High-resolution mapping of quantitative trait loci by selective recombinant genotyping

Targeted Recombinant Progeny: a design for ultra-high resolution mapping of Quantitative Trait Loci in crosses between inbred or pure lines

BACKGROUND

High-resolution mapping of the loci (QTN) responsible for genetic variation in quantitative traits is essential for positional cloning of candidate genes, and for effective marker assisted selection. The confidence interval (QTL) flanking the point estimate of QTN-location is proportional to the number of individuals in the mapping population carrying chromosomes recombinant in the given interval. Consequently, many designs for high resolution QTN mapping are based on increasing the proportion of recombinants in the mapping population. The "Targeted Recombinant Progeny" (TRP) design is a new design for high resolution mapping of a target QTN in crosses between pure, or inbred lines. It is a three-generation procedure generating a large number of recombinant individuals within a QTL previously shown to contain a QTN. This is achieved by having individuals that carry chromosomes recombinant across the target QTL interval as parents of a large mapping population; most of whom will therefore carry recombinant chromosomes targeted to the given QTL. The TRP design is particularly useful for high resolution mapping of QTN that differentiate inbred or pure lines, and hence are not amenable to high resolution mapping by genome-wide association tests.

RESULTS

In the absence of residual polygenic variation, population sizes required for achieving given mapping resolution by the TRP-F2 design relative to a standard F2 design ranged from 0.289 for a QTN with standardized allele substitution effect = 0.2, mapped to an initial QTL of 0.2 Morgan to 0.041 for equivalent QTN mapped to an initial QTL of 0.02 M. In the presence of residual polygenic variation, the relative effectiveness of the TRP design ranges from 1.068 to 0.151 for the same initial QTL intervals and QTN effect. Thus even in the presence of polygenic variation, the TRP can still provide major savings. Simulation showed that mapping by TRP should be based on 30-50 markers spanning the initial interval; and on at least 50 or more G2 families representing this number of recombination points,.

CONCLUSIONS

The TRP design can be an effective procedure for achieving high and ultra-high mapping resolution of a target QTN previously mapped to a known confidence interval (QTL).

Genetic mapping of quantitative phenotypic traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae has become a favorite production organism in industrial biotechnology presenting new challenges to yeast engineers in terms of introducing advantageous traits such as stress tolerances. Exploring subspecies diversity of S. cerevisiae has identified strains that bear industrially relevant phenotypic traits. Provided that the genetic basis of such phenotypic traits can be identified inverse engineering allows the targeted modification of production strains. Most phenotypic traits of interest in S. cerevisiae strains are quantitative, meaning that they are controlled by multiple genetic loci referred to as quantitative trait loci (QTL). A straightforward approach to identify the genetic basis of quantitative traits is QTL mapping which aims at the allocation of the genetic determinants to regions in the genome. The application of high-density oligonucleotide arrays and whole-genome re-sequencing to detect genetic variations between strains has facilitated the detection of large numbers of molecular markers thus allowing high-resolution QTL mapping over the entire genome. This review focuses on the basic principle and state of the art of QTL mapping in S. cerevisiae. Furthermore we discuss several approaches developed during the last decade that allow down-scaling of the regions identified by QTL mapping to the gene level. We also emphasize the particular challenges of QTL mapping in nonlaboratory strains of S. cerevisiae.

Combining DNA pooling with selective recombinant genotyping for increased efficiency in fine mapping

One of the key steps in positional cloning and marker-aided selection is to identify marker(s) tightly linked to the target gene (i.e., fine mapping). Selective genotyping such as selective recombinant genotyping (SRG) is commonly used in fine mapping for cost-saving. To further decrease genotyping effort and rapidly screen for tightly linked markers, we propose here a combined DNA pooling and SRG strategy. A two-stage pooled genotyping can be used for identifying recombinants between a pair of flanking markers more efficiently, and a joint use of bulked DNA analysis and two-stage pooling can also save cost for genotyping recombinants. The combined DNA pooling and SRG strategy can further be extended to fine mapping for polygenic traits. The numerical results based on hypothetical scenarios and an illustrative application to fine mapping of a mutant gene, called xl(t), in rice suggest that the proposed strategy can remarkably reduce genotyping amount compared with the conventional SRG.

Fine mapping and association analysis of a quantitative trait locus for milk production traits on Bos taurus autosome 4

To fine map a quantitative trait locus (QTL) affecting milk production traits previously associated with microsatellite RM188, we implemented an interval mapping analysis by using microsatellite markers in a large Israeli Holstein half-sib sire family, and linkage disequilibrium (LD) mapping in a large set of US Holstein bulls. Interval mapping located the target QTL to the near vicinity of RM188. For the LD mapping, we identified 42 single nucleotide polymorphisms (SNP) in 15 genes in a 12-Mb region on bovine chromosome 4. A total of 24 tag SNP were genotyped in 882 bulls belonging to the University of California Davis archival collection of Holstein bull DNA samples with predicted transmitted ability phenotypes. Marker-to-marker LD analysis revealed 2 LD blocks, with intrablock r(2) values of 0.10 and 0.46, respectively; outside the blocks, r(2) values ranged from 0.002 to 0.23. A standard additive/dominance model using the generalized linear model procedure of SAS and the regression module of HelixTree software were used to test marker-trait associations. Single nucleotide polymorphism 9 on ARL4A, SNP10 on XR_027435.1, SNP12 on ETV1, SNP21 on SNX13, and SNP24 were significantly associated with milk production traits. We propose the interval encompassing ARL4A and SNX13 genes as a candidate region in bovine chromosome 4 for a concordant QTL related to milk protein traits in dairy cattle. Functional studies are needed to confirm this result.

An optimal DNA pooling strategy for progressive fine mapping

We present a cost-effective DNA pooling strategy for fine mapping of a single Mendelian gene in controlled crosses. The theoretical argument suggests that it is potentially possible for a single-stage pooling approach to reduce the overall experimental expense considerably by balancing costs for genotyping and sample collection. Further, the genotyping burden can be reduced through multi-stage pooling. Numerical results are provided for practical guidelines. For example, the genotyping effort can be reduced to only a small fraction of that needed for individual genotyping at a small loss of estimation accuracy or at a cost of increasing sample sizes slightly when recombination rates are 0.5% or less. An optimal two-stage pooling scheme can reduce the amount of genotyping to 19.5%, 14.5% and 6.4% of individual genotyping efforts for identifying a gene within 1, 0.5, and 0.1 cM, respectively. Finally, we use a genetic data set for mapping the rice xl(t) gene to demonstrate the feasibility and efficiency of the DNA pooling strategy. Taken together, the results demonstrate that this DNA pooling strategy can greatly reduce the genotyping burden and the overall cost in fine mapping experiments.

Progressive fine mapping in experimental populations: an improved strategy toward positional cloning

Genetic mapping is one of the key steps in positional cloning. The traditional mapping strategies typically require to genotype a set of markers that are nearly evenly or randomly distributed across the genome or a region of interest. Such "grid" strategies work with low efficiency. We propose an improved mapping strategy by integrating the principle of one-dimensional optimization and information on physical map into the standard mapping procedure used in experimental populations. Computer simulations based on a set of empirical data suggest that our new procedure can reduce the number of markers required for genotyping to less than one-fourth of that of the standard procedure. An illustrative application also demonstrates a pronounced reduction of the burden in genotyping. The proposed strategy offers a quick and cost-effective access to the target gene for positional cloning without any extra expense except for making use of genomic sequence data. A Microsoft Excel spreadsheet, for performing easy calculations described in this article, is available on request from the authors.

Genetical genomics: combining gene expression with marker genotypes in poultry

Microarrays have been widely implemented across the life sciences, although there is still debate on the most effective uses of such transcriptomics approaches. In genetical genomics, gene expression measurements are treated as quantitative traits, and genome regions affecting expression levels are denoted as expression QTL (eQTL). The detected eQTL can represent a locus that lies close to the gene that is being controlled (cis-acting) or one or more loci that are unlinked to the gene that is being controlled (trans-acting). One powerful outcome of genetical genomics is the reconstruction of genetic pathways underlying complex trait variation. Because of the modest size of experiments to date, genetical genomics may fall short of its promise to unravel genetic networks. We propose to combine expression studies with fine mapping of functional trait loci. This synergistic approach facilitates the implementation of genetical genomics for species without inbred resources but is equally applicable to model species. Among livestock species, poultry is well placed to embrace this technology with the availability of the chicken genome sequence, microarrays for various platforms, as well as experimental populations in which QTL have been mapped. In the buildup toward full-blown eQTL studies, we can study the effects of known candidate genes or marked QTL at the gene expression level in more focused studies. To demonstrate the potential of genetical genomics, we have identified the cis and trans effects for a functional BW QTL on chicken chromosome 4 in breast tissue samples from chickens with contrasting QTL genotypes.

Fractioned DNA pooling: a new cost-effective strategy for fine mapping of quantitative trait loci

Selective DNA pooling (SDP) is a cost-effective means for an initial scan for linkage between marker and quantitative trait loci (QTL) in suitable populations. The method is based on scoring marker allele frequencies in DNA pools from the tails of the population trait distribution. Various analytical approaches have been proposed for QTL detection using data on multiple families with SDP analysis. This article presents a new experimental procedure, fractioned-pool design (FPD), aimed to increase the reliability of SDP mapping results, by "fractioning" the tails of the population distribution into independent subpools. FPD is a conceptual and structural modification of SDP that allows for the first time the use of permutation tests for QTL detection rather than relying on presumed asymptotic distributions of the test statistics. For situations of family and cross mapping design we propose a spectrum of new tools for QTL mapping in FPD that were previously possible only with individual genotyping. These include: joint analysis of multiple families and multiple markers across a chromosome, even when the marker loci are only partly shared among families; detection of families segregating (heterozygous) for the QTL; estimation of confidence intervals for the QTL position; and analysis of multiple-linked QTL. These new advantages are of special importance for pooling analysis with SNP chips. Combining SNP microarray analysis with DNA pooling can dramatically reduce the cost of screening large numbers of SNPs on large samples, making chip technology readily applicable for genomewide association mapping in humans and farm animals. This extension, however, will require additional, nontrivial, development of FPD analytical tools.

Natural and artificially induced genetic variability in crop and model plant species for plant systems biology

The sequencing of plant genomes which was completed a few years ago for Arabidopsis thaliana and Oryza sativa is currently underway for numerous crop plants of commercial value such as maize, poplar, tomato grape or tobacco. In addition, hundreds of thousands of expressed sequence tags (ESTs) are publicly available that may well represent 40-60% of the genes present in plant genomes. Despite its importance for life sciences, genome information is only an initial step towards understanding gene function (functional genomics) and deciphering the complex relationships between individual genes in the framework of gene networks. In this chapter we introduce and discuss means of generating and identifying genetic diversity, i.e., means to genetically perturb a biological system and to subsequently analyse the systems response, e.g., the changes in plant morphology and chemical composition. Generating and identifying genetic diversity is in its own right a highly powerful resource of information and is established as an invaluable tool for systems biology.

High-resolution quantitative trait locus analysis reveals multiple diabetes susceptibility loci mapped to intervals<800 kb in the species-conserved Niddm1i of the GK rat

Niddm1i, a 16-Mb locus within the major diabetes QTL in the diabetic GK rat, causes impaired glucose tolerance in the congenic NIDDM1I strain. Niddm1i is homologous to both human and mouse regions linked with type 2 diabetes susceptibility. We employed multiple QTL analyses of congenic F2 progeny selected for one recombination event within Niddm1i combined with characterization of subcongenic strains. Fine mapping located one hyperglycemia locus within 700 kb (Niddm1i4, P=5x10(-6)). Two adjacent loci were also detected, and the GK allele at Niddm1i2 (500 kb) showed a glucose-raising effect, whereas it had a glucose-lowering effect at Niddm1i3 (400 kb). Most proximally, Niddm1i1 (800 kb) affecting body weight was identified. Experimental data from subcongenics supported the four loci. Sorcs1, one of the two known diabetes susceptibility genes in the region, resides within Niddm1i3, while Tcf7l2 maps outside all four loci. Multiple-marker QTL analysis incorporating the effect of cosegregating QTL as cofactors together with genetically selected progeny can remarkably enhance resolution of QTL. The data demonstrate that the species-conserved Niddm1i is a composite of at least four QTL affecting type 2 diabetes susceptibility and that two adjacent QTL (Niddm1i2GK and Niddm1i3GK) act in opposite directions.

High-resolution quantitative trait locus mapping reveals sign epistasis controlling ovariole number between two Drosophila species

Identifying the genes underlying genetically complex traits is of fundamental importance for medicine, agriculture, and evolutionary biology. However, the level of resolution offered by traditional quantitative trait locus (QTL) mapping is usually coarse. We analyze here a trait closely related to fitness, ovariole number. Our initial interspecific mapping between Drosophila sechellia (8 ovarioles/ovary) and D. simulans (15 ovarioles/ovary) identified a major QTL on chromosome 3 and a minor QTL on chromosome 2. To refine the position of the major QTL, we selected 1038 additional recombinants in the region of interest using flanking morphological markers (selective phenotyping). This effort generated approximately one recombination event per gene and increased the mapping resolution by approximately seven times. Our study thus shows that using visible markers to select for recombinants can efficiently increase the resolution of QTL mapping. We resolved the major QTL into two epistatic QTL, QTL3a and QTL3b. QTL3a shows sign epistasis: it has opposite effects in two different genetic backgrounds, the presence vs. the absence of the QTL3b D. sechellia allele. This property of QTL3a allows us to reconstruct the probable order of fixation of the QTL alleles during evolution.

Genetic dissection of proteinuria in the Sabra rat

The pathophysiology underlying proteinuria remains incompletely understood and warrants further research. We currently initiated the investigation of the genetic basis of proteinuria in the Sabra rat, a model of salt susceptibility that we showed previously to be also a model of spontaneous proteinuria that is unrelated to salt loading or development of hypertension. We applied the total genome scan strategy in 75 F2male animals derived from a cross between SBH/y, which are prone to develop proteinuria, and SBN/y, which are relatively resistant to the development of proteinuria. Animals were subjected to uninephrectomy (UNx) to accelerate the development of proteinuria and were provided chow with a low salt content, thus avoiding the development of hypertension. Urinary protein excretion was monitored before UNx and monthly thereafter for 8 mo. The genotype of F2was determined with microsatellite markers. The data were analyzed for cosegregation by ANOVA and for genetic linkage with a novel multifaceted statistical genetic paradigm. We detected three proteinuria-related quantitative trait loci (QTL) that were associated with the salt sensitivity (H) alleles from SBH/y: SUP2, SUP17, and SUP20 on rat chromosomes (Chr) 2, 17, and 20. We detected an additional QTL on Chr 3, SUP3, that was associated with the salt resistance (N) alleles from SBN/y. A temporal effect was noted: QTL SUP2 and SUP17 surfaced at months 7–8, QTL SUP20 at months 6–8, and QTL SUP3 at months 5–6. The QTL emerging from this study lead us a step closer to identifying the genes associated with and elucidating the pathophysiology of proteinuria.

Selection bias in quantitative trait loci mapping

A simulation study was performed to see whether selection affected quantitative trait loci (QTL) mapping. Populations under random selection, under selection among full-sib families, and under selection within a full-sib family were simulated each with heritability of 0.3, 0.5, and 0.7. They were analyzed with the marker spacing of 10 cM and 20 cM. The accuracy for QTL detection decreased for the populations under selection within full-sib family. Estimates of QTL effects and positions differed (P < .05) from their input values. The problems could be ignored when mapping a QTL for the populations under selection among full-sib families. A large heritability helped reduction of such problems. When the animals were selected within a full-sib family, the QTL was detected for the populations with heritability of 0.5 or larger using the marker spacing of 10 cM, and with heritability of 0.7 using the marker spacing of 20 cM. This study implied that when selection was introduced, the accuracy for QTL detection decreased and the estimates of QTL effects were biased. A caution was warranted on the decision of data (including selected animals to be genotyped) for QTL mapping.

Improving quantitative trait loci mapping resolution in experimental crosses by the use of genotypically selected samples

One of the key factors contributing to the success of a quantitative trait locus (QTL) mapping experiment is the precision with which QTL positions can be estimated. We show, using simulations, that QTL mapping precision for an experimental cross can be increased by the use of a genotypically selected sample of individuals rather than an unselected sample of the same size. Selection is performed using a previously described method that optimizes the complementarity of the crossover sites within the sample. Although the increase in precision is accompanied by a decrease in QTL detection power at markers distant from QTL, only a modest increase in marker density is needed to obtain equivalent power over the whole map. Selected samples also show a slight reduction in the number of false-positive QTL. We find that two features of selected samples independently contribute to these effects: an increase in the number of crossover sites and increased evenness in crossover spacing. We provide an empirical formula for crossover enrichment in selected samples that is useful in experimental design and data analysis. For QTL studies in which the phenotyping is more of a limiting factor than the generation of individuals and the scoring of genotypes, selective sampling is an attractive strategy for increasing genome-wide QTL map resolution.

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.

Mapping Modifiers Affecting Muscularity of the Myostatin Mutant (MstnCmpt-dl1Abc) Compact Mouse

The hypermuscular Compact phenotype was first noted in a line of mice selected for high body weight and protein content. A new line, based on mice showing the Compact phenotype, was formed and selected for maximum expression of the Compact phenotype. Previously we mapped and identified a 12-bp deletion in the myostatin gene, denoted MstnCmpt-dl1Abc, which can be considered as a major gene responsible for the hypermuscular phenotype. Genetic analysis revealed that full expression of the hypermuscular phenotype requires the action of modifier loci in addition to MstnCmpt-dl1Abc. To map these modifier loci, an interspecific F2 population was generated between Comp9, an inbred line homozygous for MstnCmpt-dl1Abc, and CAST/Ei, an inbred line generated from Mus musculus castaneus. Selective DNA pooling and genotyping, separately by gender, was carried out within a subpopulation of the F2 consisting of individuals homozygous for MstnCmpt-dl1Abc. Significant association with hypermuscularity at a false discovery rate (FDR) of 0.05 was found for markers on chromosomes 3, 5, 7, 11, 16, and X. In all cases, the marker allele derived from the Comp9 parent showed a higher frequency in the hypermuscular group and the CAST/Ei allele in the normal group. The modifier loci apparently exerted their effects on muscularity only in the presence of MstnCmpt-dl1Abc.