Identification of a major quantitative trait locus for resistance to maize rough dwarf virus in a Chinese maize inbred line X178 using a linkage map based on 514 gene-derived single nucleotide polymorphisms

2011 ◽  
Vol 30 (2) ◽  
pp. 615-625 ◽  
Author(s):  
Li-yu Shi ◽  
Zhuan-fang Hao ◽  
Jian-feng Weng ◽  
Chuan-xiao Xie ◽  
Chang-lin Liu ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Single Nucleotide Polymorphisms ◽  
Linkage Map ◽  
Inbred Line ◽  
Quantitative Trait ◽  
Major Quantitative Trait Locus ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Maize Rough Dwarf Virus ◽  
Trait Locus

scholarly journals Quantifying functional impact of non-coding variants with multi-task Bayesian neural network

2019 ◽  
Vol 36 (5) ◽  
pp. 1397-1404
Author(s):  
Chencheng Xu ◽  
Qiao Liu ◽  
Jianyu Zhou ◽  
Minzhu Xie ◽  
Jianxing Feng ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Single Nucleotide Polymorphisms ◽  
Quantitative Trait ◽  
Large Scale ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Functional Impact ◽  
Coding Regions ◽  
Trait Locus

Abstract Motivation Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. Results We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. Availability and implementation Code can be downloaded from https://github.com/Zoesgithub/MtBNN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome wide association mapping of yield and other agronomic traits in rice: Narrative literature review

2021 ◽  
Vol 12 (3) ◽  
pp. 354-367
Author(s):  
Muhammad Asad ◽  
Izzah Ihsan ◽  
Muther Mansoor Qaisrani ◽  
Hafiz Muhammad Zeeshan Raza ◽  
Jallat Khan
Keyword(s):  
Quantitative Trait Locus ◽  
Single Nucleotide Polymorphisms ◽  
Association Mapping ◽  
Quantitative Trait ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Breeding Populations ◽  
Genome Wide ◽  
Trait Locus

Based on previous recombination actions and LD (linkage disequilibrium) throughout the genome, genome wide association mapping studies often are employed to find Quantitative trait locus in varied collections of crop germplasm. Generally, diverse panel’s genotyped using high density Single nucleotide polymorphism (SNP) panels are used to test a broad variety of haplotypes and alleles, as well as to track recombination divisions throughout the genome. GWAS, on the other hand, have rarely been used in breeding populations. We studied association mapping for agricultural parameters such as yield and its constituents in a breeding inhabitants of top irrigated tropical rice progenies so that the findings could be used to breeding more directly than those from a diverse panel. GWAS was undertaken with the specific purpose of accelerating selection in the breeding population, and the sample was genotyped with 71,710 Single nucleotide polymorphisms using genotyping-by-sequencing. We found 52 Quantitative trait locus QTL for 11 agronomic characteristics using this breeding panel, including substantial impact Quantitative trait loci (QTLs) for flowering period as well as grain width, grain length, grain length-breadth ratio. Furthermore we discovered haplotypes that may be applied to choose plants for our population with smaller stature (plant height), fast blooming time, with high yield, demonstrating the value of association mapping for advising breeding choices in breeding populations. Furthermore, we explore at how genomic-assisted selection models might be built using the newly discovered important Single nucleotide polymorphisms (SNPs) and deep insight into the genetic structure of these quantitative traits.

scholarly journals Identification of A Major Quantitative Trait Locus for Grain Weight In Rice Using Microsatellite Marker

2017 ◽  
Vol 21 (4) ◽  
Author(s):  
Mariana Susilowati ◽  
Hajrial Aswidinnoor ◽  
Wening Enggarini ◽  
Kurniawan Rudi Trijatmiko
Keyword(s):  
Quantitative Trait Locus ◽  
Microsatellite Marker ◽  
Quantitative Trait ◽  
Major Quantitative Trait Locus ◽  
Grain Weight ◽  
Trait Locus
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