An experimental assessment of in silico haplotype association mapping in laboratory mice.
ABSTRACT: To assess the utility of haplotype association mapping (HAM) as a quantitative trait locus (QTL) discovery tool, we conducted HAM analyses for red blood cell count (RBC) and high density lipoprotein cholesterol (HDL) in mice. We then experimentally tested each HAM QTL using published crosses or new F2 intercrosses guided by the haplotype at the HAM peaks.The HAM for RBC, using 33 classic inbred lines, revealed 8 QTLs; 2 of these were true positives as shown by published crosses. A HAM-guided (C57BL/6J x CBA/J)F2 intercross we carried out verified 2 more as true positives and 4 as false positives. The HAM for HDL, using 81 strains including recombinant inbred lines and chromosome substitution strains, detected 46 QTLs. Of these, 36 were true positives as shown by published crosses. A HAM-guided (C57BL/6J x A/J)F2 intercross that we carried out verified 2 more as true positives and 8 as false positives. By testing each HAM QTL for RBC and HDL, we demonstrated that 78% of the 54 HAM peaks were true positives and 22% were false positives. Interestingly, all false positives were in significant allelic association with one or more real QTL.Because type I errors (false positives) can be detected experimentally, we conclude that HAM is useful for QTL detection and narrowing. We advocate the powerful and economical combined approach demonstrated here: the use of HAM for QTL discovery, followed by mitigation of the false positive problem by testing the HAM-predicted QTLs with small HAM-guided experimental crosses.
Project description:Quantitative trait locus (QTL) analyses of intercross populations between widely used mouse inbred strains provide a powerful approach for uncovering genetic factors that influence susceptibility to atherosclerosis. Epistatic interactions are common in complex phenotypes and depend on genetic backgrounds. To dissect genetic architecture of atherosclerosis, we analyzed F2 progeny from a cross between apolipoprotein E-null mice on DBA/2J (DBA-apoE) and C57BL/6J (B6-apoE) genetic backgrounds and compared the results with those from two previous F2 crosses of apolipoprotein E-null mice on 129S6/SvEvTac (129-apoE) and DBA-apoE backgrounds, and B6-apoE and 129-apoE backgrounds. In these round-robin crosses, in which each parental strain was crossed with two others, large-effect QTLs are expected to be detectable at least in two crosses. On the other hand, observation of QTLs in one cross only may indicate epistasis and/or absence of statistical power. For atherosclerosis at the aortic arch, Aath4 on chromosome (Chr)2:66 cM follows the first pattern, with significant QTL peaks in (DBAx129)F2 and (B6xDBA)F2 mice but not in (B6x129)F2 mice. We conclude that genetic variants unique to DBA/2J at Aath4 confer susceptibility to atherosclerosis at the aortic arch. A similar pattern was observed for Aath5 on chr10:35 cM, verifying that the variants unique to DBA/2J at this locus protect against arch plaque development. However, multiple loci, including Aath1 (Chr1:49 cM), and Aath2 (Chr1:70 cM) follow the second type of pattern, showing significant peaks in only one of the three crosses (B6-apoE x 129-apoE). As for atherosclerosis at aortic root, the majority of QTLs, including Ath29 (Chr9:33 cM), Ath44 (Chr1:68 cM) and Ath45 (Chr2:83 cM), was also inconsistent, being significant in only one of the three crosses. Only the QTL on Chr7:37 cM was consistently suggestive in two of the three crosses. Thus QTL analysis of round-robin crosses revealed the genetic architecture of atherosclerosis.
Project description:BACKGROUND:The functional allele of the rice gene DEEPER ROOTING 1 (DRO1) increases the root growth angle (RGA). However, wide natural variation in RGA is observed among rice cultivars with the functional DRO1 allele. To elucidate genetic factors related to such variation, we quantitatively measured RGA using the basket method and analyzed quantitative trait loci (QTLs) for RGA in three F2 mapping populations derived from crosses between the large RGA-type cultivar Kinandang Patong and each of three accessions with varying RGA: Momiroman has small RGA and was used to produce the MoK-F2 population; Yumeaoba has intermediate RGA (YuK-F2 population); Tachisugata has large RGA (TaK-F2 population). All four accessions belong to the same haplotype group of functional DRO1 allele. RESULTS:We detected the following statistically significant QTLs: one QTL on chromosome 4 in MoK-F2, three QTLs on chromosomes 2, 4, and 6 in YuK-F2, and one QTL on chromosome 2 in TaK-F2. Among them, the two QTLs on chromosome 4 were located near DRO2, which has been previously reported as a major QTL for RGA, whereas the two major QTLs for RGA on chromosomes 2 (DRO4) and 6 (DRO5) were novel. With the LOD threshold reduced to 3.0, several minor QTLs for RGA were also detected in each population. CONCLUSION:Natural variation in RGA in rice cultivars carrying functional DRO1 alleles may be controlled by a few major QTLs and by several additional minor QTLs.
Project description:Using chromosome substitution strains (CSS), we previously identified a large quantitative trait locus (QTL) for conditioned fear (CF) on mouse chromosome 10. Here, we used an F2 cross between CSS-10 and C57BL/6J (B6) to localize that QTL to distal chromosome 10. That QTL accounted for all the difference between CSS-10 and B6. We then produced congenic strains to fine-map that interval. We identified two congenic strains that captured some or all the QTL. The larger congenic strain (Line 1: 122.387121-129.068?Mb; build 37) appeared to account for all the difference between CSS-10 and B6. The smaller congenic strain (Line 2: 127.277-129.068?Mb) was intermediate between CSS-10 and B6. We used haplotype mapping followed by quantitative polymerase chain reaction to identify one gene that was differentially expressed in both lines relative to B6 (Rnf41) and one that was differentially expressed between only Line 1 and B6 (Shmt2). These cis-eQTLs may cause the behavioral QTLs; however, further studies are required to validate these candidate genes. More generally, our observation that a large QTL mapped using CSS and F2 crosses can be dissected into multiple smaller QTLs shows a weaknesses of two-stage approaches that seek to use coarse mapping to identify large regions followed by fine-mapping. Indeed, additional dissection of these congenic strains might result in further subdivision of these QTL regions. Despite these limitations, we have successfully fine-mapped two QTLs to small regions and identified putative candidate genes, showing that the congenic approach can be effective for fine-mapping QTLs.
Project description:Genetic variation contributes to individual differences in obesity, but defining the exact relationships between naturally occurring genotypes and their effects on fatness remains elusive. As a step toward positional cloning of previously identified body composition quantitative trait loci (QTLs) from F2 crosses of mice from the C57BL/6ByJ and 129P3/J inbred strains, we sought to recapture them on a homogenous genetic background of consomic (chromosome substitution) strains. Male and female mice from reciprocal consomic strains originating from the C57BL/6ByJ and 129P3/J strains were bred and measured for body weight, length, and adiposity. Chromosomes 2, 7, and 9 were selected for substitution because previous F2 intercross studies revealed body composition QTLs on these chromosomes. We considered a QTL confirmed if one or both sexes of one or both reciprocal consomic strains differed significantly from the host strain in the expected direction after correction for multiple testing. Using these criteria, we confirmed two of two QTLs for body weight (Bwq5-6), three of three QTLs for body length (Bdln3-5), and three of three QTLs for adiposity (Adip20, Adip26 and Adip27). Overall, this study shows that despite the biological complexity of body size and composition, most QTLs for these traits are preserved when transferred to consomic strains; in addition, studying reciprocal consomic strains of both sexes is useful in assessing the robustness of a particular QTL.
Project description:Anopheles gambiae females are the world's most successful vectors of human malaria. However, a fraction of these mosquitoes is refractory to Plasmodium development. L3-5, a laboratory selected refractory strain, encapsulates transforming ookinetes/early oocysts of a wide variety of Plasmodium species. Previous studies on these mosquitoes showed that one major (Pen1) and two minor (Pen2, Pen3) autosomal dominant quantitative trait loci (QTLs) control the melanotic encapsulation response against P. cynomolgi B, a simian malaria originating in Malaysia.We have investigated the response of L3-5 to infection with P. cynomolgi Ceylon, a different but related parasite species, in crosses with the susceptible strain 4Arr. Refractoriness to this parasite is incompletely recessive. Infection and genotyping of F2 intercross females at genome-spanning microsatellite loci revealed that 3 autosomal QTLs control encapsulation of this species. Two loci map to the regions containing Pen2 and Pen3. The novel QTL maps to chromosome 3R, probably to polytene division 32 or 33. Thus the relative contribution of any QTL to oocyst encapsulation varies with the species of parasite. Further, different QTLs were most readily identified in different F2 families. This, like the F1 data, suggests that L3-5 is not genetically homogeneous and that somewhat different pathways may be used to achieve an encapsulation response.We have shown here that different QTLs are involved in responses against different Plasmodium parasites.
Project description:Susceptibility to osteoporotic fracture is influenced by genetic factors that can be dissected by whole-genome linkage analysis in experimental animal crosses. The aim of this study was to characterize quantitative trait loci (QTLs) for biomechanical and two-dimensional dual-energy X-ray absorptiometry (DXA) phenotypes in reciprocal F2 crosses between diabetic GK and normo-glycemic F344 rat strains and to identify possible co-localization with previously reported QTLs for bone size and structure. The biomechanical measurements of rat tibia included ultimate force, stiffness and work to failure while DXA was used to characterize tibial area, bone mineral content (BMC) and areal bone mineral density (aBMD). F2 progeny (108 males, 98 females) were genotyped with 192 genome-wide markers followed by sex- and reciprocal cross-separated whole-genome QTL analyses. Significant QTLs were identified on chromosome 8 (tibial area; logarithm of odds (LOD)?=?4.7 and BMC; LOD?=?4.1) in males and on chromosome 1 (stiffness; LOD?=?5.5) in females. No QTLs showed significant sex-specific interactions. In contrast, significant cross-specific interactions were identified on chromosome 2 (aBMD; LOD?=?4.7) and chromosome 6 (BMC; LOD?=?4.8) for males carrying F344mtDNA, and on chromosome 15 (ultimate force; LOD?=?3.9) for males carrying GKmtDNA, confirming the effect of reciprocal cross on osteoporosis-related phenotypes. By combining identified QTLs for biomechanical-, size- and qualitative phenotypes (pQCT and 3D CT) from the same population, overlapping regions were detected on chromosomes 1, 3, 4, 6, 8 and 10. These are strong candidate regions in the search for genetic risk factors for osteoporosis.
Project description:Previously, we identified the regions of chromosomes 10q12-q31 and 15p16-q21 harbor quantitative trait loci (QTLs) for lumbar volumetric bone mineral density (vBMD) in female F2 rats derived from Fischer 344 (F344) x Lewis (LEW) and Copenhagen 2331 (COP) x Dark Agouti (DA) crosses. The purpose of this study is to identify the candidate genes within these QTL regions contributing to the variation in lumbar vBMD. RNA was extracted from bone tissue of F344, LEW, COP, and DA rats. Microarray analysis was performed using Affymetrix Rat Genome 230 2.0 Arrays. Genes differentially expressed among the rat strains were then ranked based on the strength of the correlation with lumbar vBMD in F2 animals derived from these rats. Quantitative PCR (qPCR) analysis was performed to confirm the prioritized candidate genes. A total of 285 genes were differentially expressed among all strains of rats with a false discovery rate less than 10%. Among these genes, 18 candidate genes were prioritized based on their strong correlation (r (2) > 0.90) with lumbar vBMD. Of these, 14 genes (Akap1, Asgr2, Esd, Fam101b, Irf1, Lcp1, Ltc4s, Mdp-1, Pdhb, Plxdc1, Rabep1, Rhot1, Slc2a4, Xpo4) were confirmed by qPCR. We identified several novel candidate genes influencing spinal vBMD in rats.
Project description:<h4>Background</h4>Numerous QTL mapping resource populations are available in livestock species. Usually they are analysed separately, although the same founder breeds are often used. The aim of the present study was to show the strength of analysing F2-crosses jointly in pig breeding when the founder breeds of several F2-crosses are the same.<h4>Methods</h4>Three porcine F2-crosses were generated from three founder breeds (i.e. Meishan, Pietrain and wild boar). The crosses were analysed jointly, using a flexible genetic model that estimated an additive QTL effect for each founder breed allele and a dominant QTL effect for each combination of alleles derived from different founder breeds. The following traits were analysed: daily gain, back fat and carcass weight. Substantial phenotypic variation was observed within and between crosses. Multiple QTL, multiple QTL alleles and imprinting effects were considered. The results were compared to those obtained when each cross was analysed separately.<h4>Results</h4>For daily gain, back fat and carcass weight, 13, 15 and 16 QTL were found, respectively. For back fat, daily gain and carcass weight, respectively three, four, and five loci showed significant imprinting effects. The number of QTL mapped was much higher than when each design was analysed individually. Additionally, the test statistic plot along the chromosomes was much sharper leading to smaller QTL confidence intervals. In many cases, three QTL alleles were observed.<h4>Conclusions</h4>The present study showed the strength of analysing three connected F2-crosses jointly. In this experiment, statistical power was high because of the reduced number of estimated parameters and the large number of individuals. The applied model was flexible and was computationally fast.
Project description:The genes underlying variation in skeletal muscle mass are poorly understood. Although many quantitative trait loci (QTLs) have been mapped in crosses of mouse strains, the limited resolution inherent in these conventional studies has made it difficult to reliably pinpoint the causal genetic variants. The accumulated recombination events in an advanced intercross line (AIL), in which mice from two inbred strains are mated at random for several generations, can improve mapping resolution. We demonstrate these advancements in mapping QTLs for hindlimb muscle weights in an AIL (n = 832) of the C57BL/6J (B6) and DBA/2J (D2) strains, generations F8-F13. We mapped muscle weight QTLs using the high-density MegaMUGA SNP panel. The QTLs highlight the shared genetic architecture of four hindlimb muscles and suggest that the genetic contributions to muscle variation are substantially different in males and females, at least in the B6D2 lineage. Out of the 15 muscle weight QTLs identified in the AIL, nine overlapped the genomic regions discovered in an earlier B6D2 F2 intercross. Mapping resolution, however, was substantially improved in our study to a median QTL interval of 12.5 Mb. Subsequent sequence analysis of the QTL regions revealed 20 genes with nonsense or potentially damaging missense mutations. Further refinement of the muscle weight QTLs using additional functional information, such as gene expression differences between alleles, will be important for discerning the causal genes.
Project description:The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.