Welcome to Acta Agronomica Sinica,

Acta Agron Sin ›› 2014, Vol. 40 ›› Issue (01): 72-79.doi: 10.3724/SP.J.1006.2014.00072

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Statistical Genetics Approach for Functional Difference Identification of Allelic Variations and Its Application

HU Wen-Ming**,KAN Hai-Hua**,WANG Wei,XU Chen-Wu*   

  1. Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, China
  • Received:2013-04-01 Revised:2013-07-25 Online:2014-01-12 Published:2013-10-01

Abstract:

Allelic variations are ubiquitous in organisms, and play important roles in regulating genes expression. In order to study the influence of number of varieties (A), average polymorphism information content (B) and total contribution of candidate genes (C) on the association analysis of candidate genes, the empirical Bayes (E-Bayes) method was applied to explore the effects of abovementioned three factors on the statistical power of candidate genes, the accuracy and precision of the estimates of genetic effects and the false discovery rate (FDR). Results were as follows: (1) With the increase of factors A, B, and C, the statistical power and the accuracy and precision of the estimates of genetic effects were all enhanced, meanwhile the FDR was decreased; (2) Factor B had a significant influence on the statistical power of candidate genes. When factor B was at a higher level, the averaged statistical power could still reach 80% even though both factors A and C remained at lower levels. When factor B was at a medium level, more varieties were needed to ensure that the statistical power could reach 80%. However, when factor B was at a lower level, even though factor A was equal to 100, the statistical power in three different levels of factor C could not reach 50%; (3) Factor B had a significant impact on the accuracy and precision of estimated effects of candidate genes. With the increase of factor B, both the accuracy and precision of effect estimates for candidate genes were improved simultaneously; (4) Factor B also had an important effect on FDR. Through a real data analysis in rice, four detected candidate genes were significantly associated with pasting temperature (PT) by our model. Therefore, the polymorphism information content is a primary factor for detecting the functional difference of alleles. In addition, more varieties and higher contribution rate also have important influence on the statistical power and the accuracy and precision of estimates of effects.

Key words: Allelic variation, Oversaturated model, Variable selection, E-Bayes

[1]Galton F. Regression towards mediocrity in hereditary stature. J Anthropol Inst Great Brit Ireland, 1886, 15: 246–263



[2]Guo M, Yang S, Rupe M, Hu B, Bickel D R, Oscar L A. Genomewide allele-specific expression analysis using Massively Parallel Signature Sequencing (MPSS™) reveals cis- and trans-effects on gene expression in maize hybrid meristem tissue. Plant Mol Biol, 2008, 66: 551–563



[3]Schaart J G, Mehli L, Schouten H J. Quantification of allele-specific expression of a gene encoding strawberry polygalacturonase-inhibiting protein (PGIP) using pyrosequencing. Plant J, 2005, 41: 493–500



[4]Yoon M Y, Moe K T, Kim D Y, Rho I R, Kim S, Kim K T, Won M K, Chung J W, Park Y J. Genetic diversity and population structure analysis of strawberry (Fragaria ? ananassa Duch.) using SSR markers. Electr J Biotechnol, 2012, 15(2): 5



[5]Adams K L, Cronn R, Percifield R, Wendel J F. Genes duplicated by polyploidy show unequal contributions to the transcriptome and organ-specific reciprocal silencing. Proc Natl Acad Sci USA, 2003, 100: 4649–4654



[6]Kolev S, Ganeva G, Christov N, Belchev I, Kostov K, Tsenov N, Rachovska G, Landgeva S, Ivanov M, Abu-Mhadi N, Todorovska E. Allele variation in loci for adaptine response and plant height and its effect on grain yeild in wheat. Biotechnol Biotechnol Equip, 2010, 24: 1807–1813



[7]Kolev S, Vassilev D, Kostov K, Todorovska E. Allele variation in loci for adaptive response in Bulgarian wheat cultivars and landraces and its effect on heading date. Plant Genet Resour Char Util, 2011, 9: 251–255



[8]Xie H-L(谢会兰). The Foundation of Molecular Markers Correlated with Rice Starch and Preliminary Detection of Its Genetic Network. MS Dissertation of Yangzhou University, 2007 (in Chinese with English abstract)



[9]Soller M, Brody T. On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor Appl Genet, 1976, 47: 35–39



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



[11]Zeng Z B. Precision mapping of quantitative trait loci. Genetics, 1994, 136: 1457–1468



[12]Li H H, Ye G Y, Wang J K. A modi?ed algorithm for the improvement of composite e interval mapping. Genetics, 2007, 175: 361–374



[13]Zeng Z B, Kao C, Basten C J. Estimating the genetic architecture of quantitative traits. Genet Res, 2000, 74: 279–289



[14]Zhang Y M, Xu S. A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity, 2005, 95: 96–104



[15]Cohen R A. Introducing the glmselect procedure for model selection. Statist & Data Anal, 31: 207–231



[16]Robin M, David D. Two-level stochastic search variable selection in GLMs with missing predictors. Int J Biostat, 2010, 6(1): 33



[17]Ntzoufras I, Forster J J, Dellaportas P. Stochastic search variable selection for log-linear models. J Stat Comput Sim, 2000, 68: 23–37



[18]Yi N, George V, Allison D B. Stochastic search variable selection for identifying multiple quantitative trait loci. Genetics, 2003, 164: 1129–1138



[19]Xu S. An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics, 2007, 63: 513–521



[20]Xu S, Jia Z. Genomewide analysis of epistatic effects for quantitative traits in barley. Genetics, 2007, 175: 1955–1963



[21]Li H H, Hearne S, Bänziger M, Li Z, Wang J. Statistical properties of QTL linkage mapping in biparental genetic populations. Heredity, 2010, 105: 257–267



[22]Li H-H(李慧慧). The analysis and solution of some common questions in quantitative traits QTL mapping. Acta Agron Sin (作物学报), 2010, 36(6): 918–931 (in Chinese with English abstract)

[1] ZHANG Yu-Kun, LU Ying, CUI Kan, XIA Shi-Tou, LIU Zhong-Song. Allelic variation and geographical distribution of TT8 for seed color in Brassica juncea Czern. et Coss. [J]. Acta Agronomica Sinica, 2022, 48(6): 1325-1332.
[2] ZHANG Fu-Yan, CHENG Zhong-Jie, CHEN Xiao-Jie, WANG Jia-Huan, CHEN Feng, FAN Jia-Lin, ZHANG Jian-Wei, YANG Bao-An. Molecular identification and breeding application of allelic variation of grain weight gene in wheat from the Yellow-Huai-River Valley [J]. Acta Agronomica Sinica, 2021, 47(11): 2091-2098.
[3] WANG Juan,DONG Cheng-Guang,LIU Li,KONG Xian-Hui,WANG Xu-Wen,YU Yu. Association Analysis and Exploration of Elite Alleles of Mechanical Harvest-Related Traits with SSR Markers in Upland Cotton Cultivars (Gossypium hirsutum L.) [J]. Acta Agron Sin, 2017, 43(07): 954-966.
[4] DONG Xue,LIU Meng,ZHAO Xian-Lin,FENG Yu-Mei,YANG Yan. Isolation and Characterization of LMW-GS Glu-A3 in Common Wheat Related Species [J]. Acta Agron Sin, 2017, 43(06): 829-838.
[5] KOU Cheng,GAO Xin,LI Li-Qun,LI Yang,WANG Zhong-Hua,LI Xue-Jun*. Composition and Selection of TaGW2-6A Alleles for Wheat Kernel Weight [J]. Acta Agron Sin, 2015, 41(11): 1640-1647.
[6] LI Wen,WAN Qian,LIU Feng-Zhen*,ZHANG Kun,ZHANG Xiu-Rong,LI Guang-Hui,WAN Yong-Shan. Allelic Variation of Transcription Factor Genes NAC4 in Arachis Species [J]. Acta Agron Sin, 2015, 41(01): 31-41.
[7] LIU Ya-Nan,XIA Xian-Chun,HE Zhong-Hu. Characterization of Dense and Erect Panicle 1 Gene (TaDep1) Located on Common Wheat Group 5 Chromosomes and Development of Allele-Specific Markers [J]. Acta Agron Sin, 2013, 39(04): 589-598.
[8] HUANG Bing-Yan,ZHANG Xin-You,MIAO Li-Juan,GAO Wei,HAN Suo-Yi,DONG Wen-Zhao,TANG Feng-Shou,LIU Zhi-Yong. Allelic Expression Variation of ahFAD2A and its Relationship with Oleic Acid Accumulation in Peanut [J]. Acta Agron Sin, 2012, 38(10): 1752-1759.
[9] LI Wei-Yu,ZHANG Bin,ZHANG Jia-Nan,CHANG Xiao-Ping,LI Run-Zhi,JING Rui-Lian. Exploring Elite Alleles for Chlorophyll Content of Flag Leaf in Natural Population of Wheat by Association Analysis [J]. Acta Agron Sin, 2012, 38(06): 962-970.
[10] WU Yong-Sheng,LI Xin-Hai,HAO Zhuan-Fang,ZHANG Shi-Huang,XIE Chuan-Xiao. Genomic DNA Sequence,Gene Structure,Conserved Domains,and Natural Alleles of Gln1-4 Gene in Maize [J]. Acta Agron Sin, 2009, 35(6): 983-991.
[11] LI Gen-Ying;XIA Xian-Chun;HE Zhong-Hu;SUN Qi-Xin;HUANG Cheng-Yan. Distribution of Grain Hardness and Puroindoline Alleles in Landraces, Historical and Current Wheats in Shandong Province [J]. Acta Agron Sin, 2007, 33(08): 1372-1374.
[12] CHEN Feng;HE Zhong-Hu;Morten Lillemo;XIA Xian-Chun. Detection of Allelic Variation for Grain Hardness in CIMMYT Common Wheats [J]. Acta Agron Sin, 2005, 31(10): 1277-1283.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!