New candidate genes associated with fat thickness in russian meat merino sheep
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Identification of candidate genes and genetic markers associated with meat productivity in sheep using genome-wide association searches (genome-wide association studies, GWAS) is a promising area of genetic research. We conducted a genome-wide search for associations of single nucleotide polymorphisms (SNPs) with thicker fat in the lumbar region in Russian beef Merino sheep. The object of the study was rams aged 12 months (n = 50). Fat density is achieved at the 1st–2nd lumbar spine level using a portable ultrasound scanner. Animal genotyping was done using Ovine Infinium HD BeadChip 600K DNA biochips. As a result of GWAS, 14 SNPs belonging to the trait “fat thickness” were identified in sheep of the Russian meat Merino breed. Most SNPs were located in the intergenic space. One SNP is localized in the exon, and two - in the intron of genes. Three substitutions are near the small nuclear RNA gene and one near the non-coding RNA gene. Based on the analyzed SNPs in the genome, five new candidate genes were proposed that are associated with growth in the lumbar region in Russian beef Merino sheep: KCNH8, MTERF4, RYBP, NDST3, ENSOARG00000004203. The study’s goal should be to study the structure of these genes for the mechanism of their action on animal phenotypes, and the identified SNPs can be used as new molecular markers in breeding work.Keywords:
Candidate gene
Genome-wide Association Study
SNP genotyping
Genetic Association
Abstract Objectives : To investigate possible obesity candidate genes in regions of porcine quantitative trait loci (QTL) for fat deposition and obesity‐related phenotypes. Research Methods and Procedures : Chromosome mapping and QTL analyses of obesity candidate genes were performed using DNA panels from a reference pig family. Statistical association analyses of these genes were performed for fat deposition phenotypes in several other commercial pig populations. Results : Eight candidate genes were mapped to QTL regions of pig chromosomes in this study. These candidate genes also served as anchor loci to determine homologous human chromosomal locations of pig fat deposition QTL. Preliminary analyses of relationships among polymorphisms of individual candidate genes and a variety of phenotypic measurements in a large number of pigs were performed. On the basis of available data, gene‐gene interactions were also studied. Discussion : Comparative analysis of obesity‐related genes in the pig is not only important for development of marker‐assisted selection on growth and fat deposition traits in the pig but also provides for an understanding of their genetic roles in the development of human obesity.
Candidate gene
Family-based QTL mapping
Polygene
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Phytophthora infestans, the causal agent of late blight, threatens potato production worldwide. Many quantitative trait loci (QTL) for late blight resistance have been mapped in several potato populations. At the same time, numerous expressed sequences tags (EST) related to late blight resistance have been deposited in databases. In order to screen for putative candidate genes associated with late blight resistance, 65 candidate genes were selected for mapping and investigation of their relationship with QTL in three diploid potato populations PCC1, BCT, and PD. In total, 26 primers from the 65 selected genes that showed PCR length polymorphism were mapped on the linkage groups of three populations. Further comparison between map location of QTL and candidate gene loci indicated that three candidate gene markers were placed in a QTL region. The locus of a putative receptor-like protein kinase b co-localized with an important QTL region on chromosome XI of PCC1. In the PD population, the Lox gene was in a QTL with moderate effect on chromosome III and two protein phosphatase loci were localized in a QTL with the largest effect on chromosome XII. These mapped candidate gene markers could be used as a bridge to other genetic maps of potato. The association of candidate genes with QTL forms the basis for further studies on the contributions of these candidate genes to natural variation for potato late blight resistance. Key words: Candidate gene, quantitative resistance loci, late blight, potato
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Phytophthora infestans
Genetic linkage
Family-based QTL mapping
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Almost all crops have been studied on QTL(quantitative trait loci) mapping with many QTL mapping methods,such as IM(interval mapping),CIM(composite interval mapping),MCIM(mixed-model based composite interval mapping) and Bayesian QTL mapping having been developed.However,these methods have had shortcomings,namey,the genomic region of the QTL detected,within which there were probably hundreds of candidate genes was still too large.In this paper,a better understanding of the molecular functions of QTLs was obtained by first briefly reviewing methods of analyzing the candidate gene within QTL intervals based on bioinformatics.Then,in order to provide a new analytical method for better use of QTLs in the future,the genetics,genome organization,gene expression and function of candidate genes located on QTL region were analyzed.
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Candidate gene
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A systematic study has been conducted of all available reports in PubMed and OMIM (Online Mendelian Inheritance in Man) to examine the genetic and molecular basis of quantitative genetic loci (QTL) of diabetes with the main focus on genes and polymorphisms. The major question is, What can the QTL tell us? Specifically, we want to know whether those genome regions differ from other regions in terms of genes relevant to diabetes. Which genes are within those QTL regions, and, among them, which genes have already been linked to diabetes? whether more polymorphisms have been associated with diabetes in the QTL regions than in the non-QTL regions.Our search revealed a total of 9038 genes from 26 type 1 diabetes QTL, which cover 667,096,006 bp of the mouse genomic sequence. On one hand, a large number of candidate genes are in each of these QTL; on the other hand, we found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may provide unexpected benefit for identifying QTL genes for diabetes.
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Family-based QTL mapping
Mendelian inheritance
Genetic linkage
Polygene
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Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.
Genome-wide Association Study
Mendelian Randomization
Genetic Association
Pathway Analysis
Association (psychology)
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Abstract Background Meta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. The combination of meta-QTL intervals, significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants. Results In our study, 884 QTL associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1, as a result, 74 meta-QTL were identified, including 19 meta-QTL for fiber length (FL), 18 meta-QTL for fiber strength (FS), 11 meta-QTL for fiber uniformity (FU), 11 meta-QTL for fiber elongation (FE), and 15 meta-QTL for micronaire (MIC). Combined with 8589 significant SNPs associated with fiber quality traits collected from 15 studies, 297 candidate genes were identified in the meta-QTL intervals, 20 of which showed high expression specifically in the developing fibers. According to the function annotations, some of the 20 key candidate genes are associated with the fiber development. Conclusions This study provides not only stable QTLs used for marker-assisted selection (MAS), but also candidate genes to uncover the molecular mechanisms for cotton fiber development.
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Family-based QTL mapping
SNP
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Leaf size is a crucial component of sesame ( Sesamum indicum L.) plant architecture and further influences yield potential. Despite that it is well known that leaf size traits are quantitative traits controlled by large numbers of genes, quantitative trait loci (QTL) and candidate genes for sesame leaf size remain poorly understood. In the present study, we combined the QTL-seq approach and SSR marker mapping to identify the candidate genomic regions harboring QTL controlling leaf size traits in an RIL population derived from a cross between sesame varieties Zhongzhi No. 13 (with big leaves) and ZZM2289 (with small leaves). The QTL mapping revealed 56 QTL with phenotypic variation explained (PVE) from 1.87 to 27.50% for the length and width of leaves at the 1/3 and 1/2 positions of plant height. qLS15-1 , a major and environmentally stable pleiotropic locus for both leaf length and width explaining 5.81 to 27.50% phenotypic variation, was located on LG15 within a 408-Kb physical genomic region flanked by the markers ZMM6185 and ZMM6206. In this region, a combination of transcriptome analysis with gene annotations revealed three candidate genes SIN_1004875 , SIN_1004882 , and SIN_1004883 associated with leaf growth and development in sesame. These findings provided insight into the genetic characteristics and variability for sesame leaf and set up the foundation for future genomic studies on sesame leaves and will serve as gene resources for improvement of sesame plant architecture.
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Genetic architecture
Leaf size
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Reduced bull fertility imposes economic losses in bovine herds. Specifically, testicular and spermatic traits are important indicators of reproductive efficiency. Several genome-wide association studies (GWAS) have identified genomic regions associated with these fertility traits. The aims of this study were as follows: 1) to perform a systematic review of GWAS results for spermatic and testicular traits in cattle and 2) to identify key functional candidate genes for these traits. The identification of functional candidate genes was performed using a systems biology approach, where genes shared between traits and studies were evaluated by a guilt by association gene prioritization (GUILDify and ToppGene software) in order to identify the best functional candidates. These candidate genes were integrated and analyzed in order to identify overlapping patterns among traits and breeds. Results showed that GWAS for testicular-related traits have been developed for beef breeds only, whereas the majority of GWAS for spermatic-related traits were conducted using dairy breeds. When comparing traits measured within the same study, the highest number of genes shared between different traits was observed, indicating a high impact of the population genetic structure and environmental effects. Several chromosomal regions were enriched for functional candidate genes associated with fertility traits. Moreover, multiple functional candidate genes were enriched for markers in a species-specific basis, taurine (Bos taurus) or indicine (Bos indicus). For the different candidate regions identified in the GWAS in the literature, functional candidate genes were detected as follows: B. Taurus chromosome X (BTX) (TEX11, IRAK, CDK16, ATP7A, ATRX, HDAC6, FMR1, L1CAM, MECP2, etc.), BTA17 (TRPV4 and DYNLL1), and BTA14 (MOS, FABP5, ZFPM2). These genes are responsible for regulating important metabolic pathways or biological processes associated with fertility, such as progression of spermatogenesis, control of ciliary activity, development of Sertoli cells, DNA integrity in spermatozoa, and homeostasis of testicular cells. This study represents the first systematic review on male fertility traits in cattle using a system biology approach to identify key candidate genes for these traits.
Genome-wide Association Study
Candidate gene
Genetic Association
Runs of Homozygosity
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Genome-wide Association Study
Candidate gene
Depression
Genetic architecture
Genetic Association
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Congenic
Candidate gene
Family-based QTL mapping
Positional cloning
Genetic linkage
Chromosomal region
Genetic architecture
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