Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits

An approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F(2)/F(3) mapping population of tetraploid wheat (Triticum durum x T. dicoccoides) is provided.     

Linkage between loci of quantitative traits and marker loci: multi-trait analysis with a single marker

An efficient approach to increase the resolution power of linkage analysis between a quantitative trait locus (QTL) and a marker is described in this paper. It is based on a counting of the correlations between the QTs of interest. Such correlations may be caused by the segregation of other genes, environmental effects and physiological limitations. Let a QT locus A/a affect two correlated traits, x and y. Then, within the framework of mixture models, the accuracy of the parameter estimates may be seriously increased, if bivariate densities f _aa(x, y), f _Aa(x, y) and f _AA(x, y) rather than the marginals are considered as the basis for mixture decomposition. The efficiency of the proposed method was demonstrated employing Monte-Carlo simulations. Several types of progeny were considered, including backcross, F₂ and recombinant inbred lines. It was shown that provided the correlation between the traits involved was high enough, a good resolution to the problem is possible even if the QTL groups are strongly overlapping for their marginal densities.     

Single- and multiple-trait mapping analysis of linked quantitative trait loci. Some asymptotic analytical approximations.

Estimating the resolution power of mapping analysis of linked quantitative trait loci (QTL) remains a difficult problem, which has been previously addressed mainly by Monte Carlo simulations. The analytical method of evaluation of the expected LOD developed in this article spreads the "deterministic sampling" approach for the case of two linked QTL for single- and two-trait analysis. Several complicated questions are addressed through this evaluation: the dependence of QTL detection power on the QTL effects, residual correlation between the traits, and the effect of epistatic interaction between the QTL for one or both traits on expected LOD (ELOD), etc. Although this method gives only an asymptotic estimation of ELOD, it allows one to get an approximate assessment of a broad spectrum of mapping situations. A good correspondence was found between the ELODs predicted by the model and LOD values averaged over Monte Carlo simulations.     

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

Fine mapping of quantitative trait loci for mastitis resistance on bovine chromosome 11

Quantitative trait loci (QTL) affecting clinical mastitis (CM) and somatic cell score (SCS) were mapped on bovine chromosome 11. The mapping population consisted of 14 grandsire families belonging to three Nordic red cattle breeds: Finnish Ayrshire (FA), Swedish Red and White (SRB) and Danish Red. The families had previously been shown to segregate for udder health QTL. A total of 524 progeny tested bulls were included in the analysis. A linkage map including 33 microsatellite and five SNP markers was constructed. We performed combined linkage disequilibrium and linkage analysis (LDLA) using the whole data set. Further analyses were performed for FA and SRB separately to study the origin of the identified QTL/haplotype and to examine if it was common in both populations. Finally, different two-trait models were fitted. These postulated either a pleiotropic QTL affecting both traits; two linked QTL, each affecting one trait; or one QTL affecting a single trait. A QTL affecting CM was fine-mapped. In FA, a haplotype having a strong association with a high negative effect on mastitis resistance was identified. The mapping precision of an earlier detected SCS-QTL was not improved by the LDLA analysis because of lack of linkage disequilibrium between the markers used and the QTL in the region.     

A Generalization of the Mixture Model in the Mapping of Quantitative Trait Loci for Progeny from a Biparental Cross of Inbred Lines

The recent advent of molecular markers has created a great potential for the understanding of quantitative inheritance. In parallel to rapid developments and improvements in molecular marker technologies, biometrical models have been constructed, refined and generalized for the mapping of quantitative trait loci (QTL). However, current models present restricitions in terms of breeding designs to which they apply. In this paper, we develop an approach for the generalization of the mixture model for progeny from a single bi-parental cross of inbred lines. Detailed derivations are given for genetic designs involving populations developed by selfing, i.e., where marker genotypes are obtained from F(x) (x >=2) individuals and where phenotypes are measured on F(y) (y >=x) individuals or families. Extensions to designs involving doubled-haploids, backcross-derived individuals and random matings are outlined. The derivations presented here can easily be combined with current QTL mapping approaches.     

Quantitative Trait Loci Analyses for Meristic Traits in Oncorhynchus mykiss

Meristic trait variation among species and populations has long been used as the basis for identification and classification of fishes. Within Oncorhynchus mykiss, there is considerable variation in meristic characters such as numbers of vertebrae, lateral line scales, fin rays, gill rakers, and pyloric caeca. In our laboratory the Oregon State University (OSU) rainbow trout and the Clearwater River (CW) steelhead trout clonal lines, produced by androgenesis, exhibit significant differences in values for meristic traits, making quantitative trait locus (QTL) analysis of these meristic characters possible. Our objective was to determine the number, location, and effects of QTL associated with meristic characters in order to test two hypotheses: (1) that QTL for different meristic traits co-localize to the same linkage group and (2) that meristic trait QTL co-localize to the same linkage group as a previously identified development rate QTL. Doubled haploid individuals, produced by androgenesis from sperm from an F₁ hybrid between the OSU and CW lines, were used to evaluate the joint segregation of each meristic phenotype and Amplified Fragment Length Polymorphic marker genotypes. Composite interval mapping revealed QTL for six of the seven traits analyzed. One QTL each for scales above the lateral line and for gill rakers co-localized to the same position. Only one QTL for scales above the lateral line co-localized to the same region as that for the development rate QTL, but a greater map resolution is necessary to determine if these loci are truly the same. We failed to detect pleiotropy for most meristic trait QTL. Our results suggest that different major loci are associated with variation in each meristic character and that the expression of these loci may be influenced by maternal and external environmental factors.     

Mapping Quantitative Trait Loci Controlling Milk Production in Dairy Cattle by Exploiting Progeny Testing

We have exploited ``progeny testing' to map quantitative trait loci (QTL) underlying the genetic variation of milk production in a selected dairy cattle population. A total of 1,518 sires, with progeny tests based on the milking performances of > 150,000 daughters jointly, was genotyped for 159 autosomal microsatellites bracketing 1645 centimorgan or approximately two thirds of the bovine genome. Using a maximum likelihood multilocus linkage analysis accounting for variance heterogeneity of the phenotypes, we identified five chromosomes giving very strong evidence (LOD score >/= 3) for the presence of a QTL controlling milk production: chromosomes 1, 6, 9, 10 and 20. These findings demonstrate that loci with considerable effects on milk production are still segregating in highly selected populations and pave the way toward marker-assisted selection in dairy cattle breeding.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

控制玉米雄穗分枝数目和雄穗重的主效QTL的定位

利用2套具有共同亲本黄早四且分别含有230个及235个家系的F_2:3群体, 结合2年多点的表型鉴定, 运用完备复合区间作图方法对不同生态环境下(2007-北京、2008-北京、2007-河南、2008-河南、2007-新疆以及2008-新疆)的玉米雄穗分枝数和雄穗重进行QTL定位。同时, 利用基于混合线性模型的QTLNetwork-2.0软件进行基因×环境互作及上位性分析。6个环境下2个群体共检测到51个与雄穗分枝数和雄穗重相关的QTL(Q/H群体32个, Y/H群体19个), 其中包括7个主效QTL, 并在Q/H群体中确定了2个重要的QTL, 即位于7.01bin的Qqtpbn7-1和位于7.02bin的Qqtw7-2。对比2个群体的定位结果, 共挖掘到3个在不同遗传背景下的“一致性”QTL, 这些在不同环境及不同遗传背景下能够稳定存在的QTL可为玉米雄穗相关性状的生产应用以及精细定位提供有价值的参考。     

Genetic and Quantitative Trait Locus Analysis for Bio-Oil Compounds after Fast Pyrolysis in Maize Cobs

Fast pyrolysis has been identified as one of the biorenewable conversion platforms that could be a part of an alternative energy future, but it has not yet received the same attention as cellulosic ethanol in the analysis of genetic inheritance within potential feedstocks such as maize. Ten bio-oil compounds were measured via pyrolysis/gas chromatography-mass spectrometry (Py/GC-MS) in maize cobs. 184 recombinant inbred lines (RILs) of the intermated B73 x Mo17 (IBM) Syn4 population were analyzed in two environments, using 1339 markers, for quantitative trait locus (QTL) mapping. QTL mapping was performed using composite interval mapping with significance thresholds established by 1000 permutations at α = 0.05. 50 QTL were found in total across those ten traits with R² values ranging from 1.7 to 5.8%, indicating a complex quantitative inheritance of these traits.     

Mapping Quantitative Trait Loci (QTL) in sheep. III. QTL for carcass composition traits derived from CT scans and aligned with a meta-assembly for sheep and cattle carcass QTL

An (Awassi × Merino) × Merino single-sire backcross family with 165 male offspring was used to map quantitative trait loci (QTL) for body composition traits on a framework map of 189 microsatellite loci across all autosomes. Two cohorts were created from the experimental progeny to represent alternative maturity classes for body composition assessment. Animals were raised under paddock conditions prior to entering the feedlot for a 90-day fattening phase. Body composition traits were derived in vivo at the end of the experiment prior to slaughter at 2 (cohort 1) and 3.5 (cohort 2) years of age, using computed tomography. Image analysis was used to gain accurate predictions for 13 traits describing major fat depots, lean muscle, bone, body proportions and body weight which were used for single- and two-QTL mapping analysis. Using a maximum-likelihood approach, three highly significant (LOD ≥ 3), 15 significant (LOD ≥ 2), and 11 suggestive QTL (1.7 ≤ LOD < 2) were detected on eleven chromosomes. Regression analysis confirmed 28 of these QTL and an additional 17 suggestive (P < 0.1) and two significant (P < 0.05) QTL were identified using this method. QTL with pleiotropic effects for two or more tissues were identified on chromosomes 1, 6, 10, 14, 16 and 23. No tissue-specific QTL were identified.A meta-assembly of ovine QTL for carcass traits from this study and public domain sources was performed and compared with a corresponding bovine meta-assembly. The assembly demonstrated QTL with effects on carcass composition in homologous regions on OAR1, 2, 6 and 21.     

控制玉米雄穗分枝数目和雄穗重的主效QTL的定位

利用2套具有共同亲本黄早四且分别含有230个及235个家系的F2：3群体,结合2年多点的表型鉴定,运用完备复合区间作图方法对不同生态环境下（2007-北京、2008-北京、2007-河南、2008-河南、2007-新疆以及2008-新疆）的玉米雄穗分枝数和雄穗重进行QTL定位。同时,利用基于混合线性模型的QTLNetwork-2.0软件进行基因×环境互作及上位性分析。6个环境下2个群体共检测到51个与雄穗分枝数和雄穗重相关的QTL（Q/H群体32个,Y/H群体19个）,其中包括7个主效QTL,并在Q/H群体中确定了2个重要的QTL,即位于7.01bin的Qqtpbn7-1和位于7.02bin的Qqtw7-2。对比2个群体的定位结果,共挖掘到3个在不同遗传背景下的＂一致性＂QTL,这些在不同环境及不同遗传背景下能够稳定存在的QTL可为玉米雄穗相关性状的生产应用以及精细定位提供有价值的参考。     

Interacting quantitative trait loci control loss of peripheral tolerance and susceptibility to autoimmune ovarian dysgenesis after day 3 thymectomy in mice

Day 3 thymectomy (D3Tx) results in a loss of peripheral tolerance mediated by CD4(+)CD25(+) T cells and the development of autoimmune ovarian dysgenesis (AOD) in A/J and (C57BL/6J x A/J)F(1) (B6AF(1)) hybrids but not in C57BL/6J mice. Quantitative trait loci (QTL) linkage analysis using a B6AF(1) x C57BL/6J backcross population verified Aod1 and Aod2 that were previously mapped as qualitative traits. Additionally, three new QTL intervals, Aod3, Aod4, and Aod5, on chromosomes 1, 2, and 7, respectively, influencing specific subphenotypes of AOD were identified. QTL linkage analysis using the A x B and B x A recombinant inbred lines verified Aod3 and confirmed linkage to H2. Aod5 colocalized with Mater, an ovarian-specific autoantigen recognized by anti-ovarian autoantibodies in the sera of D3Tx mice. Sequence analysis of Mater identified allelic, strain-specific splice variants between A/J and C57BL/6J mice making it an attractive candidate gene for Aod5. Interaction analysis revealed significant epistatic effects between Aod1-5 and Gasa2, a locus associated with susceptibility to D3Tx-induced autoimmune gastritis, as well as with H2. These results indicate that the QTL controlling D3Tx-induced autoimmune phenomenon are both organ specific and more generalized in their effects with respect to the genesis and activity of the immunoregulatory mechanisms maintaining peripheral tolerance.     

远交群体动态性状基因定位的似然分析Ⅰ.理论方法

受动物遗传育种中用来估计动态性状育种值的随机回归测定日模型思想的启发 ,将关于时间 (测定日期 )的Legendre多项式镶嵌在遗传模型的每个遗传效应中 ,以刻画QTL对动态性状变化过程的作用 ,从而建立起动态性状基因定位的数学模型。利用远交设计群体 ,阐述了动态性状基因定位的似然分析原理 ,推导了定位参数似然估计的EM法两步求解过程。结合动态性状遗传分析的特点和普通数量性状基因定位研究进展 ,还提出了有关动态性状基因定位进一步研究的设想     

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.     

Quantitative Trait Locus Analysis of Plasma Cholesterol and Triglyceride Levels in KK × RR F2 Mice

A previous quantitative trait locus (QTL) study on hyperlipidemia in C57BL/6J x KK-Ay/a F2 mice identified three significant cholesterol QTLs (Cq1 and Cq2 on chromosome 1, and Cq3 on chromosome 3), and a suggestive triglyceride QTL on chromosome 9. An alternative analysis of this study identified a novel cholesterol QTL on chromosome 9 (Cq4), and a significant triglyceride QTL on chromosome 9 (Tgq1). In the present study, QTL analysis was performed on KK x RR F2 mice. A significant cholesterol QTL (Cq5, lod score 5.6) was identified on chromosome 9, and a significant triglyceride QTL (Tgq2, lod score 4.7) was identified on chromosome 8. The Cq5 locus was mapped to a region similar to the Cq4 locus. On the other hand, the Tgq2 locus overlapped with the QTL region responsible for glucose intolerance (Giq1) that was identified in a previous study. The results suggest that a different combination of QTLs is involved in the trait when a different counterpart strain is used. Identification of distinct, but related traits in an identical chromosomal region will facilitate revealing the responsible gene.     

性状遗传力与QTL方差对标记辅助选择效果的影响

在采用动物模型标记辅助最佳线性无偏预测方法对个体育种值进行估计的基础上,模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形,并系统地比较了性状遗传力和QTL方差对标记辅助选择所获得的遗传进展、QTL增效基因频率和群体近交系数变化的影响。结果表明：在对高遗传力和QTL方差较小的性状实施标记辅助选择时,可望获得更大的遗传进展;遗传力越高,QTL方差越大,则QTL增效基因频率的上升速度越快;遗传力较高时,群体近交系数上升的速度较为缓慢,而QTL方差对群体近交系数上升速度的影响则不甚明显。结合前人关于标记辅助选择相对效率的研究结果,可以认为：当选择性状的遗传力和QTL方差为中等水平时,标记辅助选择可望获得理想的效果。     

多个连锁数量性状位点的多性状最优化选择

一种扩展的方法能够在多个世代对具有多个数量性状位点的多性状选择进行最优化。这种方法的基础是在目标雨数中用综合遗传值替代单个性状遗传值,并在整个规划期内最大化所有世代选择反应的加权和。利用多阶段系统优化控制理论,整个最优化问题通过一个向前和向后的迭代循环解决。用一个实际育种猪群的育种参数来评价该方法的选择效果,并和标准QTL选择和常规BLUP选择进行比较。结果表明,优化选择要优于标准QTL选择和常规BLUP选择。经济权重对优化选择的影响较明显,随着达100kg日龄赋予的经济权重的增加,优化选择的优势越明显。优化选择通过两种方式增加总选择反应：1）选择早期减少一部分QTL选择反应;2）对达100kgH龄给予更大的权重。选择后期优化累积贴现选择比优化终端选择给予达100kgH龄更大的权重。     

Genome-wide mapping of Quantitative Trait Loci for fatness,fat cell characteristics and fat metabolism in three porcine F2 crosses

Background

QTL affecting fat deposition related performance traits have been considered in several studies and mapped on numerous porcine chromosomes. However, activity of specific enzymes, protein content and cell structure in fat tissue probably depend on a smaller number of genes than traits related to fat content in carcass. Thus, in this work traits related to metabolic and cytological features of back fat tissue and fat related performance traits were investigated in a genome-wide QTL analysis. QTL similarities and differences were examined between three F₂ crosses, and between male and female animals.

Methods

A total of 966 F₂ animals originating from crosses between Meishan (M), Pietrain (P) and European wild boar (W) were analysed for traits related to fat performance (11), enzymatic activity (9) and number and volume of fat cells (20). Per cross, 216 (M × P), 169 (W × P) and 195 (W × M) genome-wide distributed marker loci were genotyped. QTL mapping was performed separately for each cross in steps of 1 cM and steps were reduced when the distance between loci was shorter. The additive and dominant components of QTL positions were detected stepwise by using a multiple position model.

Results

A total of 147 genome-wide significant QTL (76 at P < 0.05 and 71 at P < 0.01) were detected for the three crosses. Most of the QTL were identified on SSC1 (between 76-78 and 87-90 cM), SSC7 (predominantly in the MHC region) and SSCX (in the vicinity of the gene CAPN6). Additional genome-wide significant QTL were found on SSC8, 12, 13, 14, 16, and 18. In many cases, the QTL are mainly additive and differ between F₂ crosses. Many of the QTL profiles possess multiple peaks especially in regions with a high marker density. Sex specific analyses, performed for example on SSC6, SSC7 and SSCX, show that for some traits the positions differ between male and female animals. For the selected traits, the additive and dominant components that were analysed for QTL positions on different chromosomes, explain in combination up to 23% of the total trait variance.

Conclusions

Our results reveal specific and partly new QTL positions across genetically diverse pig crosses. For some of the traits associated with specific enzymes, protein content and cell structure in fat tissue, it is the first time that they are included in a QTL analysis. They provide large-scale information to analyse causative genes and useful data for the pig industry.