Combining Data From Multiple Inbred Line Crosses Improves the Power and Resolution of Quantitative Trait Loci Mapping

Li, Renhua; Lyons, Malcolm A.; Wittenburg, Henning; Paigen, Beverly; Churchill, Gary A.

doi:10.1534/genetics.104.033993

Cited by 97 publications

(123 citation statements)

References 35 publications

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“…A LOD difference of 2.0 was significant (Pp0.05) in a simulation study with a sample size of 200 intercross mice. 23 For the detection of QTLs, genome-wide scans were performed using the forward selection interval mapping approach. 24 Interactions between QTLs were tested by a twodimensional grid search as described previously.…”

Section: Qtl Mappingmentioning

confidence: 99%

A unique genetic defect on chromosome 3 is responsible for juvenile obesity in the Berlin Fat Mouse

et al. 2010

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Objective: This study aimed at the mapping and estimation of genetic and sex effects contributing to the obese phenotype of the Berlin Fat Mouse Inbred line 860 (BFMI860). This mouse line is predisposed for juvenile obesity. BFMI860 mice accumulate 24% total fat mass at 10 weeks of age under a standard maintenance diet. Design: A total of 471 mice of a (BFMI860 Â C57BL/6NCrl) F 2 intercross population were fed a standard maintenance diet and were analysed for body composition at 10 weeks when they finished their rapid growth phase. Results: The most striking result was the identification of a novel obesity locus on chromosome 3 (Chr 3) at 40 Mb, explaining 39% of the variance of total fat mass in the F 2 population under a standard diet. This locus was named jObes1 (juvenile obesity 1). The BFMI860 allele effect was recessive. Males and females homozygous at jObes1 had on average 3.0 and 3.3 g more total fat mass at 10 weeks than the other two genotype classes, respectively. The effect was evident in all white adipose tissues, brown adipose tissue and also in liver. The position of the Chr 3 effect is syntenic to an obesity locus in humans. Additional loci for total fat mass and different white adipose tissue weights with minor effects were detected on mouse Chr 5 and 6. Another locus on Chr 4 had influence especially on liver weight. Many loci including jObes1 affected males and females to a different extent. Conclusion:The major locus on Chr 3 for juvenile obesity and its interaction with sex is unique and makes the BFMI860 mice an interesting resource for the discovery of novel genetic factors predisposing obesity, which might also contribute to obesity in humans. The results suggested that metabolic and regulatory pathways differed between the sexes.

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Section: Qtl Mappingmentioning

confidence: 99%

A unique genetic defect on chromosome 3 is responsible for juvenile obesity in the Berlin Fat Mouse

et al. 2010

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“…There are several possible ways to combine data from different studies to detect consensus QTLs and narrow QTL confidence intervals. One way would be to reanalyze the initial datasets in a combined way as was done in mouse by assuming a common biallelic mode of inheritance in several mapping populations (Li et al 2005a). The rice mapping populations have characteristics that would fit such a design well.…”

Section: Introductionmentioning

confidence: 99%

Rice Root Genetic Architecture: Meta-analysis from a Drought QTL Database

Courtois

Ahmadi

Khowaja

et al. 2009

Rice

253

185

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During the last 10 years, a large number of quantitative trait loci (QTLs) controlling rice root morphological parameters have been detected in several mapping populations by teams interested in improving drought resistance in rice. Compiling these data could be extremely helpful in identifying candidate genes by positioning consensus QTLs with more precision through meta-QTL analysis. We extracted information from 24 published papers on QTLs controlling 29 root parameters including root number, maximum root length, root thickness, root/ shoot ratio, and root penetration index. A web-accessible database of 675 root QTLs detected in 12 populations was constructed. This database includes also all QTLs for drought resistance traits in rice published between 1995 and 2007. The physical position on the pseudochromosomes of the markers flanking each QTL was determined. An overview of the number of root QTLs in 5-Mb segments covering the whole genome revealed the existence of "hot spots," The 32 trait × chromosome combinations comprising six or more QTLs were subjected to a meta-QTL analysis using the software package MetaQTL. The method enabled us both to determine the likely number of true QTLs in these areas using an Akaike information criterion and to estimate their position. The meta-QTL confidence intervals were notably reduced and, for the smallest ones, encompassed only a few genes.

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“…Combined-cross analysis provides increased power and resolution due to larger sample sizes but also provides a means of identifying QTL with effects that depend on their genetic background (Li et al 2005;Blanc et al 2006;Guo et al 2006;Jagodic and Olsson 2006;Malmanger et al 2006). A number of lines of evidence suggest that many of the drug-sensitivity QTL that we identified have effects that depend on their genetic background.…”

Section: Discussionmentioning

confidence: 91%

“…A combined-cross analysis has increased power over a single-cross analysis due to its larger sample size and makes it possible to explicitly test for QTL with crossspecific effects (Li et al 2005). A combined-cross analysis was conducted on all three pairs of crosses: MY-MS, MY-YS, and MS-YS, where M, Y, and S represent the M22, YPS163, and S288c parental strains.…”

Section: Resultsmentioning

confidence: 99%

A Combined-Cross Analysis Reveals Genes With Drug-Specific and Background-Dependent Effects on Drug Sensitivity in Saccharomyces cerevisiae

Fay

2009

Genetics

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Effective pharmacological therapy is often inhibited by variable drug responses and adverse drug reactions. Dissecting the molecular basis of different drug responses is difficult due to complex interactions involving multiple genes, pathways, and cellular processes. We previously found a single nucleotide polymorphism within cystathionine b-synthase (CYS4) that causes multi-drug sensitivity in a vineyard strain of Saccharomyces cerevisiae. However, not all variation was accounted for by CYS4. To identify additional genes influencing drug sensitivity, we used CYS4 as a covariate and conducted both single-and combined-cross linkage mapping. After eliminating numerous false-positive associations, we identified 16 drug-sensitivity loci, only 3 of which had been previously identified. Of 4 drug-sensitivity loci selected for validation, 2 showed replicated associations in independent crosses, and two quantitative trait genes within these regions, AQY1 and MKT1, were found to have drug-specific and background-dependent effects. Our results suggest that drug response may often depend on interactions between genes with multi-drug and drug-specific effects.

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Combining Data From Multiple Inbred Line Crosses Improves the Power and Resolution of Quantitative Trait Loci Mapping

Cited by 97 publications

References 35 publications

A unique genetic defect on chromosome 3 is responsible for juvenile obesity in the Berlin Fat Mouse

A unique genetic defect on chromosome 3 is responsible for juvenile obesity in the Berlin Fat Mouse

Rice Root Genetic Architecture: Meta-analysis from a Drought QTL Database

A Combined-Cross Analysis Reveals Genes With Drug-Specific and Background-Dependent Effects on Drug Sensitivity in Saccharomyces cerevisiae

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