Fine mapping of mouse QTLs for fatness using SNP data

Quantitative trait loci (QTLs), as determined in crossbred studies, are a valuable resource to identify genes responsible for the corresponding phenotypic variances. Due to their broad chromosomal extension of some dozens of megabases, further steps are necessary to bring the number of candidate genes that underlie the detected effects to a reasonable order of magnitude. We use a set of 13,370 SNPs to identify informative haplotype blocks in 22 mouse QTLs for fatness. About half of the genes in a typical QTL overlap with haplotype blocks, which are different for the two base mouse lines, and which, thus, qualify for further analysis. For these genes we collect four more pieces of evidence for association with fat accumulation, namely (1) homology to genes identified in a Caenorhabditis elegans knock-out experiment as fat decreasing or fat increasing, (2) the overexpression of the genes in mouse fat, liver, muscle, or hypothalamus tissues, (3) the occurrence of a gene in several independently found QTLs, and (4) the information provided by gene ontology, to achieve a ranked list of 131 candidate genes. Ten genes fulfill three or four of the above sketched criteria and are discussed briefly, 121 further genes fulfilling two criteria are provided as on-line material. Viewing the genomic region of fatness-related QTLs under several different aspects is appropriate to assess the many thousands of genes that reside in such QTLs and to produce lists of more robust candidate genes.     

Detection of quantitative trait loci for growth and fatness in pigs

A quantitative trait locus (QTL) analysis of growth and fatness data from a three-generation experimental cross between Meishan (MS) and Large White (LW) pig breeds is presented. Six boars and 23 F1 sows, the progeny of six LW boars and six MS sows, produced 530 F2 males and 573 F2 females. Nine growth traits, i.e. body weight at birth and at 3, 10, 13, 17 and 22 weeks of age, average daily gain from birth to 3 weeks, from 3 to 10 weeks and from 10 to 22 weeks of age, as well as backfat thickness at 13, 17 and 22 weeks of age and at 40 and 60 kg live weight were analysed. Animals were typed for a total of 137 markers covering the entire porcine genome. Analyses were performed using two interval mapping methods: a line-cross (LC) regression method where founder lines were assumed to be fixed for different QTL alleles and a half-/full-sib (HFS) maximum likelihood method where allele substitution effects were estimated within each half-/full-sib family. Both methods revealed highly significant gene effects for growth on chromosomes 1, 4 and 7 and for backfat thickness on chromosomes 1, 4, 5, 7 and X, and significant gene effects on chromosome 6 for growth and backfat thickness. Suggestive QTLs were also revealed by both methods on chromosomes 2 and 3 for growth and 2 for backfat thickness. Significant gene effects were detected for growth on chromosomes 11, 13, 14, 16 and 18 and for backfat thickness on chromosome 8, 10, 13 and 14. LW alleles were associated with high growth rate and low backfat thickness, except for those of chromosome 7 and to a lesser extent early-growth alleles on chromosomes 1 and 2 and backfat thickness alleles on chromosome 6.     

Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping

A novel and robust method for the fine-scale mapping of genes affecting complex traits, which combines linkage and linkage-disequilibrium information, is proposed. Linkage information refers to recombinations within the marker-genotyped generations and linkage disequilibrium to historical recombinations before genotyping started. The identity-by-descent (IBD) probabilities at the quantitative trait locus (QTL) between first generation haplotypes were obtained from the similarity of the marker alleles surrounding the QTL, whereas IBD probabilities at the QTL between later generation haplotypes were obtained by using the markers to trace the inheritance of the QTL. The variance explained by the QTL is estimated by residual maximum likelihood using the correlation structure defined by the IBD probabilities. Unlinked background genes were accounted for by fitting a polygenic variance component. The method was used to fine map a QTL for twinning rate in cattle, previously mapped on chromosome 5 by linkage analysis. The data consisted of large half-sib families, but the method could also handle more complex pedigrees. The likelihood of the putative QTL was very small along most of the chromosome, except for a sharp likelihood peak in the ninth marker bracket, which positioned the QTL within a region <1 cM in the middle part of bovine chromosome 5. The method was expected to be robust against multiple genes affecting the trait, multiple mutations at the QTL, and relatively low marker density.     

Fine mapping of Ath6, a quantitative trait locus for atherosclerosis in mice

Ath6 is a novel quantitative trait locus associated with differences in susceptibility to atherosclerosis between C57BL/6J (B6) and C57BLKS/J (BKS) inbred mouse strains. Combining data from an intercross and a backcross (1593 meioses) between mice from B6 and BKS strains and from The Jackson Laboratory interspecific backcross panels, (C57BL/6J ×Mus spretus) F₁× C57BL/6J and (C57BL/6J × SPRET/Ei) F₁× SPRET/Ei, we constructed a consensus genetic map and narrowed Ath6 to a 1.07 ± 0.26 cM interval between the anonymous DNA marker D12Pgn4 and the gene Nmyc1. This region is near the proximal end of murine Chromosome (Chr) 12, which is homologous to the human chromosomal region 2p24-p25. Marker order in the Ath6 region was concordant among the two crosses and The Jackson Laboratory interspecific backcross panels. This high resolution map rules out candidate genes encoding apolipoprotein B, syndecan 1, and Adam17. The two Ath6 crosses have a combined potential resolution of 0.06 cM. Received: 12 September 2000 / Accepted: 22 February 2001     

Fine mapping of a quantitative trait locus for osteochondrosis on horse chromosome 2

In this study, we refine a quantitative trait locus for equine osteochondrosis (OC) on horse chromosome (ECA) 2 to a genome-wide significant interval at 20.08-30.94 Mb. The marker set contained 27 newly developed microsatellites equidistantly distributed over ECA2 and 44 nucleotide polymorphisms, located in 16 positional candidate genes for OC. Genotyping was performed in 211 Hanoverian horses from 14 paternal half-sib groups. A NCDN-associated SNP and haplotype were significantly associated with OC in fetlock and/or hock joints. This study is a further step towards the identification of genes responsible for OC in horses.     

Fine mapping of quantitative trait loci for mastitis resistance on bovine chromosome 11

Quantitative trait loci (QTL) affecting clinical mastitis (CM) and somatic cell score (SCS) were mapped on bovine chromosome 11. The mapping population consisted of 14 grandsire families belonging to three Nordic red cattle breeds: Finnish Ayrshire (FA), Swedish Red and White (SRB) and Danish Red. The families had previously been shown to segregate for udder health QTL. A total of 524 progeny tested bulls were included in the analysis. A linkage map including 33 microsatellite and five SNP markers was constructed. We performed combined linkage disequilibrium and linkage analysis (LDLA) using the whole data set. Further analyses were performed for FA and SRB separately to study the origin of the identified QTL/haplotype and to examine if it was common in both populations. Finally, different two-trait models were fitted. These postulated either a pleiotropic QTL affecting both traits; two linked QTL, each affecting one trait; or one QTL affecting a single trait. A QTL affecting CM was fine-mapped. In FA, a haplotype having a strong association with a high negative effect on mastitis resistance was identified. The mapping precision of an earlier detected SCS-QTL was not improved by the LDLA analysis because of lack of linkage disequilibrium between the markers used and the QTL in the region.     

Fine mapping dissects pleiotropic growth quantitative trait locus into linked loci

A recurring issue in studies of quantitative trait loci (QTLs) is whether QTLs that appear to have pleiotropic effects are indeed caused by pleiotropy at single loci or by linked QTLs. Previous work identified a QTL that affected tail length in mice and the lengths of various bones, including the humerus, ulna, femur, tibia, and mandible. The effect of this QTL on tail length has since been found to be due to multiple linked QTLs and so its apparently pleiotropic effects may have been due to linked QTLs with distinct effects. In the present study we examined a line of mice segregating only for a 0.94-Mb chromosomal region known to contain a subset of the QTLs influencing tail length. We measured a number of skeletal dimensions, including the lengths of the skull, mandible, humerus, ulna, femur, tibia, calcaneus, metatarsus, and a tail bone. The QTL region was found to have effects on the size of the mandible and length of the tail bone, with little or no effect on the other traits. Using a randomization approach, we rejected the null hypothesis that the QTL affected all traits equally, thereby demonstrating that the pleiotropic effects reported earlier were due to linked loci with distinct effects. This result underlines the possibility that seemingly pleiotropic effects of QTLs may frequently be due to linked loci and that high-resolution mapping will often be required to distinguish between pleiotropy and linkage.     

Fine structure mapping of the barley chromosome-1 centromere region containing malting-quality QTLs

 Current techniques for quantitative trait locus (QTLs) analyses provide only approximate locations of QTLs on chromosomes. Further resolution of identified QTL regions is often required for detailed characterization. An important region containing malting-quality QTLs on barley (Hordeum vulgare L.) chromosome 1 was identified by previous QTL analyses in a Steptoe×Morex cross. This region contains two putative adjacent overlapping QTLs, each of which has effects on malt-extract percentage, α-amylase activity, diastatic power, and malt β-glucan content. All favorable alleles for these traits are attributed to Morex. The objective of the present study was fine structure mapping of this complex QTL region to elucidate whether these two putative overlapping QTLs are really one QTL. Another question was whether the apparently overlapping QTLs are due to the pleiotropic effects of a single gene, or the independent effects of several genes. A high-resolution map in the target region was developed which spans approximately 27 cM. Molecular-marker-assisted backcrossing was employed to create isogenic lines with a Steptoe background differing only in the region containing the QTLs of interest. A total of 32 different recombinants were identified, of which eight most-informative isogenic lines plus one reconstructed Steptoe control were selected for field testing. The additive effects on malt-extract percentage, α-amylase activity, diastatic power, and malt β-glucan content from eight isogenic lines were calculated based on malting data from three locations. By comparing the significant additive effects among isogenic lines carrying different Morex fragments, two QTLs each for malt extract and for α-amylase, and two to three for diastatic power were identified in certain environments and resolved into 1–8-cM genome fragments. There was a significant QTL×environment interaction for diastatic power, and there are indications that epistatic interactions for malt β-glucan content occur between the QTLs on chromosome 1 and QTLs on other chromosomes. Received : 4 June 1997 / Accepted : 19 June 1997     

Quantitative trait loci affecting fatness in the chicken

An F2 chicken population of 442 individuals from 30 families, obtained by crossing a broiler line with a layer line, was used for detecting and mapping Quantitative Trait Loci (QTL) affecting abdominal fat weight, skin fat weight and fat distribution. Within-family regression analyses using 102 microsatellite markers in 27 linkage groups were carried out with genome-wide significance thresholds. The QTL for abdominal fat weight were found on chromosomes 3, 7, 15 and 28; abdominal fat weight adjusted for carcass weight on chromosomes 1, 5, 7 and 28; skin and subcutaneous fat on chromosomes 3, 7 and 13; skin fat weight adjusted for carcass weight on chromosomes 3 and 28; and skin fat weight adjusted for abdominal fat weight on chromosomes 5, 7 and 15. Interactions of the QTL with sex or family were unimportant and, for each trait, there was no evidence for imprinting or of multiple QTL on any chromosome. Significant dominance effects were obtained for all but one of the significant locations for QTL affecting the weight of abdominal fat, none for skin fat and one of the three QTL affecting fat distribution. The magnitude of each QTL ranged from 3.0 to 5.2% of the residual phenotypic variation or 0.2-0.8 phenotypic standard deviations. The largest additive QTL (on chromosome 7) accounted for more than 20% of the mean weight of abdominal fat. Significant positive and negative QTL were identified from both lines.     

Fine mapping of collagen-induced arthritis quantitative trait loci in an advanced intercross line

The generation of advanced intercross lines (AIL) is a powerful approach for high-resolution fine mapping of quantitative trait loci (QTLs), because they accumulate much more recombination events compared with conventional F2 intercross and N2 backcross. However, the application of this approach is severely hampered by the requirements of excessive resources to maintain such crosses, i.e., in terms of animal care, space, and time. Therefore, in this study, we produced an AIL to fine map collagen-induced arthritis (CIA) QTLs using comparatively limited resources. We used only 308 (DBA/1 x FVB/N)F11/12 AIL mice to refine QTLs controlling the severity and onset of arthritis as well as the Ab response and T cell subset in CIA, namely Cia2, Cia27, and Trmq3. These QTLs were originally identified in (DBA/1 x FVB/N)F2 progeny. The confidence intervals of the three QTLs were refined from 40, 43, and 48 Mb to 12, 4.1, and 12 Mb, respectively. The data were complemented by the use of another QTL fine-mapping approach, haplotype analysis, to further refine Cia2 into a 2-Mb genomic region. To aid in the search for candidate genes for the QTLs, genome-wide expression profiling was performed to identify strain-specific differentially expressed genes within the confidence intervals. Of the 1396 strain-specific differentially expressed genes, 3, 3, and 12 genes were within the support intervals of the Cia2, Cia27, and Trmq3, respectively. In addition, this study revealed that Cia27 and Trmq3 controlling anti-CII IgG2a Ab and CD4:CD8 T cell ratio, respectively, also regulated CIA clinical phenotypes.     

Fine mapping of QTLs of chromosome 2 affecting the fruit architecture and composition of tomato

Negative correlations between quality traits and fruit size may hamper the breeding of fresh market tomato varieties for better organoleptic qualities. In a recent QTL analysis, QTLs with large effects on fruit weight, locule number and several quality traits were detected in the distal 50 cM of chromosome 2, but favorable alleles for fruit weight and locule number were unfavorable to quality traits. Substitution mapping was undertaken to determine whether the effects were due to a single QTL or to several tightly linked QTLs. Several chromosomal segments were characterized using near-isogenic lines. Five of them appeared to be involved in one or several traits. Considering the five segments from the top to the bottom of the region, the QTLs detected in each segment controlled the variation of: (1) fruit weight, (2) soluble solids content and dry matter weight, (3) fruit weight, (4) locule number and (5) fruit weight, dry matter weight, total sugars, titratable acidity and soluble solids content. This last cluster illustrates an antagonism between fruit weight and four quality traits, as favorable alleles are not conferred by the same parent in both cases. Nevertheless, several antagonistic QTLs were separated from each other in the first four segments, holding the promise for marker-assisted improvement of fruit quality traits without compromising the fruit size.     

Fine mapping of the Bmgr5 quantitative trait locus for allogeneic bone marrow engraftment in mice

To identify novel mechanisms regulating allogeneic hematopoietic cell engraftment, we used forward genetics and previously described identification, in mice, of a bone marrow (BM) engraftment quantitative trait locus (QTL), termed Bmgr5. This QTL confers dominant and large allele effects for engraftment susceptibility. It was localized to chromosome 16 by quantitative genetic techniques in a segregating backcross bred from susceptible BALB.K and resistant B10.BR mice. We now report verification of the Bmgr5 QTL using reciprocal chromosome 16 consomic strains. The BM engraftment phenotype in these consomic mice shows that Bmgr5 susceptibility alleles are not only sufficient but also indispensable for conferring permissiveness for allogeneic BM engraftment. Using panels of congenic mice, we resolved the Bmgr5 QTL into two separate subloci, termed Bmgr5a (Chr16:14.6–15.8 Mb) and Bmgr5b (Chr16:15.8–17.6 Mb), each conferring permissiveness for the engraftment phenotype and both fine mapped to an interval amenable to positional cloning. Candidate Bmgr5 genes were then prioritized using whole exome DNA sequencing and microarray gene expression data. Further studies are warranted to elucidate the genetic interaction between the Bmgr5a and Bmgr5b QTL and identify causative genes and underlying gene variants. This may lead to new approaches for overcoming the problem of graft rejection in clinical hematopoietic cell transplantation.     

Confirmation of quantitative trait loci affecting fatness in chickens

In this report we describe the analysis of an advanced intercross line (AIL) to confirm the quantitative trait locus (QTL) regions found for fatness traits in a previous study. QTL analysis was performed on chromosomes 1, 3, 4, 15, 18, and 27. The AIL was created by random intercrossing in each generation from generation 2 (G₂) onwards until generation 9 (G₉) was reached. QTL for abdominal fat weight (AFW) and/or percentage abdominal fat (AF%) on chromosomes 1, 3 and 27 were confirmed in the G₉ population. In addition, evidence for QTL for body weight at the age of 5 (BW5) and 7 (BW7) weeks and for the percentage of intramuscular fat (IF%) were found on chromosomes 1, 3, 15, and 27. Significant evidence for QTL was detected on chromosome 1 for BW5 and BW7. Suggestive evidence was found on chromosome 1 for AFW, AF% and IF%, on chromosome 15 for BW5, and on chromosome 27 for AF% and IF%. Furthermore, evidence on the chromosome-wise level was found on chromosome 3 for AFW, AF%, and BW7 and on chromosome 27 for BW5. For chromosomes 4 and 18, test statistics did not exceed the significance threshold.     

Fine linkage mapping enables dissection of closely linked quantitative trait loci for seed dormancy and heading in rice

Two quantitative trait loci (QTLs) for seed dormancy (tentatively designated Sdr1) and heading date (Hd8) have been mapped to approximately the same region on chromosome 3 by interval mapping of backcross inbred lines derived from crosses between the rice cultivars Nipponbare (japonica) and Kasalath (indica). To clarify whether Sdr1 and Hd8 could be dissected genetically, we carried out fine-scale mapping with an advanced backcross progeny. We selected a BC₄F₁ plant, in which a small chromosomal region including Sdr1 and Hd8, on the short arm of chromosome 3, remained heterozygous, whereas all the other chromosomal regions were homozygous for Nipponbare. Days-to-heading and seed germination rate in the BC₄F₂ plants showed continuous variation. Ten BC₄F₂ plants with recombination in the vicinity of Sdr1 and Hd8 were selected on the basis of the genotypes of the restriction fragment length polymorphism (RFLP) markers flanking both QTLs. Genotypes of those plants for Sdr1 and Hd8 were determined by advanced progeny testing of BC₄F₄ families. Sdr1 was mapped between the RFLP markers R10942 and C2045, and co-segregated with C1488. Hd8 was also mapped between C12534S and R10942. Six recombination events were detected between Sdr1 and Hd8. These results clearly demonstrate that Sdr1 and Hd8 were tightly linked. Nearly isogenic lines for Sdr1 and Hd8 were selected by marker-assisted selection.Communicated by D. Mackill     

Genome-wide association study for growth and fatness traits in Chinese Sujiang pigs

Growth and fatness traits are complex and economically important traits in the pig industry. The molecular basis underlying porcine growth and fatness traits remains largely unknown. To uncover genetic loci and candidate genes for these traits, we explored the GeneSeek GGP Porcine 80K SNP chip to perform a GWAS for seven growth and fatness traits in 365 individuals from the Sujiang pig, a recently developed breed in China. We identified two, 17, one and 11 SNPs surpassing the suggestively significant threshold (P < 1.86 × 10⁻⁵) for body weight, chest circumference, chest width and backfat thickness respectively. Of these SNPs, 20 represent novel genetic loci, and five and four SNPs were respectively associated with chest circumference and backfat thickness at a genome-wide significant threshold (P < 9.31 × 10⁻⁷). Eight SNPs had a pleiotropic effect on both chest circumference and backfat thickness. The most remarkable locus resided in a region between 72.95 and 76.27 Mb on pig chromosome 4, harboring a number of previously reported quantitative trait loci related to backfat deposition. In addition to two reported genes (PLAG1 and TAS2R38), we identified four genes including GABRB3, ZNF106, XKR4 and MGAM as novel candidates for body weight and backfat thickness at the mapped loci. Our findings provide insights into the genetic architecture of porcine growth and fatness traits and potential markers for selective breeding of Chinese Sujiang pigs.     

Fine mapping of a major quantitative trait locus that regulates pod shattering in soybean

Legumes represent the second most important family of crop plants, accounting for ~27 % of the world’s crop production. While some legumes are grown as forages or vegetables, most crop legumes are grown for harvesting their nutritious seeds. The legume seeds are contained in the pod, which is composed of a single seed-bearing carpel that, when matures, splits open along two seams, a process called pod dehiscence or pod shattering. Pod shattering before or during harvest causes yield losses of grain legumes. Moreover, the dominant shattering trait of the wild progenitors is a limiting factor for efficient introgression of value-added traits into elite breeding lines. Knowledge of the genetic mechanisms underlying pod shattering will facilitate breeding of shattering-resistant varieties, expedite introgression of agronomically favorable traits from wild species to elite breeding lines, and enrich our understanding of the evolution of seed dispersal and crop domestication in diverse crop species. Here we report fine mapping of a major quantitative trait locus (designated as qPDH1) that regulates pod shattering in soybean (Glycine max). A combination of linkage and association mapping allowed us to delimit the qPDH1 locus within a 47-kb region on chromosome 16. The data reported here will facilitate positional cloning of the underlying gene and the development of breeder-friendly genetic markers for marker-assisted selection in soybean.     

Fine mapping of quantitative trait loci for acid phosphatase activity in maize leaf under low phosphorus stress

Acid phosphatase (APase) is very important in phosphorus (P) scavenging and remobilization in plants. The aim of this study was the fine mapping of quantitative trait loci (QTL) for APase activity (APA) in maize (Zea mays L.) leaf. The QTL for APA were studied in the F2:3 population derived from the cross 082 × Ye107 under low P stress in two sites. A significant difference in APA was found between 082 (P-efficient genotype) and Ye107 (P-deficient genotype). Each environment was analyzed to identify the QTL. Six QTL for APA were found, comprising two QTL at Beibei (BB) and four QTL at Hechuan (HC), China. A QTL denoted as AP9 showed a stable expression under different environments on chromosome 9, and explained 10.21 and 16.81 % of phenotypic variation at BB and HC, respectively. For the fine mapping of this QTL, seven individuals selected via marker-assisted selection in the BC3F1 population were used to produce the BC3F2 lines by selfing and to allow recombination within the region containing the target QTL. High-resolution genetic and physical maps were further constructed for the fine mapping of AP9 using 12 simple sequence repeat markers and the BC3F2 population consisting of 1,441 individuals. As a result, the location of AP9 was narrowed down to a 546-kb fragment on chromosome 9.     

Combined analyses of data from quantitative trait loci mapping studies. Chromosome 4 effects on porcine growth and fatness

For many species several similar QTL mapping populations have been produced and analyzed independently. Joint analysis of such data could be used to increase power to detect QTL and evaluate population differences. In this study, data were collated on almost 3000 pigs from seven different F(2) crosses between Western commercial breeds and either the European wild boar or the Chinese Meishan breed. Genotypes were available for 31 markers on chromosome 4 (on average 8.3 markers per population). Data from three traits common to all populations (birth weight, mean backfat depth at slaughter or end of test, and growth rate from birth to slaughter or end of test) were analyzed for individual populations and jointly. A QTL influencing birth weight was detected in one individual population and in the combined data, with no significant interaction of the QTL effect with population. A QTL affecting backfat that had a significantly greater effect in wild boar than in Meishan crosses was detected. Some evidence for a QTL affecting growth rate was detected in all populations, with no significant differences between populations. This study is the largest F(2) QTL analysis achieved in a livestock species and demonstrates the potential of joint analysis.