The genetic variance for multiple linked quantitative trait loci conditional on marker information in a crossed population

In the prediction of genetic values and quantitative trait loci (QTLs) mapping via the mixed model method incorporating marker information in animal populations, it is important to model the genetic variance for individuals with an arbitrary pedigree structure. In this study, for a crossed population originated from different genetic groups such as breeds or outbred strains, the variance of additive genetic values for multiple linked QTLs that are contained in a chromosome segment, especially the segregation variance, is investigated assuming the use of marker data. The variance for a finite number of QTLs in one chromosomal segment is first examined for the crossed population with the general pedigree. Then, applying the concept of the expectation of identity-by-descent proportion, an approximation to the mean of the conditional probabilities for the linked QTLs over all loci is obtained, and using it an expression for the variance in the case of an infinite number of linked QTLs marked by flanking markers is derived. It appears that the approach presented can be useful in the segment mapping using, and in the genetic evaluation of, crosses with general pedigrees in the population of concern. The calculation of the segregation variance through the current approach is illustrated numerically, using a small data-set.     

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.     

Aroma in rice: genetic analysis of a quantitative trait

A new approach was developed which succeeded in tagging for the first time a major gene and two QTLs controlling grain aroma in rice. It involved a combination of two techniques, quantification of volatile compounds in the cooking water by gas chromatography, and molecular marker mapping. Four types of molecular marker were used (RFLPs, RAPDs, STSs, isozymes). Evaluation and mapping were performed on a doubled haploid line population which (1) conferred a precise character evaluation by enabling the analysis of large quantities of grains per genotype and (2) made possible the comparison of gas chromatography results and sensitive tests. The population size (135 lines) provided a good mapping precision. Several markers on chromosome 8 were found to be closely linked to a major gene controlling the presence of 2-acetyl-1-pyrroline (AcPy), the main compound of rice aroma. Moreover, our results showed that AcPy concentration in plants is regulated by at least two chromosomal regions. Estimations of recombination fractions on chromosome 8 were corrected for strong segregation distortion. This study confirms that AcPy is the major component of aroma. Use of the markers linked to AcPy major gene and QTLs for marker-assisted selection by successive backcrosses may be envisaged.     

Detection of quantitative trait loci on chromosome 5R of rye (Secale cereale L.)

 Progenies of an F₂ mapping population were analyzed for quantitative traits to detect QTLs by using marker information from F₂ plants for chromosome 5R. The mapping population was segregating for the major dwarfing gene Ddw1 and the gene Hp1 for hairy peduncle. The only QTL determining plant height was located between HP1 and Ddw1 on the distal part of chromosome 5RL. At the same position a QTL for peduncle length was found, and this trait was closely related to plant height (r=0.895). Since Hp1 and Ddw1 are dominant marker loci, no dominance effect could be estimated. The QTLs for spike length and the number of florets were located near the centromere on 5RL. These two traits were correlated with r=0.824 and showed partial dominance, but these traits were not correlated to plant height and peduncle length. Homoeologous relationships between the QTLs mapped for the first time in rye and those mapped in other Triticeae members are discussed. Received: 8 June 1998 / Accepted: 8 October 1998     

Limitations of a two-step moment method for mapping linked multiple QTL

As an alternative to multiple-interval mapping a two-step moment method was recently proposed to map linked multiple quantitative trait loci (QTLs). The advantage of this moment method was supposed to be its simplicity and computational efficiency, especially in detecting closely linked QTLs within a marker bracket, but also in mapping QTLs in different marker intervals. Using simulations it is shown that the two-step moment method may give poor results compared with multiple-interval mapping, irrespective of whether the QTLs are in the same or in different marker intervals, especially if linked QTLs are in repulsion. The criteria of comparison are number of identified QTLs, likelihood ratio test statistics, means and empirical standard errors of the QTL position and QTL effects estimates, and the accuracy of the residual variance estimates. Further, the joint conditional probabilities of QTL genotypes for two putative QTLs within a marker interval were derived and compared with the unmodified approach ignoring the non-independence of the conditional probabilities.     

70个水稻微卫星标记染色体位置的更正

微卫星标记(SSR)因其操作简单和稳定可靠的特点而成为一种重要的分子标记,被广泛应用于遗传作图和种质鉴定等方面。但其在染色体上位置的正确性将直接影响到基因定位的正确性和后续研究的方向。利用美国国家生物信息技术中心(NCBI)网站的Blast程序,将2740个SSR标记的前后引物序列与水稻粳稻品种日本晴基因组进行比对,共发现70个标记位于另一条染色体,对这70个标记重新锚定的染色体进行了更正。这将有助于今后水稻分子标记遗传连锁图的正确构建。     

Approaches to interval mapping of QTL in a multigeneration pedigree: the example of porcine chromosome 4

Quantitative trait loci (QTLs) have been mapped in many studies of F2 populations derived from crosses between diverse lines. One approach to confirming these effects and improving the mapping resolution is genetic chromosome dissection through a backcrossing programme. Analysis by interval mapping of the data generated is likely to provide additional power and resolution compared with treating data marker by marker. However, interval mapping approaches for such a programme are not well developed, especially where the founder lines were outbred. We explore alternative approaches to analysis using, as an example, data from chromosome 4 in an intercross between wild boar and Large White pigs where QTLs have been previously identified. A least squares interval mapping procedure was used to study growth rate and carcass traits in a subsequent second backcross generation (BC2). This procedure requires the probability of inheriting a wild boar allele for each BC2 animal for locations throughout the chromosome. Two methods for obtaining these probabilities were compared: stochastic or deterministic. The two methods gave similar probabilities for inheriting wild boar alleles and, hence, gave very similar results from the QTL analysis. The deterministic approach has the advantage of being much faster to run but requires specialized software. A QTL for fatness and for growth were confirmed and, in addition, a QTL for piglet growth from weaning at 5 weeks up to 7 weeks of age and another for carcass length were detected.     

A comparison of interval mapping procedures including the QTL-cluster mapping

Using the deterministic sampling, patterns of the log-likelihood surfaces expected in some interval mapping procedures for estimating the position of, and the effect for, QTL(s) were investigated for the situations where a single QTL or closely linked QTLs are contained in a chromosome segment bracketed with two markers. The mapping procedures compared were the conventional, likelihood-based interval mapping (IM), the regression interval mapping (RIM), and the QTL-cluster mapping (CM) in which the conditional probabilities of transmission of the whole segment marked by the flanking markers were taken into consideration. The half-sib design was used here, and several cases of the true genetic model were considered, differing in the number of QTLs contained in the marker interval, the linkage phase for the sire, and the magnitude of the QTL(s) effect. For the true genetic models where a single QTL or closely linked QTLs being in coupling phase are contained in the interval, with (R)IM, clear global maxima of the log-likelihood were observed within the range of the marker interval. It was shown that the estimates of the QTL(s) effect at the marked segment level are expected to be unbiased. On the other hand, in a setting where the linkage phase for the linked QTLs located in the interval was different from coupling and repulsion, there was found a ridge along the interval for the log-likelihood surface with (R)IM, indicating the dependency between the estimates of the position of, and the effect for, the putative QTL. For this case, it was found that the position of the putative QTL could be estimated as that of one of the flanking markers, and the estimate of the QTL effect be biased. In contrast, it was revealed that CM is expected to provide the unbiased estimate of the QTL(s)-effect at the segment level for any case of the true genetic models considered here. If the aim is for marker-assisted selection rather than mapping closely linked QTLs individually, then the CM approach is considered to be useful.     

Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data.

A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the locationwise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/mjs.     

Inferences regarding the numbers and locations of QTLs under multiple-QTL models using interval mapping and composite interval mapping

This paper examines the properties of likelihood maps generated by interval mapping (IM) and composite interval mapping (CIM), two widely used methods for detecting quantitative trait loci (QTLs). We evaluate the usefulness of interpretations of entire maps, rather than only evaluating summary statistics that consider isolated features of maps. A simulation study was performed in which traits with varying genetic architectures, including 20-40 QTLs per chromosome, were examined with both IM and CIM under different marker densities and sample sizes. IM was found to be an unreliable tool for precise estimation of the number and locations of individual QTLs, although it has greater power for simply detecting the presence of QTLs than CIM. The ability of CIM to resolve the correct number of QTLs and to estimate their locations correctly is good if there are three or fewer QTLs per 100 centiMorgans, but can lead to erroneous inferences for more complex architectures. When the underlying genetic architecture of a trait consists of several QTLs with randomly distributed effects and locations likelihood profiles were often indicative of a few underlying genes of large effect. Studies that have detected more than a few QTLs per chromosome should be interpreted with caution.     

Genetic dissection of root growth in rice (Oryza sativa L.). II: mapping quantitative trait loci using molecular markers

Drought is a major abiotic stress of upland rice, and good root growth has been associated with drought avoidance. We report on the genetic mapping of root growth traits in an F₂ population derived from two drought-resistant rice varieties, ‘Bala’ and ‘Azucena’. Restriction fragment length polymorphism (RFLP) between the parents was 32%, and a molecular map with 71 marker loci and 17 linkage groups covering 1280 cM was produced. Quantitative trait loci (QTLs) for eight root growth characteristics were mapped using phenotype data obtained in a hydroponic screen previously described in a companion paper. Using a significance threshold of LOD 2.4, we observed one QTL for maximum root length after 28 days growth on chromosome 11. It had a LOD score of 6.9, explained nearly 30% of the variation and appeared to be largely additive in effect. QTLs for maximum root length after 3, 7, 14 and 21 days of growth were also revealed. Some root-length QTLs, including that on chromosome 11, varied greatly with developmental stage. One QTL for root volume and two QTLs for adventitious root thickness were detected. No QTLs were detected for the length of cells in the mature (fully expanded) zone of adventitious root tips. The results obtained are discussed in the context of previous reports on mapping root growth parameters in rice.     

Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms

Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) without requiring line-crossing experiments. We previously proposed a Bayesian approach for simultaneously mapping multiple QTLs by a regression method that directly incorporates estimates of the population structure. In the present study, we extended our method to analyze ordinal and censored traits, since both types of traits are common in the evaluation of germplasm collections. Ordinal-probit and tobit models were employed to analyze ordinal and censored traits, respectively. In both models, we postulated the existence of a latent continuous variable associated with the observable data, and we used a Markov-chain Monte Carlo algorithm to sample the latent variable and determine the model parameters. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses based on highly polymorphic marker data, which were generated by coalescent simulations, showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant markers that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in rice germplasm collections.     

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among <Emphasis Type="Italic">Oryza sativa</Emphasis> L. germplasms

One way to use a crop germplasm collection directly to map QTLs without using line-crossing experiments is the whole genome association mapping. A major problem with association mapping is the presence of population structure, which can lead to both false positives and failure to detect genuine associations (i.e., false negatives). Particularly in highly selfing species such as Asian cultivated rice, high levels of population structure are expected and therefore the efficiency of association mapping remains almost unknown. Here, we propose an approach that combines a Bayesian method for mapping multiple QTLs with a regression method that directly incorporates estimates of population structure. That is, the effects due to both multiple QTLs and population structure were included in our statistical model. We evaluated the efficiency of our approach in simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our model could suppress both false-positive and false-negative rates and the error of estimation of genetic effects over single QTL models, indicating that our model has statistically desirable attributes over single QTL models. As real traits, we analyzed the size and shape of milled rice grains and found significant markers that may be linked to QTLs reported previously. Association mapping should have good prospects in highly selfing species such as rice if proper methods are adopted. Our approach will be useful for the whole genome association mapping of various selfing crop species.     

Association mapping for pre-harvest sprouting resistance in white winter wheat

Association mapping identified quantitative trait loci (QTLs) and the markers linked to pre-harvest sprouting (PHS) resistance in an elite association mapping panel of white winter wheat comprising 198 genotypes. A total of 1,166 marker loci including DArT and SSR markers representing all 21 chromosomes of wheat were used in the analysis. General and mixed linear models were used to analyze PHS data collected over 4 years. Association analysis identified eight QTLs linked with 13 markers mapped on seven chromosomes. A QTL was detected on each arm of chromosome 2B and one each on chromosome arms 1BS, 2DS, 4AL, 6DL, 7BS and 7DS. All except the QTL on 7BS are located in a location similar to previous reports and, if verified, the QTL on 7BS is likely to be novel. Principal components and the kinship matrix were used to account for the presence of population structure but had only a minor effect on the results. Although, none of the QTLs was highly significant across all environments, a QTL on the long arm of chromosome 4A was detected in three different environments and also using the best linear unbiased predictions over years. Although previous reports have identified this as a major QTL, its effects were minor in our biparental mapping populations. The results of this study highlight the benefits of association mapping and the value of using elite material in association mapping for plant breeding programs.     

不同遗传背景及环境中水稻（Oryza sativa L.）穗长的QTLs和上位性分析

以粳稻Ａｚｕｃｅｎａ为父本与灿稻ＩＲ６４杂交发展的一双单倍体（ＤＨ） 本,与灿稻ＩＲ１５５２杂交发展的一重组自交系（ＲＩ）群体为材料,应用分子标记图说对２个群体在大田答舅栽２个环境下的穗长进行ＱＴＬｓ及上位性效应分析,ＤＨ群体中共检测６个穗长ＱＴＬｓ,位于第１、４长染色体上的３个ＱＴＬｓ,,在２个环境中稳定表达,未检测一闰性效应,加性效应为穗长遗传主效应,ＲＩ群体中,共检测到３个穗长ＱＴＬｓ及６对     

GBS-SNP and SSR based genetic mapping and QTL analysis for drought tolerance in upland cotton

A recombinant inbred line mapping population of intra-species upland cotton was generated from a cross between the drought-tolerant female parent (AS2) and the susceptible male parent (MCU13). A linkage map was constructed deploying 1,116 GBS-based SNPs and public domain-based 782 SSRs spanning a total genetic distance of 28,083.03 cM with an average chromosomal span length of 1,080.12 cM with inter-marker distance of 10.19 cM.A total of 19 quantitative trait loci (QTLs) were identified in nine chromosomes for field drought tolerance traits. Chromosomes 3 and 8 harbored important drought tolerant QTLs for chlorophyll stability index trait while for relative water content trait, three QTLs on chromosome 8 and one QTL each on chromosome 4, 12 were identified. One QTL on each chromosome 8, 5, and 7, and two QTLs on chromosome 15 linking to proline content were identified. For the nitrate reductase activity trait, two QTLs were identified on chromosome 3 and one on each chromosome 8, 13, and 26. To complement our QTL study, a meta-analysis was conducted along with the public domain database and resulted in a consensus map for chromosome 8. Under field drought stress, chromosome 8 harbored a drought tolerance QTL hotspot with two in-house QTLs for chlorophyll stability index (qCSI01, qCSI02) and three public domain QTLs (qLP.FDT_1, qLP.FDT_2, qCC.ST_3). Identified QTL hotspot on chromosome 8 could play a crucial role in exploring abiotic stress-associated genes/alleles for drought trait improvement.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12298-021-01041-y.     

Fine mapping quantitative trait loci affecting milk production traits on bovine chromosome 6 in a Chinese Holstein population

To fine map the previously detected quantitative trait loci （QTLs） affecting milk production traits on bovine chromosome 6 （BTA6）, 15 microsatellite markers situated within an interval of 14.3 cM spanning from BMS690 to BM4528 were selected and 918 daughters of 8 sires were genotyped. Two mapping approaches, haplotype sharing based LD mapping and single marker regression mapping, were used to analyze the data. Both approaches revealed a quantitative trait locus （QTL） with significant effects on milk yield, fat yield and protein yield located in the segment flanked by markers BMS483 and MNB209, which spans a genetic distance of 0.6 cM and a physical distance of 1.5 Mb. In addition, the single marker regression mapping also revealed a QTL affecting fat percentage and protein percentage at marker DIK2291. Our fine mapping work will facilitate the cloning of candidate genes underlying the QTLs for milk production traits.     

Quantitative trait loci mapping for fatty acid contents in the backfat on porcine chromosomes 1, 13, and 18

A partial genome scan using microsatellite markers was conducted in order to detect quantitative trait loci (QTLs) for 10 fatty acid contents of the backfat in a pig reference population. Two QTLs were found by studying SSC1, SSC13, and SSC18, where QTLs had already been identified for backfat thickness. A QTL was located between marker loci S0113 and SW974 on chromosome 1; this QTL was only significantly detected (P < 0.05) for linoleic acid. The other QTL was discovered between markers S0062 and S0120 on chromosome 18, and its significance only showed (P < 0.05) for myristic acid. The two QTLs mapped to the same location as the backfat thickness QTL. A third of the phenotypic variation was explained for linoleic acid by the QTL on chromosome 1, and a quarter for myristic acid by the QTL on chromosome 18. Further studies on fine mapping and positional comparative candidate gene analyses will be the next step toward a better understanding of the genetic architecture of fatty acid contents.     

Combined genetic and physiological analysis of a locus contributing to quantitative variation

The natural variation of many traits is controlled by multiple genes, individually referred to as quantitative trait loci (QTL), that interact with the environment to determine the ultimate phenotype of any individual. A QTL has yet to be described molecularly, in part because strategies to systematically identify them are underdeveloped and because the subtle nature of QTLs prevents the application of standard methods of gene identification. Therefore, it will be necessary to develop a systematic approach(es) for the identification of QTLs based upon the numerous positional data now being accumulated through molecular marker analyses. We have characterized a QTL by the following three-step approach: (1) identification of a QTL in complex populations, (2) isolation and genetic mapping of this QTL in near-isogenic lines, and (3) identification of a candidate gene using map position and physiological criteria. Using this approach we have characterized a plant height QTL in maize that maps to chromosome 9 near the centromere. Both map position and physiological criteria suggest the gibberillin biosynthesis gene dwarf3 as a candidate gene for this QTL.     

QTL sequencing strategy to map genomic regions associated with resistance to ascochyta blight in chickpea

Whole‐genome sequencing‐based bulked segregant analysis (BSA) for mapping quantitative trait loci (QTL) provides an efficient alternative approach to conventional QTL analysis as it significantly reduces the scale and cost of analysis with comparable power to QTL detection using full mapping population. We tested the application of next‐generation sequencing (NGS)‐based BSA approach for mapping QTLs for ascochyta blight resistance in chickpea using two recombinant inbred line populations CPR‐01 and CPR‐02. Eleven QTLs in CPR‐01 and six QTLs in CPR‐02 populations were mapped on chromosomes Ca1, Ca2, Ca4, Ca6 and Ca7. The QTLs identified in CPR‐01 using conventional biparental mapping approach were used to compare the efficiency of NGS‐based BSA in detecting QTLs for ascochyta blight resistance. The QTLs on chromosomes Ca1, Ca4, Ca6 and Ca7 overlapped with the QTLs previously detected in CPR‐01 using conventional QTL mapping method. The QTLs on chromosome Ca4 were detected in both populations and overlapped with the previously reported QTLs indicating conserved region for ascochyta blight resistance across different chickpea genotypes. Six candidate genes in the QTL regions identified using NGS‐based BSA on chromosomes Ca2 and Ca4 were validated for their association with ascochyta blight resistance in the CPR‐02 population. This study demonstrated the efficiency of NGS‐based BSA as a rapid and cost‐effective method to identify QTLs associated with ascochyta blight in chickpea.