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. 2012 Feb;190(2):459-73.
doi: 10.1534/genetics.111.135095. Epub 2011 Dec 5.

Quantitative trait Loci association mapping by imputation of strain origins in multifounder crosses

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Quantitative trait Loci association mapping by imputation of strain origins in multifounder crosses

Jin J Zhou et al. Genetics. 2012 Feb.

Abstract

Although mapping quantitative traits in inbred strains is simpler than mapping the analogous traits in humans, classical inbred crosses suffer from reduced genetic diversity compared to experimental designs involving outbred animal populations. Multiple crosses, for example the Complex Trait Consortium's eight-way cross, circumvent these difficulties. However, complex mating schemes and systematic inbreeding raise substantial computational difficulties. Here we present a method for locally imputing the strain origins of each genotyped animal along its genome. Imputed origins then serve as mean effects in a multivariate Gaussian model for testing association between trait levels and local genomic variation. Imputation is a combinatorial process that assigns the maternal and paternal strain origin of each animal on the basis of observed genotypes and prior pedigree information. Without smoothing, imputation is likely to be ill-defined or jump erratically from one strain to another as an animal's genome is traversed. In practice, one expects to see long stretches where strain origins are invariant. Smoothing can be achieved by penalizing strain changes from one marker to the next. A dynamic programming algorithm then solves the strain imputation process in one quick pass through the genome of an animal. Imputation accuracy exceeds 99% in practical examples and leads to high-resolution mapping in simulated and real data. The previous fastest quantitative trait loci (QTL) mapping software for dense genome scans reduced compute times to hours. Our implementation further reduces compute times from hours to minutes with no loss in statistical power. Indeed, power is enhanced for full pedigree data.

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Figures

Figure 1
Figure 1
The eight-way funnel breeding scheme for generating recombinant inbred (RI) strains.
Figure 2
Figure 2
Imputation performance for Collaborative Cross (CC) mice with and without pedigree information.
Figure 3
Figure 3
Overall imputation accuracy and mouse 414’s accuracy as a function of the logarithm of the tuning constant λ. Mouse 414 appears in the last generation of the pedigree and gives the poorest imputation results.
Figure 4
Figure 4
Four-way cross QTL association mapping of a univariate trait using windows 51 SNPs long and pedigree structure information. The vertical line represents the QTL location. The horizontal line represents the genome-wide significance threshold after Bonferroni correction.
Figure 5
Figure 5
Four-way cross QTL association mapping of a univariate trait using windows 51 SNPs long and excluding pedigree structure. The vertical line represents the QTL location. The horizontal line represents the genome-wide significance threshold after Bonferroni correction.
Figure 6
Figure 6
Four-way cross QTL association mapping of a univariate trait using the program EMMA. The vertical line represents the QTL location. The horizontal line represents the genome-wide significance threshold after Bonferroni correction.
Figure 7
Figure 7
q-q plot of the adjusted MENDEL P-values for the simulated data assuming no pedigree structure information.
Figure 8
Figure 8
q-q plot of the adjusted EMMA P-values for the simulated data.
Figure 9
Figure 9
eQTL mapping of the Ttf2 gene on chromosome 3 using results from EMMA and MENDEL. Six founder strains were used: C57Bl6, DBA2, A, AKR, ILn, and RIII. The solid curve displays the –log10(P-values) from MENDEL’s association test. EMMA’s results are displayed as asterisks. The physical location of the Ttf2 gene is shown by the small solid rectangle near the x-axis directly under the dominant peak. The tick marks at the top of the graph are the locations of the SNPs used by EMMA.

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