Identification of genes controlling collagen-induced arthritis in mice: striking homology with susceptibility loci previously identified in the rat.

The susceptibility to collagen-induced arthritis in the highly susceptible DBA/1 mouse has earlier been shown to be partly controlled by the MHC class II gene Aq. To identify susceptibility loci outside of MHC, we have made crosses between DBA/1 and the less susceptible B10.Q strain, both expressing the MHC class II gene Aq. Analysis of 224 F2 intercross mice with 170 microsatellite markers in a genome-wide scan suggested 4 quantitative trait loci controlling arthritis susceptibility located on chromosomes 6, 7, 8, and 10. The locus on chromosome 6 (Cia6), which was associated with arthritis onset, yielded a logarithm of odds score of 4.7 in the F2 intercross experiment and was reproduced in serial backcross experiments. Surprisingly, the DBA/1 allele had a recessive effect leading to a delay in arthritis onset. The suggestive loci on chromosomes 7 and 10 were associated with arthritis severity rather than onset, and another suggestive locus on chromosome 8 was most closely associated with arthritis incidence. The loci on chromosomes 7, 8, and 10 all appeared to contain disease-promoting alleles derived from the DBA/1 strain. Interestingly, most of the identified loci were situated in chromosomal regions that are homologous to regions in the rat genome containing susceptibility genes for arthritis; the mouse Cia6 locus is homologous with the rat Cia3, Pia5, Pia2, and Aia3; the locus on chromosome 7 (Cia7) is homologous with the rat Cia2; and the locus on chromosome 10 (Cia8) is homologous with the rat Cia4.     

Mapping genetic loci that regulate lipid levels in a NZB/B1NJxRF/J intercross and a combined intercross involving NZB/B1NJ, RF/J, MRL/MpJ, and SJL/J mouse strains

The NZB/B1NJ (NZB) mouse strain exhibits high cholesterol and HDL levels in blood compared with several other strains of mice. To study the genetic regulation of blood lipid levels, we performed a genome-wide linkage analysis in 542 chow-fed F2 female mice from an NZBxRF/J (RF) intercross and in a combined data set that included NZBxRF and MRL/MpJxSJL/J intercrosses. In the NZBxRF F2 mice, the cholesterol and HDL concentrations were influenced by quantitative trait loci (QTL) on chromosome (Chr) 5 [logarithm of odds (LOD) 17-19; D5Mit10] that was in the region identified earlier in crosses involving NZB mice, but two QTLs on Chr 12 (LOD 4.7; D12Mit182) and Chr 19 (LOD 5.7; D19Mit1) were specific to the NZBxRF intercross. Triglyceride levels were affected by two novel QTLs at D12Mit182 (LOD 8.7) and D15Mit13 (LOD 3.5). The combined-cross linkage analysis (1,054 mice, 231 markers) 1) identified four shared QTLs (Chrs 5, 7, 14, and 17) that were not detected in one of the parental crosses and 2) improved the resolution of two shared QTLs. In summary, we report additional loci regulating lipid levels in NZB mice that had not been identified earlier in crosses involving the NZB strain of mice. The identification of shared loci from multiple crosses increases confidence toward finding the QTL gene.     

Quantitative trait loci associated with body weight and abdominal fat traits on chicken chromosomes 3, 5 and 7

Body weight and abdominal fat traits in meat-type chickens are complex and economically important factors. Our objective was to identify quantitative trait loci (QTL) responsible for body weight and abdominal fat traits in broiler chickens. The Northeast Agricultural University Resource Population (NEAURP) is a cross between broiler sires and Baier layer dams. We measured body weight and abdominal fat traits in the F(2) population. A total of 362 F(2) individuals derived from four F(1) families and their parents and F(0) birds were genotyped using 29 fluorescent microsatellite markers located on chromosomes 3, 5 and 7. Linkage maps for the three chromosomes were constructed and interval mapping was performed to identify putative QTLs. Nine QTL for body weight were identified at the 5% genome-wide level, while 15 QTL were identified at the 5% chromosome-wide level. Phenotypic variance explained by these QTL varied from 2.95 to 6.03%. In particular, a QTL region spanning 31 cM, associated with body weight at 1 to 12 weeks of age and carcass weight at 12 weeks of age, was first identified on chromosome 5. Three QTLs for the abdominal fat traits were identified at the 5% chromosome-wide level. These QTLs explained 3.42 to 3.59% of the phenotypic variance. This information will help direct prospective fine mapping studies and can facilitate the identification of underlying genes and causal mutations for body weight and abdominal fat traits.     

Association of a lithogenic Abcg5/Abcg8 allele on Chromosome 17 (Lith9) with cholesterol gallstone formation in PERA/EiJ mice

To examine further the genetic determinants of cholesterol gallstone susceptibility in inbred mice, we performed quantitative trait locus (QTL) analysis of an intercross of gallstone-susceptible PERA/EiJ and gallstone-resistant DBA/2J inbred mice. Three hundred twenty-four F₂ offspring were phenotyped for cholelithiasis during consumption of a lithogenic diet and genotyped using microsatellite markers. Linkage analysis was performed by interval mapping. In addition, we analyzed the combined datasets from this cross and from an independent cross of strain PERA and gallstone-resistant I/Ln mice. QTL mapping detected one significant new gallstone susceptibility (Lith) locus on Chromosome 13 (Lith15). A second significant QTL on Chr 6 (Lith16) confirmed a previous QTL. Furthermore, suggestive QTLs confirmed Lith loci from previous crosses on Chromosomes 1, 2, 5, 16 and X. QTL analysis of the dataset derived from the combined crosses increased the detection power and narrowed confidence intervals of Lith loci on Chromosomes 2, 6, 13, and 16. Moreover, the analysis of combined datasets revealed a shared QTL between both crosses on Chromosome 17 (Lith9). Significantly higher mRNA expression of Abcg5 and Abcg8 in strain PERA compared with strains I/Ln and DBA/2 further substantiated that the PERA allele of Abcg5/Abcg8 was responsible for lithogenicity underlying Lith9.     

Genetic dissection of collagen-induced arthritis in Chromosome 10 quantitative trait locus speed congenic rats: evidence for more than one regulatory locus and sex influences

Rat Chromosome 10 (RNO10) harbors Cia5, a non-MHC quantitative trait locus (QTL) that regulates the severity of type II collagen-induced arthritis (CIA) in DAxF344 and DAxBN F2 rats. CIA is an animal model with many features that resemble rheumatoid arthritis. To facilitate analysis of Cia5 independently of the other CIA regulatory loci on other chromosomes, DA recombinant QTL speed congenic rats, DA.F344(Cia5), were generated. These QTL congenic rats have a large chromosomal segment containing Cia5 (interval size < or =80.1 cM) from CIA-resistant F344 rats introgressed into their genome. Phenotypic analyses of these rats for susceptibility and severity of CIA confirmed that Cia5 is an important disease-modifying locus. CIA severity was significantly lower in the Cia5 congenic rats than in DA controls. We also generated DA Cia5 speed sub-congenic rats, DA.F344(Cia5a), which had a smaller segment of the F344 genome, Cia5a, comprising only the distal q-telomeric end (interval size < or = 22.5 cM) of Cia5, introgressed into their genome. DA.F344(Cia5a) sub-congenic rats also exhibited reduced CIA disease severity compared with the parental DA rats. The regulatory effects in both congenic strains were sex influenced. The disease-ameliorating effect of the larger fragment, Cia5, was greater in males than in females, but the effect of the smaller fragment, Cia5a, was greater in females. We also present an improved genetic linkage map covering the Cia5/Cia5a region, which we have integrated with two rat radiation hybrid maps. Comparative homology analysis of this genomic region with mouse and human chromosomes was also undertaken. Regulatory loci for multiple autoimmune/inflammatory diseases in rats (RNO10), mice (MMU11), and humans (HSA17 and HSA5q23-q31) map to chromosomal segments homologous to Cia5 and Cia5a.     

Backcross and partial advanced intercross analysis of nonobese diabetic gene-mediated effects on collagen-induced arthritis reveals an interactive effect by two major loci

Genetic segregation analysis between NOD and C57BL strains have been used to identify loci associated with autoimmune disease. Only two loci (Cia2 and Cia9) had earlier been found to control development of arthritis, whereas none of the previously identified diabetes loci was of significance for arthritis. We have now made a high-powered analysis of a backcross of NOD genes on to the B10.Q strain for association with collagen-induced arthritis. We could confirm relevance of both Cia2 and Cia9 as well as the interaction between them, but we did not identify any other significant arthritis loci. Immune cellular subtyping revealed that Cia2 was also associated with the number of blood macrophages. Congenic strains of the Cia2 and Cia9 loci on the B10.Q background were made and used to establish a partial advanced intercross (PAI). Testing the PAI mice for development of collagen-induced arthritis confirmed the loci and the interactions and also indicated that at least two genes contribute to the Cia9 locus. Furthermore, it clearly showed that Cia2 is dominant protective but that the protection is not complete. Because these results may indicate that the Cia2 effect on arthritis is not only due to the deficiency of the complement C5, we analyzed complement functions in the Cia2 congenics as well as the PAI mice. These data show that not only arthritis but also C5-dependent complement activity is dominantly suppressed, confirming that C5 is one of the major genes explaining the Cia2 effect.     

Genetic linkage between a sexually selected trait and X chromosome meiotic drive

Previous studies on the stalk-eyed fly, Cyrtodiopsis dalmanni, have shown that males with long eye-stalks win contests and are preferred by females, and artificial selection on male relative eye span alters brood sex-ratios. Subsequent theory proposes that X-linked meiotic drive can catalyse the evolution of mate preferences when drive is linked to ornament genes. Here we test this prediction by mapping meiotic drive and quantitative trait loci (QTL) for eye span. To map QTL we genotyped 24 microsatellite loci using 1228 F2 flies from two crosses between lines selected for long or short eye span. The crosses differed by presence or absence of a drive X chromosome, X(D), in the parental male. Linkage analysis reveals that X(D) dramatically reduces recombination between X and X(D) chromosomes. In the X(D) cross, half of the F2 males carried the drive haplotype, produced partially elongated spermatids and female-biased broods, and had shorter eye span. The largest QTL mapped 1.3cM from drive on the X chromosome and explained 36% of the variation in male eye span while another QTL mapped to an autosomal region that suppresses drive. These results indicate that selfish genetic elements that distort the sex-ratio can influence the evolution of exaggerated traits.     

Quantitative trait loci affecting body weight and fatness from a mouse line selected for extreme high growth.

Quantitative trait loci (QTL) influencing body weight were mapped by linkage analysis in crosses between a high body weight selected line (DU6) and a control line (DUKs). The two mouse lines differ in body weight by 106% and in abdominal fat weight by 100% at 42 days. They were generated from the same base population and maintained as outbred colonies. Determination of line-specific allele frequencies at microsatellite markers spanning the genome indicated significant changes between the lines on 15 autosomes and the X chromosome. To confirm these effects, a QTL analysis was performed using structured F2 pedigrees derived from crosses of a single male from DU6 with a female from DUKs. QTL significant at the genome-wide level were mapped for body weight on chromosome 11; for abdominal fat weight on chromosomes 4, 11, and 13; for abdominal fat percentage on chromosomes 3 and 4; and for the weights of liver on chromosomes 4 and 11, of kidney on chromosomes 2 and 9, and of spleen on chromosome 11. The strong effect on body weight of the QTL on chromosome 11 was confirmed in three independent pedigrees. The effect was additive and independent of sex, accounting for 21-35% of the phenotypic variance of body weight within the corresponding F2 populations. The test for multiple QTL on chromosome 11 with combined data from all pedigrees indicated the segregation of two loci separated by 36 cM influencing body weight.     

Detection of quantitative trait loci for backfat thickness and intramuscular fat content in pigs (Sus scrofa).

In an experimental cross between Meishan and Dutch Large White and Landrace lines, 619 F(2) animals and their parents were typed for molecular markers covering the entire porcine genome. Associations were studied between these markers and two fatness traits: intramuscular fat content and backfat thickness. Association analyses were performed using interval mapping by regression under two genetic models: (1) an outbred line-cross model where the founder lines were assumed to be fixed for different QTL alleles; and (2) a half-sib model where a unique allele substitution effect was fitted within each of the 19 half-sib families. Both approaches revealed for backfat thickness a highly significant QTL on chromosome 7 and suggestive evidence for a QTL at chromosome 2. Furthermore, suggestive QTL affecting backfat thickness were detected on chromosomes 1 and 6 under the line-cross model. For intramuscular fat content the line-cross approach showed suggestive evidence for QTL on chromosomes 2, 4, and 6, whereas the half-sib analysis showed suggestive linkage for chromosomes 4 and 7. The nature of the QTL effects and assumptions underlying both models could explain discrepancies between the findings under the two models. It is concluded that both approaches can complement each other in the analysis of data from outbred line crosses.     

Quantitative trait loci that regulate plasma lipid concentration in hereditary obese KK and KK-Ay mice

To identify quantitative trait loci (QTLs) responsible for regulating plasma lipid concentration associated with obesity, linkage analysis was carried out on the 190 F2 progeny of a cross between C57BL/6J female and KK-Ay (Ay allele at the agouti locus congenic) male. In F2 a/a (agouti locus genotype) mice, two QTLs were identified on chromosome 1 and a QTL on chromosome 3 for total-cholesterol. A QTL for HDL-cholesterol was identified on chromosome 1 and a QTL for NEFA on chromosome 9. In F2 Ay/a mice, two QTLs for HDL-cholesterol were found on chromosome 1. Loci for other lipids with suggestive linkage were also identified. In both F2 mice, one QTL on chromosome 1 for total- and HDL-cholesterol was mapped near D1Mit150, in the vicinity of the apolipoprotein A-II (Apoa2) locus. Seven nucleotide substitutions out of 309 nucleotide apolipoprotein A-II cDNA sequences were identified between KK and C57BL/6J. The Ay allele may be an indication of the plasma lipid levels, but its influence was less apparent than in the case of weight control. The loci for lipids were not on identical chromosomes with those previously identified for obesity, suggesting that hyperlipidemia in KK does not coincidentally occur with obesity.     

Genetic analysis of cholesterol accumulation in inbred mice.

Genetic linkage analysis in the laboratory mouse identified chromosomal regions containing genes that contribute to cholesterol accumulation in the liver and plasma. Comparisons between five inbred strains of mice obtained from the Jackson Laboratory (DBA/2, AKR, C57BL/6, SJL, and 129P3) revealed a direct correlation between intestinal cholesterol absorption and susceptibility to diet-induced hypercholesterolemia. This correlation was lost in the F1 generation arising from crosses between high- and low-absorbing strains. Linkage analyses in AKxD recombinant inbred strains and 129xSJL129F1 N2 backcross mice identified four quantitative trait loci (QTL) that influenced Liver cholesterol accumulation (Lcho1-4) and one locus that affected Plasma cholesterol accumulation (Pcho1). These loci map to five chromosomes and, with one exception, are different from the seven QTL identified previously that influence intestinal cholesterol absorption. We conclude that a large number of genes affects the amount of cholesterol absorbed in the small intestine and its accumulation in the liver and plasma of inbred mice.     

The arthritis severity quantitative trait loci Cia4 and Cia6 regulate neutrophil migration into inflammatory sites and levels of TNF-alpha and nitric oxide

Neutrophils are required for the development of arthritis, and their migration into the synovial tissue coincides with the onset of clinical disease. Synovial neutrophil numbers also correlate with rheumatoid arthritis disease activity and severity. We hypothesized that certain arthritis severity genes regulate disease via the regulation of neutrophil migration into the joint. This hypothesis was tested in the synovial-like air pouch model injected with carrageenan using arthritis-susceptible DA and arthritis-resistant F344 rats. DA had nearly 3-fold higher numbers of exudate neutrophils compared with F344 (p < 0.001). Five DA.F344(QTL) strains congenic for severity loci and protected from autoimmune arthritis were studied. Only DA.F344(Cia4) (chromosome 7) and DA.F344(Cia6) (chromosome 8) congenics had significantly lower exudate neutrophil counts compared with DA. TNF-alpha levels were 2.5-fold higher in DA exudates as compared with F344 exudates, and that difference was accounted for by the Cia4 locus. Exudate levels of NO, a known inhibitor of neutrophil chemotaxis, were higher in F344, compared with DA, and that difference was accounted for by Cia6. This is the first time that non-MHC autoimmune arthritis loci are found to regulate three central components of the innate immune response implicated in disease pathogenesis, namely neutrophil migration into an inflammatory site, as well as exudate levels of TNF-alpha and NO. These observations underscore the importance of identifying the Cia4 and Cia6 genes, and suggest that they should generate useful novel targets for development of new therapies.     

Identification of heading date quantitative trait locus Hd6 and characterization of its epistatic interactions with Hd2 in rice using advanced backcross progeny

A backcrossed population (BC(4)F(2)) derived from a cross between a japonica rice variety, Nipponbare, as the recurrent parent and an indica rice variety, Kasalath, as the donor parent showed a long-range variation in days to heading. Quantitative trait loci (QTL) analysis revealed that two QTL, one on chromosome 3, designated Hd6, and another on chromosome 2, designated Hd7, were involved in this variation; and Hd6 was precisely mapped as a single Mendelian factor by using progeny testing (BC(4)F(3)). The nearly isogenic line with QTL (QTL-NIL) that carries the chromosomal segment from Kasalath for the Hd6 region in Nipponbare's genetic background was developed by marker-assisted selection. In a day-length treatment test, the QTL-NIL for Hd6 prominently increased days to heading under a 13.5-hr day length compared with the recurrent parent, Nipponbare, suggesting that Hd6 controls photoperiod sensitivity. QTL analysis of the F(2) population derived from a cross between the QTL-NILs revealed existence of an epistatic interaction between Hd2, which is one of the photoperiod sensitivity genes detected in a previous analysis, and Hd6. The day-length treatment tests of these QTL-NILs, including the line introgressing both Hd2 and Hd6, also indicated an epistatic interaction for photoperiod sensitivity between them.     

Mapping of quantitative trait loci for kernel row number in maize across seven environments

Genetic factors controlling quantitative inheritance of grain yield and its components have been intensively investigated during recent decades using diverse populations in maize (Zea mays L.). Notwithstanding this, quantitative trait loci (QTL) for kernel row number (KRN) with large and consistent effect have not been identified. In this study, a linkage map of 150 simple sequence repeat (SSR) loci was constructed by using a population of 500 F2 individuals derived from a cross between elite inbreds Ye478 and Dan340. The linkage map spanned a total of 1478 cM with an average interval of 10.0 cM. A total of 397 F2:3 lines were evaluated across seven diverse environments for mapping QTL for KRN. Some QTL for grain yield and its components had previously been confirmed with this population across environments. A total of 13 QTL for KRN were identified, with each QTL explaining from 3.0 to 17.9% of phenotypic variance. The gene action for KRN was mainly additive to partial dominance. A large-effect QTL (qkrn7) with partial dominance effect accounting for 17.9% of the phenotypic variation for KRN was identified on chromosome 7 near marker umc1865 with consistent gene effect across seven diverse environments. This study has laid a foundation for map-based cloning of this major QTL and for developing molecular markers for marker-assisted selection of high KRN.     

QTL underlying the resistance to soybean aphid (<Emphasis Type="Italic">Aphis glycines</Emphasis> Matsumura) through isoflavone-mediated antibiosis in soybean cultivar ‘Zhongdou 27’

Soybean aphid (Aphis glycines Matsumura) results in severe yield loss of soybean in many soybean-growing countries of the world. A few loci have been previously identified to be associated with the aphid resistance in soybean. However, none of them was via isoflavone-mediated antibiosis process. The aim of the present study was to conduct genetic analysis of aphid resistance and to identify quantitative trait loci (QTL) underlying aphid resistance in a Chinese soybean cultivar with high isoflavone content. One hundred and thirty F_5:6 derived recombinant inbred lines from the ‘Zhongdou 27’ × ‘Jiunong 20’ cross were used. Two QTL were directly associated with resistance to aphid as measured by aphid damage index. qRa_1, close to Satt470 on soybean linkage group (LG) A2 (chromosome 8), was consistently detected for 3- and 4-week ratings and explained a large portion of phenotypic variations ranging from 25 to 35%. qRa_2, close to Satt144 of LG F (chromosome 13), was detected for 3- and 4-week ratings and could explain 7 and 11% of the phenotypic variation, respectively. These two QTL were highly associated with high isoflavone content and both positive alleles were derived from ‘Zhongdou 27’, a cultivar with higher isoflavone content. The results revealed that higher individual or total isoflavones contents in soybean lines could protect soybean against aphid attack. These two QTL detected jointly provide potential for marker-assisted selection to improve the resistance of soybean cultivars to aphid along with the increase of isoflavone content.     

QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

At harvest traits such as seed weight are the sum of development and responses to stresses over the growing season and particularly during the reproductive phase of growth. The aim here was to measure quantitative trait loci (QTL) underlying the seed weight from early development to drying post harvest. One hundred forty-three F(5) derived recombinant inbred lines (RILs) developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were used for the analysis of QTL underlying mean 100-seed weight at six different developmental stages. QTL x Environment interactions (QE) were analyzed by a mixed genetic mode based on 3 years' data. At an experiment-wise threshold of a=0.05 and by single-point analysis 94 QTL unaffected by QE underlay the mean seed weight at different developmental stages. Sixty-eight QTL affected by QE that also underlay mean seed weight were identified. From the 162 QTL 42 could be located on 12 linkage groups by composite interval mapping (LOD>2.0). The numbers, locations and types of the QTL and the genetic effects were different at each developmental stage. On linkage group C2 the distantly linked QTL swC2-1, swC2-2 and swC2-3 each affected mean seed weight throughout the different developmental stages. The DNA markers linked to the QTL possessed potential for use in marker-assisted selection for soybean seed size. The identification of QTL with genetic main effects and QE interaction effects suggested that such interactions might significantly alter seed weight during seed development.     

The genetic architecture of Drosophila sensory bristle number

We have mapped quantitative trait loci (QTL) for Drosophila mechanosensory bristle number in six recombinant isogenic line (RIL) mapping populations, each of which was derived from an isogenic chromosome extracted from a line selected for high or low, sternopleural or abdominal bristle number and an isogenic wild-type chromosome. All RILs were evaluated as male and female F(1) progeny of crosses to both the selected and the wild-type parental chromosomes at three developmental temperatures (18 degrees, 25 degrees, and 28 degrees ). QTL for bristle number were mapped separately for each chromosome, trait, and environment by linkage to roo transposable element marker loci, using composite interval mapping. A total of 53 QTL were detected, of which 33 affected sternopleural bristle number, 31 affected abdominal bristle number, and 11 affected both traits. The effects of most QTL were conditional on sex (27%), temperature (14%), or both sex and temperature (30%). Epistatic interactions between QTL were also common. While many QTL mapped to the same location as candidate bristle development loci, several QTL regions did not encompass obvious candidate genes. These features are germane to evolutionary models for the maintenance of genetic variation for quantitative traits, but complicate efforts to understand the molecular genetic basis of variation for complex traits.     

The Genetic Basis of Variation in Clean Lineages of Saccharomyces cerevisiae in Response to Stresses Encountered during Bioethanol Fermentations

Saccharomyces cerevisiae is the micro-organism of choice for the conversion of monomeric sugars into bioethanol. Industrial bioethanol fermentations are intrinsically stressful environments for yeast and the adaptive protective response varies between strain backgrounds. With the aim of identifying quantitative trait loci (QTL''s) that regulate phenotypic variation, linkage analysis on six F1 crosses from four highly divergent clean lineages of S. cerevisiae was performed. Segregants from each cross were assessed for tolerance to a range of stresses encountered during industrial bioethanol fermentations. Tolerance levels within populations of F1 segregants to stress conditions differed and displayed transgressive variation. Linkage analysis resulted in the identification of QTL''s for tolerance to weak acid and osmotic stress. We tested candidate genes within loci identified by QTL using reciprocal hemizygosity analysis to ascertain their contribution to the observed phenotypic variation; this approach validated a gene (COX20) for weak acid stress and a gene (RCK2) for osmotic stress. Hemizygous transformants with a sensitive phenotype carried a COX20 allele from a weak acid sensitive parent with an alteration in its protein coding compared with other S. cerevisiae strains. RCK2 alleles reveal peptide differences between parental strains and the importance of these changes is currently being ascertained.     

Genetic dissection of ``OLETF', a rat model for non-insulin-dependent diabetes mellitus

To elucidate the genetic factors underlying non-insulin-dependent diabetes mellitus (NIDDM), we performed genome-wide quantitative trait locus (QTL) analysis, using the Otsuka Long-Evans Tokushima Fatty (OLETF) rat. The OLETF rat is an excellent animal model of NIDDM because the features of the disease closely resemble human NIDDM. Genetic dissection with two kinds of F2 intercross progeny, from matings between the OLETF rat and non-diabetic control rats F344 or BN, allowed us to identify on Chromosome (Chr) 1 a major QTL associated with features of NIDDM that was common to both crosses. We also mapped two additional significant loci, on Chrs 7 and 14, in the (OLETF × F344)F₂ cross alone, and designated these three loci as Diabetes mellitus, OLETF type Dmo 1, Dmo2 and Dmo3 respectively. With regard to suggestive QTLs, we found loci on Chrs 10, 11, and 16 that were common to both crosses, as well as loci on Chrs 5 and 12 in the (OLETF × F344)F₂ cross and on Chrs 4 and 13 in the (OLETF × BN)F₂ cross. Our results showed that NIDDM in the OLETF rat is polygenic and demonstrated that different genetic backgrounds could affect ``fitness' for QTLs and produce different phenotypic effects from the same locus. Received: 9 October 1997 / Accepted: 29 January 1998     

Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast

Several quantitative trait loci (QTL) mapping strategies can successfully identify major-effect loci, but often have poor success detecting loci with minor effects, potentially due to the confounding effects of major loci, epistasis, and limited sample sizes. To overcome such difficulties, we used a targeted backcross mapping strategy that genetically eliminated the effect of a previously identified major QTL underlying high-temperature growth (Htg) in yeast. This strategy facilitated the mapping of three novel QTL contributing to Htg of a clinically derived yeast strain. One QTL, which is linked to the previously identified major-effect QTL, was dissected, and NCS2 was identified as the causative gene. The interaction of the NCS2 QTL with the first major-effect QTL was background dependent, revealing a complex QTL architecture spanning these two linked loci. Such complex architecture suggests that more genes than can be predicted are likely to contribute to quantitative traits. The targeted backcrossing approach overcomes the difficulties posed by sample size, genetic linkage, and epistatic effects and facilitates identification of additional alleles with smaller contributions to complex traits.