欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2013, Vol. 39 ›› Issue (01): 1-11.doi: 10.3724/SP.J.1006.2013.00001

• 作物遗传育种·种质资源·分子遗传学 •    下一篇

QTL作图中零假设检验统计量分布特征及LOD临界值估计方法研究

孙子淇,李慧慧,张鲁燕,王建康*   

  1. 中国农业科学院作物科学研究所 / 农作物基因资源与基因改良国家重大科学工程 / CIMMYT中国办事处, 北京100081
  • 收稿日期:2012-05-13 修回日期:2012-09-05 出版日期:2013-01-12 网络出版日期:2012-11-14
  • 通讯作者: 王建康, E-mail: wangjk@caas.net.cn, jkwang@cgiar.org, Tel: 010-82105846
  • 基金资助:

    本研究由国家自然科学基金项目(31000540)资助。

Properties of the Test Statistic under Null Hypothesis and the Calculation of LOD Threshold in Quantitative Trait Loci (QTL) Mapping

SUN Zi-Qi,LI Hui-Hui,ZHANG Lu-Yan,WANG Jian-Kang*   

  1. Institute of Crop Sciences / National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences / CIMMYT China Office, Beijing 100081, China
  • Received:2012-05-13 Revised:2012-09-05 Published:2013-01-12 Published online:2012-11-14
  • Contact: 王建康, E-mail: wangjk@caas.net.cn, jkwang@cgiar.org, Tel: 010-82105846

RichHTML

PDF

1878

被引次数

2

摘要：

研究QTL作图的零假设检验统计量分布特征, 可以帮助我们选取合适的LOD临界值, 以控制全基因组第一类错误的概率。本文利用模拟方法, 研究了QTL作图中单个扫描位点的似然比检验(LRT)统计量在零假设下的分布特征、影响最大LOD统计量累积分布的因素以及不同群体在不同标记密度下有效独立检验次数与染色体长度的关系。结果表明, 在定位加显性效应QTL的一维扫描和定位上位性互作QTL的二维扫描中, 单个扫描位置上的LRT统计量均服从卡方分布, 其自由度等于检测QTL遗传参数的个数; 染色体个数、群体大小和表型测量误差对零假设下检验统计量的分布没有影响, 即不影响LOD临界值的选取, 而群体类型、标记密度和染色体长度有明显影响, BC1、RIL和F2三种类型的群体中, BC1群体的临界值最小, F2群体的临界值最大, 标记越密、染色体越长, 对应的LOD临界值越大; QTL一维扫描中有效独立检验次数与染色体长度呈正比, 二维扫描中有效独立检验次数与染色体长度呈二次幂关系。借助Bonferroni矫正, 给出了全基因组显著性水平与单个扫描位点显著性水平间的关系, 因此, 研究者可根据作图群体的群体类型、标记密度和基因组长度, 很方便地确定特定全局显著性概率水平下的LOD临界值。

关键词: QTL作图, 似然比检验, LOD统计量, 零假设, 显著性水平, 独立检验次数

Abstract:

Selecting an appropriate LOD threshold is of great interest in QTL mapping studies. Many approaches can be considered to calculate the critical value throughout a genome, such as simulation-based method, analytical approximation, and empirical method based on permutation test. Many tests are conducted in QTL mapping, which are not mutually independent because the linkage relationship of adjacent markers on chromosomes. In order to declare a significant QTL at a genome-wide significance level, it is necessary to understand the behavior of test statistic under null hypothesis in QTL mapping and to deal with the dependent multiple-test problem arising in the genome-wide test. Our objectives in this study were (1) to investigate the properties of LRT (likelihood ratio test) statistic of one-point scanning under null hypothesis in QTL mapping, (2) to determine the factors affecting the cumulative distribution of maximum LOD score, and (3) to identify the relationship between the effective number of independent tests and the length of chromosome by simulation method. Results indicated that the LRT test statistic in one-dimensional scanning of additive-dominant QTL and two-dimensional scanning of epistatic QTL followed chi-square distributions, and the degree of freedom (df) was equal to the number of genetic parameters to be estimated. For example, degree of freedom in recombinant inbred lines (RIL) population was equal to 1 in one dimensional or two dimensional scanning. Degree of freedom in F2 populations was equal to 2 in one-dimensional scanning and 4 in two-dimensional scanning. Number of chromosome, population size and phenotyping error variance did not have any effect on the distribution of LRT under null hypothesis, and therefore will not affect the selection of LOD threshold. On the contrary, population type, genome size and marker density had significant impacts. For BC1, RIL and F2 populations, the threshold was the smallest in BC1 population and the highest in F2 population. Higher marker density  and longer chromosome resulted in higher LOD threshold. It was identified that the effective number of independent tests (Meff) was proportional to the length of chromosome in one-dimensional scanning of additive-dominant QTL. In two-dimensional scanning of epistatic QTL, it was identified that Meffwas in a squared relationship to the length of chromosome. With the help of Bonferroni correction, we could acquire the relationship between point-wise and genome-wide significance levels. Therefore, it is convenient to calculate the threshold LOD in QTL mapping, given the genome-wide significance level, the population type, marker density and genome size.

Key words: QTL mapping, Likelihood ratio test, LOD score, Null hypothesis, Significance level, Number of independent tests

[1]Lander E S, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics, 1989, 121: 185–199



[2]van Ooijen J W. LOD significance thresholds for QTL analysis in experimental populations of diploid species. Heredity, 1999, 83: 613–624



[3]Dupuis J, Siegmund D. Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics, 1999, 151: 373–386



[4]Feingold E, Brown P O, Siegmund D. Gaussian models for genetic linkage analysis using complete high-resolution maps of identity by descent. Am J Hum Genet, 1993, 53: 234–251



[5]Rebaï A, Goffinet B, Mangin B. Approximate thresh olds of interval mapping tests for QTL detection. Genetics, 1994, 138: 235–240



[6]Dupuis J. Statistical Problems Associated with Mapping Complex and Quantitative Traits from Genomic Mismatch Scanning Data. PhD Dissertation of Stanford University, 1994



[7]Kruglyak L, Lander E S. A nonparametric approach for mapping quantitative trait loci. Genetics, 1995, 139: 1421–1428



[8]Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genet, 1995, 11: 241–247



[9]Piepho H P. A quick method for computing approximate thresholds for quantitative trait loci detection. Genetics, 2001, 157: 425–432



[10]Zou F, Fine J P, Hu J H, Lin D Y. An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait loci. Genetics, 2004, 168: 2307–2316



[11]Chang M N, Wu R, Wu S S, Casella G. Score statistics for mapping quantitative trait loci. Stat Appl Genet Mol Biol, 2009, 8(1): Article 16



[12]Churchill G A, Doerge R W. Empirical threshold values for quantitative trait mapping. Genetics, 1994, 138: 963–971



[13]Cheverud J M. A simple correction for multiple comparisons in interval mapping genome scans. Heredity, 2001, 87: 52–58



[14]Nyholt D R. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet, 2004, 74: 765–769



[15]Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity, 2005, 95: 221–227



[16]Gao X, Starmer J, Martin E R. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genetic Epidemiology, 2008, 32: 361–369



[17]Li H, Ribaut J M, Li Z, Wang J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor Appl Genet, 2008, 116: 243–260



[18]Zhang L, Li H, Li Z, Wang J. Interactions between markers can be caused by the dominance effect of QTL. Genetics, 2008, 180: 1177–1190



[19]Zhang L, Li H, Wang J. Statistical power of inclusive composite interval mapping in detecting digenic epistasis showing common F2 segregation ratios. J Integr Plant Biol, 2012, 54: 270–279



[20]Doerge R W, Rebaï A. Significance thresholds for QTL interval mapping tests. Heredity, 1996, 76: 459–464



[21]Wang J, Chapman S C, Bonnett D G, Rebetzke G J. Simultaneous selection of major and minor genes: use of QTL to increase selection efficiency of coleoptiles length of wheat (Triticum aestivum L.). Theor Appl Genet, 2009, 119: 65–74



[22]Ribaut J M, Hoisington D A, Deutsch J A, Jiang C, González-de-León D. Identification of quantitative trait loci under drought conditions in tropical maize: 1. Flowering parameters and the anthesis-silking interval. Theor Appl Genet, 1996, 92: 905–914



[23]Zhang L, Wang S, Li H, Deng Q, Zheng A, Li S, Li P, Li Z, Wang J. Effects of missing marker and segregation distortion on QTL mapping in F2 populations. Theor Appl Genet, 2010, 121: 1071–1082



[24]Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping. Genetics, 2007, 175: 361–374



[25]Li H-H(李慧慧), Zhang L-Y(张鲁燕), Wang J-K(王建康). Analytical answers to frequently asked questions in quantitative trait locus mapping. Acta Agron Sin (作物学报), 2010, 36(6): 918–931 (in Chinese with English abstract)
[1] 李书宇, 黄杨, 熊洁, 丁戈, 陈伦林, 宋来强. 甘蓝型油菜早熟性状QTL定位及候选基因筛选[J]. 作物学报, 2021, 47(4): 626-637.
[2] 李慧慧,张鲁燕,王建康. 数量性状基因定位研究中若干常见问题的分析与解答[J]. 作物学报, 2010, 36(06): 918-931.
[3] 王建康. 数量性状基因的完备区间作图方法[J]. 作物学报, 2009, 35(2): 239-245.
[4] 王学枫;汤在祥;王亚民;宋雯;徐辰武. 基于NC III和TTC设计的胚乳性状QTL区间作图方法[J]. 作物学报, 2008, 34(10): 1734-1743.
[5] 敖雁;徐辰武. 四向杂交设计QTL分析的极大似然方法[J]. 作物学报, 2006, 32(01): 51-56.
[6] 李杰勤;张启军;叶少平;赵兵;梁永书;彭勇;吴发强;王世全;李平. 四种不同QTL作图方法的比较研究[J]. 作物学报, 2005, 31(11): 1473-1477.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2