scholarly journals Molecular advances on agricultural crop improvement to meet current cultivating demands

2019 ◽  
Vol 12 (2) ◽  
pp. 39-60
Author(s):  
T. Margaritopoulou ◽  
D. Milioni
Keyword(s):  
Genomic Selection ◽  
Quantitative Traits ◽  
Seed Quality ◽  
Association Studies ◽  
Crop Improvement ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Agricultural Crop ◽  
Marker Assisted Breeding ◽  
Breeding Programs

Abstract Sunflower, maize and potato are among the world’s principal crops. In order to improve various traits, these crops have been genetically engineered to a great extent. Even though molecular markers for simple traits such as, fertility, herbicide tolerance or specific pathogen resistance have been successfully used in marker-assisted breeding programs for years, agronomical important complex quantitative traits like yield, biotic and abiotic stress resistance and seed quality content are challenging and require whole genome approaches. Collections of genetic resources for these crops are conserved worldwide and represent valuable resources to study complex traits. Nowadays technological advances and the availability of genome sequence have made novel approaches on the whole genome level possible. Molecular breeding, including both transgenic approach and marker-assisted breeding have facilitated the production of large amounts of markers for high density maps and allowed genome-wide association studies and genomic selection in sunflower, maize and potato. Marker-assisted selection related to hybrid performance has shown that genomic selection is a successful approach to address complex quantitative traits and to facilitate speeding up breeding programs in these crops in the future.

scholarly journals Whole-Genome Sequencing of the NARO World Rice Core Collection (WRC) as the Basis for Diversity and Association Studies

2020 ◽  
Vol 61 (5) ◽  
pp. 922-932 ◽  
Author(s):  
N Tanaka ◽  
M Shenton ◽  
Y Kawahara ◽  
M Kumagai ◽  
H Sakai ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Core Collection ◽  
Sequence Data ◽  
Association Studies ◽  
Crop Improvement ◽  
Heading Date ◽  
Principal Component ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Population Structure Analysis

Abstract Genebanks provide access to diverse materials for crop improvement. To utilize and evaluate them effectively, core collections, such as the World Rice Core Collection (WRC) in the Genebank at the National Agriculture and Food Research Organization, have been developed. Because the WRC consists of 69 accessions with a high degree of genetic diversity, it has been used for >300 projects. To allow deeper investigation of existing WRC data and to further promote research using Genebank rice accessions, we performed whole-genome resequencing of these 69 accessions, examining their sequence variation by mapping against the Oryza sativa ssp. japonica Nipponbare genome. We obtained a total of 2,805,329 single nucleotide polymorphisms (SNPs) and 357,639 insertion–deletions. Based on the principal component analysis and population structure analysis of these data, the WRC can be classified into three major groups. We applied TASUKE, a multiple genome browser to visualize the different WRC genome sequences, and classified haplotype groups of genes affecting seed characteristics and heading date. TASUKE thus provides access to WRC genotypes as a tool for reverse genetics. We examined the suitability of the compact WRC population for genome-wide association studies (GWASs). Heading date, affected by a large number of quantitative trait loci (QTLs), was not associated with known genes, but several seed-related phenotypes were associated with known genes. Thus, for QTLs of strong effect, the compact WRC performed well in GWAS. This information enables us to understand genetic diversity in 37,000 rice accessions maintained in the Genebank and to find genes associated with different phenotypes. The sequence data have been deposited in DNA Data Bank of Japan Sequence Read Archive (DRA) (Supplementary Table S1).

scholarly journals Assessing the potential for assisted gene flow using past introduction of Norway spruce in Southern Sweden: Local adaptation and genetic basis of quantitative traits in trees

2018 ◽  
Author(s):  
Pascal Milesi ◽  
Mats Berlin ◽  
Jun Chen ◽  
Marion Orsucci ◽  
Lili Li ◽  
...  
Keyword(s):  
Gene Flow ◽  
Local Adaptation ◽  
Norway Spruce ◽  
Quantitative Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Southern Sweden ◽  
Breeding Programs

AbstractNorway spruce (Picea abies) is a dominant conifer species of major economic importance in Northern Europe. Extensive breeding programs were established to improve phenotypic traits of interest. In southern Sweden seeds used to create progeny tests were collected on about 3000 trees of outstanding phenotype (“plus” trees) across the region. Some were of local origin but many were recent introductions from the rest of the natural range. The mixed origin of the trees together with partial sequencing of the exome of >1,500 of these trees and phenotypic data retrieved from the Swedish breeding program offered us a unique opportunity to dissect the genetic basis of local adaptation of three quantitative traits (height, diameter and budburst). Through a combination of multivariate analyses and genome-wide association studies, we showed that there was a very strong effect of geographical origin on growth (height and diameter) and phenology (budburst) with trees from southern origins outperforming local provenances. Association studies also indicated that growth traits were highly polygenic and budburst somewhat less. Hence, our results suggest that assisted gene flow and genomic selection approaches could help alleviating the effect of climate change on P. abies breeding programs in Sweden.

47 Genomic Studies of Reproductive Performance in American Mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 26-27
Author(s):  
Shafagh Valipour ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Genetic Correlation ◽  
Genomic Selection ◽  
Reproductive Performance ◽  
Genetic Parameters ◽  
Association Studies ◽  
American Mink ◽  
Reproductive Traits ◽  
Gestation Length ◽  
Genome Wide Association Studies ◽  
Breeding Programs

Abstract Improving reproductive efficiency is one of the main breeding goals in mink farming. Understanding the genetics of reproductive performance is essential for the development of effective breeding programs in mink. The objectives of this study are to 1) estimate the phenotypic and genetic parameters for litter sizes (LS), mortality rate at birth. (MB) and gestation-length (GL) traits; 2) perform genome-wide association studies (GWAS) for these reproductive traits; 3) implement GWAS results in the selection of mink for reproductive performance; 4) explore the potential for genomic selection in mink. The detailed reproductive performance on 3,500 female mink has been collected at the Canadian Center for Fur Animal Research at Dalhousie University (Truro, NS, Canada), in which, 1,000 of them will be genotyped with Affymetrix 50k SNP panel. A series of univariate and bivariate analyses were implemented in ASREML software to estimate the genetic and phenotypic parameters. Heritability estimates (±SE) were low-to-moderate, ranged from 0.06±0.02 for total number born to 0.23±0.03 for GL. High positive genetic correlations (±SE) were observed between LS traits, ranged from 0.59±0.18 to 0.85±0.11. There was a moderate genetic correlation (±SE) between MB and total number of kits born (0.46±0.15). However, MB had a favorable strong negative genetic correlation (±SE) with the number of weaned kits (–0.75±0.16). These estimated genetic parameters can be incorporated into Canadian mink breeding programs. Considering the low-to-moderate heritability of reproduction traits, the availability of the mink reference genome and genotyping panel will provide opportunities to accelerate mink breeding through genomics. The results of this project will contribute significantly to the current genetic knowledge of reproductive traits and identify the opportunities for genetic improvement through the application of genomics. The overall project aim is to develop a cost-effective, low-density panel of markers for the implementation of genomic selection for reproductive performance in mink.

166 Livestock Resiliency: Concepts and Approaches

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 89-90
Author(s):  
Christine F Baes ◽  
Filippo Miglior ◽  
Flavio S Schenkel ◽  
Ellen Goddard ◽  
Gerrit Kistemaker ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Correlations ◽  
Performance Testing ◽  
Breeding Value ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Breeding Programs ◽  
Fertility Traits

Abstract Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically important traits. Genomic information is particularly helpful when traits have low heritability. The objective here is to provide a framework for including health, welfare, efficiency, and fertility traits taken from large-scale genetic and genomic analyses and identifying areas of potential improvement in terms of trait definition and performance testing. General tendencies between trait groups confirmed that a number of moderate unfavourable correlations (+/-0.20 or higher) exist between economically important trait complexes and health, welfare, and fertility traits. A number of trait complexes were identified in which “closer-to-biology” phenotypes could provide clear improvements to routine genetic and genomic selection programs. Here we outline development of these phenotypes and describe their collection. While conventional variance component estimation methods have underpinned the genomic component of some traits of economic interest, performance testing for health, welfare, efficiency, and fertility traits remains an elusive goal for breeding programs. Although our results are encouraging, there is much to be done in terms of trait definition and obtaining better measures of physiological parameters for wide-scale application in breeding programs. Close collaboration between veterinarians, physiologists, and geneticists is necessary to attain meaningful advancement in such areas. We would like to acknowledge the support and funding from all national and international partners involved in the RDGP project through the Large Scale Applied Research Project program from Genome Canada

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

scholarly journals Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 599
Author(s):  
Miguel A. Gutierrez-Reinoso ◽  
Pedro M. Aponte ◽  
Manuel Garcia-Herreros
Keyword(s):  
Dairy Cattle ◽  
Production Efficiency ◽  
Association Studies ◽  
Phenotypic Traits ◽  
Progeny Testing ◽  
Genome Wide Association Studies ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Increased Risk ◽  
Selection Of

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Gabriel Costa Monteiro Moreira ◽  
Clarissa Boschiero ◽  
Aline Silva Mello Cesar ◽  
James M. Reecy ◽  
Thaís Fernanda Godoy ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Association Studies ◽  
Fat Deposition ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Genome Wide
Sign in / Sign up

Export Citation Format

Share Document