Binary Trait Mapping in Experimental Crosses With Selective Genotyping

Selective genotyping is an efficient strategy for mapping quantitative trait loci. For binary traits, where there are only two distinct phenotypic values (e.g., affected/unaffected or present/absent), one may consider selective genotyping of affected individuals, while genotyping none or only some of the unaffecteds. If selective genotyping of this sort is employed, the usual method for binary trait mapping, which considers phenotypes conditional on genotypes, cannot be used. We present an alternative approach, instead considering genotypes conditional on phenotypes, and compare this to the more standard method of analysis, both analytically and by example. For studies of rare binary phenotypes, we recommend performing an initial genome scan with all affected individuals and an equal number of unaffecteds, followed by genotyping the full cross in genomic regions of interest to confirm results from the initial screen.WE consider the problem of mapping genetic loci contributing to a binary trait in an experimental cross with selective genotyping. There are two clear approaches for linkage analysis with a binary trait. Typically, we compare the proportion of affected individuals across genotype groups (Xu and Atchley 1996). Alternatively, we can compare genotype frequencies between affected and unaffected individuals, similar to Henshall and Goddard (1999). Beyond these two basic approaches, binary trait mapping has seen fundamental advances in regression models (McIntyre et al. 2001; Deng et al. 2006), extensions to multiple-QTL mapping (Coffman et al. 2005; Chen and Liu 2009), and the development of Bayesian algorithms (Yi and Xu 2000; Huang et al. 2007). However, the original data structure and approach have remained intact. Existing methods for binary trait mapping largely require the availability of genotype and phenotype data for a representative sample of both affected and unaffected individuals, and we have not yet seen a well-developed framework for binary trait mapping in the presence of selective genotyping.It is not uncommon to see genotype data on affected individuals only, in which case the above methods cannot be used. Instead, we can compare observed genotype frequencies to the expected segregation ratios given the cross type, in a test for segregation distortion (see Faris et al. 1998; Lambrides et al. 2004). For example, the expected segregation proportions for an intercross are 1:2:1. The observed genotypes can then be described by a multinomial model, and statistically significant deviation from the expected segregation ratios among the genotyped affected individuals would suggest genotype–phenotype association. Gene mapping approaches that model genotypes rather than phenotypes have been developed extensively in the analysis of affected human relative pairs (see, for example, Risch 1990; Holmans 1993; Hauser and Boehnke 1998). In the analysis of experimental crosses, however, this type of approach has been developed primarily for the identification of monogenic mutants (Moran et al. 2006).Once all affected individuals are genotyped, an investigator may go on to genotype unaffected individuals. With this genotyping strategy in mind, we present several potential methods of analysis that might be applied in this context. First, we consider a standard analysis of the genotyped individuals, with disease proportions compared across genotype groups (Xu and Atchley 1996). Having omitted ungenotyped individuals, this method of analysis appears invalid because the estimated disease proportions are biased upward, reflecting an overrepresentation of affecteds in the set of genotyped individuals under consideration. As an alternative, we develop a reverse approach with genotype frequencies compared across phenotype groups. Because selective genotyping does provide a representative sample of genotypes for each phenotype group, this reverse approach does not face the bias in parameter estimation seen with the standard approach. We further extend the reverse approach to incorporate a segregation assumption, as is necessary for an affecteds only analysis. Finally, we present a full-likelihood analysis accounting for selective genotyping, similar to that suggested by Lander and Botstein (1989) for quantitative traits. We develop the full-likelihood approach both with and without incorporating an assumption on the genotype segregation proportions.Having put forth each of these methods, we derive analytic relationships among them. These relationships provide important insight regarding application of the presented methods under selective genotyping. Most notably, we find that making a segregation assumption can lead to spurious evidence of a QTL, but is necessary to treat the case of affecteds only genotyping. We demonstrate properties of the methods in an analysis of recovery from infection by Listeria monocytogenes in intercross mice and further compare power of the methods through computer simulations. Finally, we synthesize our analytical and simulation results to offer more general suggestions for the analysis of binary trait data with selective genotyping.     

基于选择基因型对数量性状进行关联分析

数量性状的遗传分析可以通过＂选择基因型＂的方式完成。本文提出了一个利用极端样本来对数量性状位点（QTL）进行关联分析的统计量T。统计量T比较上极端群体样本中具有纯合子标记的性状值差异。通过计算机模拟考察了无关联情形时T的分布和Ⅰ型错误率,结果表明,在各种样本选择策略下,T的分布近似于χ^2-分布,Ⅰ型错误率接近设定的显著性水平。同时,考察了各种遗传模型下不同遗传率,不同样本大小,及不同样本选择阈值对T的统计功效的影响,结果表明,T的功效随着标记和QTL间连锁不平衡程度的增强及遗传率和样本大小的增大而增大,当样本选择阈值更严格时,功效也越大。     

Association Mapping in Outbred Populations: Power and Efficiency When Genotyping Parents and Phenotyping Progeny

We develop expressions for the power to detect associations between parental genotypes and offspring phenotypes for quantitative traits. Three different “indirect” experimental designs are considered: full-sib, half-sib, and full-sib–half-sib families. We compare the power of these designs to detect genotype–phenotype associations relative to the common, “direct,” approach of genotyping and phenotyping the same individuals. When heritability is low, the indirect designs can outperform the direct method. However, the extra power comes at a cost due to an increased phenotyping effort. By developing expressions for optimal experimental designs given the cost of phenotyping relative to genotyping, we show how the extra costs associated with phenotyping a large number of individuals will influence experimental design decisions. Our results suggest that indirect association studies can be a powerful means of detecting allelic associations in outbred populations of species for which genotyping and phenotyping the same individuals is impractical and for life history and behavioral traits that are heavily influenced by environmental variance and therefore best measured on groups of individuals. Indirect association studies are likely to be favored only on purely economical grounds, however, when phenotyping is substantially less expensive than genotyping. A web-based application implementing our expressions has been developed to aid in the design of indirect association studies.     

Full Genotyping of a Highly Polymorphic Human Gene Trait by Time-Resolved Fluorescence Resonance Energy Transfer

The ability of detecting the subtle variations occurring, among different individuals, within specific DNA sequences encompassed in highly polymorphic genes discloses new applications in genomics and diagnostics. DQB1 is a gene of the HLA-II DQ locus of the Human Leukocyte Antigens (HLA) system. The polymorphisms of the trait of the DQB1 gene including codons 52–57 modulate the susceptibility to a number of severe pathologies. Moreover, the donor-receiver tissue compatibility in bone marrow transplantations is routinely assessed through crossed genotyping of DQB and DQA. For the above reasons, the development of rapid, reliable and cost-effective typing technologies of DQB1 in general, and more specifically of the codons 52–57, is a relevant although challenging task. Quantitative assessment of the fluorescence resonance energy transfer (FRET) efficiency between chromophores labelling the opposite ends of gene-specific oligonucleotide probes has proven to be a powerful tool to type DNA polymorphisms with single-nucleotide resolution. The FRET efficiency can be most conveniently quantified by applying a time-resolved fluorescence analysis methodology, i.e. time-correlated single-photon counting, which allows working on very diluted template specimens and in the presence of fluorescent contaminants. Here we present a full in-vitro characterization of the fluorescence responses of two probes when hybridized to oligonucleotide mixtures mimicking all the possible genotypes of the codons 52–57 trait of DQB1 (8 homozygous and 28 heterozygous). We show that each genotype can be effectively tagged by the combination of the fluorescence decay constants extrapolated from the data obtained with such probes.     

Cross-Tissue and Tissue-Specific eQTLs: Partitioning the Heritability of a Complex Trait

Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a disproportionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to the Wellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (∼10%); GWAS SNPs explained over 50% of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index ≥ 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.     

Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by meta-analysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.YIELD is the most important and complex trait in crops. It reflects the interaction of the environment with all growth and development processes that occur throughout the life cycle (Quarrie et al. 2006). Crop yield is directly and multiply determined by yield-component traits (such as seed weight and seed number). Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that “fine-mapped” quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al. 2005; Tian et al. 2006b; Burstin et al. 2007; Xie et al. 2008; Xing et al. 2008; Xue et al. 2008), seed weight (Ishimaru 2003; Song et al. 2005; Shomura et al. 2008; Wang et al. 2008; Xie et al. 2006, 2008; Xing et al. 2008; Xue et al. 2008), flowering time (Cockram et al. 2007; Song et al. 2007; Xie et al. 2008; Xue et al. 2008), plant height (Salamini 2003; Ashikari et al. 2005; Xie et al. 2008; Xue et al. 2008), branching (Clark et al. 2006; Burstin et al. 2007; Xing et al. 2008), biomass yield (Quarrie et al. 2006; Burstin et al. 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001; Brown 2002; Yuan et al. 2002; Waller et al. 2005; Zhang 2007; Warrington et al. 2008), and root architecture (Hochholdinger et al. 2008).Many experiments have explored the genetic basis of yield and yield-associated traits (yield components and yield-related traits) in crops. Summaries of identified QTL have been published for wheat (MacCaferri et al. 2008), barley (Von Korff et al. 2008), rice, and maize (http://www.gramene.org/). The results show several common patterns. First, QTL for yield and yield-associated traits tend to be clustered in the genome, which suggests that the QTL of the yield-associated traits have pleiotropic effects on yield. Second, this kind of pleiotropy has not been well analyzed genetically. The QTL for yield (complicated factor), therefore, have not been associated with any yield-associated traits (relatively simple factors, such as plant height). Therefore, they are unlikely to predict accurately potential candidate genes for yield. Third, only a few loci (rarely >10) have been found for each of these traits. Thus, the genetic architecture of yield has remained ambiguous. Fourth, trials were carried out in a few environments and how the mode of expression of QTL for these complex traits might respond in different environments is unclear.In this study, the genetic architecture of crop yield was analyzed through the QTL mapping of seed yield and eight yield-associated traits in two related populations of rapeseed (Brassica napus) that were grown in 10 natural environments. The complexity of the genetic architecture of seed yield was demonstrated by QTL meta-analysis. The idea of estimating indicator QTL (QTL of yield-associated traits, which are defined as the potential genetic determinants of the colocalized QTL for yield) for yield QTL in conjunction with the identification of candidate genes is described.     

综合性状及其分量的多元条件分析

提出基于混合线性模型的多元条件分析方法,用于分析复杂综合性状的分量对其目标性状的贡献。定义了贡献率和贡献遗传效应两个概念,贡献率测定给定分量性状的遗传变异对目标性状的贡献比率,贡献遗传效应衡量给定分量性状的遗传效应对目标性状的贡献值。运用所提出的新方法分析了棉花籽棉产量的3个分量对产量的贡献。     

Selective DNA Pooling for Determination of Linkage between a Molecular Marker and a Quantitative Trait Locus

Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: ``selective DNA pooling') takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F(2) and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.     

Molecular Identification,Genotyping, Phenotyping,and Antifungal Susceptibilities of Medically Important Trichosporon,Apiotrichum, and Cutaneotrichosporon Species

Mycopathologia - Recently, Trichosporon taxonomy has been reevaluated and new genera of the Trichosporonaceae family have been described. Here, 26 clinical isolates were submitted for...     

Estimation of Effects of Quantitative Trait Loci in Large Complex Pedigrees

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was <2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.     

Soft Selective Sweeps in Complex Demographic Scenarios

Adaptation from de novo mutation can produce so-called soft selective sweeps, where adaptive alleles of independent mutational origin sweep through the population at the same time. Population genetic theory predicts that such soft sweeps should be likely if the product of the population size and the mutation rate toward the adaptive allele is sufficiently large, such that multiple adaptive mutations can establish before one has reached fixation; however, it remains unclear how demographic processes affect the probability of observing soft sweeps. Here we extend the theory of soft selective sweeps to realistic demographic scenarios that allow for changes in population size over time. We first show that population bottlenecks can lead to the removal of all but one adaptive lineage from an initially soft selective sweep. The parameter regime under which such “hardening” of soft selective sweeps is likely is determined by a simple heuristic condition. We further develop a generalized analytical framework, based on an extension of the coalescent process, for calculating the probability of soft sweeps under arbitrary demographic scenarios. Two important limits emerge within this analytical framework: In the limit where population-size fluctuations are fast compared to the duration of the sweep, the likelihood of soft sweeps is determined by the harmonic mean of the variance effective population size estimated over the duration of the sweep; in the opposing slow fluctuation limit, the likelihood of soft sweeps is determined by the instantaneous variance effective population size at the onset of the sweep. We show that as a consequence of this finding the probability of observing soft sweeps becomes a function of the strength of selection. Specifically, in species with sharply fluctuating population size, strong selection is more likely to produce soft sweeps than weak selection. Our results highlight the importance of accurate demographic estimates over short evolutionary timescales for understanding the population genetics of adaptation from de novo mutation.     

Plant Trait Assembly Affects Superiority of Grazer's Foraging Strategies in Species-Rich Grasslands

Background

Current plant – herbivore interaction models and experiments with mammalian herbivores grazing plant monocultures show the superiority of a maximizing forage quality strategy (MFQ) over a maximizing intake strategy (MI). However, there is a lack of evidence whether grazers comply with the model predictions under field conditions.

Methodology/Findings

We assessed diet selection of sheep (Ovis aries) using plant functional traits in productive mesic vs. low-productivity dry species-rich grasslands dominated by resource-exploitative vs. resource-conservative species respectively. Each grassland type was studied in two replicates for two years. We investigated the first grazing cycle in a set of 288 plots with a diameter of 30 cm, i.e. the size of sheep feeding station. In mesic grasslands, high plot defoliation was associated with community weighted means of leaf traits referring to high forage quality, i.e. low leaf dry matter content (LDMC) and high specific leaf area (SLA), with a high proportion of legumes and the most with high community weighted mean of forage indicator value. In contrast in dry grasslands, high community weighted mean of canopy height, an estimate of forage quantity, was the best predictor of plot defoliation. Similar differences in selection on forage quality vs. quantity were detected within plots. Sheep selected plants with higher forage indicator values than the plot specific community weighted mean of forage indicator value in mesic grasslands whereas taller plants were selected in dry grasslands. However, at this scale sheep avoided legumes and plants with higher SLA, preferred plants with higher LDMC while grazing plants with higher forage indicator values in mesic grasslands.

Conclusions

Our findings indicate that MFQ appears superior over MI only in habitats with a predominance of resource-exploitative species. Furthermore, plant functional traits (LDMC, SLA, nitrogen fixer) seem to be helpful correlates of forage quality only at the community level.     

Genome Wide Analysis of Flowering Time Trait in Multiple Environments via High-Throughput Genotyping Technique in Brassica napus L.

The prediction of the flowering time (FT) trait in Brassica napus based on genome-wide markers and the detection of underlying genetic factors is important not only for oilseed producers around the world but also for the other crop industry in the rotation system in China. In previous studies the low density and mixture of biomarkers used obstructed genomic selection in B. napus and comprehensive mapping of FT related loci. In this study, a high-density genome-wide SNP set was genotyped from a double-haploid population of B. napus. We first performed genomic prediction of FT traits in B. napus using SNPs across the genome under ten environments of three geographic regions via eight existing genomic predictive models. The results showed that all the models achieved comparably high accuracies, verifying the feasibility of genomic prediction in B. napus. Next, we performed a large-scale mapping of FT related loci among three regions, and found 437 associated SNPs, some of which represented known FT genes, such as AP1 and PHYE. The genes tagged by the associated SNPs were enriched in biological processes involved in the formation of flowers. Epistasis analysis showed that significant interactions were found between detected loci, even among some known FT related genes. All the results showed that our large scale and high-density genotype data are of great practical and scientific values for B. napus. To our best knowledge, this is the first evaluation of genomic selection models in B. napus based on a high-density SNP dataset and large-scale mapping of FT loci.     

Identification of SSR Markers for Salt-tolerance in Rice Variety CSR10 by Selective Genotyping

A population of 171 F3 genotypes derived from a cross between CSR10 (salt tolerant, indica) and Taraori Basmati (HBC19) was evaluated for various salt-tolerance attributes at vegetative stage using a hydroponic culture system. Substantial variation was observed in F₃ population for relative growth rate (range 0.065–0.187), Na-K ratio (0.023–0.376) and visual injury symptoms (score 1–9). The mean individual score of CSR10 × HBC19 F₃ plants ranged from 1.7 to 9.0 with mean value of 5.07. Seven of the F₃ plants showed transgressive segregation for salt tolerance. F₃ individuals at both extremes of the response distribution were selected and genotyped using 30 SSR markers displaying polymorphism between the two parental genotypes. As many as 18/30 SSR markers showed distorted segregation ratios among the 30 selected salt-tolerant and salt-sensitive CSR10 × HBC19 F3 plants. Linear regression analysis showed significant association of three markers (RM162 mapped on chromosome 6, and RM209 and RM287 on chromosome 11) with relative growth rate and two markers (RM212 on chromosome 1 and RM206 on chromosome 11) with Na-K ratio explaining 31.3% and 25.6% of phenotypic variation, respectively.