Candidate gene-based association genetics analysis of herbage quality traits in perennial ryegrass (Lolium perenne L.)

2013 ◽  
Vol 64 (3) ◽  
pp. 244 ◽  
Author(s):  
L. W. Pembleton ◽  
J. Wang ◽  
N. O. I. Cogan ◽  
J. E. Pryce ◽  
G. Ye ◽  
...  
Keyword(s):  
Association Mapping ◽  
Candidate Gene ◽  
Perennial Ryegrass ◽  
Association Studies ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Near Infrared Reflectance ◽  
Genome Wide ◽  
Herbage Quality

Due to the complex genetic architecture of perennial ryegrass, based on an obligate outbreeding reproductive habit, association-mapping approaches to genetic dissection offer the potential for effective identification of genetic marker–trait linkages. Associations with genes for agronomic characters, such as components of herbage nutritive quality, may then be utilised for accelerated cultivar improvement using advanced molecular breeding practices. The objective of the present study was to evaluate the presence of such associations for a broad range of candidate genes involved in pathways of cell wall biosynthesis and carbohydrate metabolism. An association-mapping panel composed from a broad range of non-domesticated and varietal sources was assembled and assessed for genome-wide sequence polymorphism. Removal of significant population structure obtained a diverse meta-population (220 genotypes) suitable for association studies. The meta-population was established with replication as a spaced-plant field trial. All plants were genotyped with a cohort of candidate gene-derived single nucleotide polymorphism (SNP) markers. Herbage samples were harvested at both vegetative and reproductive stages and were measured for a range of herbage quality traits using near infrared reflectance spectroscopy. Significant associations were identified for ~50% of the genes, accounting for small but significant components of phenotypic variance. The identities of genes with associated SNPs were largely consistent with detailed knowledge of ryegrass biology, and they are interpreted in terms of known biochemical and physiological processes. Magnitudes of effect of observed marker–trait gene association were small, indicating that future activities should focus on genome-wide association studies in order to identify the majority of causal mutations for complex traits such as forage quality.

scholarly journals Genome-Wide Association Studies Identifying Multiple Loci Associated With Alfalfa Forage Quality

2021 ◽  
Vol 12 ◽  
Author(s):  
Sen Lin ◽  
Cesar Augusto Medina ◽  
O. Steven Norberg ◽  
David Combs ◽  
Guojie Wang ◽  
...  
Keyword(s):  
Animal Feed ◽  
Lignin Content ◽  
Association Studies ◽  
Forage Quality ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Near Infrared Reflectance ◽  
Fiber Digestibility ◽  
Genome Wide

Autotetraploid alfalfa is a major hay crop planted all over the world due to its adaptation in different environments and high quality for animal feed. However, the genetic basis of alfalfa quality is not fully understood. In this study, a diverse panel of 200 alfalfa accessions were planted in field trials using augmented experimental design at three locations in 2018 and 2019. Thirty-four quality traits were evaluated by Near Infrared Reflectance Spectroscopy (NIRS). The plants were genotyped using a genotyping by sequencing (GBS) approach and over 46,000 single nucleotide polymorphisms (SNPs) were obtained after variant calling and filtering. Genome-wide association studies (GWAS) identified 28 SNP markers associated with 16 quality traits. Among them, most of the markers were associated with fiber digestibility and protein content. Phenotypic variations were analyzed from three locations and different sets of markers were identified by GWAS when using phenotypic data from different locations, indicating that alfalfa quality traits were also affected by environmental factors. Among different sets of markers identified by location, two markers were associated with nine traits of fiber digestibility. One marker associated with lignin content was identified consistently in multiple environments. Putative candidate genes underlying fiber-related loci were identified and they are involved in the lignin and cell wall biosynthesis. The DNA markers and associated genes identified in this study will be useful for the genetic improvement of forage quality in alfalfa after the validation of the markers.

Genome-wide association studies identified loci contribute to phenotypic variance of gastric cancer

Gut ◽  
2017 ◽  
Vol 67 (7) ◽  
pp. 1366-1368 ◽  
Author(s):  
Caiwang Yan ◽  
Meng Zhu ◽  
Tongtong Huang ◽  
Fei Yu ◽  
Guangfu Jin
Keyword(s):  
Gastric Cancer ◽  
Association Studies ◽  
Genome Wide Association ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Genome-wide association studies provide insights into the genetic determination of fruit traits of pear

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ming-Yue Zhang ◽  
Cheng Xue ◽  
Hongju Hu ◽  
Jiaming Li ◽  
Yongsong Xue ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Molecular Breeding ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Fruit Traits ◽  
Stone Cell ◽  
Genome Wide ◽  
Fruit Quality Traits

AbstractPear is a major fruit tree crop distributed worldwide, yet its breeding is a very time-consuming process. To facilitate molecular breeding and gene identification, here we have performed genome-wide association studies (GWAS) on eleven fruit traits. We identify 37 loci associated with eight fruit quality traits and five loci associated with three fruit phenological traits. Scans for selective sweeps indicate that traits including fruit stone cell content, organic acid and sugar contents might have been under continuous selection during breeding improvement. One candidate gene, PbrSTONE, identified in GWAS, has been functionally verified to be involved in the regulation of stone cell formation, one of the most important fruit quality traits in pear. Our study provides insights into the complex fruit related biology and identifies genes controlling important traits in pear through GWAS, which extends the genetic resources and basis for facilitating molecular breeding in perennial trees.

scholarly journals Genome-wide association study reveals candidate genes for flowering time in cowpea (Vigna unguiculata [L.] Walp)

2021 ◽  
Author(s):  
Dev Paudel ◽  
Rocheteau Dareus ◽  
Julia Rosenwald ◽  
Maria Munoz-Amatriain ◽  
Esteban Rios
Keyword(s):  
Flowering Time ◽  
Candidate Genes ◽  
Vigna Unguiculata ◽  
Association Studies ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Human Consumption ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Genome Wide

Cowpea (Vigna unguiculata [L.] Walp., diploid, 2n = 22) is a major crop used as a protein source for human consumption as well as a quality feed for livestock. It is drought and heat tolerant and has been bred to develop varieties that are resilient to changing climates. Plant adaptation to new climates and their yield are strongly affected by flowering time. Therefore, understanding the genetic basis of flowering time is critical to advance cowpea breeding. The aim of this study was to perform genome-wide association studies (GWAS) to identify marker trait associations for flowering time in cowpea using single nucleotide polymorphism (SNP) markers. A total of 367 accessions from a cowpea mini-core collection were evaluated in Ft. Collins, CO in 2019 and 2020, and 292 accessions were evaluated in Citra, FL in 2018. These accessions were genotyped using the Cowpea iSelect Consortium Array that contained 51,128 SNPs. GWAS revealed seven reliable SNPs for flowering time that explained 8-12% of the phenotypic variance. Candidate genes including FT, GI, CRY2, LSH3, UGT87A2, LIF2, and HTA9 that are associated with flowering time were identified for the significant SNP markers. Further efforts to validate these loci will help to understand their role in flowering time in cowpea, and it could facilitate the transfer of some of this knowledge to other closely related legume species.

scholarly journals Heritability jointly explained by host genotype and microbiome: will improve traits prediction?

2020 ◽  
Author(s):  
Denis Awany ◽  
Emile R Chimusa
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Heritability Estimate ◽  
Substantial Part ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Host Genotype ◽  
Genome Wide ◽  
Heritability Estimation

Abstract As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of ‘heritability’, geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host’s microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.

scholarly journals Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 669 ◽  
Author(s):  
Peter S. Kristensen ◽  
Just Jensen ◽  
Jeppe R. Andersen ◽  
Carlos Guzmán ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Prediction ◽  
Association Studies ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Breeding Programs ◽  
Genome Wide ◽  
Flour Yield

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype–environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

scholarly journals Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model

2017 ◽  
Author(s):  
Haohan Wang ◽  
Xiang Liu ◽  
Yunpeng Xiao ◽  
Ming Xu ◽  
Eric P. Xing
Keyword(s):  
Population Structure ◽  
Association Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Confounding Factors ◽  
Genetic Loci ◽  
Genome Wide

AbstractGenome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.

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