doi: 10.1111/age.12311
A composite method for mapping quantitative trait loci without interference of female achiasmatic and gender effects in silkworm, Bombyx mori C. Li*†, W. Zuo*†, X. Tong*†, H. Hu*†, L. Qiao‡, J. Song*†, G. Xiong*†, R. Gao*†, F. Dai*† and C. Lu*† *State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqi-ng 400716, China. †Key Laboratory for Sericulture Functional Genomics and Biotechnology of Agricultural Ministry, Southwest University, Chongqing 400716, China. ‡Institute of Entomology and Molecular Biology, College of Life Sciences, Chongqing Normal University, Chongqing 401331, China.
Summary
The silkworm, Bombyx mori, is an economically important insect that was domesticated more than 5000 years ago. Its major economic traits focused on by breeders are quantitative traits, and an accurate and efficient QTL mapping method is necessary to explore their genetic architecture. However, current widely used QTL mapping models are not well suited for silkworm because they ignore female achiasmate and gender effects. In this study, we propose a composite method combining rational population selection and special mapping methods to map QTL in silkworm. By determining QTL for cocoon shell weight (CSW), we demonstrated the effectiveness of this method. In the CSW mapping process, only 56 markers were used and five loci or chromosomes were detected, more than in previous studies. Additionally, loci on chromosomes 1 and 11 dominated and accounted for 35.10% and 15.03% of the phenotypic variance respectively. Unlike previous studies, epistasis was detected between loci on chromosomes 11 and 22. These mapping results demonstrate the power and convenience of this method for QTL mapping in silkworm, and this method may inspire the development of similar approaches for other species with special genetic characteristics. Keywords Bombyx mori, cocoon shell weight, genetic architecture, population selection, quantitative trait loci mapping
Introduction Economic traits of crops and farm animals are usually quantitative traits, and identification and cloning of quantitative trait genes (QTGs) paves the way for improving these traits. Although novel methods based on linkage analysis, such as QTL isogenic recombinant analysis (Peleman et al. 2005), and linkage disequilibrium approaches, such as identity-by-descent analysis (Grapes et al. 2006; Ren et al. 2011) and genome-wide association studies (Haines et al. 2005), have been developed recently, QTL mapping is still a prerequisite or necessary complement for the fine mapping of QTGs (Manenti et al. 2009; Brachi et al. 2010; Motte et al. 2014). Address for correspondence F. Dai, State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqi-ng 400716, China. E-mail: [email protected] Accepted for publication 15 April 2015
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The silkworm, Bombyx mori, was domesticated more than 5000 years ago for the production of silk fibers and is the basis of sericulture (Xiang 1995), a pillar industry for numerous countries and areas. Therefore, improvement in the economically important traits of silkworm is a major target of geneticists and breeders. As with the traits of other farm animals, major economic traits, including cocoonrelated traits such as cocoon weight, cocoon shell weight (CSW) and cocoon shell ratio, are controlled by multiple genes that exhibit quantitative characteristics (Xiang 2005b). Thus, QTL mapping is the first and fundamental step toward studying molecular mechanisms and screening and identifying candidate genes for functional validation. Several studies mapping QTL for silkworm cocoon-related traits using composite interval mapping (CIM) have been reported (Zhan et al. 2009; Mirhoseini et al. 2010; Zhang et al. 2010). Composite interval mapping and other existing mapping models were designed based on the genetic characteristics of plants. However, silkworms are insects that have very different genetic characteristics compared
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
QTL mapping method for silkworm with these plants, particularly female achiasmatic and gender effects, which greatly influence mapping results. Consequently, direct application of existing QTL mapping models may significantly bias the mapping results and obstruct the identification and fine mapping of candidate genes. Accordingly, it is necessary to develop suitable methods to improve QTL mapping for silkworm that will facilitate the identification of genes controlling economically important traits. Xu et al. (2011) proposed a new statistical model to analyze QTL in silkworm F2 populations in which the achiasmatic and gender effects were considered. However, several issues with this proposed model still need to be resolved. Only QTL on autosomes could be detected, and detecting epistasis between interacting loci on the same chromosome also proved problematic. Modifications to this protocol are needed if it is to facilitate QTL mapping in silkworm. In this study, we focused on a rational population selection strategy to avoid interference from female achiasmatic and gender effects, and developed a composite method that combines population selection and special mapping methods. We then tested the effectiveness of the method by analyzing the genetic architecture of CSW, an important economically relevant trait and a primary target for silkworm breeders. The method identified five QTL or chromosomes, and epistasis was detected for one pair.
Materials and methods Silkworm strains The inbred Dazao (IS-Dazao) and breeder stock 872B parental strains were obtained from the silkworm resource bank at Southwest University. These strains have a homozygous genetic background and were used as parents to produce the linkage analysis population (BC1F), (ISDazao 9 872B) 9 IS-Dazao, and the mapping analysis populations (BC1M): BCf [IS-Dazao 9 (IS-Dazao 9 872B)] and BCr [872B 9 (IS-Dazao 9 872B] (Fig. 1). Altogether, 187 male BCf individuals and 192 male BCr individuals were selected randomly to generate the mimic F2 population (miF2) (Fig. 1). Silkworms were reared on fresh mulberry leaves under a 12-h/12-h light/dark photoperiod at 24 °C.
Phenotypic and statistical description The cocoons of IS-Dazao and 872B were selected and photographed by camera (Canon 5D mark III) at the eyecoloring stage of pupa; then both were cross-cut at the middle part of the cocoons and photographed under the stereomicroscope (C-DSS230, Nikon Corporation). The cocoons of groups including parents, F1 and the segregation populations were paired at the eye-coloring stage of pupa to distinguish sexes, and the CSW was measured in the two
Figure 1 General outline of the mapping method. EH and EL represent the subpopulation with extreme high and extreme low cocoon shell weight respectively; BCf and BCr represent the two backcross population crossed with the IS-Dazao and 872B respectively; and the discontinuous line in the blue frame represents the genotypes of the marker on the ith chromosome in each subpopulation.
sexed populations. The phenotypic means of these populations (two strains and their two sexed populations) were calculated using the Average function in EXCEL (Microsoft Office 2010, Microsoft Corp.). The P-values of paired comparisons were computed using t-tests and one-way analysis of variance in EXCEL and SPSS 21 (IBM Corp.) respectively. The frequency distribution of the segregation population was depicted in EXCEL and ORIGINPRO 8 (OriginLab) and modified in ADOBE ILLUSTRATOR CS6 (Adobe Systems Inc.).
Step one: linkage analysis to identify the linked autosomes A selective genotyping strategy was used to determine the chromosomes linked with CSW. In this step, 24 male individuals with extremely high CSW and 24 males with extremely low CSW were selected from the linkage analysis population to make the phenotype-extreme subpopulations. The two subpopulations comprised about 20% of the male individuals (n = 292) in the linkage analysis population, which made it have the largest detection power (Darvasi & Soller 1992). Silkworm has 27 pairs of autosomes and one pair of sex chromosomes. To conduct the linkage analysis
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Li et al. on all the chromosomes, we selected 26 polymorphic markers (we failed to find a polymorphic marker on chromosome 16) on the autosomes, including 12 simple sequence repeat (SSR) (Miao et al. 2005) markers and 14 PCR-designed markers. These markers were genotyped in the phenotype-extreme subpopulations, the P-values between markers on each chromosome and CSW were computed by a t-test, and the corresponding negative logarithm values were computed in EXCEL. At the same time, a single SSR marker on chromosome 1 was used to ensure the accuracy of sex determination.
targets for QTL mapping. Because of the important role of chromosome 1 in determining the economic traits of silkworm (Xiang 2005b), it was also selected as the target, and markers on these chromosomes were developed and genotyped in a mimetic F2 (miF2) population from a double backcross group (generated by backcrossing the male F1 individuals with its two parents) to map the loci and to estimate their genetic effects including additive and dominance effects and epistasis. Both steps were conducted in male-only populations to avoid inference from gender effects. The general outline of the method is shown in Fig. 1.
Step two: mapping analysis on the target chromosomes
Statistical description of phenotypic data
Because crossover occurs in male silkworm, the mapping analysis on the linked autosomes and the sex chromosomes could be conducted in a miF2 population from the backcross of male F1 and its two parents. Thirty PCR markers were specifically developed on the chromosomes, and these markers were genotyped in the miF2 population. The QTL and the corresponding genetic effects (including additive effects, dominance and epistasis) were mapped and estimated by the CIM-based mixed linear model in QTLNETWORK 2.2 (Yang et al. 2007, 2008). A permutation test with 1000 replicates was performed to determine threshold at the 5% probability level. The primers for the markers used for linkage and mapping analysis are listed in Table S1. The genetic map, including all the markers, was constructed based on their position on the Physical-Genetic map in KAIKObase (http://sgp.dna.affrc.go.jp/KAIKObase/) (Shimomura et al. 2009). All PCR markers were designed based on the SilkDB (http://silkworm.genomics.org.cn/) (Duan et al. 2010) and KAIKObase.
Two silkworm strains, IS-Dazao and 872B, were selected as the parents to produce the cross-populations for QTL analysis. CSW varies greatly between the IS-Dazao and 872B parent strains, with IS-Dazao having a smaller cocoon (Fig. 2a) than 872B. We compared the subtle structure of cocoon cross-sections, which revealed that the cocoon shell of the IS-Dazao is thinner than that of 872B (Fig. 2b). Statistical comparison of the phenotypic mean of the two strains showed that the CSW of 872B was more than twice that of IS-Dazao for both male and female silkworms (Fig. 2c). Using these two strains, we made the linkage and mapping analysis populations and assessed the frequency distribution of the mapping populations. The results showed that the distribution of CSW did not follow a normal distribution but, rather, a curve with three distinct peaks (Fig. 3).
Results General outline of the composite method This method can be treated as a two-step approach that includes linkage analysis and mapping analysis. Both steps integrate rational population selection and another specific method. In the first step, a selective genotyping strategy was used to determine the relationship between markers on each chromosome and the trait of interest in a linkage population from BC1F. The linkage population consisted of two subpopulations with opposite phenotypes. From the subpopulations, chromosomes linked with traits of interest could be determined except for chromosome 1, the silkworm sex chromosome, because the genotypes of markers on this chromosome have no association with phenotypic differentiation and are related only to the sex (Xiang 2005a). Due to achiasmatic effects in female silkworms, linkage analysis could be simplified to the linkage analysis of a single marker on each chromosome (Xiang 2005a). Chromosomes linked with the trait of interest were subsequently selected as
Linkage and mapping analysis: several loci and a pair of epistasis effect controlling CSW Using the above procedure, we constructed a genetic map consisting of 56 markers (Fig. S1) that included 26 markers for linkage analysis and 30 markers for mapping analysis. In the linkage population, we identified chromosomes linked with CSW. The results showed that four chromosomes (8, 11, 21, 22) were significantly linked with this trait. Chromosome 11 and 22 each had an extremely low P-value, indicating a strong linkage with CSW (Fig 4a; Table S2); therefore, chromosomes 11, 22 and 1 were chosen for the subsequent mapping analysis. QTL and the corresponding genetic effects on CSW were analyzed in the mapping population, which showed that each chromosome had a single locus (Fig. 4b), named csw1, csw2 and csw3 respectively. These QTL accounted for more than 58% of the phenotypic variance in the mapping population. Among the three loci, csw1 on chromosome 1 contributed the largest effect on CSW, accounting for 35.10% of the phenotypic variance, which could be further divided into additive (34.1%) and dominance (1%) effects. The remaining QTL, csw2 and csw3, accounted for 15.03% and 4.70% of the variance respectively, which also include additive and
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
QTL mapping method for silkworm (a)
(b)
(c)
Figure 2 Phenotypic description. (a) Cocoon of IS-Dazao and 872B, the scale is 1 cm. (b) Cross-cutting subtle structure of the same part marked by the black line, the scale is 1 mm. (c) Mean CSW of IS-Dazao and 872B. (Student’s t-test; **P < 0.01)
Figure 3 Frequency distribution of the segregating population. Total represents the population consisting of BCf and BCr without any selection; the miF2 represents the mapping population selected from the total population. Both populations comprised male individuals.
dominance effects. Besides these single QTL effects, we also identified one QTL on chromosome 11 without the single effect that also affected CSW through an epistasis effect with csw3 on chromosome 22 (Fig. 4c); this was termed cswE (Table 1).
Discussion In this research, we developed a composite QTL mapping method to detect loci for quantitative traits in silkworm. Unlike conventional QTL models, we divided QTL mapping into a two-step procedure that resulted in several advantages. First, the proposed method avoided interference from gender effects. When both sexes are used for QTL mapping, the gender effect will bulge, which may partially mask the effect of QTL on the autosomes, and loci or chromosomes contributing subtle effects may be ignored during mapping. Because only the male individuals were used in this method, gender effect was avoided and the effects of the all loci were estimated accurately. Therefore, in theory, more loci or chromosomes were detected. Second, this method proved
more efficient for mapping QTL in silkworm because it took advantage of female achiasmatic effect by a rational population selection strategy. Only 27 markers on different chromosomes were required to identify chromosomes linked with the trait of interest, and only a relatively small number of markers developed specifically for the linked chromosomes were genotyped to determine the location of QTL on the chromosomes, which was both time- and labor efficient. To investigate the effectiveness of the developed method, we analyzed the genetic architecture of CSW. Under the guidance of this method, the BC1F population was generated for linkage analysis and the BC1M population was generated for mapping analysis. Of particular note was the frequency distribution of the CSW mapping population that did not follow a normal distribution but, rather, a curve with three distinct peaks. The most rational explanation may be the genetic architecture. According to the polygene theory of Nilsson-Ehle (1909), quantitative trait distributions should follow a normal distribution. However, published QTL studies have included quantitative traits with non-normal or partial-normal distributions, and QTL mapping indicated that several genes with major or minor effects controlled these traits together (Fan et al. 2006; Jun et al. 2013; Marshall et al. 2013; Uga et al. 2013). Therefore, if polygenes with non-equal effects control traits, the distributions may be partially normal or non-normal. The distribution of CSW in this study is similar with that of these traits, which indicates that the genetic architecture may consist of several major and some minor effect genes. Subsequent mapping results supported this conclusion (discussed below). By this method, we analyzed the genetic architecture of the CSW and compared its efficiency with that of previous studies (Table S3) (Zhan et al. 2009; Mirhoseini et al. 2010; Zhang et al. 2010). The comparison of results indicated that the proposed method is time and labor effective and powerful. During the mapping procedure, only 56 markers were used, which is much fewer than in other studies, but without any marker density loss (5.63 cM) on the target chromosomes for mapping analysis. Because the
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(b)
(c)
Figure 4 Results of the linkage and mapping analysis. (a) Significance values for each chromosome (Student’s t-test; *P < 0.05; **P < 0.01). The red star represents the sex chromosome.; (b) F-value of the intervals on the linked chromosomes. (c) QTLNETWORK on the linked chromosomes; the red circle (A) represents the addictive effect; the red square (D) represents the dominance effect; the orange line (E) represents the epistasis effect; and the black circle (NA) is the non-addictive effect, which means that the locus has no single effect but can interact with other loci to affect the value of trait of interest (Yang et al. 2007, 2008) . Table 1 Summary of mapping results. Variance Genetic architecture
QTL
Chromosome
Interval
Position
Range
Additive (%)
Dominance (%)
Total (%)
Single QTL
csw1 csw2 csw3 cswE csw3
1 11 22 11 22
Mk1–Mk2 Mk15–Mk16 Mk28–Mk29 Mk11–Mk12 Mk28–Mk29
0.0 23.0 37.0 2.0 37.0
0.0–4.0 14.0–30.0 30.0–44.0 0.0–7.0 30.0–44.0
34.10 15.00 4.70
1.00 0.03 0.00
35.10 15.03 4.70 3.92
Epistasis
conventional method requires genotype markers covering the whole genome, a much larger number of markers should be developed to meet a comparable marker density; for instance, 692 markers were used in the research of Zhan et al. (2009) to get a genetic map with a marker density of 6.3 cM. At the same time, more loci, as well a pair of epistases, were detected by the composite method (Table S3). The reason why more loci and the pair of epistases were found may be that the gender effect is avoided in this method, which concurs with the previous analysis.
However, only 58.75% phenotypic variance was explained by the mapping result, which is less than in some previous studies, such as 59.16% and 73.69% found in the research of Mirhoseini et al. (2010) and Zhang et al. (2010) respectively (Table S3). The reason may be as follows. On the one hand, only 78 F2 and 44 BC individuals were used in their studies respectively, which makes the sample a little small. The relatively small sample may lead to an overestimation of the genetic effect. On the other hand, in the linkage analysis stage of the composite method, four chromosomes
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
QTL mapping method for silkworm linked with CSW were detected. However, only two of them were selected as targets for the following mapping because of their extremely low P-values. This artificial negligence may lead to some loci and corresponding variance loss. Therefore, a mapping analysis on all chromosomes linked to the trait of interest is necessary if general genetic architecture is the goal of research. However, it may be enough to analyze the extremely linked chromosomes if one wants to detect several QTLs with major effect for the following fine mapping and QTG identification, which may give researchers some flexibility in applying the method. In summary, the composite method developed in this study proved powerful for mapping QTL in silkworm and saved time and labor compared to previously published methods. This method could facilitate studies of the molecular mechanisms of quantitative traits and silkworm breeding and may inspire similar approaches for QTL mapping of other species with complicated genetic characteristics.
Acknowledgements This work was funded by the Hi-Tech Research and Development 863 Program of China (Grant No. 2013AA102507), the National Basic Research 973 Program of China (Grant No. 2012CB114600), the National Natural Science Foundation of China (No. 31372379, No. 31472153) and Fundamental Research Funds for the Central Universities in China (No. XDJK2013A001, No. XDJK2013A021, No. XDJK2013C129).
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Manenti G., Galvan A., Pettinicchio A., Trincucci G., Spada E., Zolin A., Milani S., Gonzalez-Neira A. & Dragani T.A. (2009) Mouse genome-wide association mapping needs linkage analysis to avoid false-positive loci. PLoS Genetics 5, e1000331. Marshall K., Mugambi J.M., Nagda S., Sonstegard T.S., Van Tassell C.P., Baker R.L. & Gibson J.P. (2013) Quantitative trait loci for resistance to Haemonchus contortus artificial challenge in Red Maasai and Dorper sheep of East Africa. Animal Genetics 44, 285–95. Miao X.X., Xub S.J., Li M.H. et al. (2005) Simple sequence repeatbased consensus linkage map of Bombyx mori. Proceedings of the National Academy of Sciences of the United States of America 102, 16303–8. Mirhoseini S.Z., Rabiei B., Potki P. & Dalirsefat S.B. (2010) Amplified fragment length polymorphism mapping of quantitative trait loci for economically important traits in the silkworm, Bombyx mori. Journal of Insect Science 10, 153. Motte H., Vercauteren A., Depuydt S., Landschoot S., Geelen D., Werbrouck S., Goormachtig S., Vuylsteke M. & Vereecke D. (2014) Combining linkage and association mapping identifies receptor-like protein kinase1 as an essential Arabidopsis shoot regeneration gene. Proceedings of the National Academy of Sciences of the United States of America 111, 8305–10. Nilsson-Ehle, H. (1909) Kreuzungsuntersuchungen an Hafer und Weizen. Lunds. Univ. Aarskr., 5, 1–122. Peleman J.D., Wye C., Zethof J., Sorensen A.P., Verbakel H., van Oeveren J., Gerats T. & van der Voort J.R. (2005) Quantitative trait locus (QTL) isogenic recombinant analysis: a method for high-resolution mapping of QTL within a single population. Genetics 171, 1341–52. Ren J., Duan Y.Y., Qiao R.M. et al. (2011) A missense mutation in PPARD causes a major QTL effect on ear size in pigs. PLoS Genetics 7, e1002043. Shimomura M., Minami H., Suetsugu Y. et al. (2009) KAIKObase: an integrated silkworm genome database and data mining tool. BMC Genomics 10, 486. Uga Y., Yamamoto E., Kanno N., Kawai S., Mizubayashi T. & Fukuoka S. (2013) A major QTL controlling deep rooting on rice chromosome 4. Scientific Reports 3, 3040. Xiang Z.H. (1995) Introduction. In: Genetics and Breeding of the Silkworm (Ed. by Z.H. Xiang), pp. 1. Chinese Agriculture Press, Beijing. Xiang Z.H. (2005a) Mutant gene of silkworm. In: Biology of Sericulture (Ed. by Z.H. Xiang). pp. 110–2. Forestry Press of China, Beijing. Xiang Z.H. (2005b) Quantitative trait genetics of silkworm. In: Biology of Sericulture (Ed. by Z.H. Xiang), pp. 242–53. Forestry Press of China, Beijing. Xu H.M., Wei C.S., Tang Y.T., Zhu Z.H., Sima Y.F. & Lou X.Y. (2011) A new mapping method for quantitative trait loci of silkworm. BMC Genetics 12, 19. Yang J., Zhu J. & Williams R.W. (2007) Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics 23, 1527–36. Yang J., Hu C., Hu H., Yu R., Xia Z., Ye X. & Zhu J. (2008) QTLNETWORK: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24, 721–3. Zhan S., Huang J., Guo Q., Zhao Y., Li W., Miao X., Goldsmith M.R., Li M. & Huang Y. (2009) An integrated genetic linkage map for silkworms with three parental combinations and its application to the mapping of single genes and QTL. BMC Genomics 10, 389.
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Supporting information Additional supporting information may be found in the online version of this article.
Figure S1 Genetic map of the markers used. Table S1 Information of the markers used in this research. PART1 shows the markers for linkage analysis; PART2 shows the markers for mapping analysis. Table S2 Significance level between the markers and CSW. Table S3 Comparison of mapping results with that of the previous research.
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
A composite method for mapping quantitative trait loci without interference of female achiasmatic and gender effects in silkworm, Bombyx mori C. Li*†, W. Zuo*†, X. Tong*†, H. Hu*†, L. Qiao‡, J. Song*†, G. Xiong*†, R. Gao*†, F. Dai*† and C. Lu*† *State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqi-ng 400716, China. †Key Laboratory for Sericulture Functional Genomics and Biotechnology of Agricultural Ministry, Southwest University, Chongqing 400716, China. ‡Institute of Entomology and Molecular Biology, College of Life Sciences, Chongqing Normal University, Chongqing 401331, China.
Summary
The silkworm, Bombyx mori, is an economically important insect that was domesticated more than 5000 years ago. Its major economic traits focused on by breeders are quantitative traits, and an accurate and efficient QTL mapping method is necessary to explore their genetic architecture. However, current widely used QTL mapping models are not well suited for silkworm because they ignore female achiasmate and gender effects. In this study, we propose a composite method combining rational population selection and special mapping methods to map QTL in silkworm. By determining QTL for cocoon shell weight (CSW), we demonstrated the effectiveness of this method. In the CSW mapping process, only 56 markers were used and five loci or chromosomes were detected, more than in previous studies. Additionally, loci on chromosomes 1 and 11 dominated and accounted for 35.10% and 15.03% of the phenotypic variance respectively. Unlike previous studies, epistasis was detected between loci on chromosomes 11 and 22. These mapping results demonstrate the power and convenience of this method for QTL mapping in silkworm, and this method may inspire the development of similar approaches for other species with special genetic characteristics. Keywords Bombyx mori, cocoon shell weight, genetic architecture, population selection, quantitative trait loci mapping
Introduction Economic traits of crops and farm animals are usually quantitative traits, and identification and cloning of quantitative trait genes (QTGs) paves the way for improving these traits. Although novel methods based on linkage analysis, such as QTL isogenic recombinant analysis (Peleman et al. 2005), and linkage disequilibrium approaches, such as identity-by-descent analysis (Grapes et al. 2006; Ren et al. 2011) and genome-wide association studies (Haines et al. 2005), have been developed recently, QTL mapping is still a prerequisite or necessary complement for the fine mapping of QTGs (Manenti et al. 2009; Brachi et al. 2010; Motte et al. 2014). Address for correspondence F. Dai, State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqi-ng 400716, China. E-mail: [email protected] Accepted for publication 15 April 2015
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The silkworm, Bombyx mori, was domesticated more than 5000 years ago for the production of silk fibers and is the basis of sericulture (Xiang 1995), a pillar industry for numerous countries and areas. Therefore, improvement in the economically important traits of silkworm is a major target of geneticists and breeders. As with the traits of other farm animals, major economic traits, including cocoonrelated traits such as cocoon weight, cocoon shell weight (CSW) and cocoon shell ratio, are controlled by multiple genes that exhibit quantitative characteristics (Xiang 2005b). Thus, QTL mapping is the first and fundamental step toward studying molecular mechanisms and screening and identifying candidate genes for functional validation. Several studies mapping QTL for silkworm cocoon-related traits using composite interval mapping (CIM) have been reported (Zhan et al. 2009; Mirhoseini et al. 2010; Zhang et al. 2010). Composite interval mapping and other existing mapping models were designed based on the genetic characteristics of plants. However, silkworms are insects that have very different genetic characteristics compared
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
QTL mapping method for silkworm with these plants, particularly female achiasmatic and gender effects, which greatly influence mapping results. Consequently, direct application of existing QTL mapping models may significantly bias the mapping results and obstruct the identification and fine mapping of candidate genes. Accordingly, it is necessary to develop suitable methods to improve QTL mapping for silkworm that will facilitate the identification of genes controlling economically important traits. Xu et al. (2011) proposed a new statistical model to analyze QTL in silkworm F2 populations in which the achiasmatic and gender effects were considered. However, several issues with this proposed model still need to be resolved. Only QTL on autosomes could be detected, and detecting epistasis between interacting loci on the same chromosome also proved problematic. Modifications to this protocol are needed if it is to facilitate QTL mapping in silkworm. In this study, we focused on a rational population selection strategy to avoid interference from female achiasmatic and gender effects, and developed a composite method that combines population selection and special mapping methods. We then tested the effectiveness of the method by analyzing the genetic architecture of CSW, an important economically relevant trait and a primary target for silkworm breeders. The method identified five QTL or chromosomes, and epistasis was detected for one pair.
Materials and methods Silkworm strains The inbred Dazao (IS-Dazao) and breeder stock 872B parental strains were obtained from the silkworm resource bank at Southwest University. These strains have a homozygous genetic background and were used as parents to produce the linkage analysis population (BC1F), (ISDazao 9 872B) 9 IS-Dazao, and the mapping analysis populations (BC1M): BCf [IS-Dazao 9 (IS-Dazao 9 872B)] and BCr [872B 9 (IS-Dazao 9 872B] (Fig. 1). Altogether, 187 male BCf individuals and 192 male BCr individuals were selected randomly to generate the mimic F2 population (miF2) (Fig. 1). Silkworms were reared on fresh mulberry leaves under a 12-h/12-h light/dark photoperiod at 24 °C.
Phenotypic and statistical description The cocoons of IS-Dazao and 872B were selected and photographed by camera (Canon 5D mark III) at the eyecoloring stage of pupa; then both were cross-cut at the middle part of the cocoons and photographed under the stereomicroscope (C-DSS230, Nikon Corporation). The cocoons of groups including parents, F1 and the segregation populations were paired at the eye-coloring stage of pupa to distinguish sexes, and the CSW was measured in the two
Figure 1 General outline of the mapping method. EH and EL represent the subpopulation with extreme high and extreme low cocoon shell weight respectively; BCf and BCr represent the two backcross population crossed with the IS-Dazao and 872B respectively; and the discontinuous line in the blue frame represents the genotypes of the marker on the ith chromosome in each subpopulation.
sexed populations. The phenotypic means of these populations (two strains and their two sexed populations) were calculated using the Average function in EXCEL (Microsoft Office 2010, Microsoft Corp.). The P-values of paired comparisons were computed using t-tests and one-way analysis of variance in EXCEL and SPSS 21 (IBM Corp.) respectively. The frequency distribution of the segregation population was depicted in EXCEL and ORIGINPRO 8 (OriginLab) and modified in ADOBE ILLUSTRATOR CS6 (Adobe Systems Inc.).
Step one: linkage analysis to identify the linked autosomes A selective genotyping strategy was used to determine the chromosomes linked with CSW. In this step, 24 male individuals with extremely high CSW and 24 males with extremely low CSW were selected from the linkage analysis population to make the phenotype-extreme subpopulations. The two subpopulations comprised about 20% of the male individuals (n = 292) in the linkage analysis population, which made it have the largest detection power (Darvasi & Soller 1992). Silkworm has 27 pairs of autosomes and one pair of sex chromosomes. To conduct the linkage analysis
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Li et al. on all the chromosomes, we selected 26 polymorphic markers (we failed to find a polymorphic marker on chromosome 16) on the autosomes, including 12 simple sequence repeat (SSR) (Miao et al. 2005) markers and 14 PCR-designed markers. These markers were genotyped in the phenotype-extreme subpopulations, the P-values between markers on each chromosome and CSW were computed by a t-test, and the corresponding negative logarithm values were computed in EXCEL. At the same time, a single SSR marker on chromosome 1 was used to ensure the accuracy of sex determination.
targets for QTL mapping. Because of the important role of chromosome 1 in determining the economic traits of silkworm (Xiang 2005b), it was also selected as the target, and markers on these chromosomes were developed and genotyped in a mimetic F2 (miF2) population from a double backcross group (generated by backcrossing the male F1 individuals with its two parents) to map the loci and to estimate their genetic effects including additive and dominance effects and epistasis. Both steps were conducted in male-only populations to avoid inference from gender effects. The general outline of the method is shown in Fig. 1.
Step two: mapping analysis on the target chromosomes
Statistical description of phenotypic data
Because crossover occurs in male silkworm, the mapping analysis on the linked autosomes and the sex chromosomes could be conducted in a miF2 population from the backcross of male F1 and its two parents. Thirty PCR markers were specifically developed on the chromosomes, and these markers were genotyped in the miF2 population. The QTL and the corresponding genetic effects (including additive effects, dominance and epistasis) were mapped and estimated by the CIM-based mixed linear model in QTLNETWORK 2.2 (Yang et al. 2007, 2008). A permutation test with 1000 replicates was performed to determine threshold at the 5% probability level. The primers for the markers used for linkage and mapping analysis are listed in Table S1. The genetic map, including all the markers, was constructed based on their position on the Physical-Genetic map in KAIKObase (http://sgp.dna.affrc.go.jp/KAIKObase/) (Shimomura et al. 2009). All PCR markers were designed based on the SilkDB (http://silkworm.genomics.org.cn/) (Duan et al. 2010) and KAIKObase.
Two silkworm strains, IS-Dazao and 872B, were selected as the parents to produce the cross-populations for QTL analysis. CSW varies greatly between the IS-Dazao and 872B parent strains, with IS-Dazao having a smaller cocoon (Fig. 2a) than 872B. We compared the subtle structure of cocoon cross-sections, which revealed that the cocoon shell of the IS-Dazao is thinner than that of 872B (Fig. 2b). Statistical comparison of the phenotypic mean of the two strains showed that the CSW of 872B was more than twice that of IS-Dazao for both male and female silkworms (Fig. 2c). Using these two strains, we made the linkage and mapping analysis populations and assessed the frequency distribution of the mapping populations. The results showed that the distribution of CSW did not follow a normal distribution but, rather, a curve with three distinct peaks (Fig. 3).
Results General outline of the composite method This method can be treated as a two-step approach that includes linkage analysis and mapping analysis. Both steps integrate rational population selection and another specific method. In the first step, a selective genotyping strategy was used to determine the relationship between markers on each chromosome and the trait of interest in a linkage population from BC1F. The linkage population consisted of two subpopulations with opposite phenotypes. From the subpopulations, chromosomes linked with traits of interest could be determined except for chromosome 1, the silkworm sex chromosome, because the genotypes of markers on this chromosome have no association with phenotypic differentiation and are related only to the sex (Xiang 2005a). Due to achiasmatic effects in female silkworms, linkage analysis could be simplified to the linkage analysis of a single marker on each chromosome (Xiang 2005a). Chromosomes linked with the trait of interest were subsequently selected as
Linkage and mapping analysis: several loci and a pair of epistasis effect controlling CSW Using the above procedure, we constructed a genetic map consisting of 56 markers (Fig. S1) that included 26 markers for linkage analysis and 30 markers for mapping analysis. In the linkage population, we identified chromosomes linked with CSW. The results showed that four chromosomes (8, 11, 21, 22) were significantly linked with this trait. Chromosome 11 and 22 each had an extremely low P-value, indicating a strong linkage with CSW (Fig 4a; Table S2); therefore, chromosomes 11, 22 and 1 were chosen for the subsequent mapping analysis. QTL and the corresponding genetic effects on CSW were analyzed in the mapping population, which showed that each chromosome had a single locus (Fig. 4b), named csw1, csw2 and csw3 respectively. These QTL accounted for more than 58% of the phenotypic variance in the mapping population. Among the three loci, csw1 on chromosome 1 contributed the largest effect on CSW, accounting for 35.10% of the phenotypic variance, which could be further divided into additive (34.1%) and dominance (1%) effects. The remaining QTL, csw2 and csw3, accounted for 15.03% and 4.70% of the variance respectively, which also include additive and
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
QTL mapping method for silkworm (a)
(b)
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Figure 2 Phenotypic description. (a) Cocoon of IS-Dazao and 872B, the scale is 1 cm. (b) Cross-cutting subtle structure of the same part marked by the black line, the scale is 1 mm. (c) Mean CSW of IS-Dazao and 872B. (Student’s t-test; **P < 0.01)
Figure 3 Frequency distribution of the segregating population. Total represents the population consisting of BCf and BCr without any selection; the miF2 represents the mapping population selected from the total population. Both populations comprised male individuals.
dominance effects. Besides these single QTL effects, we also identified one QTL on chromosome 11 without the single effect that also affected CSW through an epistasis effect with csw3 on chromosome 22 (Fig. 4c); this was termed cswE (Table 1).
Discussion In this research, we developed a composite QTL mapping method to detect loci for quantitative traits in silkworm. Unlike conventional QTL models, we divided QTL mapping into a two-step procedure that resulted in several advantages. First, the proposed method avoided interference from gender effects. When both sexes are used for QTL mapping, the gender effect will bulge, which may partially mask the effect of QTL on the autosomes, and loci or chromosomes contributing subtle effects may be ignored during mapping. Because only the male individuals were used in this method, gender effect was avoided and the effects of the all loci were estimated accurately. Therefore, in theory, more loci or chromosomes were detected. Second, this method proved
more efficient for mapping QTL in silkworm because it took advantage of female achiasmatic effect by a rational population selection strategy. Only 27 markers on different chromosomes were required to identify chromosomes linked with the trait of interest, and only a relatively small number of markers developed specifically for the linked chromosomes were genotyped to determine the location of QTL on the chromosomes, which was both time- and labor efficient. To investigate the effectiveness of the developed method, we analyzed the genetic architecture of CSW. Under the guidance of this method, the BC1F population was generated for linkage analysis and the BC1M population was generated for mapping analysis. Of particular note was the frequency distribution of the CSW mapping population that did not follow a normal distribution but, rather, a curve with three distinct peaks. The most rational explanation may be the genetic architecture. According to the polygene theory of Nilsson-Ehle (1909), quantitative trait distributions should follow a normal distribution. However, published QTL studies have included quantitative traits with non-normal or partial-normal distributions, and QTL mapping indicated that several genes with major or minor effects controlled these traits together (Fan et al. 2006; Jun et al. 2013; Marshall et al. 2013; Uga et al. 2013). Therefore, if polygenes with non-equal effects control traits, the distributions may be partially normal or non-normal. The distribution of CSW in this study is similar with that of these traits, which indicates that the genetic architecture may consist of several major and some minor effect genes. Subsequent mapping results supported this conclusion (discussed below). By this method, we analyzed the genetic architecture of the CSW and compared its efficiency with that of previous studies (Table S3) (Zhan et al. 2009; Mirhoseini et al. 2010; Zhang et al. 2010). The comparison of results indicated that the proposed method is time and labor effective and powerful. During the mapping procedure, only 56 markers were used, which is much fewer than in other studies, but without any marker density loss (5.63 cM) on the target chromosomes for mapping analysis. Because the
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Figure 4 Results of the linkage and mapping analysis. (a) Significance values for each chromosome (Student’s t-test; *P < 0.05; **P < 0.01). The red star represents the sex chromosome.; (b) F-value of the intervals on the linked chromosomes. (c) QTLNETWORK on the linked chromosomes; the red circle (A) represents the addictive effect; the red square (D) represents the dominance effect; the orange line (E) represents the epistasis effect; and the black circle (NA) is the non-addictive effect, which means that the locus has no single effect but can interact with other loci to affect the value of trait of interest (Yang et al. 2007, 2008) . Table 1 Summary of mapping results. Variance Genetic architecture
QTL
Chromosome
Interval
Position
Range
Additive (%)
Dominance (%)
Total (%)
Single QTL
csw1 csw2 csw3 cswE csw3
1 11 22 11 22
Mk1–Mk2 Mk15–Mk16 Mk28–Mk29 Mk11–Mk12 Mk28–Mk29
0.0 23.0 37.0 2.0 37.0
0.0–4.0 14.0–30.0 30.0–44.0 0.0–7.0 30.0–44.0
34.10 15.00 4.70
1.00 0.03 0.00
35.10 15.03 4.70 3.92
Epistasis
conventional method requires genotype markers covering the whole genome, a much larger number of markers should be developed to meet a comparable marker density; for instance, 692 markers were used in the research of Zhan et al. (2009) to get a genetic map with a marker density of 6.3 cM. At the same time, more loci, as well a pair of epistases, were detected by the composite method (Table S3). The reason why more loci and the pair of epistases were found may be that the gender effect is avoided in this method, which concurs with the previous analysis.
However, only 58.75% phenotypic variance was explained by the mapping result, which is less than in some previous studies, such as 59.16% and 73.69% found in the research of Mirhoseini et al. (2010) and Zhang et al. (2010) respectively (Table S3). The reason may be as follows. On the one hand, only 78 F2 and 44 BC individuals were used in their studies respectively, which makes the sample a little small. The relatively small sample may lead to an overestimation of the genetic effect. On the other hand, in the linkage analysis stage of the composite method, four chromosomes
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QTL mapping method for silkworm linked with CSW were detected. However, only two of them were selected as targets for the following mapping because of their extremely low P-values. This artificial negligence may lead to some loci and corresponding variance loss. Therefore, a mapping analysis on all chromosomes linked to the trait of interest is necessary if general genetic architecture is the goal of research. However, it may be enough to analyze the extremely linked chromosomes if one wants to detect several QTLs with major effect for the following fine mapping and QTG identification, which may give researchers some flexibility in applying the method. In summary, the composite method developed in this study proved powerful for mapping QTL in silkworm and saved time and labor compared to previously published methods. This method could facilitate studies of the molecular mechanisms of quantitative traits and silkworm breeding and may inspire similar approaches for QTL mapping of other species with complicated genetic characteristics.
Acknowledgements This work was funded by the Hi-Tech Research and Development 863 Program of China (Grant No. 2013AA102507), the National Basic Research 973 Program of China (Grant No. 2012CB114600), the National Natural Science Foundation of China (No. 31372379, No. 31472153) and Fundamental Research Funds for the Central Universities in China (No. XDJK2013A001, No. XDJK2013A021, No. XDJK2013C129).
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Supporting information Additional supporting information may be found in the online version of this article.
Figure S1 Genetic map of the markers used. Table S1 Information of the markers used in this research. PART1 shows the markers for linkage analysis; PART2 shows the markers for mapping analysis. Table S2 Significance level between the markers and CSW. Table S3 Comparison of mapping results with that of the previous research.
© 2015 Stichting International Foundation for Animal Genetics, 46, 426–432
