Limits...
Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping.

Crooks L, Sahana G, de Koning DJ, Lund MS, Carlborg O - BMC Proc (2009)

Bottom Line: Generally the power to detect QTL was high and the Type 1 error was low.Estimates of QTL locations were generally very accurate.No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, SE-75007 Uppsala, Sweden. Lucy.Crooks@hgen.slu.se

ABSTRACT
As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis.

No MeSH data available.


Related in: MedlinePlus

Chromosomal positions of simulated QTL. Each simulated chromosome (Chr) is 100 cM long. QTL are indicated on the right-hand side of each chromosome and their position in cM on the left-hand side. No QTL were simulated on chromosome 6.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2654496&req=5

Figure 1: Chromosomal positions of simulated QTL. Each simulated chromosome (Chr) is 100 cM long. QTL are indicated on the right-hand side of each chromosome and their position in cM on the left-hand side. No QTL were simulated on chromosome 6.

Mentions: The data available for fine-mapping and genome-wide association analyses consisted of a simulated four-generation pedigree of 4,665 individuals [13]. Phased biallelic marker genotypes were given at 0.1 cM intervals for six chromosomes, each 100 cM long. Hence, chromosome-wide haplotypes containing 1,000 markers per chromosome were given. Fifty biallelic QTL with additive effects were simulated. Details of these QTL are given in Table 1 and their genomic locations are illustrated in Figure 1. For six QTL, the location was pre-defined and their effects were chosen so that the QTL explained a fixed proportion of the genetic variance. The genetic variance for each QTL was calculated as 2p(1 - p)α2, where p is the minor allele frequency in the four generations and α is the average effect of allelic substitution (average change in genotypic value when one allele is randomly substituted for the other, which we calculated from the data using the formula in [14]). The locations and effects of the remaining QTLs were randomly sampled. A normally distributed error term was added to the genetic value for each individual to give a genetic variance of 0.3 times the phenotypic variance. No QTL were simulated on chromosome 6, making it a control for false positives. The number of QTL simulated on chromosomes 1–5 was 10, 13, 6, 10 and 11, respectively. None of the QTL was located at a marker position and therefore the QTL genotypes were unknown to the participants. The average effect of allelic substitution for the QTL varied from <0.01 to 0.75. One QTL was, by chance, fixed in the base population; the minor allele frequencies of the other QTLs ranged from 0.04 to 0.47.


Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping.

Crooks L, Sahana G, de Koning DJ, Lund MS, Carlborg O - BMC Proc (2009)

Chromosomal positions of simulated QTL. Each simulated chromosome (Chr) is 100 cM long. QTL are indicated on the right-hand side of each chromosome and their position in cM on the left-hand side. No QTL were simulated on chromosome 6.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654496&req=5

Figure 1: Chromosomal positions of simulated QTL. Each simulated chromosome (Chr) is 100 cM long. QTL are indicated on the right-hand side of each chromosome and their position in cM on the left-hand side. No QTL were simulated on chromosome 6.
Mentions: The data available for fine-mapping and genome-wide association analyses consisted of a simulated four-generation pedigree of 4,665 individuals [13]. Phased biallelic marker genotypes were given at 0.1 cM intervals for six chromosomes, each 100 cM long. Hence, chromosome-wide haplotypes containing 1,000 markers per chromosome were given. Fifty biallelic QTL with additive effects were simulated. Details of these QTL are given in Table 1 and their genomic locations are illustrated in Figure 1. For six QTL, the location was pre-defined and their effects were chosen so that the QTL explained a fixed proportion of the genetic variance. The genetic variance for each QTL was calculated as 2p(1 - p)α2, where p is the minor allele frequency in the four generations and α is the average effect of allelic substitution (average change in genotypic value when one allele is randomly substituted for the other, which we calculated from the data using the formula in [14]). The locations and effects of the remaining QTLs were randomly sampled. A normally distributed error term was added to the genetic value for each individual to give a genetic variance of 0.3 times the phenotypic variance. No QTL were simulated on chromosome 6, making it a control for false positives. The number of QTL simulated on chromosomes 1–5 was 10, 13, 6, 10 and 11, respectively. None of the QTL was located at a marker position and therefore the QTL genotypes were unknown to the participants. The average effect of allelic substitution for the QTL varied from <0.01 to 0.75. One QTL was, by chance, fixed in the base population; the minor allele frequencies of the other QTLs ranged from 0.04 to 0.47.

Bottom Line: Generally the power to detect QTL was high and the Type 1 error was low.Estimates of QTL locations were generally very accurate.No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, SE-75007 Uppsala, Sweden. Lucy.Crooks@hgen.slu.se

ABSTRACT
As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis.

No MeSH data available.


Related in: MedlinePlus