Large-scale SNP genotyping in crosses between outbred lines: how useful is it?

Ledur, Mônica Corrêa; Navarro, Nicolas; Pérez‐Enciso, Miguel

doi:10.1038/hdy.2009.149

Cited by 23 publications

(18 citation statements)

References 23 publications

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“…The genotypes of 932 F 2 animals were simulated using gene-dropping [38] theory, by conditioning on a real pedigree and on the haplotypes of the 55 F 1 parents (6 males and 49 females) from the real F 2 population. The haplotypes were estimated at a high accuracy from the genotypes of the F 1 parents and 19 F 0 ancestors (4 Duroc boars and 15 Pietrain sows), using the software MERLIN [39].…”

Section: Methodsmentioning

confidence: 99%

“…As an example, there were 1,405 SNP on chromosome 12 that were spread over 64.2 Mb, and the ensuing average distance between markers was 0.04573 Mb. By assuming a recombination rate of 1 cM per Mb [38], the number of recombinations in chromosome 12 was drawn from a Poisson distribution with parameter equal to 64.2 / 100 = 0.642. The next step was to assign the resulting gametes carrying these recombinations of the F 1 genotypes to their F 2 progeny.…”

Section: Methodsmentioning

confidence: 99%

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Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

et al. 2013

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BackgroundF2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios.ResultsSelection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90.ConclusionsCombining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

et al. 2013

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“…For an F 2 population, the power of detecting QTLs could reach 0.90 when the sample size is 500 and the heritability exceeds 0.04 (Satagopan et al, 2007). If the heritability reaches 0.18, the power of detecting QTLs would be close to one in an F 2 population including 200 individuals from two outbred lines crossing by 50 k SNP panels (Ledur et al, 2010). In fact, the heritability of body weight is generally no more than 0.25 in chickens (Wolc et al, 2009;Chen et al, 2011); however, many studies have successfully detected QTLs for body weights by GWAS (Gu et al, 2011;Xie et al, 2012).…”

Section: Population Structurementioning

confidence: 99%

A genome-wide association study identifies major loci affecting the immune response against infectious bronchitis virus in chicken

Luo

et al. 2014

Infection, Genetics and Evolution

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Coronaviruses are a hot research topic because they can cause severe diseases in humans and animals. Infectious bronchitis virus (IBV), belonging to gamma-coronaviruses, causes a highly infectious respiratory viral disease and can result in catastrophic economic losses to the poultry industry worldwide. Unfortunately, the genetic basis of the host immune responses against IBV is poorly understood. In the present study, the antibody levels against IBV post-immunization were measured by an enzyme-linked immunosorbent assay in the serum of 511 individuals from a commercial chicken (Gallus gallus) population. A genome-wide association study using 43,211 single nucleotide polymorphism markers was performed to identify the major loci affecting the immune response against IBV. This study detected 20 significant (P<1.16 × 10(-6)) effect single nucleotide polymorphisms for the antibody level against IBV. These single nucleotide polymorphisms were distributed on five chicken chromosomes (GGA), involving GGA1, GGA3, GGA5, GGA8, and GGA9. The genes in the 1-Mb windows surrounding each single nucleotide polymorphism with significant effect for the antibody level against IBV were associated with many biological processes or pathways related to immunity, such as the defense response and mTOR signaling pathway. A genomic region containing a cluster of 13 beta-defensin (GAL1-13) and interleukin-17F genes on GGA3 probably plays an important role in the immune response against IBV. In addition, the major loci significantly associated with the antibody level against IBV on GGA1 and GGA5 could explain about 12% and 13% of the phenotypic variation, respectively. This study suggested that the chicken genome has several important loci affecting the immune response against IBV, and increases our knowledge of how to control outbreaks of infectious bronchitis.

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“…The currently available chicken 60 k SNP panel provides genome coverage and map resolution unavailable from microsatellite markers and has the potential of much improved accuracy in finding the exact QTL locations. A recent study [10] showed that designed populations such as F2 populations for genome-wide association studies (GWAS) were advantageous over random populations in reducing false discovery rate (FDR) and in improving mapping accuracy. In this article, we report results of a genome-wide association analysis of chicken body weight using the chicken 60 k SNP panel in a chicken F2 resource population derived from the cross between Silky Fowl and White Plymouth Rock, which are two chicken breeds with highly divergent phenotypes in growth rate and body weight.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study of Body Weight in Chicken F2 Resource Population

Feng

et al. 2011

PLoS ONE

147

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Chicken body weight is an economically important trait and great genetic progress has been accomplished in genetic selective for body weight. To identify genes and chromosome regions associated with body weight, we performed a genome-wide association study using the chicken 60 k SNP panel in a chicken F2 resource population derived from the cross between Silky Fowl and White Plymouth Rock. A total of 26 SNP effects involving 9 different SNP markers reached 5% Bonferroni genome-wide significance. A chicken chromosome 4 (GGA4) region approximately 8.6 Mb in length (71.6–80.2 Mb) had a large number of significant SNP effects for late growth during weeks 7–12. The LIM domain-binding factor 2 (LDB2) gene in this region had the strongest association with body weight for weeks 7–12 and with average daily gain for weeks 6–12. This GGA4 region was previously reported to contain body weight QTL. GGA1 and GGA18 had three SNP effects on body weight with genome-wide significance. Some of the SNP effects with the significance of “suggestive linkage” overlapped with previously reported results.

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Large-scale SNP genotyping in crosses between outbred lines: how useful is it?

Cited by 23 publications

References 23 publications

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

A genome-wide association study identifies major loci affecting the immune response against infectious bronchitis virus in chicken

Genome-Wide Association Study of Body Weight in Chicken F2 Resource Population

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