Development of SNP Markers for Norovirus Related FUT2 Gene in Oyster

Mapping short reads to a reference genome is an essential step in many next-generation sequencing (NGS) analyses. In plants with large genomes, a large fraction of the reads can align to multiple locations of the genome with equally good alignment scores. How to map these ambiguous reads to the genome is a challenging problem with big impacts on the downstream analysis. Traditionally, the default method is to assign an ambiguous read randomly to one of the many potential locations. In this study, we explore two alternative methods that are based on the hypothesis that the possibility of an ambiguous read being generated by a location is proportional to the total number of reads produced by that location: (1) the enrichment method that assigns an ambiguous read to the location that has produced the most reads among all the potential locations, (2) the probability method that assigns an ambiguous read to a location based on a probability proportional to the number of reads the location produces. We systematically compared the performance of the proposed methods with that of the default random method. Our results showed that the enrichment method produced better results than the default random method and the probability method in the discovery of single nucleotide polymorphisms (SNPs). Not only did it produce more SNP markers, but it also produced SNP markers with better quality, which was demonstrated using multiple mainstay genomic analyses, including genome-wide association studies (GWAS), minor allele distribution, population structure, and genomic prediction.

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Comparative genome-wide mapping versus extreme pool-genotyping and development of diagnostic SNP markers linked to QTL for adult plant resistance to stripe rust in common wheat

Theoretical and Applied Genetics ◽

10.1007/s00122-018-3113-7 ◽

2018 ◽

Vol 131 (8) ◽

pp. 1777-1792 ◽

Cited By ~ 12

Author(s):

Jianhui Wu ◽

Shuo Huang ◽

Qingdong Zeng ◽

Shengjie Liu ◽

Qilin Wang ◽

...

Keyword(s):

Common Wheat ◽

Stripe Rust ◽

Plant Resistance ◽

Adult Plant Resistance ◽

Snp Markers ◽

Adult Plant ◽

Comparative Genome ◽

Genome Wide ◽

Pool Genotyping ◽

Mapping Versus

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Prototype for developing SNP markers from GWAS and biparental QTL for rice panicle and grain traits

ael ◽

10.1002/ael2.20047 ◽

2021 ◽

Vol 6 (2) ◽

Author(s):

Georgia C. Eizenga ◽

Aaron K. Jackson ◽

Jeremy D. Edwards

Keyword(s):

Snp Markers

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Development and application of rose (Rosa chinensis Jacq.) SNP markers based on SLAF-seq technology

Genetic Resources and Crop Evolution ◽

10.1007/s10722-021-01215-3 ◽

2021 ◽

Author(s):

Ao-Nan Xia ◽

Ao-Ao Yang ◽

Xian-Shui Meng ◽

Gui-Zhi Dong ◽

Xiao-Juan Tang ◽

...

Keyword(s):

Snp Markers ◽

Rosa Chinensis

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High resolution mapping and candidate gene identification of downy mildew race 16 resistance in spinach

BMC Genomics ◽

10.1186/s12864-021-07788-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Gehendra Bhattarai ◽

Wei Yang ◽

Ainong Shi ◽

Chunda Feng ◽

Braham Dhillon ◽

...

Keyword(s):

Candidate Genes ◽

Downy Mildew ◽

Association Analysis ◽

Spinacia Oleracea ◽

Genotyping By Sequencing ◽

Significant Snps ◽

Snp Markers ◽

Resistance Locus ◽

Downy Mildew Resistance ◽

Mildew Resistance

Abstract Background Downy mildew, the most devastating disease of spinach (Spinacia oleracea L.), is caused by the oomycete Peronospora effusa [=P. farinosa f. sp. spinaciae]. The P. effusa shows race specificities to the resistant host and comprises 19 reported races and many novel isolates. Sixteen new P. effusa races were identified during the past three decades, and the new pathogen races are continually overcoming the genetic resistances used in commercial cultivars. A spinach breeding population derived from the cross between cultivars Whale and Lazio was inoculated with P. effusa race 16 in an environment-controlled facility; disease response was recorded and genotyped using genotyping by sequencing (GBS). The main objective of this study was to identify resistance-associated single nucleotide polymorphism (SNP) markers from the cultivar Whale against the P. effusa race 16. Results Association analysis conducted using GBS markers identified six significant SNPs (S3_658,306, S3_692697, S3_1050601, S3_1227787, S3_1227802, S3_1231197). The downy mildew resistance locus from cultivar Whale was mapped to a 0.57 Mb region on chromosome 3, including four disease resistance candidate genes (Spo12736, Spo12784, Spo12908, and Spo12821) within 2.69–11.28 Kb of the peak SNP. Conclusions Genomewide association analysis approach was used to map the P. effusa race 16 resistance loci and identify associated SNP markers and the candidate genes. The results from this study could be valuable in understanding the genetic basis of downy mildew resistance, and the SNP marker will be useful in spinach breeding to select resistant lines.

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GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study

Scientific Reports ◽

10.1038/s41598-021-85203-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mahdi Akbarzadeh ◽

Saeid Rasekhi Dehkordi ◽

Mahmoud Amiri Roudbar ◽

Mehdi Sargolzaei ◽

Kamran Guity ◽

...

Keyword(s):

Lipid Profile ◽

Genetic Study ◽

Prediction Accuracy ◽

Significant Snps ◽

Snp Markers ◽

Linear Unbiased Prediction ◽

Genetic Prediction ◽

Heritability Estimation ◽

Precise Prediction ◽

Best Linear Unbiased

AbstractIn recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.

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Combining QTL-seq and linkage mapping to fine map a candidate gene in qCTS6 for cold tolerance at the seedling stage in rice

BMC Plant Biology ◽

10.1186/s12870-021-03076-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Luomiao Yang ◽

Jingguo Wang ◽

Zhenghong Han ◽

Lei Lei ◽

Hua Long Liu ◽

...

Keyword(s):

Cold Stress ◽

Cold Tolerance ◽

Regulatory Gene ◽

Snp Markers ◽

Seedling Stage ◽

Pcr Analysis ◽

Sequencing Data ◽

Coding Region ◽

Rice Varieties ◽

Major Qtl

Abstract Background Cold stress caused by low temperatures is an important factor restricting rice production. Identification of cold-tolerance genes that can stably express in cold environments is crucial for molecular rice breeding. Results In this study, we employed high-throughput quantitative trait locus sequencing (QTL-seq) analyses in a 460-individual F2:3 mapping population to identify major QTL genomic regions governing cold tolerance at the seedling stage in rice. A novel major QTL (qCTS6) controlling the survival rate (SR) under low-temperature conditions of 9°C/10 days was mapped on the 2.60-Mb interval on chromosome 6. Twenty-seven single-nucleotide polymorphism (SNP) markers were designed for the qCST6 region based on re-sequencing data, and local QTL mapping was conducted using traditional linkage analysis. Eventually, we mapped qCTS6 to a 96.6-kb region containing 13 annotated genes, of which seven predicted genes contained 13 non-synonymous SNP loci. Quantitative reverse transcription PCR analysis revealed that only Os06g0719500, an OsbZIP54 transcription factor, was strongly induced by cold stress. Haplotype analysis confirmed that +376 bp (T>A) in the OsbZIP54 coding region played a key role in regulating cold tolerance in rice. Conclusion We identified OsbZIP54 as a novel regulatory gene associated with rice cold-responsive traits, with its Dongfu-104 allele showing specific cold-induction expression serving as an important molecular variation for rice improvement. This result is expected to further exploration of the genetic mechanism of rice cold tolerance at the seedling stage and improve cold tolerance in rice varieties by marker-assisted selection.

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