scholarly journals Genomic selection in salmonids: new discoveries and future perspectives

2021 ◽  
Author(s):  
Edo D’Agaro ◽  
Andea Favaro ◽  
Stefano Matiussi ◽  
Pier Paolo Gibertoni ◽  
Stefano Esposito
Keyword(s):  
Disease Resistance ◽  
Genomic Selection ◽  
Association Studies ◽  
Single Step ◽  
Molecular Techniques ◽  
Breeding Systems ◽  
Breeding Value ◽  
Genome Wide Association Studies ◽  
Entire Genome ◽  
Breeding Programmes

AbstractOver the past 20 years, the introduction of new molecular techniques has given a new impetus to genetic and genomic studies of fishes. The main traits selected in the aquaculture sector conform to the polygenic model, and, thus far, effective breeding programmes based on genome-wide association studies (GWAS) and marker-assisted selection (MAS) have been applied to simple traits (e.g. disease resistance and sexual maturation of salmonids) and known Quantitative Trait Loci (QTLs). Genomic selection uses the genomic relationships between candidate loci and SNPs distributed over the entire genome and in tight linkage disequilibrium (LD) with genes that encode the traits. SNP (low and high density) arrays are used for genotyping thousands of genetic markers (single nucleotide polymorphisms, SNPs). The genomic expected breeding value (GEBV) of selection candidates is usually calculated by means of the GBLUP or ssGBLUP (single step) methods. In recent years, in several aquaculture breeding programmes, the genomic selection method has been applied to different fish and crustacean species. While routine implementation of genomic selection is now largely carried out in Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss), it is expected that, in the near future, this method will progressively spread to other fish species. However, genomic selection is an expensive method, so it will be relevant mostly for traits of high economic value. In several studies (using different salmonid species), the accuracy of the GEBVs varied from 0.10 to 0.80 for different traits (e.g. growth rate and disease resistance) compared to traditional breeding methods based on geneology. Genomic selection applied to aquaculture species has the potential to improve selection programmes substantially and to change ongoing fish breeding systems. In the long term, the ability to use low-pass genome sequencing methods, low-cost genotyping and novel phenotyping techniques will allow genomic selection to be applied to thousands of animals directly at the farm level.

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

2021 ◽  
Author(s):  
Cesar A Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Agronomic Traits ◽  
Association Studies ◽  
Breeding Value ◽  
Genome Wide Association Studies ◽  
Genome Wide

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

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 Genetic Basis of Mastitis Resistance in Dairy Cattle – A Review / Podstawy Genetyczne Odporności Krów Mlecznych Na Zapalenie Wymienia – Artykuł Przeglądowy

2013 ◽  
Vol 13 (4) ◽  
pp. 663-673 ◽  
Author(s):  
Grażyna Sender ◽  
Agnieszka Korwin-Kossakowska ◽  
Adrianna Pawlik ◽  
Karima Galal Abdel Hameed ◽  
Jolanta Oprządek
Keyword(s):  
Dairy Cattle ◽  
Association Studies ◽  
Genetic Resistance ◽  
Clinical Mastitis ◽  
Future Research ◽  
Complex Nature ◽  
Candidate Gene Approach ◽  
Genome Wide Association Studies ◽  
Polygenic Control ◽  
Breeding Programmes

Abstract Mastitis is one of the most important mammary gland diseases impacting lactating animals. Resistance to this disease could be improved by breeding. There are several selection methods for mastitis resistance. To improve the natural genetic resistance of cows in succeeding generations, current breeding programmes use somatic cell count and clinical mastitis cases as resistance traits. However, these methods of selection have met with limited success. This is partly due to the complex nature of the disease. The limited progress in improving udder health by conventional selection procedures requires applying information on molecular markers of mastitis susceptibility in marker-assisted selection schemes. Mastitis is under polygenic control, so there are many genes that control this trait in many loci. This review briefly describes genome-wide association studies which have been carried out to identify quantitative trait loci associated with mastitis resistance in dairy cattle worldwide. It also characterizes the candidate gene approach focus on identifying genes that are strong candidates for the mastitis resistance trait. In the conclusion of the paper we focus our attention on future research which should be conducted in the field of the resistance to mastitis.

scholarly journals Meta-Analysis Reveals Consensus Genomic Regions Associated with Multiple Disease Resistance in Wheat ( Triticum Aestivum L.)

2021 ◽  
Author(s):  
Dinesh Kumar Saini ◽  
Amneek Chahal ◽  
Neeraj Pal ◽  
Puja Srivast ◽  
Pushpendra Kumar Gupta
Keyword(s):  
Disease Resistance ◽  
Physical Map ◽  
Association Studies ◽  
Meta Analysis ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Karnal Bunt ◽  
Genome Wide Association Studies ◽  
Head Blight ◽  
Multiple Disease Resistance

Abstract In wheat, meta-QTLs (MQTLs), and candidate genes (CGs) were identified for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the five diseases. As many as 493 QTLs were available from these studies, which were distributed in five diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103; fusarium head blight (FHB), 184; karnal bunt (KB), 66, and loose smut (LS), 14. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of initial QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were verified using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identified which were further investigated for differential expression using data from five transcriptome studies, resulting in 194 differentially expressed genes (DEGs). Among the DEGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for fine mapping of MDR genes and marker-assisted breeding.

scholarly journals Genome-wide association studies: a powerful tool for neurogenomics

2010 ◽  
Vol 28 (1) ◽  
pp. E2 ◽  
Author(s):  
Matthew C. Cowperthwaite ◽  
Deepankar Mohanty ◽  
Mark G. Burnett
Keyword(s):  
Neurological Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Great Promise ◽  
Genome Wide Association Studies ◽  
Entire Genome ◽  
Genome Wide ◽  
New Generation ◽  
Theoretical Issues ◽  
Gwa Studies

As their power and utility increase, genome-wide association (GWA) studies are poised to become an important element of the neurosurgeon's toolkit for diagnosing and treating disease. In this paper, the authors review recent findings and discuss issues associated with gathering and analyzing GWA data for the study of neurological diseases and disorders, including those of neurosurgical importance. Their goal is to provide neurosurgeons and other clinicians with a better understanding of the practical and theoretical issues associated with this line of research. A modern GWA study involves testing hundreds of thousands of genetic markers across an entire genome, often in thousands of individuals, for any significant association with a particular disease. The number of markers assayed in a study presents several practical and theoretical issues that must be considered when planning the study. Genome-wide association studies show great promise in our understanding of the genes underlying common neurological diseases and disorders, as well as in leading to a new generation of genetic tests for clinicians.

Genome-wide association and genomic selection in animal breedingThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽  
2010 ◽  
Vol 53 (11) ◽  
pp. 876-883 ◽  
Author(s):  
Ben Hayes ◽  
Mike Goddard
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Relationship Matrix ◽  
Genome Wide Association Studies ◽  
Simple Method ◽  
Breeding Values ◽  
Genome Wide ◽  
A Genome

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.

scholarly journals Genome-wide association study and genomic predictions for resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing

2017 ◽  
Author(s):  
Agustín Barría ◽  
Kris A. Christensen ◽  
Katharina Correa ◽  
Ana Jedlicki ◽  
Jean P. Lhorente ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
Coho Salmon ◽  
Association Studies ◽  
Oncorhynchus Kisutch ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Selection Models ◽  
Genome Wide ◽  
Piscirickettsia Salmonis

ABSTRACTPiscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming. Current treatments have been ineffective for the control of the disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) which were experimentally challenged against P. salmonis were sequenced using ddRAD sequencing. A total of 4,174 SNP markers were identified in the population. These markers were used to perform a GWAS and testing genomic selection models. One SNP related with iron availability was genome-wide significantly associated with resistance to P. salmonis defined as day of death. Genomic selection models showed similar accuracies and predictive abilities than traditional pedigree-based best linear unbiased prediction (PBLUP) method.

scholarly journals Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Enrico Mancin ◽  
Daniela Lourenco ◽  
Matias Bermann ◽  
Roberto Mantovani ◽  
Ignacy Misztal
Keyword(s):  
Population Structure ◽  
Beef Cattle ◽  
Dairy Cattle ◽  
Genetic Relatedness ◽  
Association Studies ◽  
Single Step ◽  
Structured Populations ◽  
Farm Animals ◽  
Snp Analysis ◽  
Genome Wide Association Studies

Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (ssGWAS) can use phenotypes from non-genotyped relatives; however, its ability to account for population structure has not been explored. Here we investigate the equivalence among ssGWAS, efficient mixed-model association expedited (EMMAX), and genomic best linear unbiased prediction GWAS (GBLUP-GWAS), and how they differ from the single-SNP analysis without correction for population structure (SSA-NoCor). We used simulated, structured populations that mimicked fish, beef cattle, and dairy cattle populations with 1040, 5525, and 1,400 genotyped individuals, respectively. Larger populations were also simulated that had up to 10-fold more genotyped animals. The genomes were composed by 29 chromosomes, each harboring one QTN, and the number of simulated SNPs was 35,000 for the fish and 65,000 for the beef and dairy cattle populations. Males and females were genotyped in the fish and beef cattle populations, whereas only males had genotypes in the dairy population. Phenotypes for a trait with heritability varying from 0.25 to 0.35 were available in both sexes for the fish population, but only for females in the beef and dairy cattle populations. In the latter, phenotypes of daughters were projected into genotyped sires (i.e., deregressed proofs) before applying EMMAX and SSA-NoCor. Although SSA-NoCor had the largest number of true positive SNPs among the four methods, the number of false negatives was two–fivefold that of true positives. GBLUP-GWAS and EMMAX had a similar number of true positives, which was slightly smaller than in ssGWAS, although the difference was not significant. Additionally, no significant differences were observed when deregressed proofs were used as pseudo-phenotypes in EMMAX compared to daughter phenotypes in ssGWAS for the dairy cattle population. Single-step GWAS accounts for population structure and is a straightforward method for association analysis when only a fraction of the population is genotyped and/or when phenotypes are available on non-genotyped relatives.

scholarly journals Disease association with frequented regions of genotype graphs

2020 ◽  
Author(s):  
Samuel Hokin ◽  
Alan Cleary ◽  
Joann Mudge
Keyword(s):  
Rare Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Disease Status ◽  
Disease Association ◽  
Genome Wide Association Studies ◽  
Entire Genome ◽  
Machine Learning Classification ◽  
Complementary Method ◽  
Genome Wide

Complex diseases, with many associated genetic and environmental factors, are a challenging target for genomic risk assessment. Genome-wide association studies (GWAS) associate disease status with, and compute risk from, individual common variants, which can be problematic for diseases with many interacting or rare variants. In addition, GWAS typically employ a reference genome which is not built from the subjects of the study, whose genetic background may differ from the reference and whose genetic characterization may be limited. We present a complementary method based on disease association with collections of genotypes, called frequented regions, on a pangenomic graph built from subjects' genomes. We introduce the pangenomic genotype graph, which is better suited than sequence graphs to human disease studies. Our method draws out collections of features, across multiple genomic segments, which are associated with disease status. We show that the frequented regions method consistently improves machine-learning classification of disease status over GWAS classification, allowing incorporation of rare or interacting variants. Notably, genomic segments that have few or no variants of genome-wide significance (p<5x10-8) provide much-improved classification with frequented regions, encouraging their application across the entire genome. Frequented regions may also be utilized for purposes such as choice of treatment in addition to prediction of disease risk.

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