Quantitative trait locus study design from an information perspective

We examine the efficiency of different genotyping and phenotyping strategies in inbred line crosses from an information perspective. This provides a mathematical framework for the statistical aspects of QTL experimental design, while guiding our intuition. Our central result is a simple formula that quantifies the fraction of missing information of any genotyping strategy in a backcross. It includes the special case of selectively genotyping only the phenotypic extreme individuals. The formula is a function of the square of the phenotype and the uncertainty in our knowledge of the genotypes at a locus. This result is used to answer a variety of questions. First, we examine the cost-information trade-off varying the density of markers and the proportion of extreme phenotypic individuals genotyped. Then we evaluate the information content of selective phenotyping designs and the impact of measurement error in phenotyping. A simple formula quantifies the information content of any combined phenotyping and genotyping design. We extend our results to cover multigenotype crosses, such as the F(2) intercross, and multiple QTL models. We find that when the QTL effect is small, any contrast in a multigenotype cross benefits from selective genotyping in the same manner as in a backcross. The benefit remains in the presence of a second unlinked QTL with small effect (explaining <20% of the variance), but diminishes if the second QTL has a large effect. Software for performing power calculations for backcross and F(2) intercross incorporating selective genotyping and marker spacing is available from http://www.biostat.ucsf.edu/sen.     

QTL detection with bidirectional and unidirectional selective genotyping: marker-based and trait-based analyses

Selective genotyping of one or both phenotypic extremes of a population can be used to detect linkage between markers and quantitative trait loci (QTL) in situations in which full-population genotyping is too costly or not feasible, or where the objective is to rapidly screen large numbers of potential donors for useful alleles with large effects. Data may be subjected to 'trait-based' analysis, in which marker allele frequencies are compared between classes of progeny defined based on trait values, or to 'marker-based' analysis, in which trait means are compared between progeny classes defined based on marker genotypes. Here, bidirectional and unidirectional selective genotyping were simulated, using population sizes and selection intensities relevant to cereal breeding. Control of Type I error was usually adequate with marker-based analysis of variance or trait-based testing using the normal approximation of the binomial distribution. Bidirectional selective genotyping was more powerful than unidirectional. Trait-based analysis and marker-based analysis of variance were about equally powerful. With genotyping of the best 30 out of 500 lines (6%), a QTL explaining 15% of the phenotypic variance could be detected with a power of 0.8 when tests were conducted at a marker 10 cM from the QTL. With bidirectional selective genotyping, QTL with smaller effects and (or) QTL farther from the nearest marker could be detected. Similar QTL detection approaches were applied to data from a population of 436 recombinant inbred rice lines segregating for a large-effect QTL affecting grain yield under drought stress. That QTL was reliably detected by genotyping as few as 20 selected lines (4.5%). In experimental populations, selective genotyping can reduce costs of QTL detection, allowing larger numbers of potential donors to be screened for useful alleles with effects across different backgrounds. In plant breeding programs, selective genotyping can make it possible to detect QTL using even a limited number of progeny that have been retained after selection.     

Joint linkage QTL analyses for partial resistance to Phytophthora sojae in soybean using six nested inbred populations with heterogeneous conditions

Partial resistance to Phytophthora sojae in soybean is controlled by multiple quantitative trait loci (QTL). With traditional QTL mapping approaches, power to detect such QTL, frequently of small effect, can be limited by population size. Joint linkage QTL analysis of nested recombinant inbred line (RIL) populations provides improved power to detect QTL through increased population size, recombination, and allelic diversity. However, uniform development and phenotyping of multiple RIL populations can prove difficult. In this study, the effectiveness of joint linkage QTL analysis was evaluated on combinations of two to six nested RIL populations differing in inbreeding generation, phenotypic assay method, and/or marker set used in genotyping. In comparison to linkage analysis in a single population, identification of QTL by joint linkage analysis was only minimally affected by different phenotypic methods used among populations once phenotypic data were standardized. In contrast, genotyping of populations with only partially overlapping sets of markers had a marked negative effect on QTL detection by joint linkage analysis. In total, 16 genetic regions with QTL for partial resistance against P. sojae were identified, including four novel QTL on chromosomes 4, 9, 12, and 16, as well as significant genotype-by-isolate interactions. Resistance alleles from PI 427106 or PI 427105B contributed to a major QTL on chromosome 18, explaining 10–45 % of the phenotypic variance. This case study provides guidance on the application of joint linkage QTL analysis of data collected from populations with heterogeneous assay conditions and a genetic framework for partial resistance to P. sojae.     

Identification of quantitative trait loci for prolificacy and growth in mice

Marker–quantitative trait locus (QTL) linkage was evaluated in F₂ intercross and backcross mouse populations derived from stocks differing dramatically in prolificacy and mature weight. A highly prolific outbred Quackenbush-Swiss mouse line, or an inbred line derived from it (16.62 ± 0.22 and 14.64 ± 0.27 pups per litter, respectively) were used as one of the grandparents in these populations. The less prolific C57BL/6J inbred mouse line (6.67 ± 0.37 pups per litter) was used as the other grandparent. Linkage was evaluated in a three-step process that involved selective genotyping of F₂ intercross progeny representing extremes for prolificacy, genotyping of the full F₂ for chromosomal regions potentially associated with prolificacy, and genotyping of the backcross for genomic regions significantly associated with prolificacy in the F₂. Segments of Chromosomes (Chrs) 2 and 4 were significantly (P < 0.05, experiment-wise error rate) associated with prolificacy, and LOD scores suggestive of linkage were observed for litter size on Chr 9 and growth on Chrs 4 and 11. Existence of growth QTL was also supported by marker effects that were significant (P < 0.05) or approaching significance (P < 0.10) in the backcross. Additive litter size QTL effects ranged from 0.56 to 0.79 pups per litter, and dominance deviations ranged from −0.56 to 1.19 pups per litter, suggesting overdominance as a possible mode of gene action in some cases. The observation of pleiotropic or linked QTL for growth and prolificacy corresponds well with results from many selection experiments identifying positively correlated responses to selection for these two traits. Received: 9 August 1997 / Accepted: 30 September 1997     

Strategies to optimize marker-assisted introgression of multiple unlinked QTL

To optimize designs to implement marker-assisted introgression programs aiming to introgress three unlinked quantitative trait loci (QTL), the present paper studies different alternatives versus a traditional backcross or intercross phase. Four alternative backcross strategies appear to be more advantageous by having 50% less genotyping load than a traditional backcross strategy tracking all three QTL at a time through a single line. A multiplication phase following the selection of homozygous animals at the three QTL as an intercross alternative allows doubling of the number of homozygous animals in a mouse model compared with the first intercross generation. Within the same model, a second intercross alternative with individuals carrying all three QTL at the first intercross results in a 12-fold increase in the number of homozygous animals obtained in the first intercross generation. The same ranges of decrease are observed in the number of animals to be genotyped and the number of genotypings when targeting a fixed number of homozygous animals. An option, with two lines each carrying two QTL through the backcross phase and coupled with the second intercross alternative, appears to be the best introgression alternative. This option requires 76% fewer genotypings, 68% fewer animals to be genotyped, and costs 75% less than an option in which all three QTL are introgressed through a single line. Received: 9 August 1999 / Accepted: 25 October 1999     

Fatness QTL on chicken chromosome 5 and interaction with sex

Quantitative trait loci (QTL) affecting fatness in male chickens were previously identified on chromosome 5 (GGA5) in a three-generation design derived from two experimental chicken lines divergently selected for abdominal fat weight. A new design, established from the same pure lines, produced 407 F2 progenies (males and females) from 4 F1-sire families. Body weight and abdominal fat were measured on the F₂ at 9 wk of age. In each sire family, selective genotyping was carried out for 48 extreme individuals for abdominal fat using seven microsatellite markers from GGA5. QTL analyses confirmed the presence of QTL for fatness on GGA5 and identified a QTL by sex interaction. By crossing one F1 sire heterozygous at the QTL with lean line dams, three recombinant backcross 1 (BC1) males were produced and their QTL genotypes were assessed in backcross 2 (BC2) progenies. These results confirmed the QTL by sex interaction identified in the F2 generation and they allow mapping of the female QTL to less than 8 Mb at the distal part of the GGA5. They also indicate that fat QTL alleles were segregating in both fat and lean lines.     

Detection of Obesity QTLs on Mouse Chromosomes 1 and 7 by Selective DNA Pooling

The inheritance of obesity has been analyzed in an intercross between the lean 129/Sv mouse strain and the obesity-prone EL/Suz mouse strain. The weights of three major fat pads were determined on 4-month-old mice, and the sum of these weights, divided by body weight, was used as an adiposity index. The strategy of selective DNA pooling was used as a primary screen to identify putative quantitative trait loci (QTLs) affecting adiposity index. DNA pools representing the leanest 15% and fattest 15% of the F2 progeny were compared for differential allelic enrichment using widely dispersed microsatellite variants. To evaluate putative QTLs, individual genotyping and interval mapping were employed to estimate QTL effects and assess statistical significance. One QTL affecting adiposity index, which accounted for 12.3% of phenotypic variance in gender-merged data, was mapped to the central region of Chromosome (Chr) 7. The QTL allele inherited from EL conferred increased adiposity. A second QTL that accounts for 6.3% of phenotypic variance was identified on Chr 1 nearD1Mit211.At both QTLs, the data are consistent with dominant inheritance of the allele contributing to obesity. The possible relationships between these QTLs and previously described obesity QTLs, major obesity mutations, and candidate genes are discussed.     

R/qtlDesign: inbred line cross experimental design

An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at .     

Simultaneous improvement and genetic dissection of grain yield and its related traits in a backbone parent of hybrid rice (Oryza sativa L.) using selective introgression

Three populations with a total of 125 BC2F3:4 introgression lines (ILs) selected for high yields from three BC2F2 populations were used for genetic dissection of rice yield and its related traits. The progeny testing in replicated phenotyping across two environments and genotyping with 140 polymorphic simple sequence repeat markers allowed the identification of 21 promising ILs that had significantly higher yields than the recurrent parent Shuhui527 (SH527). A total of 94 quantitative trait loci (QTL) were identified using the selective introgression method based on Chi-squared (χ ²) and multi-locus probability tests and the RSTEP-LRT method based on stepwise regression. These QTL were mostly mapped to 12 clusters on seven rice chromosomes. Several important properties of the QTL affecting grain yield (GY) and its related traits were revealed. The first one was the presence of strong and frequent non-random associations between or among QTL that affect low-heritability traits (GY and spikelet number per panicle, SN) in the ILs with high trait values. Second, beneficial alleles at 88.9 % GY and 75 % SN QTL for increased productivity were from the donors, suggesting that direct phenotypic selection for high yield in our introgression breeding program was a powerful way to transfer beneficial alleles at many loci from the donors into SH527. Third, most QTL were in clusters with large effects on multiple traits, which should be the focal points in further investigations and marker-assisted selection in rice. The majority of the QTL identified were expressed only in one of the environments, suggesting that differential expression of QTL in different environments is the primary genetic basis of genotype × environment interaction. Finally, a large variation in both the direction and magnitude of QTL effects was detected for different donor alleles at seven QTL in the same genetic background and environments. This finding suggests the possible presence of functional diversity among the donor alleles at these loci. The promising ILs and QTL identified provide valuable materials and genetic information for further improving the yield potential of SH527, which is a backbone restorer of hybrid rice in China.     

Detection of marker–QTL associations by studying change in marker frequencies with selection

The value of selective genotyping for the detection of QTL has already been studied from a theoretical point of view but with the assumption of a negligible contribution of the QTL to the phenotypic variance. For predicting change in gene frequency, we show that this assumption is only valid for less than 0.05 and for a proportion selected higher than 1%. Therefore, we develop a study of the optimization of selective genotyping without assumption on QTL effect, with selection either of both tails (bidirectional genotyping or BSG) or only one tail (unidirectional genotyping or USG). For a given population size of phenotyped plants the optimal proportion selected for selective genotyping is around 30% for each tail. For the same investment as in ANOVA, by investing more in phenotyping than in genotyping when the cost ratio of genotyping to phenotyping is higher than 1, the optimal proportion selected appears to be between 10 and 20% for each tail. It is mainly affected by the cost ratio and decreases when the cost ratio increases. At this optimum, BSG is competitive with ANOVA, or even more powerful, when the cost ratio is higher than 1. USG can also be competitive when the cost ratio is higher than 2. Using experimental data from two populations of about 300 F4 inbred families of maize, it was verified that BSG at the optimum gives the same results as ANOVA or is better whereas USG is less powerful or equivalent.     

The effects of selective genotyping on estimates of proportion of recombination between linked quantitative trait loci

Selective genotyping is the marker assay of only the more extreme phenotypes for a quantitative trait and is intended to increase the efficiency of quantitative trait loci (QTL) mapping. We show that selective genotyping can bias estimates of the recombination frequency between linked QTLs — upwardly when QTLs are in repulsion phase, and downwardly when QTLs are in coupling phase. We examined these biases under simple models involving two QTLs segregating in a backcross or F₂ population, using both analytical models and computer simulations. We found that bias is a function of the proportion selected, the magnitude of QTL effects, distance between QTLs and the dominance of QTLs. Selective genotyping thus may decrease the power of mapping multiple linked QTLs and bias the construction of a marker map. We suggest a large proportion than previously suggested (50%) or the entire population be genotyped if linked QTLs of large effects (explain > 10% phenotypic variance) are evident. New models need to be developed to explicitly incorporate selection into QTL map construction.     

Quantitative trait locus and haplotype mapping in closely related inbred strains identifies a locus for open field behavior

Quantitative trait locus (QTL) mapping in the mouse typically utilizes inbred strains that exhibit significant genetic and phenotypic diversity. The development of dense SNP panels in a large number of inbred strains has eliminated the need to maximize genetic diversity in QTL studies as plenty of SNP markers are now available for almost any combination of strains. We conducted a QTL mapping experiment using both a backcross (N₂) and an intercross (F₂) between two genetically similar inbred mouse strains: C57BL/6J (B6) and C57L/J (C57). A set of additive QTLs for activity behaviors was identified on Chrs 1, 9, 13, and 15. We also identified additive QTLs for anxiety-related behaviors on Chrs 7, 9, and 16. A QTL on Chr 11 is sex-specific, and we revealed pairwise interactions between QTLs on Chrs 1 and 13 and Chrs 10 and 18. The Chr 9 activity QTL accounts for the largest amount of phenotypic variance and was not present in our recent analysis of a B6 × C58/J (C58) intercross (Bailey et al. in Genes Brain Behav 7:761–769, 2008). To narrow this QTL interval, we used a dense SNP haplotype map with over 7 million real and imputed SNP markers across 74 inbred mouse strains (Szatkiewicz et al. in Mamm Genome 19(3):199–208, 2008). Evaluation of shared and divergent haplotype blocks among B6, C57, and C58 strains narrowed the Chr 9 QTL interval considerably and highlights the utility of QTL mapping in closely related inbred strains.     

Detection of a quantitative trait locus for both foliage and tuber resistance to late blight [Phytophthora infestans (Mont.) de Bary] on chromosome 4 of a dihaploid potato clone (Solanum tuberosum subsp. tuberosum)

Linkage analysis, Kruskal–Wallis analysis, interval mapping and graphical genotyping were performed on a potato diploid backcross family comprising 120 clones segregating for resistance to late blight. A hybrid between the Solanum tuberosum dihaploid clone PDH247 and the long-day-adapted S. phureja clone DB226(70) had been crossed to DB226(70) to produce the backcross family. Eighteen AFLP primer combinations provided 186 and 123 informative maternal and paternal markers respectively, with 63 markers in common to both parents. Eleven microsatellite (SSR) markers proved useful for identifying chromosomes. Linkage maps of both backcross parents were constructed. The results of a Kruskal–Wallis analysis, interval mapping and graphical genotyping were all consistent with a QTL or QTLs for blight resistance between two AFLP markers 30 cM apart on chromosome 4, which was identified by a microsatellite marker. The simplest explanation of the results is a single QTL with an allele from the dihaploid parent conferring resistance to race 1, 4 of P. infestans in the foliage in the glasshouse and to race 1, 2, 3, 4, 6, 7 in the foliage in the field and in tubers from glasshouse raised plants. The QTL was of large effect, and explained 78 and 51% of the variation in phenotypic scores for foliage blight in the glasshouse and field respectively, as well as 27% of the variation in tuber blight. Graphical genotyping and the differences in blight scores between the parental clones showed that all of the foliage blight resistance is accounted for by chromosome 4, whereas undetected QTLs for tuber resistance probably exist on other chromosomes. Graphical genotyping also explained the lack of precision in mapping the QTL(s) in terms of lack of appropriate recombinant chromosomes.     

Efficiency of selective genotyping for genetic analysis of complex traits and potential applications in crop improvement

Selective genotyping of individuals from the two tails of the phenotypic distribution of a population provides a cost efficient alternative to analysis of the entire population for genetic mapping. Past applications of this approach have been confounded by the small size of entire and tail populations, and insufficient marker density, which result in a high probability of false positives in the detection of quantitative trait loci (QTL). We studied the effect of these factors on the power of QTL detection by simulation of mapping experiments using population sizes of up to 3,000 individuals and tail population sizes of various proportions, and marker densities up to one marker per centiMorgan using complex genetic models including QTL linkage and epistasis. The results indicate that QTL mapping based on selective genotyping is more powerful than simple interval mapping but less powerful than inclusive composite interval mapping. Selective genotyping can be used, along with pooled DNA analysis, to replace genotyping the entire population, for mapping QTL with relatively small effects, as well as linked and interacting QTL. Using diverse germplasm including all available genetics and breeding materials, it is theoretically possible to develop an “all-in-one plate” approach where one 384-well plate could be designed to map almost all agronomic traits of importance in a crop species. Selective genotyping can also be used for genomewide association mapping where it can be integrated with selective phenotyping approaches. We also propose a breeding-to-genetics approach, which starts with identification of extreme phenotypes from segregating populations generated from multiple parental lines and is followed by rapid discovery of individual genes and combinations of gene effects together with simultaneous manipulation in breeding programs.     

Interval mapping of quantitative trait loci with selective DNA pooling data

Selective DNA pooling is an efficient method to identify chromosomal regions that harbor quantitative trait loci (QTL) by comparing marker allele frequencies in pooled DNA from phenotypically extreme individuals. Currently used single marker analysis methods can detect linkage of markers to a QTL but do not provide separate estimates of QTL position and effect, nor do they utilize the joint information from multiple markers. In this study, two interval mapping methods for analysis of selective DNA pooling data were developed and evaluated. One was based on least squares regression (LS-pool) and the other on approximate maximum likelihood (ML-pool). Both methods simultaneously utilize information from multiple markers and multiple families and can be applied to different family structures (half-sib, F2 cross and backcross). The results from these two interval mapping methods were compared with results from single marker analysis by simulation. The results indicate that both LS-pool and ML-pool provided greater power to detect the QTL than single marker analysis. They also provide separate estimates of QTL location and effect. With large family sizes, both LS-pool and ML-pool provided similar power and estimates of QTL location and effect as selective genotyping. With small family sizes, however, the LS-pool method resulted in severely biased estimates of QTL location for distal QTL but this bias was reduced with the ML-pool.     

Volume measures for linkage disequilibrium

Background

A major QTL for fatness and growth, denoted FAT1, has previously been detected on pig chromosome 4q (SSC4q) using a Large White – wild boar intercross. Progeny that carried the wild boar allele at this locus had higher fat deposition, shorter length of carcass, and reduced growth. The position and the estimated effects of the FAT1 QTL for growth and fatness have been confirmed in a previous study. In order to narrow down the QTL interval we have traced the inheritance of the wild boar allele associated with high fat deposition through six additional backcross generations.

Results

Progeny-testing was used to determine the QTL genotype for 10 backcross sires being heterozygous for different parts of the broad FAT1 region. The statistical analysis revealed that five of the sires were segregating at the QTL, two were negative while the data for three sires were inconclusive. We could confirm the QTL effects on fatness/meat content traits but not for the growth traits implying that growth and fatness are controlled by distinct QTLs on chromosome 4. Two of the segregating sires showed highly significant QTL effects that were as large as previously observed in the F₂ generation. The estimates for the remaining three sires, which were all heterozygous for smaller fragments of the actual region, were markedly smaller. With the sample sizes used in the present study we cannot with great confidence determine whether these smaller effects in some sires are due to chance deviations, epistatic interactions or whether FAT1 is composed of two or more QTLs, each one with a smaller phenotypic effect. Under the assumption of a single locus, the critical region for FAT1 has been reduced to a 3.3 cM interval between the RXRG and SDHC loci.

Conclusion

We have further characterized the FAT1 QTL on pig chromosome 4 and refined its map position considerably, from a QTL interval of 70 cM to a maximum region of 20 cM and a probable region as small as 3.3 cM. The flanking markers for the small region are RXRG and SDHC and the orthologous region of FAT1 in the human genome is located on HSA1q23.3 and harbors approximately 20 genes. Our strategy to further refine the map position of this major QTL will be i) to type new markers in our pigs that are recombinant in the QTL interval and ii) to perform Identity-By-Descent (IBD) mapping across breeds that have been strongly selected for lean growth.     

A New Simple Method for Improving QTL Mapping Under Selective Genotyping

The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitted in the model for QTL analysis. Compared to the existing methods, the main feature of our model is that we first provide a simple way for obtaining the distribution of QTL genotypes for the ungenotyped individuals and then use it, rather than the population distribution of QTL genotypes as in the existing methods, to fit the ungenotyped individuals in model construction. Another feature is that the proposed method is developed on the basis of a multiple-QTL model and has a simple estimation procedure similar to that for complete genotyping. As a result, the proposed method has the ability to provide better QTL resolution, analyze QTL epistasis, and tackle multiple QTL problem under selective genotyping. In addition, a truncated normal mixture model based on a multiple-QTL model is developed when only the genotyped individuals are considered in the analysis, so that the two different types of models can be compared and investigated in selective genotyping. The issue in determining threshold values for selective genotyping in QTL mapping is also discussed. Simulation studies are performed to evaluate the proposed methods, compare the different models, and study the QTL mapping properties in selective genotyping. The results show that the proposed method can provide greater QTL detection power and facilitate QTL mapping for selective genotyping. Also, selective genotyping using larger genotyping proportions may provide roughly equivalent power to complete genotyping and that using smaller genotyping proportions has difficulties doing so. The R code of our proposed method is available on http://www.stat.sinica.edu.tw/chkao/.     

Selective genotyping with epistasis can be utilized for a major quantitative trait locus mapping in hypertension in rats

Epistasis used to be considered an obstacle in mapping quantitative trait loci (QTL) despite its significance. Numerous epistases have proved to be involved in quantitative genetics. We established a backcross model that demonstrates a major QTL for hypertension (Ht). Seventy-eight backcrossed rats (BC), derived from spontaneously hypertensive rats (SHR) and normotensive Fischer 344 rats, showed bimodal distribution of systolic blood pressure (BP) values and a phenotypic segregation ratio consistent with 1:1. In this backcross analysis, sarco(endo)plasmic reticulum Ca(2+)-dependent ATPase (Serca) II heterozygotes showed widespread bimodality in frequency distribution of BP values and obviously demonstrated Ht. First, in genome-wide screening, Mapmaker/QTL analysis mapped Ht at a locus between D1Mgh8 and D1Mit4 near Sa in all 78 BC. The peak logarithm of the odds (LOD) score reached 5.3. Second, Serca II heterozygous and homozygous BC were analyzed separately using Mapmaker/QTL. In the 35 Serca II heterozygous BC, the peak LOD score was 3.8 at the same locus whereas it did not reach statistical significance in the 43 Serca II homozygotes. Third, to map Ht efficiently, we selected 18 Serca II heterozygous BC with 9 highest and 9 lowest BP values. In these 18 BC, the peak LOD score reached 8.1. In 17 of the 18, D1Mgh8 genotypes (homo or hetero) qualitatively cosegregated with BP phenotypes (high or low) (P < 0.0001, by chi-square analysis). In conclusion, selective genotyping with epistasis can be utilized for a major QTL mapping near Sa on chromosome 1 in SHR.     

Genetic Modifiers ofLeprAssociated with Variability in Insulin Production and Susceptibility to NIDDM

In an attempt to identify the genetic basis for susceptibility to non-insulin-dependent diabetes mellitus within the context of obesity, we generated 401 genetically obeseLepr^fa/Lepr^faF2 WKY13M intercross rats that demonstrated wide variation in multiple phenotypic measures related to diabetes, including plasma glucose concentration, percentage of glycosylated hemoglobin, plasma insulin concentration, and pancreatic islet morphology. Using selective genotyping genome scanning approaches, we have identified three quantitative trait loci (QTLs) on Chr. 1 (LOD 7.1 for pancreatic morpholology), Chr. 12 (LOD 5.1 for body mass index and LOD 3.4 for plasma glucose concentration), and Chr. 16 (P< 0.001 for genotype effect on plasma glucose concentration). The obese F2 progeny demonstrated sexual dimorphism for these traits, with increased diabetes susceptibility in the males appearing at approximately 6 weeks of age, as sexual maturation occurred. For each of the QTLs, the linked phenotypes demonstrated sexual dimorphism (more severe affection in males). The QTL on Chr. 1 maps to a region vicinal to that previously linked to adiposity in studies of diabetes susceptibility in the nonobese Goto–Kakizaki rat, which is genetically closely related to the Wistar counterstrain we employed. Several candidate genes, including tubby (tub), multigenic obesity 1 (Mob1), adult obesity and diabetes (Ad), and insulin-like growth factor-2 (Igf2), map to murine regions homologous to the QTL region identified on rat Chr. 1.