QTL analysis and fine mapping of a major QTL conferring kernel size in maize (Zea mays)

doi:10.21203/rs.3.rs-20357/v1

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QTL analysis and fine mapping of a major QTL conferring kernel size in maize (Zea mays)

https://doi.org/10.21203/rs.3.rs-20357/v1

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posted 06 Apr, 2020

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Background：Kernel size are important agronomic traits for grain yield in maize. The objective of this study is to map QTLs for kernel size, fine map a stable QTL qKL-2 and predict candidate genes for kernel length in maize. A total 199 F2:3 lines were obtained from selfing F2 individuals from the cross SG5/SG7. In this study, a high density genetic linkage map with 3305 bin-markers spanning a total length of 2236.66 cM on 10 chromosomes was applied for primary QTL mapping. Composite interval mapping (CIM) method was used for detecting QTLs in three environments of F2 and F2:3 populations.

Results：The result showed that a total ten QTLs for kernel size were detected, among which five QTLs for kernel length (KL) and five QTLs for kernel width (KW). Two stable QTLs qKW-1 and qKL-2 were mapped in all three environments. In order to validate and fine map qKL-2 , near isogenic lines (NILs) were developed by continuous backcross between SG5 as the donor parent and SG7 as the recurrent parent. Marker assisted selection was conducted from BC2F1 generation with molecular markers near qKL-2. Secondary linkage map with six markers around the objective region was developed for fine mapping qKL-2. Finally, qKL-2 was mapped in a 1.95Mb physical interval on maize chromosome 9 by blasting with Zea_Mays_B73 v4 genome. The results were confirmed with selected overlapping recombinant chromosomes. A total 11 out of 40 protein coding genes in the identified interval differentially expressed after conducting transcriptomic analysis between the two parents. GRMZM2G006080, which encodes receptor-like protein kinase FERONIA was predicted as candidate gene to control kernel size.

Conclusions：A total ten QTLs for kernel size were identified. Two stable QTLs qKL-2 and qKW-2 were mapped in three envionments. Major QTL qKL-2 for KL was validated and fine mapped in a 1.95Mb physical interval. GRMZM2G006080 was predicted as candidate gene for qKL-2 to control kernel length. The work will not only help to understand the mechanisms that control kernel size of maize, but also provide new gene for marker-assisted selection in further studies.

Plant Molecular Biology and Genetics

fine mapping

maize

kernel size

NILs

candidate gene

Maize is one of the most important crops served as food or animal feed[1] and plays a special role in sustaining food security[2]. To obtain high grain yield is one of the most important objectives for maize breeding. Yield traits are complex quantitative traits controlled by multiple genes. Kernel size traits are considered as important yield traits in improving grain yield process[3]. Kernel size which are thought of important component of grain yield and are consisted of kernel length, width and thickness and determines kernel weight [4]. Kernel size traits, especially kernel length have been revealed to significantly correlate with grain yield [5].Therefore, improving kernel size have great meaning in maize breeding.

During the twentieth century, a lot of maize grain yield traits improvement works from phenotypic level were conducted [6]. However, it is difficult to illustrate the specific reasons that yield formation affected from only phenotypic level. Along with the first genetic linkage map of maize published in 1986 [7], Molecular markers based on polymerase chain reaction (PCR) technology have greatly developed and applied for constructing genetic maps. Then, more and more QTLs controlling important agronomic traits in maize were detected by analyzing phenotypic value and genotypic value based on constructed genetic maps. These identified QTLs were distributed on all 10 maize chromosomes [8]. Till now, more than 150 QTLs for kernel size or weight have been identified by using different populations in maize (Gramene QTL database). Many QTL mapping or fine mapping works for kernel size or weight have been carried out recent years[2, 9, 10].

Great achievements in QTL mapping or isolating underlying genes for kernel size have made in many species such as rice [11–15], Arabidopsis thaliana [16, 17], soybean [18, 19], wheat [20, 21], especially in rice, genes control kernel size or weight such as GS3[22], GS5[11], qGL3[23], GW2[13], GW8[24], GS2[25], qGW7/GL7[26] have been successfully cloned. Compared with studies on kernel size in species mentioned above, studies on identifying and cloning kernel size related genes has lagged behind in maize. To a certain extent, the reason for this might be low QTL mapping accuracy and low QTL mapping resolution based on genetic linkage maps with low marker density [27]. Along with the development in the next-generation sequencing (NGS) technologies and the continuous declining cost of genotyping, it is possible to develop high-density genetic linkage maps in maize. In addition, it is critical important that QTLs should be validated and fine mapped for applying in further marker assisted breeding process. Near isogenic line (NIL) is one of the most widely accepted populations commonly used in QTL fine mapping. NILs have been successfully used in confirming and fine- mapping QTLs in many species, such as rice [28–30], wheat [31, 32]. In maize, Nie et al. 2019 fine mapped a major QTL qkrnw4 associated with kernel row number by the use of a near-isogenic line (NIL) [33]. Gao et al. 2019 fine mapped qLRI4 conferring leaf rolling index by using NIL populations [34]. Yang et al. 2018 fine mapped a major QTL qkc7.03 to a 416.27 kb physical interval for kernel cracking with NILs developed [35].

Based on above, the purposes of this study were. 1) to map QTLs for kernel size in three environments by using F₂ and F_2:3 populations from the same cross SG5/SG7. 2) to validate and fine map the identified major QTL qKL-2 by using BC₃F₁ NILs. 3) RNA-seq technology was applied to reveal DEGs between SG5 and SG7 and overlay these DEGs on to the fine mapped physical intervals of qKL-2 to predict candidate genes responsible for kernel length.

Segregation population development and phenotyping analysis

Phenotypic values of F₂population were collected for two kernel size traits i.e., kernel length (KL) and kernel width (KW) in 2016 while phenotypic values of F_2:3 populations were collected for the two traits in 2018 and 2019. Phenotypic mean values of KL and KW in F₂ and F_2:3were shown in Table 1. The difference of KL between SG5 and SG7 is extremely significant (P<0.01, Figure 3(a), Figure 3(b)). The phenotypic value of SG5 and SG7 were significantly different in both KL and KW traits. The two observed kernel size traits both displayed as bell shaped normal distribution (Supplemental Fig.1). Pearson’s correlation coefficients between KL and KW is 0.20, 0.25 and 0.24 in F₂-2016, F_2:3-2018 and F_2:3-2019, respectively. The correlation coefficients between KL and KW were all significant in three environments.

QTL mapping

CIM procedure was applied for mapping QTLs conferring KL and KW. Ten QTLs were mapped for KL and KW in F₂ and F_2:3 populations. In F₂ population, two KL QTLs were mapped and both located on maize chromosome 9, three KW QTLs were mapped and located on maize chromosomes 3 and 8. In F_2:3-2018 population, four KL QTLs were mapped and located on maize chromosome 3,7, and 9, three KW QTLs were mapped and located on maize chromosomes 3 and 8. In F_2:3-2019 population, three KL QTLs were mapped on maize chromosome 7, and 9, three KW QTLs were mapped and located on maize chromosomes 3 and 8. Manhattan plots of QTL mapping results were shown in Fig. 1. The physical intervals for all ten detected QTLs spanned from 2.30 to 14.35 Mb by comparison to reference genome (B73 RefGen_v3). The phenotypic variation explained by each QTL ranged from 8.4 to 23.0% in a trait, with means of 14.25, 14.03 and 13.67% for traits KL in F₂-2016, F_2:3-2018 and F_2:3-2019 respectively while means of 14.63, 12.97 and 10.83% for traits KW in three environments. The LOD scores range from 4.0 (qKL-7) to 9.5(qKW-1). Information of QTL identified are summarized in Table 2. The results showed that, qKL-2 for KL and qKW-1 were both detected in all three environments (highlighted with green color circle in Fig. 2). Major QTLs qKW-2, qKL-7, qKW-3 and qKL-10 were detected repeatedly in two environments. (highlighted with blue color circle in Fig. 2). To sum up, QTLs qKL-2 and qKW-1 detected in this study are considered to be stable QTLs (Table 2). However, results above are primary mapping results, which need to be validated and fine mapped in further study.

NILs development and qKL-2 fine mapping

In 2017 to 2019, a total of 998 BC₃F₁ NILs of qKL-2 with SG7 background were developed by continuous backcross method. A secondary linkage map with six separated markers around qKL-2 was generated. The sequences of the associated markers were listed Supplemental Table 1. The total length of the map was 43.35cM and the genetic distances between each two adjacent markers were 16.75, 8.39, 0.80, 5.67 and 11.74 cM, respectively. The physical positions of the six markers were 115.23, 130.51, 133.34,135.29, 139.75 and 153.88Mb on chromosome 9 by blasting maize B73 RefGen_v4(Figure 3(c)). Major QTL qKL-2 was detected on the secondary linkage map of NILs based on CIM method in QTL Cartographer v2.5. The peak of LOD value indicating that qKL-2 was most likely located between SSR3 and SSR4 (Figure 3 (c)). The additive effect of qKL-2 was 0.97mm and the phenotypic variation was 16.0%. To confirm the narrowed qKL-2 interval, Five kind of recombinant types named Class 1 to Class 5 were selected from 998 BC₃F₁ individuals, and the progeny test of homozygous segregants indicated the interval to a 1.95-Mb region flanked by the markers SSR3 and SSR4 (table 3). There are significant difference between phenotypic values of the two set of recombinant types Class 2,3 and Class 1, 4, 5(Figure 3 (d)). The loci of SSR3 and SSR4 are homozygous in Class 2,3 while heterozygous in Class 1, 4, 5. The results that qKL-2 located in SSR3 to SSR4 interval also supported by selected overlapping recombinant chromosomes.

Candidate genes prediction for qKL-2

RNA-seq procedure was conducted on an Illumina NovaSeq instrument with 18 RNA samples from grains in different developmental stages of plants. The 1.95-Mb physical intervals of qKL-2, encompass 40 protein coding genes, (figure 3(e)). After DEGs analysis, a total of 11 protein coding genes left in the physical intervals (Table 4). Previous studies indicated that FERONIA receptor kinase controls seed size in Arabidopsis thaliana[41]. GRMZM2G006080 encodes receptor-like protein kinase FERONIA and was predicted as candidate gene of qKL-2 which most likely responsible for kernel length.

Kernel size traits which controlled by multiple genes are important components of grain yield in maize. Grain yield was influenced significantly by kernel size, especially kernel length [4, 5]. Stable QTLs are of great significance for marker-assisted breeding while false positive QTLs are no use. Normally, two steps i.e. primary mapping and fine mapping are needed for QTL analysis unless experiments were conducted in multiple environments with as many as sample size and marker numbers. In this study, primary mapping were carried out in two environments, three kernel size QTLs qKL-2 and qKW-1 detected in all three environments were stable. The two QTLs could be benefit for further marker assisted breeding. Advanced population was developed by continuous backcross with markers assisted selection for validating and fine mapping qKL-2. Finally, qKL-2 was mapped in a 1.95-Mb interval on maize chromosome 9, the results was confirmed with overlapping recombinant chromosomes. It is of critical importance that the less genes the better in target QTL interval for map based cloning. In this study, RNA-seq technology was applied for transcriptomic analyzing DEGs between SG5 and SG7 grains in different developmental stages. DEGs identified were overlaid on to the qKL-2 intervals. After DEGs analysis, only 11 protein coding genes left in the QTLs qKL-2 intervals (Table 4). The potential functional genes in QTLs physical intervals decreased significantly after DEGs analysis. Further comparative genomics analysis was applied for predicting candidate genes. The evidence on studies of rice or arabidopsis thaliana showed that kernel size was regulated by multiple signaling pathways mainly include, ubiquitin-proteasome degradation [42], transcription factor pathway, phytohormone signaling pathway and G protein independent pathway. Yu et al. 2014 conclude that receptor kinase FERONIA involved in a signaling pathway to negatively regulate the elongation of integument cells, leading to control of the seed size in Arabidopsis thaliana [41]. Therefore, GRMZM2G006080, which encodes receptor-like protein kinase FERONIA was predicted as candidate gene to control kernel size. The predicted candidate gene will be not only helpful for underlying genetic mechanism for kernel size, but also provide basis for improving kernel size traits in maize.

In this study, a total ten QTLs for kernel size were detected, among which five QTLs for kernel length (KL) and five QTLs for kernel width (KW). Two stable QTLs qKL-2 and qKW-2 were mapped in three envionments. Major QTL qKL-2 for KL was validated and fine mapped in a 1.95 Mb physical interval on maize chromosome 9 by blasting with Zea_Mays_B73 v4 genome. The results were confirmed with selected overlapping recombinant chromosomes. GRMZM2G006080, which encodes receptor-like protein kinase FERONIA was predicted as candidate gene to control kernel length.

Primary population development and phenotypic evaluation

Maize inbred lines SG5 and SG7 were obtained from Institute of Grain and Oil, Liupanshui Academy of Agricultural Sciences, Liupanshui, China. F₁ hybrid seeds were obtained from crossing two maize inbred lines SG5 and SG7 in Liupanshui, Guizhou province of China in 2013. The hybrid seeds were planted in the same location in 2014 summer. A total of 199 F₂ individuals were developed by planting F₁ seeds at the Panxian Maize Breeding Station in Sanya, Hainan Island of China in winter 2014. Next, a F_2:3segregation population with 199 lines was developed from selfing each F₂ individuals. A total of 199 lines of the F_2:3 population was planted in field with a randomized block design of three replications, single-row plot with row spacing 50cm, each plot 15 plants with plants spacing 35cm. Kernel size traits included kernel length(KL) and kernel width (KW) were investigated by using electronic digital calipers. In F_2:3, Kernel size traits of 8 ears in 8 single plants located in the middle of each plot were chosen for evaluating phenotypic value after harvesting and drying naturally step. In F₂, phenotypic values of KL and KW were estimated by mean values from three replications of 10 kernels randomly selected from middle parts of the ear. In F_2:3, KL and KW were estimated by mean values from three replications of 10 kernels randomly selected from bulked kernels of middle parts of 8 ears.

QTL mapping

Methods of extracting genomic DNA, sequencing genotype, grouping sequence data, identifying single nucleotide polymorphisms (SNPs) and constructing high-density linkage map results were exhibited in our previous study [36]. Forward regression model of CIM method in QTL Cartographer v2.5 was applied for QTL mapping with walking speed was 1 cM. The logarithm of odds (LOD) value for detected traits was set up by 1,000 times permutations results. The LOD significance threshold for declaring QTL was 3.86 for CIM based upon the permutation analysis. QTL statistics were also reported for those in which the LOD score exceeded 2.5. LOD peaks were used for determining the position of a significant QTL on chromosomes. The additive effect value of a QTL is positive indicate that the increase in phenotypic value for a trait is provided by SG5 alleles while negative indicate the decrease in phenotypic value for a trait is provided by SG7 alleles. MapChart 2.32 software [37] was used for drawing the graphic of QTLs positions on 10 chromosomes of maize. Physical intervals of the QTLs mapped in F₂ and F_2:3 populations respectively were compared together and QTLs which have identical physical intervals in both two populations will be regarded as stable QTLs. The QTL mapping software carried out here also evaluates additive, dominance effect and the phenotypic variation by a QTL identified.

NILs development and qKL-2 fine mapping

NILs population was developed by using continuous backcross method for qKL-2 locus. Marker assisted selection started from BC₂F₁ generation with SSR molecular markers near qKL-2 between SG5 as the donor parent and SG7 as the recurrent parent. Primers of SSR markers were all selected from primers developed by Xu et al. 2013 based on resequencing maize genome results [38]. Phenotypic value for each of BC₃F₁ individual was investigated the same to method conducted in F₂population, which used for primary mapping in this study. Young healthy leaves from each of the 998 BC₃F₁ individuals were collected and frozen in liquid nitrogen, and then transferred to a −80◦C freezer. Genomic DNA of all individuals were extracted following the manufacturer’s protocols with the Plant Genomic DNA Kit (TIANGEN, Beijing, China). DNA degradation and contamination were monitored on 1% agarose gels. DNA purity was checked using the NanoPhotometer R spectrophotometer (IMPLEN, CA, USA). DNA concentration was measured using Qubit R DNA Assay Kit in Qubit R 2.0 Flurometer (Life Technologies, CA, USA). Secondary linkage map with selected markers around qKL-2 was generated by using JoinMap 3.0 software [39]with a logarithum of the odds (LOD) critical value 10.0 as the threshold. CIM method in QTL Cartographer v2.5 was applied for QTL mapping with walking speed 1 cM.

Sample preparation, RNA isolation and data analysis

The two parents SG5 and SG7 were planted in the field. Ear shoots were covered by ear shoot bags before corn silk grow out. SG5 and SG7 plants in the middle of the plot were chosen for selfing at the same day at the stage of the corn silk length became about 5cm. Grains of the selfing parents’ plants were sampled at the different developmental stages (5 days, 10 days, and 15 days after selfing) with each sample of three biological replicates in parallel. All collected samples were immediately frozen in liquid nitrogen and transferred to −80 °C storage environment. Methods of extracting total RNA, checking RNA purity, measuring RNA concentration, assessing RNA integrity and performing RNA-seq were exhibited in our previous study[40]. A total of 18 collected samples were sequenced at the Illumina NovaSeq platform. Softwares for building reference genome index, aligning paired-end clean reads to the reference genome, counting reads numbers mapped to each gene, calculating expected number of fragments per kilobase of transcript sequence per millions base pairs (FPKM) and analyzing differential expression between SG5 and SG7 were exhibited in our previous study[40].

Candidate genes prediction for qKL-2

In this study, RNA-seq data was used for mining differentially expressed genes (DEGs) between SG5 and SG7. Pair-wise comparison of transcriptomes between SG5 and SG7 was conducted for detecting DEGs. The DEGs obtained were overlaid on to physical intervals of qKL-2 to predict candidate genes for kernel length in maize. The detected DEGs were annotated on the basis of database for maize gene annotation accessible at MaizeGDB website http://www.maizegdb.org.

BLAST Basic local alignment search tool

CIM Composite interval mapping

LOD Logarithm of odds

QTL Quantitative trait loci

NIL Near isogenic line

SNP Single nucleotide polymorphisms

DEG Differentially expressed gene

Ethics approval and consent to participate:

Not applicable.

Consent for publication:

Not applicable.

Availability of data and materials:

Data supporting the current study can be obtained by contacting the corresponding author ([email protected]). Seed of SG5 and SG7 is available from the authors upon request.

Competing Interests

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Funding:

This work was supported by the Natural Science Foundation of China (Grant #31460359), Research Foundation for Advanced Talents of Qingdao Agricultural University (Grant 6631119035), the Natural Science Foundation of Guizhou Province of China (Grant #Qian Kehe J word [2014]2155 and Grant #Qian Kehe LH word [2015]7605)

Author Contributions

GW and YZ have finished in developing F₂, F_2:3 and BC₁F₃ population. ZZ, XZ, MJ and MC have finished phenotypes investigation. CS have finished genotyping of F_2:3 progeny, analyzing the data, and drafted the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors thank Shunliang Gong for the helping in providing SG5 and SG7 materials and developing the primary mapping populations.

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Table 1. Descriptive statistics of traits in the F₂and F_2:3 mapping population of maize derived from the cross of SG5 and SG7.

Generations	Trait	SG5(mm)	SG7(mm)	Min(mm)	Max(mm)	Mean(mm)	Std.Dev.(mm)
F₂	KL	9.93	8.99	8.07	12.87	10.42	0.85
	KW	8.07	11.17	8.03	11.80	9.98	0.78
F_2:3-2018	KL	9.93	8.99	8.49	13.21	10.34	0.74
	KW	8.07	11.17	8.24	12.12	10.07	0.74
F_2:3-2019	KL	9.93	8.99	8.40	13.35	10.34	0.75
	KW	8.07	11.17	8.28	12.22	10.08	0.73

Table 2. QTL identified for KL and KW traits of maize using high-density SNP bin-map from composite interval mapping (CIM). The logarithm of odds (LOD) value 3.86 for detected traits was set up by 1,000 times permutations results (in blod). QTL statistics were also reported for those in which the LOD score exceeded 2.5 but less than 3.86 (in no blod).

Environments	QTL	Chr	Flanking markers	Positions (Mb)	Interval (Mb)	Physical length (Mb)	LOD	ADD^a	DOM^b	R²(%)
	Kernel length (KL) trait
F₂-2016	*qKL-1*	9	mk3093-mk3100	126.05	124.90-127.55	2.65	8.4	0.34	0.18	14.8
	*qKL-2*	9	mk3106-mk3114	134.65	133.20-135.75	2.55	7.7	0.26	0.24	13.7
	qKL-3	3	mk1343-mk1350	217.85	215.90-218.1	2.20	2.9	-0.13	-0.17	4.7
	qKL-4	2	mk624-mk638	2.75	0.10-3.55	3.45	2.5	0.06	0.254	4.2
	qKL-5	10	mk3228-mk3236	105.05	96.45-109.2	12.75	3.8	-0.19	-0.14	6.4
F_2:3-2018	*qKL-2*	9	mk3105-mk3110	134.65	131.55-134.75	3.20	8.1	0.23	0.25	15.3
	qKL-4	2	Mk624-mk634	2.85	0.10-3.00	2.90	3.1	-0.02	0.31	5.4
	*qKL-6*	3	mk1145-mk1167	115.20	103.65-117.85	14.2	4.2	-0.38	0.17	12.1
	*qKL-7*	7	mk2510-mk2525	143.00	137.30-145.70	8.40	5.1	-0.40	0.32	13.4
	qKL-8	7	mk2618-mk2622	174.6.	174.35-174.90	0.55	3.5	-0.28	0.04	6.5
	qKL-9	8	mk2689-mk2701	21.85	21.60-33.80	12.20	3.1	-0.30	0.20	8.0
	*qKL-10*	9	mk3077-mk3084	115.15	113.65-120.70	7.05	8.1	0.30	0.19	15.3
F_2:3-2019	*qKL-2*	9	mk3100-mk3104	128.0.	127.55 -130.05	2.50	6.9	0.27	0.17	13.4
	qKL-4	2	mk624-mk637	2.85	0.10-3.45	3.35	2.6	0.00	0.27	4.5
	qKL-6	3	mk1158-mk1167	115.2.	114.80-117.85	3.05	3.9	-0.37	0.19	11.3
	*qKL-7*	7	mk2517-mk2525	143.00	141.25-145.70	4.45	4.0	0.40	0.34	13.0
	qKL-8	7	mk2617-mk2622	174.60	174.05- 174.90	0.85	3.7	-0.26	-0.01	5.9
	*qKL-10*	9	mk3077-mk3084	115.15	113.65-120.70	7.05	7.6	0.28	0.20	14.6
	qKL-11	5	mk2183-mk2188	215.30	214.55-215.55	1.00	2.6	0.25	-0.26	5.9
	qKL-12	6	mk2298-mk2303	149.80	143.05-157.50	14.45	2.5	0.09	0.24	4.4
	Kernel width (KW) trait
F₂-2016	*qKW-1*	3	mk1042-mk1060	30.85	30.20-44.55	14.35	9.5	-0.54	0.17	23.0
	*qKW-2*	8	mk2806-mk2814	148.95	144.55-151.00	6.45	5.8	-0.34	-0.02	10.0
	*qKW-3*	8	mk2814-mk2820	152.25	151.00-157.95	6.95	5.9	-0.35	0.00	10.9
	qKW-4	1	mk577-mk603	292.20	288.45-295.55	7.10	3.1	-0.29	0.09	7.2
	qKW-5	2	mk667-mk673	17.05	13.60-17.55	3.95	3.4	-0.23	-0.07	4.5
	qKW-6	2	mk676-mk686	24.05	19.55-24.35	4.80	3.7	-0.19	-0.13	6.1
	qKW-7	10	mk3284-mk3294	145.50	144.70-146.95	2.25	3.0	-0.15	0.33	4.9
F_2:3-2018	*qKW-1*	3	mk1040-mk1047	30.85	29.30-32.75	3.45	5.8	-0.37	-0.04	9.9
	*qKW-2*	8	mk2811-mk2814	148.95	147.80-151.00	3.20	5.8	-0.43	0.13	14.5
	qKW-5	2	mk667-mk676	16.70	13.60-19.55	5.95	3.2	-0.12	-0.20	5.3
	*qKW-8*	8	mk2799-2808	141.80	140.30-145.60	5.30	6.2	-0.43	0.11	14.5
	qKW-9	2	mk657-mk662	9.70	9.30-11.30	2.00	3.8	-0.06	-0.29	6.2
F_2:3-2019	*qKW-1*	3	mk1035-mk1042	28.95	27.10-30.20	3.10	5.5	-0.47	0.19	14.5
	*qKW-3*	8	mk2814-mk2817	152.25	151.00-153.30	2.30	5.5	-0.33	-0.01	9.6
	qKW-4	1	mk579-mk591	292.55	289.45- 293.25	3.80	2.5	-0.23	0.05	5.0
	qKW-6	2	mk676-mk686	23.75	19.55-24.35	4.80	3.8	-0.17	-0.14	6.2
	*qKW-10*	8	mk2826-mk2835	163.25	160.70-164.95	4.25	5.0	-0.24	-0.14	8.4

^a Estimated additive effect

^b Estimated dominance effect

Table 3. Statistical analysis of phenotypic values from different kinds of recombinant types of NILs around the qKL-2 region

	No.of Recombinants	Grain length/mm
class 1	28	10.85±0.14
class 2	33	9.88±0.13
class 3	3	9.99±0.16
class 4	20	10.71±0.19
class 5	47	10.89±0.11

Table 4. Differentially expressed genes out of 40 protein coding genes in 1.95-Mb physical interval on chromosome 9 and candidate gene predicted for qKL-2.

geneID（B73 RefGen_v3）	start(bp)	end(bp)	length	Annotation	LogFC^d or RCP1/RCP2^e
geneID（B73 RefGen_v3）	start(bp)	end(bp)	length	Annotation	Day5^f	Day10	Day15
GRMZM2G099101	132128943	132133712	2349	Endoglucanase 9	0.36	0.42	1.66
GRMZM5G899188	132973437	132974250	814	17.0 kDa class II heat shock protein	2.03	0.98	3.23
GRMZM2G398288	131627588	131635485	4521	Phospholipid-transporting ATPase 1	-0.40^g	0.36	-0.22
GRMZM2G027437	131778230	131786437	1277		-0.55	-0.45	-0.79
GRMZM2G006080	131829772	131832943	3172	Receptor-like protein kinase FERONIA	0.41	-0.09	-0.04
GRMZM2G309327	131924112	131925141	1030	Probable calcium-binding protein	-1.87	-2.20	-1.41
GRMZM2G159500	133066105	133068297	2048	NAC domain-containing protein	-0.76	-0.22	-0.21
GRMZM2G102382	133137426	133140041	1413	Thioredoxin-like 1-2, chloroplastic	0.85	0.95	1.01
GRMZM2G102657	132797892	132800350	2053		0/9.54	0/18.55	0/7.55
GRMZM2G404249	132803660	132804501	842	17.0 kDa class II heat shock protein	1.70	2.29	1.27
GRMZM5G875954	132964847	132972815	1206	Selenium-binding protein 2	-5.07	-3.20	-1.20

d: Log 2 ratio, number of folds the gene is differentially expressed in RNA-seq;

e: different of readcounts between P₁ and P₂;

f: day5，day10，day15 indicate grain samples collected after selfing 5 days, 10 days and 15 days between the two parents SG5 and SG7.

g: Positive sign indicates gene transcript expressed high in SG5 while negative sign indicates gene transcript expressed high in SG7

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posted 06 Apr, 2020

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QTL analysis and fine mapping of a major QTL conferring kernel size in maize (Zea mays)

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