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John A H Benzie - One of the best experts on this subject based on the ideXlab platform.
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sibship assignment to the founders of a bangladeshi catla catla Breeding Population
Genetics Selection Evolution, 2019Co-Authors: Matthew G Hamilton, Wagdy Mekkawy, John A H BenzieAbstract:Catla catla (Hamilton) fertilised spawn was collected from the Halda, Jamuna and Padma rivers in Bangladesh from which approximately 900 individuals were retained as ‘candidate founders’ of a Breeding Population. These fish were fin-clipped and genotyped using the DArTseq platform to obtain, 3048 single nucleotide polymorphisms (SNPs) and 4726 silicoDArT markers. Using SNP data, individuals that shared no putative parents were identified using the program COLONY, i.e. 140, 47 and 23 from the Halda, Jamuna and Padma rivers, respectively. Allele frequencies from these individuals were considered as representative of those of the river Populations, and genomic relationship matrices were generated. Then, half-sibling and full-sibling relationships between individuals were assigned manually based on the genomic relationship matrices. Many putative half-sibling and full-sibling relationships were found between individuals from the Halda and Jamuna rivers, which suggests that catla sampled from rivers as spawn are not necessarily representative of river Populations. This has implications for the interpretation of past Population genetics studies, the sampling strategies to be adopted in future studies and the management of broodstock sourced as river spawn in commercial hatcheries. Using data from individuals that shared no putative parents, overall multi-locus pairwise estimates of Wright’s fixation index (FST) were low (≤ 0.013) and the optimum number of clusters using unsupervised K-means clustering was equal to 1, which indicates little genetic divergence among the SNPs included in our study within and among river Populations.
Jose M Yanez - One of the best experts on this subject based on the ideXlab platform.
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whole genome linkage disequilibrium and effective Population size in a coho salmon oncorhynchus kisutch Breeding Population using a high density snp array
Frontiers in Genetics, 2019Co-Authors: Agustin Barria, Kris A Christensen, Grazyella Massako Yoshida, Ana Jedlicki, Jean P Lhorente, William S Davidson, Jong S Leong, Eric Rondeau, Ben F Koop, Jose M YanezAbstract:The estimation of linkage disequilibrium between molecular markers within a Population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different Populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon Breeding Population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the Breeding Population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r 2 was 0.10. The Average r 2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r 2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r 2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective Population size of 43 was estimated for the Population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon Population. Furthermore, based on the contemporary N e, a new mate allocation strategy is suggested to increase the effective Population size.
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whole genome linkage disequilibrium and effective Population size in a coho salmon oncorhynchus kisutch Breeding Population using a high density snp array
Frontiers in Genetics, 2019Co-Authors: Agustin Barria, Kris A Christensen, Grazyella Massako Yoshida, Ana Jedlicki, Jean P Lhorente, William S Davidson, Jong S Leong, Eric Rondeau, Ben F Koop, Jose M YanezAbstract:The estimation of linkage disequilibrium between molecular markers within a Population is critical when establishing the minimum number of markers required for association studies, genomic selection and for inferring historical events influencing different Populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon Breeding Population using a high density chip SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the Breeding Population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r2 was 0.10. The Average r2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at distances lower than 1 kb and up to 10 Mb, respectively. An r2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (> 0.20 at distances lower than 1 Mb). Average r2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective Population size of 43 was estimated for the Population 10 generation ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the used panel SNPs allows to capture high-resolution information in the farmed coho salmon Population. Furthermore, based on the contemporary Ne, a new mate allocation strategy is suggested to increase the effective Population size.
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whole genome linkage disequilibrium and effective Population size in a coho salmon oncorhynchus kisutch Breeding Population
bioRxiv, 2018Co-Authors: Agustin Barria, Kris A Christensen, Grazyella Massako Yoshida, Ana Jedlicki, Jean P Lhorente, William S Davidson, Jose M YanezAbstract:The estimation of linkage disequilibrium between molecular markers within a Population is critical when establishing the minimum number of markers required for association studies, genomic selection and for inferring historical events influencing different Populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon Breeding Population using ddRAD genomic markers. Linkage disequilibrium was estimated between a total of 7,505 SNPs found in 62 individuals (33 dams and 29 sires) from the Breeding Population. The makers encompass all 30 coho salmon chromosomes and comprise 1,655.19 Mb of the genome. The average density of markers per chromosome ranged from 3.45 to 6.11 per 1 Mbp. The minor allele frequency averaged 0.20 (with a range from 0.08 to 0.50). The overall average linkage disequilibrium among SNPs pairs measured as r2 was 0.054. The average r2 value decreased with increasing physical distance, with values ranging from 0.37 to 0.054 at distances lower than 1 kb and up to 10 Mb, respectively. An r2 threshold of 0.1 was reached at distance of approximately 1.3 Mb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (> 0.20 at distances lower than 1 Mb). Average r2 values were lower than 0.1 for all chromosomes at distances greater than 4 Mb. Linkage disequilibrium values suggest that whole genome association and selection studies could be performed using about 75,000 SNPs in aquaculture Populations (depending on the trait under investigation). From the identified SNPs, an effective Population size of 100 was estimated for the Population 10 generation ago, and 1,000, for 139 generations ago. Based on the extent of r2 decay, we suggest that at least 75,000 SNPs would be necessary for an association mapping study. Over 100,000 SNPs would be necessary for a high power study, in the current coho salmon Population.
Bruce Topp - One of the best experts on this subject based on the ideXlab platform.
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genomic selection and genetic gain for nut yield in an australian macadamia Breeding Population
BMC Genomics, 2021Co-Authors: Katie Oconnor, Mobashwer Alam, Craig Hardner, Ben J Hayes, Robert J Henry, Bruce ToppAbstract:Improving yield prediction and selection efficiency is critical for tree Breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant Breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the Breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia Breeding program with comparison to traditional Breeding methods. This study evaluated the prediction accuracy of GS in a macadamia Breeding Population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated Breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related Populations and prediction across unrelated Populations. Narrow-sense heritability of yield and yield stability was low (h2 = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related Populations and 0.14 when predicted across unrelated Populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related Populations. Predicted genetic gain of yield using GS in related Populations was 474 g/year, more than double that of traditional Breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years. The results of this study indicate that the incorporation of GS for yield into the Australian macadamia Breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.
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Population structure genetic diversity and linkage disequilibrium in a macadamia Breeding Population using snp and silicodart markers
Tree Genetics & Genomes, 2019Co-Authors: Katie Oconnor, Mobashwer Alam, Catherine J Nock, Abdul Baten, Craig Hardner, Andrzej Kilian, Ben J Hayes, Bruce ToppAbstract:Macadamia (Macadamia integrifolia Maiden & Betche, Macadamia tetraphylla L.A.S. Johnson and their hybrids) is grown commercially around the world for its high-quality edible kernel. Traditional Breeding efforts involve crossing varieties to produce thousands of progeny seedlings for evaluation. Cultivar improvement for nut yield using component traits and genomics are options for macadamia Breeding, but accurate knowledge of genetic diversity and structure of the Breeding Population is required. This study reports allelic diversity within and between families of 295 seedling offspring from 29 parents, Population structure and the extent of linkage disequilibrium (LD) in the Population. Genotyping generated 19,527 silicoDArT and 5329 SNP markers, and, after filtering, 16,171 silicoDArTs and 4113 SNPs were used for diversity analyses. LD decay was initially rapid at short distances, but low-level LD persisted for long distances, with an average r2 = 0.124 for SNPs within 1 kb of each other. The seedling Population was relatively genetically diverse and very similar to that of the 29 parents. The diversity (HE = 0.255 for progeny and 0.250 for parents) among these individuals indicates the level of diversity at the wider Population level in the Breeding programme, though the Population appears less diverse than other fruit crops. Macadamia progeny was moderately differentiated (FST = 0.401) and formed k = 3 distinct clusters, which represents M. integrifolia germplasm separating from two different hybrid groups. There was low to no relationship between heterozygosity and performance for nut yield among progeny. These findings will inform future genomic studies of the Australian macadamia Breeding programme, such as genome-wide association studies and genomic selection, where knowledge and control of Population structure are vital.
Kevin P. Smith - One of the best experts on this subject based on the ideXlab platform.
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assessing genomic selection prediction accuracy in a dynamic barley Breeding Population
The Plant Genome, 2015Co-Authors: Ahmad H. Sallam, Jean-luc Jannink, Jeffrey B. Endelman, Kevin P. SmithAbstract:Prediction accuracy of genomic selection (GS) has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant Breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley Breeding Population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP) markers. The Breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of Breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training Population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RRBLUP), to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training Populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation Populations (marker allele frequency, Population structure, and linkage disequilibrium, LD) as well as characteristics of the trait (genetic architecture and heritability, H2). Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait H2 in the training Population and simpler trait architecture were associated with greater prediction accuracy. Genomic selection is touted as a marker-based Breeding approach that complements traditional markerassisted selection (MAS) and phenotypic selection. In traditional MAS, favorable alleles or genes for relatively simply inherited traits are mapped and then molecular markers linked to those alleles are used to select individuals to use as parents or to advance from segregating Breeding Populations (Bernardo, 2008). Marker-assisted selection is more effective than phenotypic selection if the tagged loci account for a large portion of the total genetic variation within the Population of selection candidates (Collins et al., 2003; Castro et al., 2003; Xu and Crouch, 2008). The limitation of traditional MAS for highly complex traits is that it captures only a small portion of the total genetic variation because it uses a limited number of selected markers (Lande and Thompson, 1990; Bernardo, 2010). Phenotypic selection is effective on quantitative traits, but is limited to stages in Breeding cycles and environments where such traits can be measured effectively, such as for advanced lines in multiple location field trials. Therefore, GS can be strategically implemented in Published in The Plant Genome 8 doi: 10.3835/plantgenome2014.05.0020 © Crop Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA An open-access publication All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. A.H. Sallam, Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, St. Paul, MN 55108; J.B. Endelman, Dep. of Horticulture, Univ. of Wisconsin-Madison, 1575 Linden Dr., Madison, WI 53706; J.-L. Jannink, USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell Univ., Ithaca, NY 14853; K.P. Smith, Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, St. Paul, MN 55108. Received 6 Jan. 2014. *Corresponding author (smith376@umn.edu). Abbreviations: BLUEs, best linear unbiased estimations; DON, deoxynivalenol; EMMA, efficient mixed-model association; FHB, Fusarium head blight; Fst, Wright’s fixation index; GS, genomic selection; H2, heritability; GEBV, genomic estimated Breeding value; LD, linkage disequilibrium; MAF, minor allele frequency; MAS, marker-assisted selection; ra, predictive ability; QTL, quantitative trait loci; REML, restricted maximum likelihood; RKHS, Reproducing Kernel Hilbert Space; RR-BLUP, random regression best linear unbiased prediction; SNP, single nucleotide polymorphism. Published March 13, 2015
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Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population.
The plant genome, 2015Co-Authors: Ahmad H. Sallam, Jean-luc Jannink, Jeffrey B. Endelman, Kevin P. SmithAbstract:Prediction accuracy of genomic selection (GS) has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant Breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley Breeding Population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP) markers. The Breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of Breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training Population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RR-BLUP), to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training Populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation Populations (marker allele frequency, Population structure, and linkage disequilibrium, LD) as well as characteristics of the trait (genetic architecture and heritability, H2 ). Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait H2 in the training Population and simpler trait architecture were associated with greater prediction accuracy.
Agustin Barria - One of the best experts on this subject based on the ideXlab platform.
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whole genome linkage disequilibrium and effective Population size in a coho salmon oncorhynchus kisutch Breeding Population using a high density snp array
Frontiers in Genetics, 2019Co-Authors: Agustin Barria, Kris A Christensen, Grazyella Massako Yoshida, Ana Jedlicki, Jean P Lhorente, William S Davidson, Jong S Leong, Eric Rondeau, Ben F Koop, Jose M YanezAbstract:The estimation of linkage disequilibrium between molecular markers within a Population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different Populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon Breeding Population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the Breeding Population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r 2 was 0.10. The Average r 2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r 2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r 2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective Population size of 43 was estimated for the Population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon Population. Furthermore, based on the contemporary N e, a new mate allocation strategy is suggested to increase the effective Population size.
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whole genome linkage disequilibrium and effective Population size in a coho salmon oncorhynchus kisutch Breeding Population using a high density snp array
Frontiers in Genetics, 2019Co-Authors: Agustin Barria, Kris A Christensen, Grazyella Massako Yoshida, Ana Jedlicki, Jean P Lhorente, William S Davidson, Jong S Leong, Eric Rondeau, Ben F Koop, Jose M YanezAbstract:The estimation of linkage disequilibrium between molecular markers within a Population is critical when establishing the minimum number of markers required for association studies, genomic selection and for inferring historical events influencing different Populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon Breeding Population using a high density chip SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the Breeding Population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r2 was 0.10. The Average r2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at distances lower than 1 kb and up to 10 Mb, respectively. An r2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (> 0.20 at distances lower than 1 Mb). Average r2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective Population size of 43 was estimated for the Population 10 generation ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the used panel SNPs allows to capture high-resolution information in the farmed coho salmon Population. Furthermore, based on the contemporary Ne, a new mate allocation strategy is suggested to increase the effective Population size.
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whole genome linkage disequilibrium and effective Population size in a coho salmon oncorhynchus kisutch Breeding Population
bioRxiv, 2018Co-Authors: Agustin Barria, Kris A Christensen, Grazyella Massako Yoshida, Ana Jedlicki, Jean P Lhorente, William S Davidson, Jose M YanezAbstract:The estimation of linkage disequilibrium between molecular markers within a Population is critical when establishing the minimum number of markers required for association studies, genomic selection and for inferring historical events influencing different Populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon Breeding Population using ddRAD genomic markers. Linkage disequilibrium was estimated between a total of 7,505 SNPs found in 62 individuals (33 dams and 29 sires) from the Breeding Population. The makers encompass all 30 coho salmon chromosomes and comprise 1,655.19 Mb of the genome. The average density of markers per chromosome ranged from 3.45 to 6.11 per 1 Mbp. The minor allele frequency averaged 0.20 (with a range from 0.08 to 0.50). The overall average linkage disequilibrium among SNPs pairs measured as r2 was 0.054. The average r2 value decreased with increasing physical distance, with values ranging from 0.37 to 0.054 at distances lower than 1 kb and up to 10 Mb, respectively. An r2 threshold of 0.1 was reached at distance of approximately 1.3 Mb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (> 0.20 at distances lower than 1 Mb). Average r2 values were lower than 0.1 for all chromosomes at distances greater than 4 Mb. Linkage disequilibrium values suggest that whole genome association and selection studies could be performed using about 75,000 SNPs in aquaculture Populations (depending on the trait under investigation). From the identified SNPs, an effective Population size of 100 was estimated for the Population 10 generation ago, and 1,000, for 139 generations ago. Based on the extent of r2 decay, we suggest that at least 75,000 SNPs would be necessary for an association mapping study. Over 100,000 SNPs would be necessary for a high power study, in the current coho salmon Population.