In a typical composite interval mapping experiment, the probability of obtaining false QTL is likely to be at least an order of magnitude greater than the nominal experiment-wise Type I error rate, as set by permutati...In a typical composite interval mapping experiment, the probability of obtaining false QTL is likely to be at least an order of magnitude greater than the nominal experiment-wise Type I error rate, as set by permutation test. F2 mapping crosses were simulated with three different genetic maps. Each map contained ten QTL on either three, six or twelve linkage groups. QTL effects were additive only, and heritability was 50%. Each linkage group had 11 evenly-spaced (10 cM) markers. Selective genotyping was used. Simulated data were analyzed by composite interval mapping with the Zmapqtl program of QTL Cartographer. False positives were minimized by using the largest feasible number of markers to control genetic background effects. Bootstrapping was then used to recover mapping power lost to the large number of conditioning markers. Bootstrapping is shown to be a useful tool for QTL discovery, although it can also produce false positives. Quantitative bootstrap support—the proportion of bootstrap replicates in which a significant likelihood maximum occurred in a given marker interval—was positively correlated with the probability that the likelihood maxima revealed a true QTL. X-linked QTL were detected with much lower power than autosomal QTL. It is suggested that QTL mapping experiments should be supported by accompanying simulations that replicate the marker map, crossing design, sample size, and method of analysis used for the actual experiment.展开更多
Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive...Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping （ICIM） is able to identify epistatic quantitative trait loci （QTLs） no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate （FDR） of ICIM epistatic mapping, by considering F2 and doubled haploid （DH） populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd （LOD） threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping （MIM） in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice （Oryza sativa L.）.展开更多
Many economically important quantita- tive traits in animals and plants are measured re- peatedly over time. These traits are called dynamic traits. Mapping QTL controlling the phenotypic pro- files of dynamic traits ...Many economically important quantita- tive traits in animals and plants are measured re- peatedly over time. These traits are called dynamic traits. Mapping QTL controlling the phenotypic pro- files of dynamic traits has become an interesting topic for animal and plant breeders. However, statistical methods of QTL mapping for dynamic traits have not been well developed. We develop a composite in- terval mapping approach to detecting QTL for dy- namic traits. We fit the profile of each QTL effect with Legendre polynomials. Parameter estimation and statistical test are performed on the regression coefficients of the polynomials under the maximum likelihood framework. Maximum likelihood estimates of QTL parameters are obtained via the EM algorithm. Results of simulation study showed that composite interval mapping can improve both the statistcial power of QTL detecting and the accuracy of parameter estimation relative to the simply interval mapping procedure where only one QTL is fit to each model. The method is developed in the context of an F2 mapping population, but extension to other types of mapping populations is straightforward.展开更多
Most often a genetic linkage map is prepared using populations obtained from two highly diverse genotypes. However, the markers from such a map may not be useful in a breeding program as these markers may not
利用花叶1号×紫茎1号杂交后代衍生的208个F2家系组建群体,构建含有95个SSR标记位点的遗传连锁图谱,该图谱包含11个连锁群,全长1457.47 c M,标记平均间距为15.34 c M。利用复合区间作图法,对株高、幼茎色、主茎色、生长习性、结荚...利用花叶1号×紫茎1号杂交后代衍生的208个F2家系组建群体,构建含有95个SSR标记位点的遗传连锁图谱,该图谱包含11个连锁群,全长1457.47 c M,标记平均间距为15.34 c M。利用复合区间作图法,对株高、幼茎色、主茎色、生长习性、结荚习性、复叶叶形和成熟叶色等农艺性状进行QTL分析,分别检测到与株高、幼茎色、主茎色、复叶叶形有关的QTL各1个,贡献率在8.49%~66.64%之间;与结荚习性有关的QTL3个,贡献率在60.32%~80.36%之间;与成熟叶色有关QTL 4个,贡献率在69.06%~87.35%之间;与生长习性有关的QTL数量最多,共26个,贡献率在58.32%~99.51%之间。上述QTL主要分布在LG1、LG2、LG4、LG8和LG10连锁群,其中LG1最少,仅检测到生长习性的1个QTL,LG4最多,包含了幼茎色、主茎色、结荚习性、生长习性、复叶叶形、成熟期叶色6个农艺性状的15个QTL;这些QTL既可以应用于绿豆育种的分子标记辅助选择,也对深入研究这些性状的遗传奠定了基础。展开更多
文摘In a typical composite interval mapping experiment, the probability of obtaining false QTL is likely to be at least an order of magnitude greater than the nominal experiment-wise Type I error rate, as set by permutation test. F2 mapping crosses were simulated with three different genetic maps. Each map contained ten QTL on either three, six or twelve linkage groups. QTL effects were additive only, and heritability was 50%. Each linkage group had 11 evenly-spaced (10 cM) markers. Selective genotyping was used. Simulated data were analyzed by composite interval mapping with the Zmapqtl program of QTL Cartographer. False positives were minimized by using the largest feasible number of markers to control genetic background effects. Bootstrapping was then used to recover mapping power lost to the large number of conditioning markers. Bootstrapping is shown to be a useful tool for QTL discovery, although it can also produce false positives. Quantitative bootstrap support—the proportion of bootstrap replicates in which a significant likelihood maximum occurred in a given marker interval—was positively correlated with the probability that the likelihood maxima revealed a true QTL. X-linked QTL were detected with much lower power than autosomal QTL. It is suggested that QTL mapping experiments should be supported by accompanying simulations that replicate the marker map, crossing design, sample size, and method of analysis used for the actual experiment.
基金supported by the HarvestPlus Challenge Program of CGIARthe Special Funds for EU Collaboration from the Ministry of Science and Technology of China(Project no.1113)the Seventh Framework Programme of European Commission(Project no.266045)
文摘Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping （ICIM） is able to identify epistatic quantitative trait loci （QTLs） no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate （FDR） of ICIM epistatic mapping, by considering F2 and doubled haploid （DH） populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd （LOD） threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping （MIM） in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice （Oryza sativa L.）.
基金supported by the National Natural Science Foundation of China(Grant No.30471236).
文摘Many economically important quantita- tive traits in animals and plants are measured re- peatedly over time. These traits are called dynamic traits. Mapping QTL controlling the phenotypic pro- files of dynamic traits has become an interesting topic for animal and plant breeders. However, statistical methods of QTL mapping for dynamic traits have not been well developed. We develop a composite in- terval mapping approach to detecting QTL for dy- namic traits. We fit the profile of each QTL effect with Legendre polynomials. Parameter estimation and statistical test are performed on the regression coefficients of the polynomials under the maximum likelihood framework. Maximum likelihood estimates of QTL parameters are obtained via the EM algorithm. Results of simulation study showed that composite interval mapping can improve both the statistcial power of QTL detecting and the accuracy of parameter estimation relative to the simply interval mapping procedure where only one QTL is fit to each model. The method is developed in the context of an F2 mapping population, but extension to other types of mapping populations is straightforward.
文摘Most often a genetic linkage map is prepared using populations obtained from two highly diverse genotypes. However, the markers from such a map may not be useful in a breeding program as these markers may not
文摘利用花叶1号×紫茎1号杂交后代衍生的208个F2家系组建群体,构建含有95个SSR标记位点的遗传连锁图谱,该图谱包含11个连锁群,全长1457.47 c M,标记平均间距为15.34 c M。利用复合区间作图法,对株高、幼茎色、主茎色、生长习性、结荚习性、复叶叶形和成熟叶色等农艺性状进行QTL分析,分别检测到与株高、幼茎色、主茎色、复叶叶形有关的QTL各1个,贡献率在8.49%~66.64%之间;与结荚习性有关的QTL3个,贡献率在60.32%~80.36%之间;与成熟叶色有关QTL 4个,贡献率在69.06%~87.35%之间;与生长习性有关的QTL数量最多,共26个,贡献率在58.32%~99.51%之间。上述QTL主要分布在LG1、LG2、LG4、LG8和LG10连锁群,其中LG1最少,仅检测到生长习性的1个QTL,LG4最多,包含了幼茎色、主茎色、结荚习性、生长习性、复叶叶形、成熟期叶色6个农艺性状的15个QTL;这些QTL既可以应用于绿豆育种的分子标记辅助选择,也对深入研究这些性状的遗传奠定了基础。
