An analytical formula to estimate confidence interval of QTL location with a saturated genetic map as a function of experimental design

Weller, J. I.; Soller, M.

doi:10.1007/s00122-004-1664-2

Cited by 28 publications

(36 citation statements)

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“…A CI of three haplotype segments included~1.5 × 10 7 bp, 15 cM and 150 genes. For a dense genome scan, Darvasi and Soller (1997) and Weller and Soller (2004) developed formula to estimate CI for QTL location as a function of experimental design, QTL substitution effect and the number of individuals analyzed. However, none of their experimental designs correspond to the granddaughter design, in which multiple families are analyzed but only a fraction of those families are segregating for the QTL.…”

Section: Weller Cole Vanraden and Wiggansmentioning

confidence: 99%

“…A total of 4917 backcross progeny are required to obtain a CI of 95% for a substitution effect of 0.25 phenotypic standard deviations. The width of the CI is inversely proportional to the square of the substitution effect relative to the residual standard deviation (Weller and Soller, 2004). For the granddaughter design, in which genetic evaluations are analyzed, the variance among sons will be approximately equal to three-fourths of the genetic variance, but only half of the substitution effect of the QTL will be passed from sons to their daughters.…”

Section: Weller Cole Vanraden and Wiggansmentioning

confidence: 99%

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Application of the a posteriori granddaughter design to the Holstein genome

Weller

Cole

VanRaden

et al. 2014

Animal

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An a posteriori granddaughter design was applied to estimate quantitative trait loci genotypes of sires with many sons in the US Holstein population. The results of this analysis can be used to determine concordance between specific polymorphisms and segregating quantitative trait loci. Determination of the actual polymorphisms responsible for observed genetic variation should increase the accuracy of genomic evaluations and rates of genetic gain. A total of 52 grandsire families, each with ⩾100 genotyped sons with genetic evaluations based on progeny tests, were analyzed for 33 traits (milk, fat and protein yields; fat and protein percentages; somatic cell score (SCS); productive life; daughter pregnancy rate; heifer and cow conception rates; service-sire and daughter calving ease; service-sire and daughter stillbirth rates; 18 conformation traits; and net merit). Of 617 haplotype segments spanning the entire bovine genome and each including~5 × 10 6 bp, 5 cM and 50 genes, 608 autosomal segments were analyzed. A total of 19 335 unique haplotypes were found among the 52 grandsires. There were a total of 133 chromosomal segment-by-trait combinations, for which the nominal probability of significance for the haplotype effect was <10 − 8 , which corresponds to genome-wide significance of <10 − 4 . The number of chromosomal regions that met this criterion by trait ranged from one for rear legs (rear view) to seven for net merit. For each of the putative quantitative trait loci, at least one grandsire family had a within-family contrast with a t-value of >3. Confidence intervals (CIs) were estimated by the nonparametric bootstrap for the largest effect for each of nine traits. The bootstrap distribution generated by 100 samples was bimodal only for net merit, which had the widest 90% CI (eight haplotype segments). This may be due to the fact that net merit is a composite trait. For all other chromosomes, the CI spanned less than a third of the chromosome. The narrowest CI (a single haplotype segment) was found for SCS. It is likely that analysis by more advanced methods could further reduce CIs at least by half. These results can be used as a first step to determine the actual polymorphisms responsible for observed quantitative variation in dairy cattle.

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Section: Weller Cole Vanraden and Wiggansmentioning

confidence: 99%

Section: Weller Cole Vanraden and Wiggansmentioning

confidence: 99%

Application of the a posteriori granddaughter design to the Holstein genome

Weller

Cole

VanRaden

et al. 2014

Animal

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“…Then Q1 and Q2 could be regarded as one QTL since the patterns are identical while Q3 is a different QTL from Q1 and Q2 because of the change in effect sign on T4. Furthermore, the appropriate QTL window size can be analytically checked using the Weller and Soller (2004) approach. In our case, the appropriateness of a 20cM peak window size was confirmed by analytically calculating the required confidence intervals for QTL location for a RIL population of our size given the magnitude of QTL effects (Weller and Soller, 2004).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the appropriate QTL window size can be analytically checked using the Weller and Soller (2004) approach. In our case, the appropriateness of a 20cM peak window size was confirmed by analytically calculating the required confidence intervals for QTL location for a RIL population of our size given the magnitude of QTL effects (Weller and Soller, 2004). For the standardized traits, this was found to be around 15cM assuming (standardized) effect size of 0.25 with sample size of 149 and heritability of 0.25.…”

Section: Discussionmentioning

confidence: 99%

“…The size of mapping populations, N, affects the precision of QTL locations as well as the precision with which genetic linkage maps can be constructed (Weller and Soller, 2004;Semagn et al, 2006). The precision of the QTL location is positively correlated with the number of individuals since there is an inverse relationship between confidence interval (CI) for QTL location and population size for any type of biparental mapping population i.e.…”

Section: Qtl Mapping Resolutionmentioning

confidence: 99%

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Statistical methods for QTL mapping and genomic prediction of multiple traits and environments: case studies in pepper

Alimi

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In this thesis we describe the results of a number of quantitative techniques that were used to understand the genetics of yield in pepper as an example of complex trait measured in a number of environments. Main objectives were; i) to propose a number of mixed models to detect QTLs for multiple traits and multiple environments, ii) to extend the multi-trait QTL models to a multi-trait genomic prediction model, iii) to study how well the complex trait yield can be indirectly predicted from its component traits, and iv) to understand the 'causal' relationships between the target trait yield and its component traits.The thesis is part of an EU-FP7 project "Smart tools for Prediction and Improvements of Crop Yield" (SPICY-http://www.spicyweb.eu/). This project generated phenotypic data from four environments using 149 individuals from the sixth generation of recombinant inbred lines obtained from intraspecific cross between large -fruited inbred pepper cultivar 'Yolo Wonder' (YW) and the hot pepper cultivar 'Criollo de Morelos 334' (CM 334). A total of 16 physiological traits were evaluated across the four trials and various types of genetic parameters were estimated. In a first analysis, the traits were univariately analyzed using linear mixed model. Trait heritabilities were generally large (ranging between 0.43 -0.96 with an average of 0.86) and mostly comparable across trials while many of the traits displayed heterosis and transgression. The same QTLs were detected across the four trials, though QTL magnitude differed for many of the traits. We also found that some QTLs affected more than one trait, suggesting QTL pleiotropy (a QTL region affecting more than one trait). We discussed our results in the light of previously reported QTLs for these and similar traits in pepper.We addressed the presence of genotype-by-environment interaction (GEI) in yield and the other traits through a multi-environment (ME) mixed model methodology with terms for QTL-by-environment interaction (QEI). We opined that yield would benefit from joint analysis with other traits and so deployed two other mixed model based multi-response QTL approaches: a multi-trait approach (MT) and a multi-trait multi-environment approach (MTME). For yield as well as the other traits, MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. Many of the detected QTLs were pleiotropic and showed quantitative QEI. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.The QTL methods considered thus far are not well suited for prediction purposes as only a limited set of QTL-related markers are used. Since the main interest of this research includes improvement of yield prediction, we explored both single-trait and multi-trait versions of genomic prediction (GP) models as alternatives to the QTL-based prediction (QP) models. This was termed direct prediction. The methods differed in their predictive accuracies with GP meth...

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Mapping Quantitative Trait Loci

Weller¹

2012

Bovine Genomics

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An analytical formula to estimate confidence interval of QTL location with a saturated genetic map as a function of experimental design

Cited by 28 publications

References 13 publications

Application of the a posteriori granddaughter design to the Holstein genome

Application of the a posteriori granddaughter design to the Holstein genome

Statistical methods for QTL mapping and genomic prediction of multiple traits and environments: case studies in pepper

Mapping Quantitative Trait Loci

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