Optimal Haplotype Block-Free Selection of Tagging SNPs for Genome-Wide Association Studies

Brazil is the largest consumer of dry edible beans (Phaseolus vulgaris L.) in the world, 70% of consumption is of the carioca variety. Although the variety has high yield, it is susceptible to several diseases, among them, anthracnose (ANT) can lead to losses of up to 100% of production. The most effective strategy to overcome ANT, a disease caused by the fungus Colletotrichum lindemuthianum, is the development of resistant cultivars. For that reason, the selection of carioca genotypes resistant to multiple ANT races and the identification of loci/markers associated with genetic resistance are extremely important for the genetic breeding process. Using a carioca diversity panel (CDP) with 125 genotypes and genotyped by BeadChip BARCBean6K_3 and a carioca segregating population AM (AND-277 × IAC-Milênio) genotyped by sequencing (GBS). Multiple interval mapping (MIM) and genome-wide association studies (GWAS) were used as mapping tools for the resistance genes to the major ANT physiological races present in the country. In general, 14 single nucleotide polymorphisms (SNPs) showed high significance for resistance by GWAS, and loci associated with multiple races were also identified, as the Co-3 locus. The SNPs ss715642306 and ss715649427 in linkage disequilibrium (LD) at the beginning of chromosome Pv04 were associated with all the races used, and 16 genes known to be related to plant immunity were identified in this region. Using the resistant cultivars and the markers associated with significant quantitative resistance loci (QRL), discriminant analysis of principal components (DAPC) was performed considering the allelic contribution to resistance. Through the DAPC clustering, cultivar sources with high potential for durable anthracnose resistance were recommended. The MIM confirmed the presence of the Co-14 locus in the AND-277 cultivar which revealed that it was the only one associated with resistance to ANT race 81. Three other loci were associated with race 81 on chromosomes Pv03, Pv10, and Pv11. This is the first study to identify new resistance loci in the AND-277 cultivar. Finally, the same Co-14 locus was also significant for the CDP at the end of Pv01. The new SNPs identified, especially those associated with more than one race, present great potential for use in marker-assisted and early selection of inbred lines.

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Abstract: P866 USE OF GENOME-WIDE ASSOCIATION STUDIES IN SELECTION OF CANDIDATE SNPS

Atherosclerosis Supplements ◽

10.1016/s1567-5688(09)70987-0 ◽

2009 ◽

Vol 10 (2) ◽

pp. e1011

Author(s):

J Bolton ◽

J Price

Keyword(s):

Association Studies ◽

Genome Wide Association ◽

Candidate Snps ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Selection Of

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Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

Algorithms for Molecular Biology ◽

10.1186/1748-7188-7-11 ◽

2012 ◽

Vol 7 (1) ◽

Cited By ~ 22

Author(s):

Tapio Pahikkala ◽

Sebastian Okser ◽

Antti Airola ◽

Tapio Salakoski ◽

Tero Aittokallio

Keyword(s):

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Matrix Operations ◽

Genome Wide ◽

Genetic Features ◽

Selection Of

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Haplotype-based genome wide association study using a novel SNP-set method : RAINBOW

10.1101/612028 ◽

2019 ◽

Cited By ~ 1

Author(s):

Kosuke Hamazaki ◽

Hiroyoshi Iwata

Keyword(s):

Haplotype Block ◽

Rare Variants ◽

Association Studies ◽

False Positives ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Alleles ◽

Genome Wide ◽

Causal Variants ◽

Novel Snp

AbstractBackgroundDiffculty in detecting rare variants is one of the problems in conventional genome wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set GWAS method named “RAINBOW” and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes.ResultsWe prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. The results of the comparison indicated that the proposed method was able to better control false positives than the others. The proposed method was also excellent at detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset. Moreover, the proposed method showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, especially when the causal variants were located very close to each other and the directions of their effects were opposite.ConclusionThe proposed method, RAINBOW, is especially superior in controlling false positives, detecting causal variants, and detecting nearby causal variants with opposite effects. By using the SNP-set approach as the proposed method, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as the R package and is available at https://github.com/KosukeHamazaki/RAINBOW.

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Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

10.1101/2021.10.28.466332 ◽

2021 ◽

Author(s):

Xing Wu ◽

Wei Jiang ◽

Christopher Fragoso ◽

Jing Huang ◽

Geyu Zhou ◽

...

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Haplotype Block ◽

Association Studies ◽

Crop Improvement ◽

Plant Genome ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Haplotype Blocks ◽

Genome Wide

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

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