遗传分析方法和软件开发及其应用
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摘要
大多数农艺性状都是数量性状,受多个基因位点共同影响和控制。这些基因位点不仅可能产生单独影响,还可能产生上位性互作效应。基因位点产生的效应大多数对环境敏感,存在基因与环境互作。传统的遗传研究方法通常用于分析较小的试验群体数据,分子标记密度较稀疏,并且仅对单个数量性状分析。然而数量性状的复杂性在生物体中的不同层次都有体现,如DNA水平,RNA水平,蛋白质水平,代谢水平,表现型水平等。为了进一步研究农作物遗传机理,研究者通常采用自然群体或者复杂的试验设计,收集一系列相互关联的农艺性状。此外,随着生物技术的快速发展,收集和获取高通量数据越来越容易和自动化,先进的技术手段被应用于农作物研究中。例如,在DNA水平中,由于快速测序和测序成本逐渐降低,SNP标记开始代替传统分子标记用于农业作物研究和应用中；在RNA水平中,研究者把基因芯片数据与其他实验数据结合一起深入研究作物遗传结构。这些给遗传统计分析方法带来巨大机遇和挑战。
本文就目前农业复杂性状遗传分析中遇到的若干问题为出发点,提出了一些相应的统计分析方法,并通过蒙特卡洛模拟和实例分析验证这些方法的有效性和可靠性。全文共分为四章,主要内容概括如下：
第一章首先简单介绍目前农业复杂性状遗传分析中遇到的若干问题以及相关的统计遗传学分析方法。由于本文涉及到统计检验和效应值估算等统计分析方法及其原理,因而简要地介绍了这些相关的统计方法。
第二章针对传统QTL连锁定位方法遇到的问题,我们进一步拓展了分析方法。本章共分为两个个小节,1.第一节介绍三位点上位性互作的搜索方法和策略。位点之间的上位性是影响复杂性状表现型变异的重要原因,许多文献证实了上位性的存在。此外,上位性被认为是遗传率缺失的重要因素,准确估计上位性有助于提高个体基因型预测值,对作物育种、疾病预防都有重要价值。然而目前主要的QTL定位方法尚无法定位三位点上位性互作。鉴于此,我们提出三位点高维互作的定位方法和策略,并通过一系列蒙特卡洛模拟验证该定位方法的有效性和准确性。2.第二节介绍多个数量性状联合QTL定位方法。农业研究领域中,一个复杂性状往往与其他多个性状具有不同程度的相关性。传统的基因定位方法仅局限于分析单个性状,忽视了性状之间的相关信息。这会带来一些问题,如不能很好控制一类错误。因而我们提出了多性状联合基因定位方法,采用Wilks'Lambda统计量检验单位点和二互作上位性,并用置换检验控制整个基因组的假阳性发生率。随机模拟验证了该方法的可行性和有效性,并且在水稻和小鼠的实例分析中我们搜索到多个显著位点。
第三章我们提出针对高通量数据的全基因组关联分析方法(GWAS)。本章也分为两小节,1.第一节介绍基于SNP标记的提出了数量性状SNP(QTS)关联分析方法。由于高通量生物技术不断发展成熟,SNP标记被广泛使用,关联分析方法也随之成为常用的分析工具。我们就目前常用的统计分析方法中存在的问题,提出了基于混合线性回归模型的QTS关联分析方法,把上位性检验以及位点与环境互作检测整合在一个模型中,采用F测验检验位点显著性,运用置换检验方法控制假阳性发生率。蒙特卡洛随机模拟和小鼠实例数据验证了方法的可行性和有效性。2.第二节介绍可对数量性状转录座(QTT)、数量性状蛋白座(QTP)和数量性状代谢座(QTM)作关联分析的方法。基因芯片是研究生物复杂性状的重要工具。已被广泛应用于疾病诊断,药物筛选,农作物育种等诸多领域。与传统的聚类分析和eQTL、pQTL定位不同,我们提出的QTT/P/M关联分析方法基于连续型性状变异,搜索与其显著相关的转录座、蛋白座和代谢座位点,包括单个显著位点和成对位点之间的上位性互作。蒙特卡洛随机模拟验证了该关联分析方法的可靠性与有效应。并且在小鼠的实例研究中,我们不仅找到了显著的转录座位点,并把结果与QTS关联分析以及QTL连锁分析定位结果结合,更进一步了解基因作用方式。
第四章介绍QTXNetwork定位分析软件QTXNetwork是一个遗传分析软件,基于C++语言开发,具有可视化界面。其适用性广,包括QTL连锁定位分析,SNP关联分析和转录座关联分析。QTXNetwork目前版本具有以下几部分功能：1.搜索候选数量性状基因座(QTL)、数量性状SNP(QTS)、数量性状转录座(QTT)、数量性状蛋白座(QTP)和数量性状代谢座(QTM)包括显著关联的单位点,上位性位点；2.估算显著位点的遗传效应值和遗传率,包括主效应,与环境互作效应,以及相对应的遗传贡献率；3.最佳基因组合和相应遗传效应预测,根据各个显著位点和相应的遗传效应估计值,预测在每个环境中的最佳基因位点组合和相应的遗传效应；4.样本个体的遗传效应值预测,根据各个显著位点和相应的遗传效应估计值,预测每个样本个体的遗传效应值,并列出效应值最大和最小的一些个体。此外,QTXNetwork的使用简单,操作方便。
Most agronomically important traits of crops are quantitative in nature, and their genetic variations are usually controlled by a set of genes, called quantitative trait loci (QTLs). There exist genes by genes interaction and genes by environments interaction. Since the genetic architecture of these traits is so complex, the trait variation is not only due to individual QTLs and their interactions, but also to the network of SNPs, RNAs, proteins, and metablites. In order to study the mechanism of complex traits, plant breeders and researchers adopt more complicated experimental design or natural population, and collect a series of correlated phenotype data. In addition, owing to advanced high-throughput biological technologies, it is convenient to acquire large-scale biological data. For instance, SNPs (Single Nucleotide Polymorphisms) become wildly used in the field of crop science, instead of conventional molecular markers, such as SSRs (Simple Sequence Repeats), AFLPs (Amplified Fragment Length Polymorphisms), RFLPs (Restriction Fragment Length Polymorphisms) and etc. Similarly, gene-expression microarrays have been combined with some other experimental approaches to find the genetic mechanism of complex traits. They bring challenges to the biostatistical methods.
We have developed new statistical methodologies and theories to settle the issues which current exist in genetics and plant breeding. We also investigate the efficiency and effectiveness of the proposed statistical methods by Monte Carlo simulations and real data analysis. The main contents of the dissertation are as follows,
1. The first chapter introduces the recent issues exist in some corresponding statistical methods. The main motivation of the dissertation is to provide some solutions to the genetic analysis. Then, we briefly introduce several recent statistical methods on hypothesis test and genetic effect estimation.
2. The second chapter expands the conventional linkage analysis, considering the challenges in recent applications. The chapter is divided into two sections.1. The first section introduces the statistical methods of multi-trait mapping for quantitative trait loci (QTLs). Most approaches can only do QTL mapping separately by a phenotype-driven method focusing on an individual trait. However, complex trait data usually contain observations on multiple traits and in multiple environments or under different treatment conditions. Therefore, several potential problems arise; for instance, determining whether multiple traits are affected by a single QTL with pleiotropic effects, or by multiple closely linked QTLs. We proposed a multivariate statistical model to conduct multiple-trait analysis, using Wilks' Lambda statistic to test the individual loci and epistatic interaction. Monte Carlo simulations and real data analysis are conducted to demonstrate the applicableness and powerfulness of the methods.2. The second section introduces a statistical strategy to mapping triplet interactions. The epistasis, interaction between loci or genes, indicates that the effect of a specific genotype combination on the genotype depends on the genetic background. A lot of works provide the evidence of significance of epistasis. In addition, the epistasis is reported to be a potential key driver of missing heritability. Identification of interactions between genes is able to improve the genetic predition, which is contributive to the disease-risk classification and plant breeding. In current, however, the mainly statistical methods for searching QTLs could not detect triplet interactions. Thus, we proposed a mapping strategy which is based on mixed linear model approach to search high order interactions. Monte Carlo simulations are performed to investigate the effectiveness and efficiency of the proposed methods.
3. The thrid chapter introduces the newly proposed statistical methods on genome-wise association study (GWAS) to analyze the high-thro ugh put biological data. The chapter is also divided into two sections.
1. The first section introduces the association analysis of quantitative trait SNPs (QTS). Owing to high-throughput genotyping technologies, simultaneous comparison of groups of loci, and density of SNPs, SNP markers are widely used in biomedical, plant and animal researches. Meanwhile, association analysis becomes a common tool to handle the large-scale data. Due to deficience of the stastitical model, we proposed a mixed linear model appraoch based assocation analysis of SNPs. It could detect individual and epistatic interacting loci, as well as estimate their main effects and loci by environment interaction effects. We also perform Monte Carlo simulations and real data analysis to demonstrate the applicableness and efficiency of the methods.2. The second section introduces the association analysis of quantitative trait transcript (QTT), quantitative trait protein (QTP), and quantitative trait metabolite (QTM). The gene-expression, protein and metabolite profiles have been applied to a wild range of biology problems. It is a kind of tool for biomarkers identification, diseases diagnosis, drug screening, and plant breeding. On the other hand, the profiles of gene-expression level, protein level and metabolite level are recently combined with other experimental approaches to identify the key mechanisms of complex traits. Contrary to the expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTL) mapping, we proposed association analysis of transcripts, proteins and metabolites, which considers the profiles data as a type of markers to study the association with an organismal quantitative trait. A series of Monte Carlo simulations are conducted to investigate the effectiveness and efficieness of the statistical appraoch. In the real case study, we integrated the QTL linkage analysis, QTS and QTT association analysis into a mapping system to elucidate the potential drivers of complex traits. Besides, it is also a trial to investigate genetic mechanism of complex traits in detailed based on a series of high-throughput data, such as SNPs, CNVs (copy number variations), gene-expression microarray, protein profiles, and metabolite profiles.
4. The fourth chapter introduces the newly developed software, which is mainly focus on statistical analysis-QTXNetwork. The software package is developed by C++to map the QTL, QTS, QTT, QTP, and QTM for complex traits, which could handle data from multiple-environment traits (METs). It could perform1) casual loci detection, including individual, two-and three-way loci,2) effect and heritability estimation of significant loci, including individual,2-and3-way effects, as well as interaction effects and heritability between the loci and environments,3) superior genotype effects prediction, predicting the best genetic effect of an individual in a specific environment based on known loci genotype,4) individual genotype effects prediction, predicting genetic effects of every individual in a specific environment based on loci detected, and listing the top and bottom ones.
本文就目前农业复杂性状遗传分析中遇到的若干问题为出发点,提出了一些相应的统计分析方法,并通过蒙特卡洛模拟和实例分析验证这些方法的有效性和可靠性。全文共分为四章,主要内容概括如下：
第一章首先简单介绍目前农业复杂性状遗传分析中遇到的若干问题以及相关的统计遗传学分析方法。由于本文涉及到统计检验和效应值估算等统计分析方法及其原理,因而简要地介绍了这些相关的统计方法。
第二章针对传统QTL连锁定位方法遇到的问题,我们进一步拓展了分析方法。本章共分为两个个小节,1.第一节介绍三位点上位性互作的搜索方法和策略。位点之间的上位性是影响复杂性状表现型变异的重要原因,许多文献证实了上位性的存在。此外,上位性被认为是遗传率缺失的重要因素,准确估计上位性有助于提高个体基因型预测值,对作物育种、疾病预防都有重要价值。然而目前主要的QTL定位方法尚无法定位三位点上位性互作。鉴于此,我们提出三位点高维互作的定位方法和策略,并通过一系列蒙特卡洛模拟验证该定位方法的有效性和准确性。2.第二节介绍多个数量性状联合QTL定位方法。农业研究领域中,一个复杂性状往往与其他多个性状具有不同程度的相关性。传统的基因定位方法仅局限于分析单个性状,忽视了性状之间的相关信息。这会带来一些问题,如不能很好控制一类错误。因而我们提出了多性状联合基因定位方法,采用Wilks'Lambda统计量检验单位点和二互作上位性,并用置换检验控制整个基因组的假阳性发生率。随机模拟验证了该方法的可行性和有效性,并且在水稻和小鼠的实例分析中我们搜索到多个显著位点。
第三章我们提出针对高通量数据的全基因组关联分析方法(GWAS)。本章也分为两小节,1.第一节介绍基于SNP标记的提出了数量性状SNP(QTS)关联分析方法。由于高通量生物技术不断发展成熟,SNP标记被广泛使用,关联分析方法也随之成为常用的分析工具。我们就目前常用的统计分析方法中存在的问题,提出了基于混合线性回归模型的QTS关联分析方法,把上位性检验以及位点与环境互作检测整合在一个模型中,采用F测验检验位点显著性,运用置换检验方法控制假阳性发生率。蒙特卡洛随机模拟和小鼠实例数据验证了方法的可行性和有效性。2.第二节介绍可对数量性状转录座(QTT)、数量性状蛋白座(QTP)和数量性状代谢座(QTM)作关联分析的方法。基因芯片是研究生物复杂性状的重要工具。已被广泛应用于疾病诊断,药物筛选,农作物育种等诸多领域。与传统的聚类分析和eQTL、pQTL定位不同,我们提出的QTT/P/M关联分析方法基于连续型性状变异,搜索与其显著相关的转录座、蛋白座和代谢座位点,包括单个显著位点和成对位点之间的上位性互作。蒙特卡洛随机模拟验证了该关联分析方法的可靠性与有效应。并且在小鼠的实例研究中,我们不仅找到了显著的转录座位点,并把结果与QTS关联分析以及QTL连锁分析定位结果结合,更进一步了解基因作用方式。
第四章介绍QTXNetwork定位分析软件QTXNetwork是一个遗传分析软件,基于C++语言开发,具有可视化界面。其适用性广,包括QTL连锁定位分析,SNP关联分析和转录座关联分析。QTXNetwork目前版本具有以下几部分功能：1.搜索候选数量性状基因座(QTL)、数量性状SNP(QTS)、数量性状转录座(QTT)、数量性状蛋白座(QTP)和数量性状代谢座(QTM)包括显著关联的单位点,上位性位点；2.估算显著位点的遗传效应值和遗传率,包括主效应,与环境互作效应,以及相对应的遗传贡献率；3.最佳基因组合和相应遗传效应预测,根据各个显著位点和相应的遗传效应估计值,预测在每个环境中的最佳基因位点组合和相应的遗传效应；4.样本个体的遗传效应值预测,根据各个显著位点和相应的遗传效应估计值,预测每个样本个体的遗传效应值,并列出效应值最大和最小的一些个体。此外,QTXNetwork的使用简单,操作方便。
Most agronomically important traits of crops are quantitative in nature, and their genetic variations are usually controlled by a set of genes, called quantitative trait loci (QTLs). There exist genes by genes interaction and genes by environments interaction. Since the genetic architecture of these traits is so complex, the trait variation is not only due to individual QTLs and their interactions, but also to the network of SNPs, RNAs, proteins, and metablites. In order to study the mechanism of complex traits, plant breeders and researchers adopt more complicated experimental design or natural population, and collect a series of correlated phenotype data. In addition, owing to advanced high-throughput biological technologies, it is convenient to acquire large-scale biological data. For instance, SNPs (Single Nucleotide Polymorphisms) become wildly used in the field of crop science, instead of conventional molecular markers, such as SSRs (Simple Sequence Repeats), AFLPs (Amplified Fragment Length Polymorphisms), RFLPs (Restriction Fragment Length Polymorphisms) and etc. Similarly, gene-expression microarrays have been combined with some other experimental approaches to find the genetic mechanism of complex traits. They bring challenges to the biostatistical methods.
We have developed new statistical methodologies and theories to settle the issues which current exist in genetics and plant breeding. We also investigate the efficiency and effectiveness of the proposed statistical methods by Monte Carlo simulations and real data analysis. The main contents of the dissertation are as follows,
1. The first chapter introduces the recent issues exist in some corresponding statistical methods. The main motivation of the dissertation is to provide some solutions to the genetic analysis. Then, we briefly introduce several recent statistical methods on hypothesis test and genetic effect estimation.
2. The second chapter expands the conventional linkage analysis, considering the challenges in recent applications. The chapter is divided into two sections.1. The first section introduces the statistical methods of multi-trait mapping for quantitative trait loci (QTLs). Most approaches can only do QTL mapping separately by a phenotype-driven method focusing on an individual trait. However, complex trait data usually contain observations on multiple traits and in multiple environments or under different treatment conditions. Therefore, several potential problems arise; for instance, determining whether multiple traits are affected by a single QTL with pleiotropic effects, or by multiple closely linked QTLs. We proposed a multivariate statistical model to conduct multiple-trait analysis, using Wilks' Lambda statistic to test the individual loci and epistatic interaction. Monte Carlo simulations and real data analysis are conducted to demonstrate the applicableness and powerfulness of the methods.2. The second section introduces a statistical strategy to mapping triplet interactions. The epistasis, interaction between loci or genes, indicates that the effect of a specific genotype combination on the genotype depends on the genetic background. A lot of works provide the evidence of significance of epistasis. In addition, the epistasis is reported to be a potential key driver of missing heritability. Identification of interactions between genes is able to improve the genetic predition, which is contributive to the disease-risk classification and plant breeding. In current, however, the mainly statistical methods for searching QTLs could not detect triplet interactions. Thus, we proposed a mapping strategy which is based on mixed linear model approach to search high order interactions. Monte Carlo simulations are performed to investigate the effectiveness and efficiency of the proposed methods.
3. The thrid chapter introduces the newly proposed statistical methods on genome-wise association study (GWAS) to analyze the high-thro ugh put biological data. The chapter is also divided into two sections.
1. The first section introduces the association analysis of quantitative trait SNPs (QTS). Owing to high-throughput genotyping technologies, simultaneous comparison of groups of loci, and density of SNPs, SNP markers are widely used in biomedical, plant and animal researches. Meanwhile, association analysis becomes a common tool to handle the large-scale data. Due to deficience of the stastitical model, we proposed a mixed linear model appraoch based assocation analysis of SNPs. It could detect individual and epistatic interacting loci, as well as estimate their main effects and loci by environment interaction effects. We also perform Monte Carlo simulations and real data analysis to demonstrate the applicableness and efficiency of the methods.2. The second section introduces the association analysis of quantitative trait transcript (QTT), quantitative trait protein (QTP), and quantitative trait metabolite (QTM). The gene-expression, protein and metabolite profiles have been applied to a wild range of biology problems. It is a kind of tool for biomarkers identification, diseases diagnosis, drug screening, and plant breeding. On the other hand, the profiles of gene-expression level, protein level and metabolite level are recently combined with other experimental approaches to identify the key mechanisms of complex traits. Contrary to the expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTL) mapping, we proposed association analysis of transcripts, proteins and metabolites, which considers the profiles data as a type of markers to study the association with an organismal quantitative trait. A series of Monte Carlo simulations are conducted to investigate the effectiveness and efficieness of the statistical appraoch. In the real case study, we integrated the QTL linkage analysis, QTS and QTT association analysis into a mapping system to elucidate the potential drivers of complex traits. Besides, it is also a trial to investigate genetic mechanism of complex traits in detailed based on a series of high-throughput data, such as SNPs, CNVs (copy number variations), gene-expression microarray, protein profiles, and metabolite profiles.
4. The fourth chapter introduces the newly developed software, which is mainly focus on statistical analysis-QTXNetwork. The software package is developed by C++to map the QTL, QTS, QTT, QTP, and QTM for complex traits, which could handle data from multiple-environment traits (METs). It could perform1) casual loci detection, including individual, two-and three-way loci,2) effect and heritability estimation of significant loci, including individual,2-and3-way effects, as well as interaction effects and heritability between the loci and environments,3) superior genotype effects prediction, predicting the best genetic effect of an individual in a specific environment based on known loci genotype,4) individual genotype effects prediction, predicting genetic effects of every individual in a specific environment based on loci detected, and listing the top and bottom ones.
引文
1. Fisher, R.A., The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh,1918.52(2):p.399-433.
2. Mackay, T.F., The genetic architecture of quantitative traits. Annu Rev Genet,2001.35:p.303-39.
3. Mackay, T.F., The genetic architecture of quantitative traits:lessons from Drosophila. Curr Opin Genet Dev,2004.14(3):p.253-7.
4. Fisher, R.A., Statistical methods for research workers.. The Eugenics Review,1925.18(2):p.148-150.
5. Comstock RE, R.H., Estimation of average dominance of genes. Iowa:Iowa State University Press,1952.
6. Hallauer AR, M.J., Quantitative genetics in maize breeding. Iowa:Iowa State University Press.,1981.
7. Gardner, C.O. and A.S. Eberhart, Analysis and interpretation of the variety cross diallel and related populations. Biometrics,1966.22(3):p.439-52.
8. Griffing, B., Concept of General and Specific Combining Ability in Relation to Diallel Crossing Systems. Australian Journal of Biological Sciences,1956.9(4):p.463-493.
9. Searle SR, C.G., McCulloch CE, Variance components. "New York:John Wiley & Sons Inc,1992.
10. Botstein, D., et al., Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics,1980.32(3):p.314-31.
11. Weber, J.L. and P.E. May, Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. American Journal of Human Genetics,1989.44(3):p.388-96.
12. Williams, J.G., et al., DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research.1990.18(22):p.6531-5.
13. Vos, P., et al., AFLP:a new technique for DNA fingerprinting. Nucleic Acids Research,1995.23(21):p. 4407-14.
14. Elston, R.C. and J. Stewart, The analysis of quantitative traits for simple genetic models from parental, F 1 andbackcross data. Genetics,1973.73(4):p.695-711.
15. Hill, A.P., Quantitative linkage:a statistical procedure for its detection and estimation. Ann Hum Genet,1975.38(4):p.439-49.
16. Soller, M., T. Brody, and A. Genizi, On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. TAG Theoretical and Applied Genetics,1976.47(1):p.35-39.
17. Lander, E.S., et al., MAPMAKER:an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics,1987.1(2):p.174-81.
18. Lander, E.S. and D. Botstein, Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics,1989.121(1):p.185-99.
19. Zeng, Z.B., Precision Mapping of Quantitative Trait Loci. Genetics,1994.136(4):p.1457-1468.
20. Zeng, Z.B., Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci U S A,1993.90(23):p.10972-6.
21. Kao, C.H., Z.B. Zeng, and R.D. Teasdale, Multiple interval mapping for quantitative trait loci. Genetics,1999.152(3):p.1203-16.
22. Zeng, Z.B., C.H. Kao, and C.J. Basten, Estimating the genetic architecture of quantitative traits. Genetical Research,1999.74(3):p.279-89.
23. Ljungberg, K., S. Holmgren, and O. Carlborg, Simultaneous search for multiple QTL using the global optimization algorithm DIRECT. Bioinformatics,2004.20(12):p.1887-95.
24. Sen, S. and G.A. Churchill, A statistical framework for quantitative trait mapping. Genetics,2001. 159(1):p.371-87.
25. Yi, N., S. Xu, and D.B. Allison, Bayesian model choice and search strategies for mapping interacting quantitative trait Loci. Genetics,2003.165(2):p.867-83.
26. Piepho, H.P. and H.G. Gauch, Jr., Marker pair selection for mapping quantitative trait loci. Genetics, 2001.157(1):p.433-44.
27. Xu, S., Estimating polygenic effects using markers of the entire genome. Genetics,2003.163(2):p. 789-801.
28. Meuwissen, T.H., B.J. Hayes, and M.E. Goddard, Prediction of total genetic value using genome-wide dense marker maps. Genetics,2001.157(4):p.1819-29.
29. Zhang, Y.M. and S. Xu, Mapping quantitative trait loci in F2 incorporatingphenotypes of F3 progeny. Genetics,2004.166(4):p.1981-93.
30. Zhang, Y.M. and S. Xu, Advanced statistical methods for detecting multiple quantitative trait loci. Recent Res Dev Genet Breed,2005.2:p.1-23.
31. Wang, D.L., et al., Mapping QTLs with epistatic effects and OTL×environment interactions by mixed linear model approaches. TAG Theoretical and Applied Genetics,1999.99(7):p.1255-1264.
32. Yang, J., J. Zhu, and R.W. Williams, Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics,2007.23(12):p.1527-1536.
33. Wang, C., J. Rutledge, and D. Gianola, Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genetics, selection, evolution:GSE,1994.26(2):p. 91-115.
34. Borecki, I.B. and B.K. Suarez, Linkage and association:basic concepts. Adv Genet,2001.42:p. 45-66.
35. Borecki, I.B. and M.A. Province, Linkage and association:basic concepts. Adv Genet,2008.60:p. 51-74.
36. Jiang, C.J. and Z.B. Zeng, Multiple-Trait Analysis of Genetic-Mapping for Quantitative Trait Loci. Genetics,1995.140(3):p.1111-1127.
37. Lange, C. and J.C. Whittaker, Mapping quantitative trait Loci using generalized estimating equations. Genetics,2001.159(3):p.1325-37.
38. Mangin, B., P. Thoquet, and N. Grimsley, Pleiotropic OTL analysis. Biometrics,1998.54(1):p.88-99.
39. Weller, J.I., et al., Application of a canonical transformation to detection of quantitative trait loci with the aid of genetic markers in a multi-trait experiment. Theoretical and Applied Genetics,1996.92(8):p. 998-1002.
40. Falconer, D.S.M., T. F. C., Introduction to Quantitative Genetics. Addison Wesley Longman, Harlow, 1996.
41. Lynch, M.W., B., Genetics and Analysis of Quantitative Traits. Sinauer Associates, Sunderland, Massachusetts,1998.
42. Phillips, P.C., Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems. Nature Reviews Genetics,2008.9(11):p.855-67.
43. Carlborg, O., et al., Simultaneous mapping of epistatic OTL in chickens reveals clusters of OTL pairs with similar genetic effects on growth. Genetical Research,2004.83(3):p.197-209.
44. Carlborg,O. and C.S. Haley, Epistasis:too often neglected in complex trait studies? Nature Reviews Genetics,2004.5(8):p.618-25.
45. Hummel, K.P., The inheritance and expression of disorganization, an unusual mutation in the mouse. Journal of Experimental Zoology,1958.137(3):p.389-423.
46. Tiret, L., et al., Synergistic effects of angiotensin-converting enzyme and angiotensin-ll type 1 receptor gene polymorphisms on risk of myocardial infarction. Lancet,1994.344(8927):p.910-3.
47. Steinmetz, L.M., et al., Dissecting the architecture of a quantitative trait locus in yeast. Nature,2002. 416(6878):p.326-30.
48. Sinha, H., et al., Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast. Genetics,2008.180(3):p.1661-70.
49. Stam, L.F. and C.C. Laurie, Molecular dissection of a major gene effect on a quantitative trait:the level of alcohol dehydrogenase expression in Drosophila melanogaster. Genetics,1996.144(4):p. 1559-64.
50. Manolio, T.A., et al., Finding the missing heritability of complex diseases. Nature,2009.461(7265):p. 747-53.
51. Eichler, E.E., et al., Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics,2010.11(6):p.446-50.
52. Chee, M., et al., Accessing genetic information with high-density DNA arrays. Science,1996. 274(5287):p.610-4.
53. Mullikin, J.C., et al., An SNP map of human chromosome 22. Nature,2000.407(6803):p.516-20.
54. Altshuler, D., et al., An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature,2000.407(6803):p.513-6.
55. Lander, E.S., Array of hope. Nature Genetics,1999.21(1 Suppl):p.3-4.
56. Lizardi, P.M., et al., Mutation detection and single-molecule counting using isothermal rolling-circle amplification. Nature Genetics,1998.19(3):p.225-32.
57. Lyamichev, V., et al., Polymorphism identification and quantitative detection of genomic DNA by invasive cleavage of oligonucleotide probes. Nat Biotechnol,1999.17(3):p.292-6.
58. Nilsson, M., et al., Padlock probes reveal single-nucleotide differences, parent of origin and in situ distribution of centromeric sequences in human chromosomes 13 and 21. Nature Genetics,1997.16(3):p. 252-5.
59. Brookes, A.J., The essence of SNPs. Gene,1999.234(2):p.177-86.
60. Terwilliger, J.D., et al.. Mapping genes through the use of linkage disequilibrium generated by genetic drift:'drift mapping'in small populations with no demographic expansion. Hum Hered,1998.48(3):p. 138-54.
61. Strittmatter, W.J. and A.D. Roses, Apolipoprotein E and Alzheimer disease. Proc Natl Acad Sci U S A, 1995.92(11):p.4725-7.
62. Greenberg, D.A., Linkage analysis of "necessary" disease loci versus "susceptibility" loci. American Journal of Human Genetics,1993.52(1):p.135-43.
63. Hodge, S.E., What association analysis can and cannot tell us about the genetics of complex disease. Am J Med Genet 1994.54(4):p.318-23.
64. Risch, N. and K. Merikangas, The future of genetic studies of complex human diseases. Science,1996. 273(5281):p.1516-7.
65. Gatz. M., et al., Heritability for Alzheimer's disease:the study of dementia in Swedish twins. J Gerontol A Biol Sci Med Sci,1997.52(2):p. M117-25.
66. Friedman, T.B., et al., A gene for congenital, recessive deafness DFNB3 maps to the pericentromeric region of chromosome 17. Nature Genetics,1995.9(1):p.86-91.
67. Kerem, B., et al., Identification of the cystic fibros is gene:genetic analysis. Science,1989.245(4922): p.1073-80.
68. Sarkar, C, et al., Human genetic bi-allelic sequences (HGBASE), a database of intra-genic polymorphisms. Mem Inst Oswaldo Cruz,1998.93(5):p.693-4.
69. Collins, F.S., L.D. Brooks, and A. Chakravarti, A DNA polymorphism discovery resource for research on human genetic variation. Genome Res,1998.8(12):p.1229-31.
70. Landegren, U., M. Nilsson, and P.Y. Kwok, Reading bits of genetic information:methods for single-nucleotide polymorphism analysis. Genome Res,1998.8(8):p.769-76.
71. Spielman, R.S., R.E. McGinnis, and W.J. Ewens, Transmission test for linkage disequilibrium:the insulin gene region and insulin-dependent diabetes mellitus (IDDM). American Journal of Human Genetics,1993.52(3):p.506-16.
72. Laan, M. and S. Paabo, Demographic history and linkage disequilibrium in human populations. Nature Genetics,1997.17(4):p.435-8.
73. Slatkin, M., Linkage disequilibrium in growing and stable populations. Genetics,1994.137(1):p. 331-6.
74. Rao, S. and S. Xu, Mapping quantitative trait loci for ordered categorical traits in four-way crosses. Heredity,1998.81 (Pt 2):p.214-24.
75. Kump, K.L., et al., Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nature Genetics.2011.43(2):p.163-8.
76. Tian. F., et al., Genome-wide association study of leaf architecture in the maize nested association mapping population. Nature Genetics,2011.43(2):p.159-62.
77. McMullen, M.D., et al., Genetic properties of the maize nested association mapping population. Science,2009.325(5941):p.737-40.
78. Balding, D.J., A tutorial on statistical methods for population association studies. Nature Reviews Genetics,2006.7(10):p.781-91.
79. Cardon, L.R. and J.I. Bell, Association study designs for complex diseases. Nature Reviews Genetics, 2001.2(2):p.91-9.
80. Fisher, R.A., The rhesus factor; a study in scientific method. Am Sci,1947.35(1):p.95-102.
81. Clark, D.L.H.a.A.G., Principles of Population Genetics. Sinauer Associates, Sunderland, MA,1997.
82. Excoffier, L. and M. Slatkin, Maximum-likelihood estimation of molecular haplotype frequencies in a diploidpopulation. Mol Biol Evol,1995.12(5):p.921-7.
83. Schaid, D.J., et al., Score tests for association between traits and haplotypes when linkage phase is ambiguous. American Journal of Human Genetics,2002.70(2):p.425-34.
84. Tzeng, J.Y., et al., Regression-based association analysis with clustered haplotypes through use of genotypes. American Journal of Human Genetics,2006.78(2):p.231-42.
85. Wang, T. and R.C. Elston, Improved power by use of a weighted score test for linkage disequilibrium mapping. American Journal of Human Genetics,2007.80(2):p.353-60.
86. Prentice, R.L. and R. Pyke, Logistic Disease Incidence Models and Case-Control Studies. Biometrika, 1979.66(3):p.403-411.
87. Seaman, S.R. and S. Richardson, Equivalence of Prospective and Retrospective Models in the Bayesian Analysis of Case-Control Studies. Biometrika,2004.91(1):p.15-25.
88. Caspi, A., et al.. Role of genotype in the cycle of violence in maltreated children. Science,2002. 297(5582):p.851-4.
89. Caspi, A., et al., Influence of life stress on depression:moderation by a polymorphism in the 5-HTT gene. Science,2003.301(5631):p.386-9.
90. Long, A.D., et al., High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics,1995.139(3):p.1273-91.
91. Nuzhdin, S.V., et al., Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci U S A,1997.94(18):p.9734-9.
92. Kenney-Hunt,J.P., et al., Pleiotropic patterns of quantitative trait loci for 70 murine skeletal traits. Genetics,2008.178(4):p.2275-88.
93. Farber, C.R. and J.F. Medrano, Fine mapping reveals sex bias in quantitative trait loci affecting growth, skeletal size and obesity-related traits on mouse chromosomes 2 and 11. Genetics,2007.175(1):p. 349-60.
94. Ober, C., D.A. Loisel, and Y. Gilad, Sex-specific genetic architecture of human disease. Nature Reviews Genetics,2008.9(12):p.911-22.
95. Purcell, S., et al., PLINK:a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics,2007.81(3):p.559-75.
96. Liu, Y., et al., Genome-wide interaction-based association analysis identified multiple new susceptibility Loci for common diseases. Plos Genetics,2011.7(3):p. e10101338.
97. Hahn, L.W., M.D. Ritchie, and J.H. Moore, Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics,2003.19(3):p.376-82.
98. Ritchie, M.D., et al., Multifactor-dirnensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. American Journal of Human Genetics,2001.69(1): p.138-47.
99. Moore, J.H. and S.M. Williams, New strategies for identifying gene-gene interactions in hypertension. Ann Med,2002.34(2):p.88-95.
100. Lou, X.Y., et al., A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. American Journal of Human Genetics,2007.80(6):p.1125-37.
101. Chen, G.B., et al., Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches. PLoS One,2011.6(2):p. e16981.
102. Brown, P.O. and D. Botstein, Exploring the new world of the genome with DNA microarrays. Nature Genetics,1999.21(1 Suppl):p.33-7.
103. Hughes, T.R., et al., Functional discovery via a compendium of expression profiles. Cell,2000.102(1): p.109-26.
104. Karp, C.L., et al., Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat Immunol,2000.1(3):p.221-6.
105. Schadt, E.E., et al., Genetics of gene expression surveyed in maize, mouse and man. Nature,2003. 422(6929):p.297-302.
106. Johnson, J.M., et al., Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science,2003.302(5653):p.2141-4.
107. Schadt, E.E., et al., A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biol,2004.5(10):p. R73.
108. Shoemaker, D.D., et al., Experimental annotation of the human genome using microarray technology. Nature,2001.409(6822):p.922-7.
109. Zhu, J., et al., An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res,2004.105(2-4):p.363-74.
110. Klose, J., et al., Genetic analysis of the mouse brain proteome. Nature Genetics,2002.30(4):p. 385-93.
111. Luscombe, N.M., et al., Genomic analysis of regulatory network dynamics reveals large topological changes. Nature,2004.431(7006):p.308-12.
112. Brem, R.B., et al., Genetic dissection of transcriptional regulation in budding yeast. Science,2002. 296(5568):p.752-5.
113. Yvert, G., et al., Trans-act ing regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genetics,2003.35(1):p.57-64.
114. Liotta, L. and E. Petricoin, Molecular profiling of human cancer. Nature Reviews Genetics,2000.1(1): p.48-56.
115. Sieberts, S.K. and E.E. Schadt, Moving toward a system genetics view of disease. Mammalian Genome, 2007.18(6-7):p.389-401.
116. Rockman, M.V. and L. Kruglyak, Genetics of global gene expression. Nature Reviews Genetics,2006. 7(11):p.862-72.
117.Farber, C.R. and A.J. Lusis, Integrating global gene expression analysis and genetics. Adv Genet,2008. 60:p.571-601.
118. Hansen, B.G., B.A. Halkier, and D.J. Kliebenstein, Identifying the molecular basis of QTLs:eOTLs add a new dimension. Trends Plant Sci,2008.13(2):p.72-7.
119. Gilad, Y., S.A. Rifkin, and J.K. Pritchard, Revealing the architecture of gene regulation:the promise of eQTL studies. Trends Genet,2008.24(8):p.408-15.
120. Cookson, W.. et al., Mapping complex disease traits with global gene expression. Nature Reviews Genetics,2009.10(3):p.184-94.
121. Jansen, R.C. and J.P. Nap, Genetical genomics:the added value from segregation. Trends Genet,2001. 17(7):p.388-91.
122. Chesler, E.J., et al., Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genetics,2005.37(3):p.233-42.
123. Ayroles, J.F., et al., Systems genetics of complex traits in Drosophila melanogaster. Nature Genetics, 2009.41(3):p.299-307.
124. Passador-Gurgel, G., et al., Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster. Nature Genetics,2007.39(2):p.264-8.
125. Harbison, S.T., et al., Co-regulated transcriptional networks contribute to natural genetic variation in Drosophila sleep. Nature Genetics,2009.41(3):p.371-5.
126. Miller, J.A., M.C. Oldham, and D.H. Geschwind, A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci,2008.28(6):p.1410-20.
127. Keller, M.P., et al., A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res,2008.18(5):p.706-16.
128. Jiang, C. and Z.B. Zeng, Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica,1997.101(1):p.47-58.
129. Doerge, R.W. and G.A. Churchill, Permutation tests for multiple loci affecting a quantitative character. Genetics,1996.142(1):p.285-94.
130. Causse, M.A., et al., Saturated molecular map of the rice genome based on an interspecific backcross population. Genetics,1994.138(4):p.1251-74.
131. HuangN., M.S., Mew T., Parco A., Guiderdoni E.. Development of an RFLP map from a doubled haploid population in rice. Rice Genetics Newsletter,1994.11:p.134-137.
132. Liu, G.F., et al., Genetic analysis of grain yield conditioned on its component traits in rice (Oryza sativa L). Australian Journal of Agricultural Research,2008.59(2):p.189-195.
133. Shepherd, J.K., et al., Behavioral and Pharmacological Characterization of the Elevated Zero-Maze as an Animal-Model of Anxiety. Psychopharmacology,1994.116(1):p.56-64.
134. Taylor, B., Recombinant inbred strains. Genetic Variation in the Laboratory Mouse,2nd edn Edited by: Lyon ML and Searle AG. Oxford University Press,1989:p.773-796.
135. Taylor, B.A., et al., Genotyping new BXD recombinant inbred mouse strains and comparison of BXD and consensus maps. Marnmalian Genome,1999.10(4):p.335-348.
136. Albrechtsen, A., et al., A Bayesian multilocus association method:allowing for higher-order interaction in association studies. Genetics,2007.176(2):p.1197-208.
137. Barton, N.H. and P.D. Keightley, Understanding quantitative genetic variation. Nature Reviews Genetics,2002.3(1):p.11-21.
138. Zhang, X.S. and W.G. Hill, Change and maintenance of variation in quantitative traits in the context of the Price equation. Theor Popul Biol,2010.77(1):p.14-22.
139. Pettersson, M., et al., Replication and explorations of high-order epistasis using a large advanced intercross line pedigree. Plos Genetics,2011.7(7):p. e1002180.
140. Levene, H., Genetic Equilibrium When More Than One Ecological Niche is Available. The American Naturalist,1953.87(836):p.331-333.
141. Gillespie, J.H. and M. Turelli, Genotype-environment interactions and the maintenance of polygenic variation. Genetics,1989.121(1):p.129-38.
142. Henderson, C.R., Progress in statistical methods applied to quantitative genetics since 1976. in Proceedings of the Second International Conference on Quantitative Genetics, edited by B. S. Weir, E. J. Elsen, M. M. Goodman and G. "Namkoong. Sinaur, Sunderland, Mass.,1988:p.85-90.
143. Manly, K.F., R.H. Cudmore, Jr., and J.M. Meer, Map Manager QTX, cross-platform software for genetic mapping. Mammalian Genome,2001.12(12):p.930-2.
144. Broman, K.W., et al., R/qtl:QTL mapping in experimental crosses. Bioinformatics,2003.19(7):p. 889-90.
145. Wang SC, B.C., Zeng ZB. Windows QTL Cartographer 2.5. In. Raleigh, NC, USA:Department of Statistics, North Carolina State University 2006; Available from: (http://statgen.ncsu.edu/qtlcart/WQTLCart.htm).
146. Yang, J., et al., QTLNetwork:mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics,2008.24(5):p.721-3.
2. Mackay, T.F., The genetic architecture of quantitative traits. Annu Rev Genet,2001.35:p.303-39.
3. Mackay, T.F., The genetic architecture of quantitative traits:lessons from Drosophila. Curr Opin Genet Dev,2004.14(3):p.253-7.
4. Fisher, R.A., Statistical methods for research workers.. The Eugenics Review,1925.18(2):p.148-150.
5. Comstock RE, R.H., Estimation of average dominance of genes. Iowa:Iowa State University Press,1952.
6. Hallauer AR, M.J., Quantitative genetics in maize breeding. Iowa:Iowa State University Press.,1981.
7. Gardner, C.O. and A.S. Eberhart, Analysis and interpretation of the variety cross diallel and related populations. Biometrics,1966.22(3):p.439-52.
8. Griffing, B., Concept of General and Specific Combining Ability in Relation to Diallel Crossing Systems. Australian Journal of Biological Sciences,1956.9(4):p.463-493.
9. Searle SR, C.G., McCulloch CE, Variance components. "New York:John Wiley & Sons Inc,1992.
10. Botstein, D., et al., Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics,1980.32(3):p.314-31.
11. Weber, J.L. and P.E. May, Abundant class of human DNA polymorphisms which can be typed using the polymerase chain reaction. American Journal of Human Genetics,1989.44(3):p.388-96.
12. Williams, J.G., et al., DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research.1990.18(22):p.6531-5.
13. Vos, P., et al., AFLP:a new technique for DNA fingerprinting. Nucleic Acids Research,1995.23(21):p. 4407-14.
14. Elston, R.C. and J. Stewart, The analysis of quantitative traits for simple genetic models from parental, F 1 andbackcross data. Genetics,1973.73(4):p.695-711.
15. Hill, A.P., Quantitative linkage:a statistical procedure for its detection and estimation. Ann Hum Genet,1975.38(4):p.439-49.
16. Soller, M., T. Brody, and A. Genizi, On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. TAG Theoretical and Applied Genetics,1976.47(1):p.35-39.
17. Lander, E.S., et al., MAPMAKER:an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics,1987.1(2):p.174-81.
18. Lander, E.S. and D. Botstein, Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics,1989.121(1):p.185-99.
19. Zeng, Z.B., Precision Mapping of Quantitative Trait Loci. Genetics,1994.136(4):p.1457-1468.
20. Zeng, Z.B., Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci U S A,1993.90(23):p.10972-6.
21. Kao, C.H., Z.B. Zeng, and R.D. Teasdale, Multiple interval mapping for quantitative trait loci. Genetics,1999.152(3):p.1203-16.
22. Zeng, Z.B., C.H. Kao, and C.J. Basten, Estimating the genetic architecture of quantitative traits. Genetical Research,1999.74(3):p.279-89.
23. Ljungberg, K., S. Holmgren, and O. Carlborg, Simultaneous search for multiple QTL using the global optimization algorithm DIRECT. Bioinformatics,2004.20(12):p.1887-95.
24. Sen, S. and G.A. Churchill, A statistical framework for quantitative trait mapping. Genetics,2001. 159(1):p.371-87.
25. Yi, N., S. Xu, and D.B. Allison, Bayesian model choice and search strategies for mapping interacting quantitative trait Loci. Genetics,2003.165(2):p.867-83.
26. Piepho, H.P. and H.G. Gauch, Jr., Marker pair selection for mapping quantitative trait loci. Genetics, 2001.157(1):p.433-44.
27. Xu, S., Estimating polygenic effects using markers of the entire genome. Genetics,2003.163(2):p. 789-801.
28. Meuwissen, T.H., B.J. Hayes, and M.E. Goddard, Prediction of total genetic value using genome-wide dense marker maps. Genetics,2001.157(4):p.1819-29.
29. Zhang, Y.M. and S. Xu, Mapping quantitative trait loci in F2 incorporatingphenotypes of F3 progeny. Genetics,2004.166(4):p.1981-93.
30. Zhang, Y.M. and S. Xu, Advanced statistical methods for detecting multiple quantitative trait loci. Recent Res Dev Genet Breed,2005.2:p.1-23.
31. Wang, D.L., et al., Mapping QTLs with epistatic effects and OTL×environment interactions by mixed linear model approaches. TAG Theoretical and Applied Genetics,1999.99(7):p.1255-1264.
32. Yang, J., J. Zhu, and R.W. Williams, Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics,2007.23(12):p.1527-1536.
33. Wang, C., J. Rutledge, and D. Gianola, Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genetics, selection, evolution:GSE,1994.26(2):p. 91-115.
34. Borecki, I.B. and B.K. Suarez, Linkage and association:basic concepts. Adv Genet,2001.42:p. 45-66.
35. Borecki, I.B. and M.A. Province, Linkage and association:basic concepts. Adv Genet,2008.60:p. 51-74.
36. Jiang, C.J. and Z.B. Zeng, Multiple-Trait Analysis of Genetic-Mapping for Quantitative Trait Loci. Genetics,1995.140(3):p.1111-1127.
37. Lange, C. and J.C. Whittaker, Mapping quantitative trait Loci using generalized estimating equations. Genetics,2001.159(3):p.1325-37.
38. Mangin, B., P. Thoquet, and N. Grimsley, Pleiotropic OTL analysis. Biometrics,1998.54(1):p.88-99.
39. Weller, J.I., et al., Application of a canonical transformation to detection of quantitative trait loci with the aid of genetic markers in a multi-trait experiment. Theoretical and Applied Genetics,1996.92(8):p. 998-1002.
40. Falconer, D.S.M., T. F. C., Introduction to Quantitative Genetics. Addison Wesley Longman, Harlow, 1996.
41. Lynch, M.W., B., Genetics and Analysis of Quantitative Traits. Sinauer Associates, Sunderland, Massachusetts,1998.
42. Phillips, P.C., Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems. Nature Reviews Genetics,2008.9(11):p.855-67.
43. Carlborg, O., et al., Simultaneous mapping of epistatic OTL in chickens reveals clusters of OTL pairs with similar genetic effects on growth. Genetical Research,2004.83(3):p.197-209.
44. Carlborg,O. and C.S. Haley, Epistasis:too often neglected in complex trait studies? Nature Reviews Genetics,2004.5(8):p.618-25.
45. Hummel, K.P., The inheritance and expression of disorganization, an unusual mutation in the mouse. Journal of Experimental Zoology,1958.137(3):p.389-423.
46. Tiret, L., et al., Synergistic effects of angiotensin-converting enzyme and angiotensin-ll type 1 receptor gene polymorphisms on risk of myocardial infarction. Lancet,1994.344(8927):p.910-3.
47. Steinmetz, L.M., et al., Dissecting the architecture of a quantitative trait locus in yeast. Nature,2002. 416(6878):p.326-30.
48. Sinha, H., et al., Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast. Genetics,2008.180(3):p.1661-70.
49. Stam, L.F. and C.C. Laurie, Molecular dissection of a major gene effect on a quantitative trait:the level of alcohol dehydrogenase expression in Drosophila melanogaster. Genetics,1996.144(4):p. 1559-64.
50. Manolio, T.A., et al., Finding the missing heritability of complex diseases. Nature,2009.461(7265):p. 747-53.
51. Eichler, E.E., et al., Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics,2010.11(6):p.446-50.
52. Chee, M., et al., Accessing genetic information with high-density DNA arrays. Science,1996. 274(5287):p.610-4.
53. Mullikin, J.C., et al., An SNP map of human chromosome 22. Nature,2000.407(6803):p.516-20.
54. Altshuler, D., et al., An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature,2000.407(6803):p.513-6.
55. Lander, E.S., Array of hope. Nature Genetics,1999.21(1 Suppl):p.3-4.
56. Lizardi, P.M., et al., Mutation detection and single-molecule counting using isothermal rolling-circle amplification. Nature Genetics,1998.19(3):p.225-32.
57. Lyamichev, V., et al., Polymorphism identification and quantitative detection of genomic DNA by invasive cleavage of oligonucleotide probes. Nat Biotechnol,1999.17(3):p.292-6.
58. Nilsson, M., et al., Padlock probes reveal single-nucleotide differences, parent of origin and in situ distribution of centromeric sequences in human chromosomes 13 and 21. Nature Genetics,1997.16(3):p. 252-5.
59. Brookes, A.J., The essence of SNPs. Gene,1999.234(2):p.177-86.
60. Terwilliger, J.D., et al.. Mapping genes through the use of linkage disequilibrium generated by genetic drift:'drift mapping'in small populations with no demographic expansion. Hum Hered,1998.48(3):p. 138-54.
61. Strittmatter, W.J. and A.D. Roses, Apolipoprotein E and Alzheimer disease. Proc Natl Acad Sci U S A, 1995.92(11):p.4725-7.
62. Greenberg, D.A., Linkage analysis of "necessary" disease loci versus "susceptibility" loci. American Journal of Human Genetics,1993.52(1):p.135-43.
63. Hodge, S.E., What association analysis can and cannot tell us about the genetics of complex disease. Am J Med Genet 1994.54(4):p.318-23.
64. Risch, N. and K. Merikangas, The future of genetic studies of complex human diseases. Science,1996. 273(5281):p.1516-7.
65. Gatz. M., et al., Heritability for Alzheimer's disease:the study of dementia in Swedish twins. J Gerontol A Biol Sci Med Sci,1997.52(2):p. M117-25.
66. Friedman, T.B., et al., A gene for congenital, recessive deafness DFNB3 maps to the pericentromeric region of chromosome 17. Nature Genetics,1995.9(1):p.86-91.
67. Kerem, B., et al., Identification of the cystic fibros is gene:genetic analysis. Science,1989.245(4922): p.1073-80.
68. Sarkar, C, et al., Human genetic bi-allelic sequences (HGBASE), a database of intra-genic polymorphisms. Mem Inst Oswaldo Cruz,1998.93(5):p.693-4.
69. Collins, F.S., L.D. Brooks, and A. Chakravarti, A DNA polymorphism discovery resource for research on human genetic variation. Genome Res,1998.8(12):p.1229-31.
70. Landegren, U., M. Nilsson, and P.Y. Kwok, Reading bits of genetic information:methods for single-nucleotide polymorphism analysis. Genome Res,1998.8(8):p.769-76.
71. Spielman, R.S., R.E. McGinnis, and W.J. Ewens, Transmission test for linkage disequilibrium:the insulin gene region and insulin-dependent diabetes mellitus (IDDM). American Journal of Human Genetics,1993.52(3):p.506-16.
72. Laan, M. and S. Paabo, Demographic history and linkage disequilibrium in human populations. Nature Genetics,1997.17(4):p.435-8.
73. Slatkin, M., Linkage disequilibrium in growing and stable populations. Genetics,1994.137(1):p. 331-6.
74. Rao, S. and S. Xu, Mapping quantitative trait loci for ordered categorical traits in four-way crosses. Heredity,1998.81 (Pt 2):p.214-24.
75. Kump, K.L., et al., Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nature Genetics.2011.43(2):p.163-8.
76. Tian. F., et al., Genome-wide association study of leaf architecture in the maize nested association mapping population. Nature Genetics,2011.43(2):p.159-62.
77. McMullen, M.D., et al., Genetic properties of the maize nested association mapping population. Science,2009.325(5941):p.737-40.
78. Balding, D.J., A tutorial on statistical methods for population association studies. Nature Reviews Genetics,2006.7(10):p.781-91.
79. Cardon, L.R. and J.I. Bell, Association study designs for complex diseases. Nature Reviews Genetics, 2001.2(2):p.91-9.
80. Fisher, R.A., The rhesus factor; a study in scientific method. Am Sci,1947.35(1):p.95-102.
81. Clark, D.L.H.a.A.G., Principles of Population Genetics. Sinauer Associates, Sunderland, MA,1997.
82. Excoffier, L. and M. Slatkin, Maximum-likelihood estimation of molecular haplotype frequencies in a diploidpopulation. Mol Biol Evol,1995.12(5):p.921-7.
83. Schaid, D.J., et al., Score tests for association between traits and haplotypes when linkage phase is ambiguous. American Journal of Human Genetics,2002.70(2):p.425-34.
84. Tzeng, J.Y., et al., Regression-based association analysis with clustered haplotypes through use of genotypes. American Journal of Human Genetics,2006.78(2):p.231-42.
85. Wang, T. and R.C. Elston, Improved power by use of a weighted score test for linkage disequilibrium mapping. American Journal of Human Genetics,2007.80(2):p.353-60.
86. Prentice, R.L. and R. Pyke, Logistic Disease Incidence Models and Case-Control Studies. Biometrika, 1979.66(3):p.403-411.
87. Seaman, S.R. and S. Richardson, Equivalence of Prospective and Retrospective Models in the Bayesian Analysis of Case-Control Studies. Biometrika,2004.91(1):p.15-25.
88. Caspi, A., et al.. Role of genotype in the cycle of violence in maltreated children. Science,2002. 297(5582):p.851-4.
89. Caspi, A., et al., Influence of life stress on depression:moderation by a polymorphism in the 5-HTT gene. Science,2003.301(5631):p.386-9.
90. Long, A.D., et al., High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics,1995.139(3):p.1273-91.
91. Nuzhdin, S.V., et al., Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci U S A,1997.94(18):p.9734-9.
92. Kenney-Hunt,J.P., et al., Pleiotropic patterns of quantitative trait loci for 70 murine skeletal traits. Genetics,2008.178(4):p.2275-88.
93. Farber, C.R. and J.F. Medrano, Fine mapping reveals sex bias in quantitative trait loci affecting growth, skeletal size and obesity-related traits on mouse chromosomes 2 and 11. Genetics,2007.175(1):p. 349-60.
94. Ober, C., D.A. Loisel, and Y. Gilad, Sex-specific genetic architecture of human disease. Nature Reviews Genetics,2008.9(12):p.911-22.
95. Purcell, S., et al., PLINK:a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics,2007.81(3):p.559-75.
96. Liu, Y., et al., Genome-wide interaction-based association analysis identified multiple new susceptibility Loci for common diseases. Plos Genetics,2011.7(3):p. e10101338.
97. Hahn, L.W., M.D. Ritchie, and J.H. Moore, Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics,2003.19(3):p.376-82.
98. Ritchie, M.D., et al., Multifactor-dirnensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. American Journal of Human Genetics,2001.69(1): p.138-47.
99. Moore, J.H. and S.M. Williams, New strategies for identifying gene-gene interactions in hypertension. Ann Med,2002.34(2):p.88-95.
100. Lou, X.Y., et al., A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. American Journal of Human Genetics,2007.80(6):p.1125-37.
101. Chen, G.B., et al., Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches. PLoS One,2011.6(2):p. e16981.
102. Brown, P.O. and D. Botstein, Exploring the new world of the genome with DNA microarrays. Nature Genetics,1999.21(1 Suppl):p.33-7.
103. Hughes, T.R., et al., Functional discovery via a compendium of expression profiles. Cell,2000.102(1): p.109-26.
104. Karp, C.L., et al., Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat Immunol,2000.1(3):p.221-6.
105. Schadt, E.E., et al., Genetics of gene expression surveyed in maize, mouse and man. Nature,2003. 422(6929):p.297-302.
106. Johnson, J.M., et al., Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science,2003.302(5653):p.2141-4.
107. Schadt, E.E., et al., A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biol,2004.5(10):p. R73.
108. Shoemaker, D.D., et al., Experimental annotation of the human genome using microarray technology. Nature,2001.409(6822):p.922-7.
109. Zhu, J., et al., An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res,2004.105(2-4):p.363-74.
110. Klose, J., et al., Genetic analysis of the mouse brain proteome. Nature Genetics,2002.30(4):p. 385-93.
111. Luscombe, N.M., et al., Genomic analysis of regulatory network dynamics reveals large topological changes. Nature,2004.431(7006):p.308-12.
112. Brem, R.B., et al., Genetic dissection of transcriptional regulation in budding yeast. Science,2002. 296(5568):p.752-5.
113. Yvert, G., et al., Trans-act ing regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genetics,2003.35(1):p.57-64.
114. Liotta, L. and E. Petricoin, Molecular profiling of human cancer. Nature Reviews Genetics,2000.1(1): p.48-56.
115. Sieberts, S.K. and E.E. Schadt, Moving toward a system genetics view of disease. Mammalian Genome, 2007.18(6-7):p.389-401.
116. Rockman, M.V. and L. Kruglyak, Genetics of global gene expression. Nature Reviews Genetics,2006. 7(11):p.862-72.
117.Farber, C.R. and A.J. Lusis, Integrating global gene expression analysis and genetics. Adv Genet,2008. 60:p.571-601.
118. Hansen, B.G., B.A. Halkier, and D.J. Kliebenstein, Identifying the molecular basis of QTLs:eOTLs add a new dimension. Trends Plant Sci,2008.13(2):p.72-7.
119. Gilad, Y., S.A. Rifkin, and J.K. Pritchard, Revealing the architecture of gene regulation:the promise of eQTL studies. Trends Genet,2008.24(8):p.408-15.
120. Cookson, W.. et al., Mapping complex disease traits with global gene expression. Nature Reviews Genetics,2009.10(3):p.184-94.
121. Jansen, R.C. and J.P. Nap, Genetical genomics:the added value from segregation. Trends Genet,2001. 17(7):p.388-91.
122. Chesler, E.J., et al., Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genetics,2005.37(3):p.233-42.
123. Ayroles, J.F., et al., Systems genetics of complex traits in Drosophila melanogaster. Nature Genetics, 2009.41(3):p.299-307.
124. Passador-Gurgel, G., et al., Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster. Nature Genetics,2007.39(2):p.264-8.
125. Harbison, S.T., et al., Co-regulated transcriptional networks contribute to natural genetic variation in Drosophila sleep. Nature Genetics,2009.41(3):p.371-5.
126. Miller, J.A., M.C. Oldham, and D.H. Geschwind, A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci,2008.28(6):p.1410-20.
127. Keller, M.P., et al., A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res,2008.18(5):p.706-16.
128. Jiang, C. and Z.B. Zeng, Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica,1997.101(1):p.47-58.
129. Doerge, R.W. and G.A. Churchill, Permutation tests for multiple loci affecting a quantitative character. Genetics,1996.142(1):p.285-94.
130. Causse, M.A., et al., Saturated molecular map of the rice genome based on an interspecific backcross population. Genetics,1994.138(4):p.1251-74.
131. HuangN., M.S., Mew T., Parco A., Guiderdoni E.. Development of an RFLP map from a doubled haploid population in rice. Rice Genetics Newsletter,1994.11:p.134-137.
132. Liu, G.F., et al., Genetic analysis of grain yield conditioned on its component traits in rice (Oryza sativa L). Australian Journal of Agricultural Research,2008.59(2):p.189-195.
133. Shepherd, J.K., et al., Behavioral and Pharmacological Characterization of the Elevated Zero-Maze as an Animal-Model of Anxiety. Psychopharmacology,1994.116(1):p.56-64.
134. Taylor, B., Recombinant inbred strains. Genetic Variation in the Laboratory Mouse,2nd edn Edited by: Lyon ML and Searle AG. Oxford University Press,1989:p.773-796.
135. Taylor, B.A., et al., Genotyping new BXD recombinant inbred mouse strains and comparison of BXD and consensus maps. Marnmalian Genome,1999.10(4):p.335-348.
136. Albrechtsen, A., et al., A Bayesian multilocus association method:allowing for higher-order interaction in association studies. Genetics,2007.176(2):p.1197-208.
137. Barton, N.H. and P.D. Keightley, Understanding quantitative genetic variation. Nature Reviews Genetics,2002.3(1):p.11-21.
138. Zhang, X.S. and W.G. Hill, Change and maintenance of variation in quantitative traits in the context of the Price equation. Theor Popul Biol,2010.77(1):p.14-22.
139. Pettersson, M., et al., Replication and explorations of high-order epistasis using a large advanced intercross line pedigree. Plos Genetics,2011.7(7):p. e1002180.
140. Levene, H., Genetic Equilibrium When More Than One Ecological Niche is Available. The American Naturalist,1953.87(836):p.331-333.
141. Gillespie, J.H. and M. Turelli, Genotype-environment interactions and the maintenance of polygenic variation. Genetics,1989.121(1):p.129-38.
142. Henderson, C.R., Progress in statistical methods applied to quantitative genetics since 1976. in Proceedings of the Second International Conference on Quantitative Genetics, edited by B. S. Weir, E. J. Elsen, M. M. Goodman and G. "Namkoong. Sinaur, Sunderland, Mass.,1988:p.85-90.
143. Manly, K.F., R.H. Cudmore, Jr., and J.M. Meer, Map Manager QTX, cross-platform software for genetic mapping. Mammalian Genome,2001.12(12):p.930-2.
144. Broman, K.W., et al., R/qtl:QTL mapping in experimental crosses. Bioinformatics,2003.19(7):p. 889-90.
145. Wang SC, B.C., Zeng ZB. Windows QTL Cartographer 2.5. In. Raleigh, NC, USA:Department of Statistics, North Carolina State University 2006; Available from: (http://statgen.ncsu.edu/qtlcart/WQTLCart.htm).
146. Yang, J., et al., QTLNetwork:mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics,2008.24(5):p.721-3.