcription, and mRNA processing. In comparison between two mountain pines, 75 outlier SNPs were found in proteins involved mainly in the gene expression and metabolism.

scholarly journals QTL Mapping by SLAF-seq and Expression Analysis of Candidate Genes for Aphid Resistance in Cucumber

2016 ◽  
Vol 7 ◽  
Author(s):  
Danna Liang ◽  
Minyang Chen ◽  
Xiaohua Qi ◽  
Qiang Xu ◽  
Fucai Zhou ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Expression Analysis ◽  
Aphid Resistance

scholarly journals Genome-Wide Association Study (GWAS) to Identify Salt-Tolerance QTLs Carrying Novel Candidate Genes in Rice During Early Vegetative Stage

Rice ◽  
2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Leila Nayyeripasand ◽  
Ghasem Ali Garoosi ◽  
Asadollah Ahmadikhah
Keyword(s):  
Association Study ◽  
Candidate Gene ◽  
Candidate Genes ◽  
Salinity Tolerance ◽  
Genome Wide Association Study ◽  
Dry Weight ◽  
Genome Wide Association ◽  
Vegetative Stage ◽  
Genome Wide ◽  
Genomic Regions

Abstract Background Rice is considered as a salt-sensitive plant, particularly at early vegetative stage, and its production is suffered from salinity due to expansion of salt affected land in areas under cultivation. Hence, significant increase of rice productivity on salinized lands is really necessary. Today genome-wide association study (GWAS) is a method of choice for fine mapping of QTLs involved in plant responses to abiotic stresses including salinity stress at early vegetative stage. In this study using > 33,000 SNP markers we identified rice genomic regions associated to early stage salinity tolerance. Eight salinity-related traits including shoot length (SL), root length (RL), root dry weight (RDW), root fresh weight (RFW), shoot fresh weight (SFW), shoot dry weight (SDW), relative water content (RWC) and TW, and 4 derived traits including SL-R, RL-R, RDW-R and RFW-R in a diverse panel of rice were evaluated under salinity (100 mM NaCl) and normal conditions in growth chamber. Genome-wide association study (GWAS) was applied based on MLM(+Q + K) model. Results Under stress conditions 151 trait-marker associations were identified that were scattered on 10 chromosomes of rice that arranged in 29 genomic regions. A genomic region on chromosome 1 (11.26 Mbp) was identified which co-located with a known QTL region SalTol1 for salinity tolerance at vegetative stage. A candidate gene (Os01g0304100) was identified in this region which encodes a cation chloride cotransporter. Furthermore, on this chromosome two other candidate genes, Os01g0624700 (24.95 Mbp) and Os01g0812000 (34.51 Mbp), were identified that encode a WRKY transcription factor (WRKY 12) and a transcriptional activator of gibberellin-dependent alpha-amylase expression (GAMyb), respectively. Also, a narrow interval on the same chromosome (40.79–42.98 Mbp) carries 12 candidate genes, some of them were not so far reported for salinity tolerance at seedling stage. Two of more interesting genes are Os01g0966000 and Os01g0963000, encoding a plasma membrane (PM) H+-ATPase and a peroxidase BP1 protein. A candidate gene was identified on chromosome 2 (Os02g0730300 at 30.4 Mbp) encoding a high affinity K+ transporter (HAK). On chromosome 6 a DnaJ-encoding gene and pseudouridine synthase gene were identified. Two novel genes on chromosome 8 including the ABI/VP1 transcription factor and retinoblastoma-related protein (RBR), and 3 novel genes on chromosome 11 including a Lox, F-box and Na+/H+ antiporter, were also identified. Conclusion Known or novel candidate genes in this research were identified that can be used for improvement of salinity tolerance in molecular breeding programmes of rice. Further study and identification of effective genes on salinity tolerance by the use of candidate gene-association analysis can help to precisely uncover the mechanisms of salinity tolerance at molecular level. A time dependent relationship between salt tolerance and expression level of candidate genes could be recognized.

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