Use of maternal information for QTL detection in a (grand)daughter design
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Abstract:
In a (grand)daughter design, maternal information is often neglected because the number of progeny per dam is limited. The number of dams per maternal grandsire (MGS), however, could be large enough to contribute to QTL detection. But dams and MGS usually are not genotyped, there are two recombination opportunities between the MGS and the progeny, and at a given location, only half the progeny receive a MGS chromosomal segment. A 3-step procedure was developed to estimate: (1) the marker phenotypes probabilities of the MGS; (2) the probability of each possible MGS haplotype; (3) the probabilities that the progeny receives either the first, or second MGS segment, or a maternal grandam segment. These probabilities were used for QTL detection in a linear model including the effects of sire, MGS, paternal QTL, MGS QTL and maternal grandam QTL. Including the grandam QTL effect makes it possible to detect QTL in the grandam population, even when MGS are not informative. The detection power, studied by simulation, was rather high, provided that MGS family size was greater than 50. Using maternal information in the French dairy cattle granddaughter design made it possible to detect 23 additional QTL genomewise significant.Keywords:
Sire
Family-based QTL mapping
Daughter
Most of the important agronomic traits of rice are quantitative in nature, which are controlled by polygenes. In conventional quantitative genetics, QTL analysis was only executed as a whole. In recent years, the construction of highly dense molecular linkage map and the development of effective biostatistical methods have revolutionized the genetic studies of quantitative traits. The joint analysis of molecular marker genotype and phenotype values of individuals or lines in different populations enabled the detection and location of quantitative trait loci (QTL). In the present paper, the principles and methods of QTL mapping were introduced, recent progresses in the studies of QTL analysis in rice were reviewed, including QTL numbers and effects, epistasis effect, interaction between QTL and environment, QTLs of related characters, developmental QTL and so on. The rice genome sequencing project has been finished, therefore in this paper, the strategy for fine mapping and cloning of QTLs in the genome era was also discussed. Application prospect of QTL analysis in rice breeding was forecasted.
Family-based QTL mapping
Polygene
Epistasis
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Quantitative trait loci, a genetic concept introduced in the 1940s, for explaining the inheritance of non-Mendelian traits, have been realized as particular fragments of chromosomes even unique genes in most crops in the 21st century. However, only very a small portion of QTL has been screened out by geneticists comparing to a great number of genes underneath the quantitative traits. These identified QTL even have been seldom used into breeding program because crop breeders may not find the QTL in their breeding populations in their field station. Several key points will be proposed to meet the challenges of QTL analysis today: a fine mapping population and the related reference genetic map, QTL evaluation in multiple environments, recognizing real QTL with small genetic effect, map integration.
Family-based QTL mapping
Mendelian inheritance
Inheritance
Trait
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With the rapid development of molecular biology, the study on genetic maps of animal is furthered by the application of some effective genetic marker, which provides a foundation to establish a highly density linkage map and a feasibility to discover quantitative trait loci(QTL),in recent years theory on QTL, the program of mapping, the test of QTL as well as research progress on QTL mapping in poultry were discussed, but,the current difficulties of application were pointed out also.
Family-based QTL mapping
Linkage (software)
Trait
Genetic linkage
Genetic linkage map
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Family-based QTL mapping
Candidate gene
Identification
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Family-based QTL mapping
Germ plasm
Inheritance
Trait
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由在以前的连接地图增加 31 个标记，包含 205 个标记的一张新基因连接地图被构造，跨越有 11.2 厘米的平均间隔的 2305.4 厘米的一个总数。在整个染色体的 genotypic 错误被统计方法检测并且手工地搬迁了。连接地图的精确显著地被改进。主要 andepistatic QTL 被印射的多重间隔(MIM ) 被 R/qtl 检测，并且主要 QTL 被证实并且精制。最后， MIM 为每谷物收益为排数字，和五 QTL 检测了七 QTL，核每排和 100 核重量。对 QTL 的基因变化的贡献为谷物产量改变了 from35.3 百分比到 61.5% 因为排数。仅仅核每排展出了在 QTL 之间的重要 epistatic 相互作用。24 epistatic QTL 被检测它在几乎所有十个染色体上散布了。大约三分之二 epistatic QTL 在主要 QTL 和另一个地点之间被观察，它没有重要效果。这些结果相当清楚地显示有很多 QTL，影响特点表情，不是直接，但是间接地通过和另外的 loci 的相互作用。因此，如果比主要 QTL 效果不更重要， epistatic QTL 效果可以起一个关键作用，在为在现在的学习的测量特点的基因变化。
Epistasis
Family-based QTL mapping
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Sire
Daughter
Trait
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QTL mapping is an important step in gene fine mapping,map-based cloning,and the efficient use of gene information in molecular breeding.Questions are frequently met and asked in the application of QTL mapping in practical genetic populations.Questions related to statistical method of QTL mapping are:what does LOD score mean? What is the relationship between the reliability of detected QTL and the LOD threshold? How to evaluate different QTL mapping methods? How to improve the QTL detection power? Questions related to genetic parameter estimation are:how to calculate the phenotypic variance explained by each detected QTL? How to determine the source of favorable alleles at detected QTL? How efficient is the selective genotyping? Can composite traits be used in QTL mapping? Questions related to linkage map and mapping populations are:does the phenotype of a trait in interest have to follow a normal distribution? Does the increase in marker density greatly improve QTL mapping power? What effects will missing markers have in QTL mapping? What effects will segregation distortion have in QTL mapping? Our objective in this paper was to give an analysis and answer to each of the 12 frequently asked questions,based on our studies in past several years.
Family-based QTL mapping
Association mapping
Positional cloning
Trait
Genetic linkage
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Advances on methods for mapping quantitative trait loci (QTL) are firstly summarized. Then, some new methods, including mapping multiple QTL, fine mapping of QTL, and mapping QTL for dynamic traits, are mainly described. Finally, some future prospects are proposed, including how to dig novel genes in the germplasm resource, map expression QTL (eQTL) by the use of all markers, phenotypes and micro-array data, identify QTL using genetic mating designs and detect viability loci. The purpose is to direct plant geneticists to choose a suitable method in the inheritance analysis of quantitative trait and in search of novel genes in germplasm resource so that more potential genetic information can be uncovered.
Family-based QTL mapping
Germ plasm
Inheritance
Trait
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Isoflavone, protein and oil are the most important quality traits in soybean. Since these quality traits are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify the complex and unclear genetic background of them. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and high-density linkage map based on whole genome resequencing to map novel QTL; and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with high logarithm of odd scores were identified through composite interval mapping (CIM) including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1034 QTL collected from previous studies. For the first-time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL respectively related to oil and protein were obtained as well. The results promote the understandings on the biosynthesis and regulation of isoflavones, protein and oil at molecular level, and facilitate the construction of molecular modular for great quality traits in soybean.
Family-based QTL mapping
Candidate gene
Genetic linkage
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