Construction of an ultrahigh-density genetic linkage map for Manihot esculenta Crantz and identification of QTL for root quantity traits

doi:10.21203/rs.3.rs-4717041/v1

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Construction of an ultrahigh-density genetic linkage map for Manihot esculenta Crantz and identification of QTL for root quantity traits

https://doi.org/10.21203/rs.3.rs-4717041/v1

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Single plant root weight, root number, and root length to width ratio are important agronomic traits that affect cassava yield. To explore the QTL related to quantitative traits such as cassava root weight, this study used Hyper-seq simplified resequencing technology to obtain two hybrid progeny from cassava cultivar SC205 as the female parent and cassava cultiva HB60 and cassava cultiva 18R as the male parent, respectively (of which the HB60 × SC205 progeny is called the A1 population, and the 18R × SC205 is called the A2 population). The genotyping of the two F1 mapping populations was conducted, and a genetic map of the maternal parents of the A1 and A2 populations was constructed by comparing the differences between the parental loci and extracting the same variant loci in the offspring and maternal parents. 18 linkage groups were identified for each parent, consistent with the haploid chromosome number of cassava. On average, each parental map had 6,280 markers, with a marker density ranging from 0.37 cM/marker to 0.43 cM/marker. Using the constructed genetic linkage map, QTL analysis of important quantitative traits such as single plant root weight, root number, and root length to width ratio were conducted, and the main QTLs associated with these traits were identified and candidate genes were identified by combining parental transcriptome data. These results help to further validate the use of hyper-seq technology in cassava breeding and provide new methods for developing future varieties.

Manihot esculenta Crantz

cassava root traits

Linkage map

QTL

Cassava (Manihot esculenta Crantz) is an important energy plant of the Euphorbiaceae family and is one of the six major grains in the world ¹, widely grown in the tropical regions of Africa, Asia, and Latin America². Characterized by its adaptability, fast growth and high yield, cassava is an important food crop, especially in the tropics and subtropics, and is of high economic and social value because of its ability to grow on more infertile soils³ and because it is not limited by seasons or moisture. For some African countries, there are about 25% of energy acquisition sources with cassava⁴. The tubers of cassava are its main economic part, and the tubers contain large amounts of starch and small amounts of protein among other nutrients. Cassava tubers can be processed into a variety of food products, can be used as animal feed, and can also be used for brewing beer and producing alcohol, which is an important source of bioethanol⁵. Cassava starch is suitable for producing high-quality modified starch because of its large particles and high transparency, and it is a dependent raw material for modified starch in some industries, which meets the needs of many aspects.

With the development of high-throughput sequencing technology, the drawing of fine genetic maps of cassava has also made great progress. In recent years, researchers have constructed multiple genetic linkage maps for cassava using RFLP (Restriction Fragment Length Polymorphism), SSR (Simple Sequence Repeat), and SNP (Single Nucleotide Polymorphism) markers. These maps cover 18 linkage groups, with an average molecular marker distance ranging from 4.51 to 4.81. The length of the linkage maps ranges from 1707.9 to 2412 centimorgans (cM) ^6–8. With the aid of these genetic maps, significant progress has been made in identifying Quantitative Trait Loci (QTL) for various traits in cassava. Boonchanawiwat et al. searched for QTL affecting the height of cassava plants and the height of the first branch by performing linkage map analysis in self-pollinated progeny⁹, Nang et al. conducted QTL analysis in a population of reciprocal cross and found that plant height and first branch height are controlled by multiple genes¹⁰. Okogbenin et al. investigated the genetic basis of early root hypertrophy in cassava by genetic analysis and QTL localization in a population of non-self-crossing parents. This study showed that early root hypertrophy is a complex trait controlled by multiple genes¹¹. Akinbo et al. identified several QTLs affecting the protein content of cassava roots through QTL analysis in a backcross population¹². Zou et al. constructed an AFSM QTL map by crossing two non-inbred parents, KU50 and SC124 (KS population) to obtain 186 cassava populations, and identified 260 candidate QTL genes for cold stress and 301 candidate QTL genes for storing root quality and yield^13,14. Okogbenin, and Ewa’s studies identified several QTLs associated with cassava plant size and yield traits ^15–17. Overall, some progress has been made in the study of QTL localization for root number in cassava, but further studies are still needed to better understand the genetic mechanism of root number in cassava and to utilize this knowledge to improve cassava varieties.

Hyper-seq is a novel, effective and flexible marker-assisted selection for genotyping based on the polymerase chain reaction (PCR). Compared to commonly used whole genome sequencing (WGS) methods such as restriction site-associated DNA tag sequencing fragments generated by, for example, type IIB restriction endonucleases (2b-RAD) and amplified fragment single-nucleotide polymorphisms and methylation (AFSM), the Hyper-seq method is much more efficient and less costly. The biggest advantage of Hyper-seq is that it can complete genome simplification, barcode addition and simplified genome sequence amplification in one step. Hyper-seq library is constructed with specially designed primers and DNA (for PCR amplification) or leaf (for direct PCR amplification) templates, without restriction enzyme digestion and barcode connection steps¹⁸.

In this study, the high-yielding SC205 variety was selected as the female parent, and two hybrid offspring populations were obtained with HB60 and 18R as the male parents, respectively (the offspring of HB60×SC205 was called A1 population, and the offspring of 18R×SC205 was called A2 population). The aim is to construct a common parental genetic linkage map for two half-sib populations and use this genetic linkage map to explore the quantitative trait loci (QTL) for tuberous root traits in cassava half-sib populations and identify candidate genes related to these traits. Genotyping was performed on two F1 populations, and by aligning the genotypes with the cassava version 8.1 genome, the parental and offspring variant sites were obtained. Differential sites between the parents were compared to obtain maternal-specific variant sites, and the offspring-specific variant sites that matched the maternal variant sites were extracted to construct the genetic map of the maternal parent in different populations. Each parental map identified 18 linkage groups, which were consistent with the haploid chromosome number of cassava, with an average of 6,280 markers related to each population. The marker density ranged from 0.37cM per marker to 0.43cM per marker. By analyzing the QTL of some important quantitative traits such as sringle plant root weight(RW), root number(RN), and root length to width ratio(RLW), the genetic map was verified, to identify the main QTL of related traits and identify the candidate genes affecting the traits by using the gene expression information. These results help to further validate Hyper-seq technology in cassava breeding and develop new approaches for future variety development.

2.1 Mapping populations and DNA extraction

In this study, the cassava cultiva SC205 variety was crossed with cassava cultiva HB60 and cassava cultiva 18R to produce two F1 populations, with 259 offspring from SC205×18R (A1 population) and 233 offspring from SC205×HB60 (A2 population), the sample name is shown in Supplementary Table S1.The populations were planted in Chengmai Meiyu Village, Hainan Province in April of 2021 and 2022, with two replicates for each planting, two rows per replicate, and six plants per row. Phenotypic data was collected in February of the following year. In October of 2021, DNA was extracted from tender leaves using the CTAB method and evaluated using nanodrop¹⁹. RNA was also extracted from the mature period of cassava root tuber of the parents for transcriptome sequencing.

2.2 Hyper-seq library preparation and sequencing

In this study, Hyper-seq-G technology is adopted, which is a new high-throughput DNA sequencing library preparation and genotyping method with extremely low cost, developed by Zou¹⁸. The DNA concentration of all samples was homogenized to 150 ng/ul-200 ng/ul, and the reaction volume was increased to 20ul by adding 1u of DNA sample, mix, F’ primer, and ddH₂O for PCR amplification reaction, and the raw data were uploaded to BGI.

2.3 Sequence analyses and genotyping

Quality control of the raw data was performed using FastQC software²⁰. The original data was divided into each sample using the Barcode and fastq-multx software (https://github.com/brwnj/fastq-multx). The AM5608.1 version genome was selected as the reference genome and the BWA²¹ was used to establish a reference genome index to generate bam files. Varients calling was performed using SAMtool and BCFtools^22,23, and high-quality SNPs (Max-missing < 0.2, MAF > 0.05) were filtered from the VCF files using PLINK software (Purcell et al. 2007). The SNP marker density was visualized using CMplot package in R, and variant sites were annotated using SNPEff software²⁴.

2.4 Transcriptome analyses and genotyping

The raw transcriptome data was analyzed using FastQC (https://github.com/s-andrews/FastQC), followed by adapter removal, base modification, trimming, and filtering of low-quality sequences using Trimmomatic software²⁵. The Hisat2²⁶ software was used to construct a reference genome index of the AM5608.1 version genome and align paired-end clean reads to the reference genome. The feature Counts²⁷ software was then used to accurately count reads, and the reads of all samples were combined into one file and exported as an RNA-Seq_Practice_countstable file. The expression levels of each gene in each sample were quantified based on the FPKM and TPM calculation formulae. Gene expression levels were obtained for different samples.

2.5 Linkage map construction

Construct a genetic linkage map of the common parent from the 259 offspring datasets of Group A1 and the 233 offspring datasets of Group A2. Compare the differential loci between the parents to obtain the parent-specific variant loci. Extract the specific variant loci shared between the offspring and the parent using VCFtools ²⁸. Filter out markers with a missing rate higher than 20% using PLINK²⁹. Identify and score the marker pairs using the QTL IciMapping³⁰ software. Retain the markers that meet the expected Mendelian segregation ratio through Fisher's exact independence test (P < 0.01) and chi-square test. Establish each linkage map based on a threshold logarithm of the odds (LOD) ratio > 4. Calculate the genetic distance between the two population linkage maps using the Kosambi mapping function. Plot the linkage maps using R software.

2.6 Trait assessment Statistical Analyses

Select three plants with similar growth vigor from the A1 and A2 cassava populations in 2022 and 2023, measure RW, RN, and RLW for each plant. Combine the data from both years and calculate the heritability of the genetic traits based on the phenotypic values using Best Linear Unbiased Estimation (BLUE) method. Using R package lme4, calculate the BLUE values for a single two-year dataset and a merged dataset 22–23, resulting in a total of 6 data groups. Perform statistical analysis and processing on the trait data, and create distribution plots for different traits across different years. Evaluate the correlation between traits using Spearman's coefficient, and assess normality, kurtosis, and skewness using the Shapiro-Wilks test (α = 0.05). And draw a bar chart of trait distribution and a normal distribution curve

2.7 QTL Detection and candidate gene analysis

MapQTL5 software³¹ was used for QTL calculation. The linkage maps and phenotype information from 2022 and 2023 were used for Multiple-QTL Mapping (MQM Mapping) to map QTL and estimate their effects³². The logarithm of odds (LOD) score for significant QTL was determined through a test analysis (1000 permutations, total error level 5%). Screening of candidate loci with LOD score > 2, by collating the loci mapped in the two populations, selecting the loci within a 50 Kb interval upstream and downstream of the candidate loci that are present in both populations and in three or more data sets as co-located loci. Collation of the co-located loci to determine the co-located QTL, and estimation of the contribution of each identified QTL to the total phenotypic variance through analysis of variance. The loci and QTL were organized based on the population and year information (e.g., 22-A1), and the final QTL were determined. The nomenclature for the identified QTL followed the convention of using the abbreviation of the trait followed by "q," the relevant chromosome name, and a numerical identifier (e.g., qRW-1a). A confidence interval of 1M flanking the QTL identification region was used for screening candidate genes.

Candidate genes were extracted from the QTL interval and functionally annotated using the cassava AM560.8.1 version genome annotation information. Important genes were selected for analysis. The gene numbers were compared with the annotation files of the AM560v8.1 reference genome to obtain the corresponding GO numbers for each gene. Candidate gene numbers and GO numbers were input into WEGO (http://wego.genomics.org.cn/) for GO functional annotation, and gene sequences were extracted using seqkit subseq³³. The input file was organized and imported into KEGG (http://kobas.cbi.pku.edu.cn/genelist/) for pathway enrichment analysis. An enrichment plot was generated using R.

3.1 SNP marker density and annotation

A total of ~ 406 Gb of sequencing data was obtained, with an average sequencing depth of 812 Mb per sample (the cassava AM560 8.1 version genome size is 617 Mb). In total, 1,215,893,952 high-quality reads were obtained, with an average of 2,461,324 reads per sample. Variant sites were extracted from the samples using BWA, SAMtools and the BCFtools variant detection, resulting in two VCF files containing variant sites for the A1 and A2 populations, with 2,417,535 and 1,376,544 variant sites, respectively. The A1 population had 161,361 unique variant sites shared with the SC205 parent (The A1 maternal parent, abbreviated as A1_SC205), while the A2 population had 121,556 unique variant sites shared with the SC205 parent (The A1 maternal parent, abbreviated as A1_SC205). To visualize the distribution of variant sites on the genome, a SNP marker density map was plotted, which showed that variant sites were densely distributed across the genome (Fig. 1A, B). The obtained variant sites were annotated to better understand their position on the genome, and annotation information and SNP statistics by type and region (Supplementary Table S2) were provided.

The specific loci were filtered using PLINK software with filtering parameters (--geno0.1 --maf0.05 --hwe0.0001). Next, QTL IciMapping software was used to screen out biased segregation molecular markers, and 5,913 in A1 and 6,648 in A2 populations were obtained to construct high-density genetic maps of the two populations maternal parent, and 18 linkage groups were identified, which were consistent with the number of haploid chromosomes in cassava. The total map distance of A1_SC205 genetic map was 2492.62 cM, the average distance between markers was 0.43cM, the length of 18 linkage groups ranged from 82.42-219.53 cM, and the longest linkage group was LG01, while the total map distance of A2_SC205 genetic map was 2445.17 cM, the average distance between markers was 0.37 cM, and the length of the linkage groups ranged from 72.10-206.95cM, and the longest linkage group was LG01 (Supplementary Table S3). In order to observe the collinearity relationship between the linkage map markers of the two populations, the common loci of the two populations were counted, and it was found that there were 1,792 identical loci, the average collinear rate of A1 was 30.37%, the maximum collinear rate was LG16, as high as 38.01%, the average collinear rate of A2 was 26.69%, and the maximum collinear rate was LG18, which was as high as 39.40% (Supplementary Table S3), in which used TBtools to draw the chromosome collinearity of the two populations (Fig. 1C) Additionally, the collinearity relationship between the linkage maps of the two populations was plotted (Supplementary Fig. 1), revealing a certain degree of collinearity on each chromosome between the two populations, laying the foundation for subsequent co-localization.

3.3 Statistical Analysis and Correlation of Phenotypic Data

The summary of the parental population's phenotypic mean values, as well as the statistical data for the different years and combined Best Linear Unbiased Estimators (BLUE) values for the correlated traits of the two populations, are presented in Supplementary Table S4. A combined analysis of the two populations revealed that the coefficient of variation (CV) for RW ranged from 0.413 to 0.656, with heritability values ranging from 0.16 to 0.23. For RN, the CV ranged from 0.244 to 0.342, with heritability values ranging from 0.48 to 0.53, while for RLW, the CV ranged from 0.207 to 0.283, with heritability values ranging from 0.48 to 0.59 (Table 1). It can be observed that in both populations, RW exhibited the highest variability with relatively lower heritability, whereas RN and RLW showed smaller trait variabilities with higher stability and heritability. This suggests that data stability may influence the magnitude of trait heritability. Additionally, the similarity in heritability between the two populations indicates a relatively stable heritability.

To assess the normality of the data, we calculated the skewness and kurtosis, which indicated an overall skewness close to 0 and kurtosis close to 3, suggesting approximate normality of the data and suitability for subsequent analysis. At the same time, we plotted the data distribution curve, which also indicates that the data approximately follows a normal distribution(Fig. 2A). Violin plots and trait error distribution plots (Fig. 2B) were generated to visualize the distribution of phenotypic data for the two populations. The plots demonstrate minimal overall variation over the two years, indicating low data error and high stability.

To investigate the interrelationships between the traits, we utilized R software to compute the trait correlation coefficients and correlations between different populations, and generated a correlation heatmap (Fig. 2CD). The correlation analysis revealed that all three traits displayed a certain degree of positive correlation in both populations, with RN showing a strong positive correlation with RW. Consistent with the strong positive correlation between RN and RW, the boxplots demonstrated similar overall trends in the data for RN and RW.

Table 1

Statistical analysis of population traits of hybrid progenies
population	Trait	Year	Mean	standarddeviation	Range	Skew	Kurtosis	CV	Heritability
A1	RN	2022	6.589	1.901	2.510–12.510	0.194	2.472	0.288	0.48
		2023	5.353	1.824	0.720–10.720	0.538	3.165	0.341
		22–23	5.738	1.620	1.57–11.57	0.079	3.064	0.282
	RW	2022	2.614	1.560	0.120–9.310	0.936	4.158	0.597	0.16
		2023	2.643	1.734	0.230–9.120	1.297	4.635	0.656
		22–23	2.513	1.474	0.08–9.103	0.934	4.357	0.587
	RLW	2022	6.788	1.556	3.290–12.330	0.635	3.516	0.229	0.58
		2023	8.695	1.956	1.340–13.860	-0.385	3.754	0.225
		22–23	7.608	1.572	0.935–12.812	-0.156	4.123	0.207
A2	RN	2022	6.585	2.204	1.00-14.160	0.419	3.318	0.335	0.53
		2023	4.684	1.600	0.720–9.720	0.438	3.753	0.342
		22–23	5.599	1.364	2.237–10.73	0.211	3.726	0.244
	RW	2022	2.161	1.219	0.140–8.670	1.394	6.670	0.564	0.23
		2023	1.563	0.791	0.060–5.090	1.010	4.917	0.506
		22–23	1.789	0.740	0.081–4.327	0.074	2.866	0.413
	RLW	2022	6.818	1.761	3.450–12.460	0.750	3.746	0.258	0.59
		2023	7.432	2.106	0.830–14.660	0.406	4.172	0.283
		22–23	7.100	1.537	2.876–11.604	0.225	2.835	0.217

3.4 QTL localization

The QTL mapping results indicated that a total of 188 QTLs were located with RLW in different years between the two populations, along with 157 QTLs associated with RN and 213 QTLs related to RW. The quantities of QTLs mapped in different populations and different years are summarized in Table 2, and detailed information is provided in Supplementary Table S5. To validate the effectiveness of the QTLs, the positional information of the mapped loci in the two populations was compared, ultimately resulting in the identification of 3 co-located RLW-related QTLs, 3 RN-related QTLs, and 9 RW-related QTLs (Table 3).

The 3 QTLs of RLW are located on chromosomes 1, 7, and 9, and the LOD value of the localized QTL qRLW-7 exceeds 3, indicating a probability of over 99.9% for the presence of a QTL in this region. This indicates that the QTL has a specific high reliability, with an average phenotypic contribution rate of 10.53%, and has been localized in both A1 and A2 populations.

The 3 QTLs of RN are located on chromosomes 3, 16, 17. Two of the QTLs have a mapping interval of less than 100bp, which has high accuracy and helps to accurately locate candidate genes related to this trait. The average cumulative contribution rate of QTLs is 22.86%, with qRN-17 phenotype contributing up to 31.2%.

The 9 QTLs of RW are mainly located on chromosomes 7, 9, 17, and 18, with two QTLs located on chromosome 18. At the same time, QTLs related to RLW and RN are also located on chromosomes 7 and 9. QRW-18a, located around 2Mb on chromosome 18, was identified in 5 sets of data from populations A1 and A2, with an LOD value of 3.34; At the same time, there are three QTL positioning intervals less than 150bp, indicating high overall positioning accuracy, and the average LOD value of QTLs exceeds 3, indicating a high possibility of the existence of the positioned QTLs.

To understand the variation in LOD scores of QTLs mapped for different traits in different years within different populations, three co-located QTLs for each trait were selected, and QTL interval and nearby LOD waveforms were plotted(Fig. 3A). The results indicated a consistent overall fluctuation in LOD scores within the mapped QTL intervals and their vicinity across the different datasets.Two linkage maps were drawn using genetic distance and markers, and QTL location information was labeled based on subsequent localization results (Fig. 3BC).

Table 2

Number of loci for different traits in different populations and years
Year	RLW	RN	RW	Total
A1-22	20	9	17	46
A1-23	38	41	31	110
A1-22 + 23	32	13	19	64
A2-22	38	41	74	153
A2-23	35	21	14	70
A2-22 + 23	25	32	58	115
Total	188	157	213	558

Table 3

Coloucation QTLs mapped for root quantity traits in Cassava
QTL_Name	Group	Trait	Position	Population	LOD	%Expl.
RLW-1	1	RLW	446,386–526,632	A1-22,A1-22 + 23,A2-23,A2-22 + 23	2.77	8
RLW-7	7	RLW	7,270,622-7,306,733	A1-22 + 23,A2-23,A2-22 + 23	3.65	14.8
RLW-9	9	RLW	56,068–75,706	A1-22 + 23,A2-22,A2-23,A2-22 + 23	2.5	8.8
RN-3	3	RN	4,862,573-4,922,415	A1-23,A1-22 + 23,A2-22,A2-23,A2-22 + 23	2.5	25.9
RN-16	16	RN	26,309,903 − 26,309,939	A1-22,A1-22 + 23,A2-23,A2-22 + 23	2.14	11.5
RN-17	17	RN	27,016,633 − 27,016,679	A1-23,A1-22 + 23,A2-22 + 23	2.85	31.2
RW-7	7	RW	3,472,827-3,497,170	A1-22,A1-22 + 23,A2-22,A2-23,A2-22 + 23	3.08	12.8
RW-9	9	RW	140,164 − 148,432	A1-22 + 23,A2-22,A2-23,A2-22 + 23	7.6	12.8
RW-10	10	RW	4,262,539-4,262,551	A1-22 + 23,A2-22,A2-23,A2-22 + 23	3.11	13.6
RW-11	11	RW	16,003,135 − 16,003,215	A1-23,A1-22 + 23,A2-22,A2-22 + 23	3.52	15.4
RW-12	12	RW	21,852,432 − 21,935,379	A1-23,A1-22 + 23,A2-22,A2-23	2.53	8.4
RW-15	15	RW	23,949,858 − 23,961,834	A1-22,A1-22 + 23,A2-22,A2-23,A2-22 + 23	4.14	10.2
RW-17	17	RW	31,297,009–31,368,543	A1-22,A1-22 + 23,A2-22,A2-23,A2-22 + 23	3.78	9.8
RW-18	18	RW	16,818,273 − 16,893,445	A1-22,A1-22 + 23,A2-23,A2-22 + 23	3.08	16.7
RW-18	18	RW	2,317,804-2,317,946	A1-22,A1-22 + 23,A2-22,A2-23,A2-22 + 23	3.34	30.5

3.5 Candidate gene ananalysis

Based on the localization results, we have identified the interval and upstream and downstream genes as candidate genes. In order to better understand the gene functions, we conducted GO and KEGG analyses. The GO enrichment analysis revealed that these genes are mainly enriched in "polar transport of auxin," "auxin transport," "cytokinin metabolic process," "ATP biosynthetic process," "auxin-activated signaling pathway," and "transcription coregulator activity" (Fig. 4A). KEGG analysis found that the candidate genes are mainly enriched in pathways such as "Protein Photosynthesis proteins," "Starch and sucrose metabolism," and "Plant hormone signal transduction" (Fig. 4B). Through screening and identification, a total of 39 genes were obtained (Table 4), including 5 candidate genes associated with RLW, 11 candidate genes associated with RN, 23 candidate genes associated with RW, 7 genes containing 5 classes of transcription factors (WARY, BES 1, bZIP, ARF, MYB). The remaining candidate genes include auxin-responsive factor, zinc finger (C3HC4-type RING finger) family protein, ethylene sensor, SNARE-like superfamily protein, plant glycogenin-like starch initiation protein, and glucose-6-phosphate dehydrogenase 6. The expression levels of the candidate genes were calculated based on QTL localization candidate genes and parental transcriptome data (Supplementary Table S6). Based on whether the expression level changes are consistent with the trend of parental traits, the candidate genes were classified into two categories, and expression level heat maps of the localized genes were plotted (Fig. 4CD).

Table 4

Candidate genes for root quantity traits in Cassava
Trait	Chromosome	Gene_ID	Physical position(bp)	Gene Function
RN	3	Manes.03G044400	3,875,045 − 3,881,657	Ankyrin repeat family protein
RN	3	Manes.03G047500	4,297,715-4,299,007	Winged-helix DNA-binding transcription factor family protein
RN	3	Manes.03G051300	4,782,760-4,784,024	WRKY
RN	3	Manes.03G053600	5,181,953-5,190,433	Zn-dependent exopeptidases superfamily protein
RN	3	Manes.03G055200	5,383,408-5,388,267	Ankyrin repeat family protein
RLW	7	Manes.07G056000	6,355,527-6,365,241	Tetratricopeptide repeat (TPR)-like superfamily protein
RW	7	Manes.07G026100	2,819,441-2,822,633	Smg-4/UPF3 family protein
RW	7	Manes.07G027700	3,050,129-3,053,172	SNARE-like superfamily protein
RW	7	Manes.07G028400	3,120,266-3,131,581	Protein phosphatase 2C family protein
RW	7	Manes.07G030500	3,349,025 − 3,352,522	Zinc finger (C3HC4-type RING finger) family protein
RW	7	Manes.07G031300	3,431,995-3,432,570	Dehydroascorbate reductase 2
RLW	9	Manes.09G001500	149,564 − 163,612	DHHC-type zinc finger family protein
RLW	9	Manes.09G000800	433,877 − 435,511	DNA/RNA helicase protein
RLW	9	Manes.09G000500	459,723 − 477,394	Ribosomal protein L32e
RLW	9	Manes.09G004500	1,145,489-1,158,661	bZIP
RW	9	Manes.09G001500	149,564 − 163,612	DHHC-type zinc finger family protein
RW	10	Manes.10G043100	4,441,036 − 4,451,646	Plant-specific GATA-type zinc finger transcription factor family protein
RW	10	Manes.10G045000	4,799,921-4,809,695	Clast3-related
RW	11	Manes.11G081500	15,096,310 − 15,098,673	Adaptin family protein
RW	11	Manes.11G090300	16,982,368 − 16,982,661	R-protein L3 B
RW	12	Manes.12G097300	21,899,393 − 21,904,241	Signal transduction histidine kinase, hybrid-type, ethylene sensor
RW	15	Manes.15G183200	24,954,254 − 24,957,013	Triosephosphate isomerase
RN	17	Manes.17G061300	26,043,602 − 26,053,323	Plant glycogenin-like starch initiation protein 3
RN	17	Manes.17G066200	26,623,698 − 26,625,728	WRKY
RN	17	Manes.17G066900	26,690,489 − 26,691,693	P-loop containing nucleoside triphosphate hydrolases superfamily protein
RN	17	Manes.17G073900	27,398,788 − 27,404,693	Auxin efflux carrier family protein
RN	17	Manes.17G074600	27,527,652 − 27,529,759	Glucose-6-phosphate dehydrogenase 6
RN	17	Manes.17G077900	27,772,133 − 27,779,912	With no lysine (K) kinase 4
RW	17	Manes.17G100500	30,715,977 − 30,722,346	Transcriptional factor B3 family protein / auxin-responsive factor AUX/IAA-related
RW	17	Manes.17G102000	30,954,184 − 30,956,884	WRKY
RW	18	Manes.18G014800	1,564,349-1,568,553	Lysine histidine transporter 1
RW	18	Manes.18G015300	1,619,465-1,621,243	Ubiquitin-like superfamily protein
RW	18	Manes.18G015400	1,638,932-1,645,840	ARF
RW	18	Manes.18G023500	2,256,111-2,258,325	MYB
RW	18	Manes.18G027800	2,392,755-2,401,894	Nodulin MtN3 family protein
RW	18	Manes.18G029600	2,509,358-2,512,562	BES1
RW	18	Manes.18G035600	3,171,581-3,174,946	Raffinose synthase family protein
RW	18	Manes.18G036300	3,195,620-3,197,448	Octicosapeptide/Phox/Bem1p family protein

Genetic map and QTL mapping precision

In this study, high-quality SNP markers were obtained using the hyper-seq simplified sequencing technology, and genetic maps were constructed for two populations. Previous studies have constructed multiple cassava maps using markers such as RFLP,, SSR, etc., with a range of 100–510 markers and map lengths from 845.2cM to 1420.3cM with spacing of 5.6cM/marker to 17.92cM/marker ^6,11,34–37. This study increased marker density based on previous work, resulting in a final genetic map spacing of 0.37cM/marker to 0.43cM/marker, providing a higher precision cassava genetic map.And 15 QTLs related to traits were identified, and the researchers³⁸ used GWAS to locate multiple significant loci for cassava agronomic traits, The positioning results of qRN-17a and qRW-18a are consistent, Ewa¹⁶ found in his study that QTLs related to RW traits were located on chromosomes 3, 4, and 7, while QTLs related to RN traits were located on chromosomes 3 and 7, consistent with some of the results of this study.

Joint analysis of trait localization results

Crop yield is usually the main goal of breeding programs. Due to the increase in world population, food demand is expected to grow by 110% in the next 30–35 years. The population of sub-Saharan Africa is expected to grow by over 120%. In this region, cassava is the second largest source of calories, accounting for about 30% of daily calorie requirements per person. Despite its importance, the average cassava yield in Africa has not significantly increased since 1961. This study further selected three traits related to yield and cassava root: single plant root number (RN), root weight (RW), and tuber aspect ratio (RLW) for QTL localization analysis based on the high-precision cassava genetic map constructed. There is a high positive correlation between the three traits, and RN and RW can effectively evaluate crop yield, while RLW can effectively evaluate tuber shape, which is also an important factor affecting yield. This study found that Manes.09G001500 is associated with RN and RW traits have been identified, and this gene belongs to the DHHC type zinc finger family protein which affects plant growth and development. OsDHHC01 was reported to increase the number of tillers and grain yield in rice and oilseed rape (Brassica napus L.) ^39,40. OsDHHC13 was preliminarily found to positively regulate the oxidative stress response⁴⁰. DHHC type zinc finger protein (DHHC) and S-acylated protein mainly serve as substrates for S-acyltransferase. S-acylation, also known as palmitoylation, is an important protein lipid modification that plays a crucial role in plant growth, development, and stress response⁴¹. Researchers have found a rapid method for screening and identifying DHHC proteins related to S-acylation proteins, and have found that OsDHHC30 participates in regulating salt tolerance in rice through S-acylation modification of OsCBL2/3⁴². It is speculated that this gene may affect the growth and development of cassava by regulating its stress resistance, thereby affecting the weight and quantity of root tubers. Further research will be conducted to elucidate the molecular mechanism by which this gene affects root tuber traits.

Meanwhile, Manes.03G051300, Manes.17G066200, Manes.17G102000 belongs to the WRKY family, in which Manes.03G051300, Manes.17G066200 is associated with RN traits and Manes.17G102000 is associated with RW. This family is an important regulator in higher plants, involved in plant growth and development. Studies have shown that the WRKY transcription factor OsWRKY78 regulates rice stem elongation and seed size⁴³ Singh et al. identified a WRKY transcription factor SIWRKY23 expressed mainly in roots of tomato, which was found to be associated with seed size⁴⁴. In cassava research, the MeWRKY20 gene was found to promote ABA accumulation⁴⁵, and high ABA levels can promote radial expansion of cortical cells and reduce root penetration ability⁴⁶. Manes.18G015400 and Manes.17G100500 belong to the AFR transcription factor family, Manes.17G073900 belongs to the Auxin efflux carrier family protein, auxin, Aux) Indole-3-acetic acid, IAA is almost involved in all processes of plant growth and development, and the vast majority of these processes are regulated by gene expression⁴⁷. Auxin response factor, ARF is a new family of transcription factors discovered by Ulmasov et al. that regulate the expression of auxin responsive genes^48,49. It can specifically interact with auxin response elements in the promoter region of auxin responsive genes, AuxRE binds to "TGTCTC" to activate or inhibit gene expression⁵⁰. The auxin signaling pathway mediated by ARF transcription affects plant growth and development by regulating cell division, elongation, and differentiation^51,52. Gutierrez et al. found that miRNA160 and miRNA167 are involved in Arabidopsis AtARF6, regulating adventitious root formation, and Arabidopsis AtARF17 is also involved in this process^53,54. Ren et al. found that the SlARF2 gene in tomatoes regulates lateral root formation⁵⁵. Carey et al. found that DnARF11 is specifically expressed in the roots of Dendrobium officinale, indicating its important role in root system establishment⁵⁶. It is speculated that these genes affect RN and RW by regulating the development of root tubers.

The candidate gene Manes.18G023500 belongs to the MYB transcription factor family, which is one of the largest transcription factor families in plants with complex functional differentiation that plays an important role in physiological and biochemical processes in plant growth and development. Studies have shown that MeMYB108 can reduce leaf shedding and regulate cassava biomass⁵⁷. In Arabidopsis, AtMYB3R1 and AtMYB3R4 (AtMYB3R1/4) act as transcriptional activation factors, expressed in proliferating tissue, regulating the cell cycle process and affecting circadian rhythms^58–60. Ectopic expression of AtMYB56 can inhibit root growth, leading to failed root regeneration after stem cell damage⁶¹. In soybeans, GmMYB81 regulates the development of soybean tissues and embryos and has a significant effect on abiotic stress⁶². It is further speculated that the MeMYB67 and MeMYB61 genes may affect cassava yield by influencing leaf shedding and regulating the cell cycle process.

Cassava RLW is not only related to yield traits, but can also effectively evaluate the shape of potato blocks. In this study, Manes.09G004500 was identified as related to RLW, which belongs to the bZIP family. This family is an important regulatory factor present in higher plants and participates in plant growth and development⁶³. Studies have shown that, BZIP is involved in regulating certain signaling pathways related to the ABA pathway. During the germination and flowering period of Arabidopsis seeds, The bZIP transcription factors ABI5 (ABA sensitive 5) and ABFs (ABRE binding factors) are key factors that regulate ABA signaling Hossain. Arabidopsis abi5 mutant is less sensitive to ABA. Due to ABA inhibiting seed germination, ABI5 can activate the expression of specific genes in seeds through ABA mediated signaling pathways, thereby promoting seed germination⁶⁴. In addition, research has found that Arabidopsis AtbZIP29 is specifically expressed in proliferative tissues, regulating the number of cells in leaf and root meristem tissues and promoting tissue regeneration by specifically binding to cell cycle regulatory factors and cell wall regeneration related genes. During the cell division cycle, BZIP29 can interact with bZIP69 to form dimers, which play a role in the root primordia, root crown, and meristem of lateral roots, thereby affecting the shape of roots⁶⁵. It is speculated that this gene may affect RLW through similar molecular mechanisms.

This study selected multi-year phenotypic data from two half sibling populations, and obtained genotype data from cassava populations using hyper seq simplified resequencing technology to establish a high-precision genetic map of cassava. By utilizing the localization information of two different populations, a co localization QTL is determined, which is equivalent to mutual validation of QTL localization between different populations, ensuring the reliability of QTL localization. Subsequently, candidate genes are determined based on QTL confidence intervals, and the trend of parental trait size changes and parental transcriptome data are used to select and verify candidate genes. This will provide an important foundation for functional gene mining and molecular assisted breeding of quantitative traits in cassava, and also provide certain genetic resources and theoretical basis for cassava improvement breeding for yield traits.

RFLP

Restriction Fragment Length Polymorphism

SSR

Simple Sequence Repeat

SNP

Single Nucleotide Polymorphism

QTL

Quantitative Trait Loci

PCR

Polymerase chain reaction

WGS

Whole genome sequencing

AFSM

Amplified Fragment Single-nucleotide polymorphisms and Methylation

BLUE

Best Linear Unbiased Estimation

sringle plant Root Weight

Root Number

RLW

Root Length to Width ratio

LOD

Logarithm of the Odds

centimorgan

Coefficient of Variation

KEGG

Kyoto Encyclopedia of Genes and Genomes

Gene Ontology

DHHC

DHHC type zinc finger protein

Author Contributions: Q.L., Z.X. and W.W. conceived and managed the experiments. L.Z., Z.W., Y.L., P.A., and S.J. conducted the experiments work. Q.L., Z.X. and Y.L. analyzed the experimental results. Q.L. wrote the manuscript. Z.X., and W.W. reviewed and contributed to improve it and revised the last version of the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate: Not applicable.

Consent for publication: Written informed consent for publication was obtained from all participants.

Acknowledgements: Thank you to all authors for their participation and contribution to this article.

Funding: This research was funded by National Natural Science Foundation of China NSFC-CG Joint Fund Key Project, Gene Network and Molecular Design Breeding of Cassava Yield and Root Quality (3181101517).

Data Availability Statement: Supporting data can be found at the National Genomics Data Center and are accessible at http://bigd.big.ac.cn/ under BioProject numbers PRJCA027761 .

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Conflicts of Interest: The authors declare no conflict of interest.

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Construction of an ultrahigh-density genetic linkage map for Manihot esculenta Crantz and identification of QTL for root quantity traits

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and Methods

2.1 Mapping populations and DNA extraction

2.2 Hyper-seq library preparation and sequencing

2.3 Sequence analyses and genotyping

2.4 Transcriptome analyses and genotyping

2.5 Linkage map construction

2.6 Trait assessment Statistical Analyses

2.7 QTL Detection and candidate gene analysis

3 Results and conclusions

3.1 SNP marker density and annotation

3.3 Statistical Analysis and Correlation of Phenotypic Data

3.4 QTL localization

3.5 Candidate gene ananalysis

4 Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

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