贝叶斯统计在QTL作图中的应用研究进展

在许多复杂情况下, 贝叶斯统计方法比经典数理统计方法能更直接解决问题, 且可有效整合部分先验信息, 但其需要高强度计算的特性曾限制了其广泛应用。近几十年来, 随着高速计算机的发展以及MCMC算法的不断提出, 贝叶斯方法已被用于群体遗传学、分子进化、连锁作图和数量遗传学等研究领域, 文章综述了数量遗传学中QTL作图的贝叶斯方法从简单到复杂的发展历程。     

QTL mapping in rice

In the past 10 years, interest in applying the tools of molecular genetics to the problem of increasing world rice production has resulted in the generation of two highly saturated, molecular linkage maps of rice, and the localization of numerous genes and quantitative trait loci (QTLs). Primary studies have identified QTLs associated with disease resistance, abiotic stress tolerance and yield potential of rice in a range of ecosystems. The ability to identify, manipulate and potentially clone individual genes involved in quantitatively inherited characters, combined with the demonstrated conservation of numerous linkage blocks among members of the grass family, emphasizes the contribution of map-based genetic analyses both to applied and to basic crop research.     

Genome-wide QTL mapping of nine body composition and bone mineral density traits in pigs

Background

Since the pig is one of the most important livestock animals worldwide, mapping loci that are associated with economically important traits and/or traits that influence animal welfare is extremely relevant for efficient future pig breeding. Therefore, the purpose of this study was a genome-wide mapping of quantitative trait loci (QTL) associated with nine body composition and bone mineral traits: absolute (Fat, Lean) and percentage (FatPC, LeanPC) fat and lean mass, live weight (Weight), soft tissue X-ray attenuation coefficient (R), absolute (BMC) and percentage (BMCPC) bone mineral content and bone mineral density (BMD).

Methods

Data on the nine traits investigated were obtained by Dual-energy X-ray absorptiometry for 551 pigs that were between 160 and 200 days old. In addition, all pigs were genotyped using Illumina’s PorcineSNP60 Genotyping BeadChip. Based on these data, a genome-wide combined linkage and linkage disequilibrium analysis was conducted. Thus, we used 44 611 sliding windows that each consisted of 20 adjacent single nucleotide polymorphisms (SNPs). For the middle of each sliding window a variance component analysis was carried out using ASReml. The underlying mixed linear model included random QTL and polygenic effects, with fixed effects of sex, housing, season and age.

Results

Using a Bonferroni-corrected genome-wide significance threshold of P < 0.001, significant peaks were identified for all traits except BMCPC. Overall, we identified 72 QTL on 16 chromosomes, of which 24 were significantly associated with one trait only and the remaining with more than one trait. For example, a QTL on chromosome 2 included the highest peak across the genome for four traits (Fat, FatPC, LeanPC and R). The nearby gene, ZNF608, is known to be associated with body mass index in humans and involved in starvation in Drosophila, which makes it an extremely good candidate gene for this QTL.

Conclusions

Our QTL mapping approach identified 72 QTL, some of which confirmed results of previous studies in pigs. However, we also detected significant associations that have not been published before and were able to identify a number of new and promising candidate genes, such as ZNF608.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0068-2) contains supplementary material, which is available to authorized users.     

Human QTL linkage mapping

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10–15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies.     

Genome‐wide association QTL mapping for teat number in a purebred population of Duroc pigs

Because of increasing litter size in Western pig breeds, additional teats are desirable to increase the capacity for nursing offspring. We applied genome‐wide SNP markers to detect QTL regions that affect teat number in a Duroc population. We phenotyped 1024 animals for total teat number. A total of 36 588 SNPs on autosomes were used in the analysis. The estimated heritability for teat number was 0.34 ± 0.05 on the basis of a genomic relationship matrix constructed from all SNP markers. Using a BayesC method, we identified a total of 18 QTL regions that affected teat number in Duroc pigs; 9 of the 18 regions were newly detected.     

Haplotypic QTL mapping in an outbred pedigree

An offspring genome can be viewed as a mosaic of chromosomal segments or haplotypes contributed by multiple founders in any quantitative trait locus (QTL) detection study but tracing these is especially complex to achieve for outbred pedigrees. QTL haplotypes can be traced from offspring back to individual founders in outbred pedigrees by combining founder-origin probabilities with fully informative flanking markers. This haplotypic method was illustrated for QTL detection using a three-generation pedigree for a woody perennial plant, Pinus taeda L. Growth rate was estimated using height measurements from ages 2 to 10 years. Using simulated and actual datasets, power of the experimental design was shown to be efficient for detecting QTLs of large effect. Using interval mapping and fully informative markers, a large QTL accounting for 11.3% of the phenotypic variance in the growth rate was detected. This same QTL was expressed at all ages for height, accounting for 7.9-12.2% of the phenotypic variance. A mixed-model inheritance was more appropriate for describing genetic architecture of growth curves in P. taeda than a strictly polygenic model. The positive QTL haplotype was traced from the offspring to its contributing founder, GP3, then the haplotypic phase for GP3 was determined by assaying haploid megagametophytes. The positive QTL haplotype was a recombinant haplotype contributed by GP3. This study illustrates the combined power of fully informative flanking markers and founder origin probabilities for (1) estimating QTL haplotype magnitude, (2) tracing founder origin and (3) determining haplotypic transmission frequency.     

Spurious linkage between markers in QTL mapping

Selective genotyping of extreme progeny is a powerful method to increase the information content per individual when looking for quantitative trait loci (QTLs) using molecular markers for which a map is known. However, if marker information from the selected individuals is used to construct the map of the markers, this can lead to distorted segregation of the markers that in turn can lead to the estimation of a spurious linkage between independently inherited markers. The mistaken estimation of linkage between independently inherited markers will occur when there are two (or more) independently inherited QTLs linked to two (or more) markers and the same individuals are used to estimate the map of the markers and to do the QTL estimation. The incorrect linkage occurs because in selecting individuals from the tails of the phenotypic distribution we will also be selecting certain combinations of the markers instead of obtaining a random sample of the true distribution of the marker genotypes. Analytical results are outlined and the analyses of a simulated data set illustrate the problems that could arise when data from individuals chosen by selective genotyping are incorrectly employed to construct a marker map. A strategy is proposed to remedy this problem.     

QTL mapping with near-isogenic lines in maize

A set of 89 near-isogenic lines (NILs) of maize was created using marker-assisted selection. Nineteen genomic regions, identified by restriction fragment length polymorphism loci and chosen to represent portions of all ten maize chromosomes, were introgressed by backcrossing three generations from donor line Tx303 into the B73 genetic background. NILs were genotyped at an additional 128 simple sequence repeat loci to estimate the size of introgressions and the amount of background introgression. Tx303 introgressions ranged in size from 10 to 150 cM, with an average of 60 cM. Across all NILs, 89% of the Tx303 genome is represented in targeted and background introgressions. The average proportion of background introgression was 2.5% (range 0–15%), significantly lower than the expected value of 9.4% for third backcross generation lines developed without marker-assisted selection. The NILs were grown in replicated field evaluations in two years to map QTLs for flowering time traits. A parallel experiment of testcrosses of each NIL to the unrelated inbred, Mo17, was conducted in the same environments to map QTLs in NIL testcross hybrids. QTLs affecting days to anthesis, days to silking, and anthesis-silk interval were detected in both inbreds and hybrids in both environments. The testing environments differed dramatically for drought stress, and different sets of QTLs were detected across environments. Furthermore, QTLs detected in inbreds were typically different from QTLs detected in hybrids, demonstrating the genetic complexity of flowering time. NILs can serve as a valuable genetic mapping resource for maize breeders and geneticists. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Statistical methods for QTL mapping in cereals

This paper gives an overview of the statistical theory suitable for mapping quantitative trait loci in experimental populations derived from inbred parents, with a particular emphasis on methodology for cereal crops. The basic theory is described, and some new areas of statistical research appropriate for mapping in cereal crops are discussed.     

玉米叶绿素含量的QTL定位

为了探讨玉米叶绿素含量的遗传规律, 以A150-3-2×Mo17杂交组配的189个F2单株作为作图群体, 构建了具有112个标记位点的玉米分子遗传图谱, 于喇叭口期和开花期分别进行了玉米叶绿素a含量(chla), 叶绿素b含量(chlb), 其他叶绿素含量(chlc)和叶绿素总含量(chlz)4个性状的测定, 并进行QTL分析。在喇叭口期和开花期共检测到32个QTL, 分布在除第6和10染色体以外的其他染色体上。在喇叭口期检测到24个QTL, 分布于第1、2、3、5、7、8和9染色体上, 叶绿素a、叶绿素b、其他叶绿素和叶绿素总含量各检测到6个QTL, 在同一区间内检测到的4个性状的QTL之间的距离在0~2 cM之间。喇叭口期检测到控制叶绿素a、叶绿素b、其他叶绿素和叶绿素总含量的4个主效QTL位于第5染色体上的umc1098~bnlg557区间, 分别可解释表型变异的11.63%、10.3%、10.77%和11.51%。开花期检测到8个QTL, 分布于第4和5染色体上。其中叶绿素a、叶绿素b、其他叶绿素和叶绿素总含量各2个QTL。标记umc1098和bnlg557之间同时存在控制喇叭口期4个叶绿素含量性状的QTL和开花期控制叶绿素a和叶绿素b的QTL。标记umc2308和bnlg386之间只存在控制开花期4个叶绿素含量性状的QTL。     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

QTL modulating ear size and erectness in pigs

Ear size and erectness are important conformation measurements in pigs. An F(2) population established by crossing European Large White (small, erect ears) with Chinese Meishan (large, flop ears) was used to study the genetic influence of the two ear traits for the first time. A linkage map incorporating 152 markers on 18 autosomal chromosomes was utilised in a genome scan for QTL. Significant QTL were found on SSC1, 5, 7, 9 and 12 for the two traits. The QTL on SSC5 and SSC7 had major effects and were significant at the genome-wide level (P < 0.01). The QTL on SSC1 for ear erectness also had a major effect and was genome-wide significant (P < 0.01). The 95% confidence interval (CI) of the ear size QTL on SSC5 spanned only 4 cM. The QTL on SSC7 for the two ear traits each had a CI of <20 cM, and their positions overlapped with those of the major QTL affecting subcutaneous fat depths on the same chromosome. This study provides insights on the complex genetic influences underlying pig ear traits and will facilitate positional candidate gene analysis to identify causative DNA variants.     

Brassinosteroid leaf unrolling QTL mapping in durum wheat

Brassinosteroids are a newly reported class of plant growth phytohormones found in plants throughout the plant kingdom. Functioning at very low concentrations, they play an essential role in improving biomass yield and stress tolerance. There are no reports in the literature of the genetic variability of responsiveness of brassinosteroids in wheat; most studies on brassinosteroids have focused on the physiological effects of exogenous addition of brassinosteroids. Our aim was to study the genetic variation in the responsiveness of a doubled haploid durum wheat population to three brassinosteroid concentrations using the leaf unrolling test, which is a simple bioassay to test brassinosteroid activity. An F₁-derived doubled haploid population of 77 individuals from the cross Strongfield/Blackbird was used to construct a genetic map of 427 molecular marker loci. The leaf unrolling test was performed on the parents and doubled haploid genotypes of the population using 0.2, 2 and 20 nM brassinosteroid concentrations. The results indicated significant differences in leaf unrolling between the two parents, doubled haploid genotypes, treatments and genotype-by-treatment combinations. Transgressive segregation beyond Strongfield of leaf unrolling was observed for all concentrations, with the strongest response at 20 nM. Putative quantitative trait loci were revealed in the intervals Xgwm2–Xbarc45 on chromosome 3A and Xwmc643a–Xwmc625a on chromosome 3B. Additional quantitative trait loci were associated with markers Xwmc48a, Xwmc511, Xwmc89a and Xgwmc692 on chromosome 4B, and Xwmc17 on chromosome 7A. This work should enhance the understanding of the relationship between stress tolerance and productivity, and responsiveness to brassinosteroids.     

MCQTL: multi-allelic QTL mapping in multi-cross design

The aim of the MCQTL software package is to perform QTL mapping in multi-cross designs. It allows the analysis of the usual populations derived from inbred lines and can link the families by assuming that the QTL locations are the same in all of them. Moreover, a diallel modelling of the QTL genotypic effects is allowed in multiple related families. The implemented model is a linear regression model. A composite interval mapping and an iterative QTL mapping are implemented to deal with multiple QTL models. Marker cofactor selections by forward or backward stepwise methods are implemented as well as computation of threshold test value by permutation. AVAILABILITY: The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org (Bioinformatics products).     

Multi-trait QTL mapping in barley using multivariate regression

Many studies of QTL locations record several different traits on the same population, but most analyses look at this information on a trait-by-trait basis. In this paper we show how the regression approach to QTL mapping of Haley & Knott (1992) may be extended to a multi-trait analysis via multivariate regression, easily programmed in statistical packages. A procedure for identifying QTL locations using forward selection and bootstrapping is proposed. The method is applied to examine the locations for QTLs for six yield characters (the number of fertile stems, the grain number of the main stem, the main stem grain weight, the single plant yield, the plot yield and the thousand grain weight) in a doubled haploid population of spring barley. Several chromosomal locations with effects on more than one trait are found. The method is also suitable for examining a single trait measured in different years or environments, and is used here to examine data on heading date, a highly heritable trait, and plot yield, a trait with moderate heritability and showing QTL-environment interactions.     

QTL mapping of fire blight resistance in apple

Fire blight caused by the bacterium Erwinia amylovora is a severe threat to apple and pear orchards worldwide. Apple varieties exhibit a wide range of relative susceptibility/tolerance to fire blight. Although, no monogenic resistance against fire blight has been identified yet, recent evidence indicates the existence of quantitative resistance. Potential sources of fire blight resistance include several wild Malus species and some apple cultivars. F1 progenies of ‘Fiesta’בDiscovery’ were inoculated with the Swiss strain Ea 610 and studied under controlled conditions to identify quantitative trait loci (QTLs) for fire blight resistance. Disease was evaluated at four time points after inoculation. Shoot lesion length and the area under disease progress curve (AUDPC) values were used for QTL analysis. One significant (LOD score of 7.5–8.1, p<0.001) QTL was identified on the linkage group 7 of ‘Fiesta’ (F7). The F7 QTL explained about 37.5–38.6% of the phenotypic variation.     

QTL mapping in autotetraploids using SNP dosage information

Key message

Dense linkage maps derived by analysing SNP dosage in autotetraploids provide detailed information about the location of, and genetic model at, quantitative trait loci.

Abstract

Recent developments in sequencing and genotyping technologies enable researchers to generate high-density single nucleotide polymorphism (SNP) genotype data for mapping studies. For polyploid species, the SNP genotypes are informative about allele dosage, and Hackett et al. (PLoS ONE 8:e63939, 2013) presented theory about how dosage information can be used in linkage map construction and quantitative trait locus (QTL) mapping for an F₁ population in an autotetraploid species. Here, QTL mapping using dosage information is explored for simulated phenotypic traits of moderate heritability and possibly non-additive effects. Different mapping strategies are compared, looking at additive and more complicated models, and model fitting as a single step or by iteratively re-weighted modelling. We recommend fitting an additive model without iterative re-weighting, and then exploring non-additive models for the genotype means estimated at the most likely position. We apply this strategy to re-analyse traits of high heritability from a potato population of 190 F₁ individuals: flower colour, maturity, height and resistance to late blight (Phytophthora infestans (Mont.) de Bary) and potato cyst nematode (Globodera pallida), using a map of 3839 SNPs. The approximate confidence intervals for QTL locations have been improved by the detailed linkage map, and more information about the genetic model at each QTL has been revealed. For several of the reported QTLs, candidate SNPs can be identified, and used to propose candidate trait genes. We conclude that the high marker density is informative about the genetic model at loci of large effects, but that larger populations are needed to detect smaller QTLs.     

QTL for the heritable inverted teat defect in pigs

The mothering ability of a sow largely depends on the shape and function of the mammary gland. The aim of this study was to identify QTL for the heritable inverted teat defect, a condition characterized by disturbed development of functional teats. A QTL analysis was conducted in a porcine experimental population based on Duroc and Berlin Miniature pigs (DUMI). The significant QTL were confirmed by linkage analysis in commercial pigs according to the affected sib pair design and refined by family-based association test (FBAT). Nonparametric linkage (NPL) analysis revealed five significant and seven suggestive QTL for the inverted teat defect in the porcine experimental population. In commercial dam lines five significant NPL values were detected. QTL regions in overlapping marker intervals or close proximity in both populations were found on SSC3, SSC4, SSC6, and SSC11. SSC6 revealed QTL in both populations at different positions, indicating the segregation of at least two QTL. The results confirm the previously proposed polygenic inheritance of the inverted teat defect and, for the first time, point to genomic regions harboring relevant genes. The investigation revealed variation of the importance of QTL in the various populations due to either differences in allele frequencies and statistical power or differences in the genetic background that modulates the impact of the liability loci on the expression of the disease. The QTL study enabled us to name a number of plausible positional candidate genes. The correspondence of QTL regions for the inverted teat defect and previously mapped QTL for teat number are in line with the etiologic relationship of these traits.