QTL analysis of early flowering of female flowers in zucchini(Cucurbita pepo L.)

Early flowering promotes early maturity, production, and the capacity to counteract biotic and abiotic stresses, making it an important agronomic trait in zucchini. The present study demonstrated that the zucchini inbred line ‘19’ consistently flowered early, taking significantly fewer days to bloom the first female flower(DFF) than the inbred line ‘113’. Genetic analysis revealed that DFF, an inheritable quantitative trait, is controlled by multiple genes. Based on the strategy of quantitative trait locus(QTL) sequencing(QTL-seq) combined with linkage analysis, three QTLs for DFF were identified on chromosomes 4, 11, and 20. This study used additional F2 populations grown under different environmental conditions for QTL mapping analysis of DFF with insertion/deletion(InDel) markers to validate these results. Using the composite interval mapping(CIM) method of R/qtl software, we only identified one major locus under all environmental conditions, located in a 117-kb candidate region on chromosome 20. Based on gene annotation, gene sequence alignment, and qRT-PCR analysis, we found that the Cp4.1LG20g08050 gene encoding a RING finger protein may be a candidate gene for the opposite regulation of early flowering in zucchini. In summary, these results lay a foundation for a better understanding of early flowering and improving early flowering-based breeding strategies in zucchini.

QTL Analysis of Rice Peduncle Vascular Bundle System and Panicle Traits

A double haploid (DH) population of rice (Oryza sativa L.) derived from anther culture of ZYQ8/JX17, a typical indica and japonica hybrid, was used for genetic analysis of rice peduncle vascular system and panicle traits. The number of large vascular bundles (LVB), the number of small vascular bundles (SVB) in the peduncle, and the panicle traits including the number of primary rachis branches (PRB), the number of spikelets per panicle (SNP), peduncle top diameter (PTD), and panicle length (PL) were investigated in the parents and DH lines. The quantitative trait loci (QTLs) for each trait were analyzed based on the constructed molecular linkage map of this population. Three QTLs for LVB (qLVB_1, qLVB_6 and qLVB_7) were detected on chromosomes 1, 6, and 7, respectively. Two putative QTLs for SVB (qSVB_4 and qSVB_6) were mapped on chromosomes 4 and 6 respectively. Four QTLs (qPRB_4a, qPRB_4b, qPRB_6 and qPRB_7) on chromosomes 4, 6, and 7, respectively, were detected for PRB. Three QTLs (qSPN_4a, qSPN_4b and qSPN_6) were identified on chromosomes 4 and 6, respectively, which could significantly affect SPN. Five QTLs for PTD (qPTD_2, qPTD_5, qPTD_6, qPTD_8 and qPTD_12) were identified on chromosomes 2, 5, 6, 8, and 12, respectively. Three QTLs for PL (qPL_4, qPL_6 and qPL_8) were detected on chromosomes 4, 6, and 8, respectively. Clustering of QTLs, such as qLVB_6, qSVB_6, qSNP_6, qPTD_6, and qPL_6 detected in the interval G122_G1314b on chromosome 6, was found. These results suggest that some QTLs for peduncle vascular bundle system are possibly responsible for the panicle traits.

QTL Analysis of Lodging-related Morphological Traits of Soybean under Two Eco-environments

[Objective] This study is to map QTLs for lodging-related morphological traits of soybean, aiming at providing basis for breeding lodging-resistant varieties. [Method] To map the QTLs for lodging-related morphological traits, an F216-F218 RIL population containing 147 lines derived from a cross between Charleston as female and Dongnong 594 as male parent were used as experimental materials in this study. Totally 164 SSR primers screened out from amplification on two parents, were employed to amplify the F216-F218 RIL population for constructing a genetic linkage map. The QTLs for nodes on main stem, stem thickness and stem weight of the F216-F218 RIL soybean population were investigated in two eco-environments for three years. [Result] Sixteen QTLs for nodes on main stem were detected in A1, B1, C2, Dla, D2, F, G, H and N linkage groups, respectively; Ten QTLs for stem thickness in A1, B1, C2, Dla, E and G linkage groups, respectively; Fifteen QTLs for stem weight in A1, A2, C2, Dla, Dlb and G linkage groups, respectively. Of these QTLs, five for nodes on main stem, one for stem thickness and six for stem weight could be detected by both CIM and MIM, accounting for 8.6%-27.0%, 9.0%- 11.0%, and 6.0%-39.0% of the general phenotypic variation, respectively. From ex- perimental data of two years, Three QTLs for nodes on main stem and two for stem weight could be detected, accounting for 8.0%-60.2% and 10.0%-23.0% of the general phenotypic variation, respectively; while no QTL for stem thickness was re- peatedly detected during more than two years. [Conclusion] Comparison of the QTLs mapped for nodes on main stem, stem thickness and stem weight indicates that these three morphological traits are closely related with lodging-resistance of soybean.

QTL analysis of leaf photosynthetic rate and related physiological traits in rice(Oryza sativa L.)

Photosynthesis is one of the most important factors that influence the biomass and yield. Recently, more attention has been paid to genetic study on rice photosynthesis and rice breeding for the physiological traits related to high efficient photosynthesis. Chlorophyll content, stomatal resistance, and transpiration rate were very important physiological traits related to photosynthesis. But until now, no genetic study on these traits has been reported. A DH population derived from anther culture of ZYQ8/JX17, a typical indica/japonica hybrid was developed,

QTL Analysis in Tetraploid Cotton

QTL analyses were performed in tetraploid cotton.An interspecific F2 population consisting of 69 plants,which was developed from the cross between Gossypium hirsutum L.,cv.Handan 208(characterized as high fiber yield) and G.barbadense L.,cv.Pima 90(characterized as excellent fiber quality),was genotyped with SSR,RAPD,SRAP,and REMAP markers.A 1029-locus linkage map

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Analysis of QTLs for the Trichome Density on the Upper and Downer Surface of Leaf Blade in Soybean [Glycine max (L.) Merr.]

Trichomes （plant hairs） are present on nearly all land plants and are known to play important roles in plant protection, specifically against insect herbivory, drought, and UV radiation. The identification of quantitative trait loci （QTL） associated with trichome density should help to interpret the molecular genetic mechanism of soybean trichome density. 184 recombinant inbred lines （RILs）, derived from a cross between soybean cultivars Kefeng 1 and Nannong 1138-2 were used as segregating population for evaluation of TDU （trichome density on the upper surface of leaf blade） and TDD （trichome density on the downer surface of leaf blade）. A total of 15 QTL were detected on molecular linkage groups （MLG） A2, Dla, Dlb, E and H by composite interval mapping （CIM） and among all the QTL, qtuA2-1, qtuD 1 a-1, qtuD lb-2, qtuH-2 qtuE-1, qtdDlb-2, and qtdH- 2 were affirmed by multiple interval mapping （MIM）. The contribution ofphenotypic variance of qtuH-2 was 31.81 and 29.4% by CIM and MIM, respectively, suggesting it might be major gene Ps loci. Only 10 pairs of main QTL interactions for TDU were detected, explained a range of 0.2-5.1% of phenotypic variations for each pair for a total of 22.8%. The QTL on MLG Dlb affecting trichome density were mapped near to Rsc-7 conditioning resistance to SMV （soybean mosaic virus）. This study showed that the genetic mechanism of trichome density was the mixed major gene and polygene inheritance, and also suggested that the causal nature between trichome density and other agronomic traits.

Analysis of QTL for Seed Dormancy and Their Response to Dry Heat Treatment in Rice (Oryza sativa L.)

Quantitative trait loci (QTL) controlling seed dormancy in rice were identified usingrecombinant inbred lines (RILs) population derived from the cross between a japonicavariety Kinmaze and an indica variety DV85. Seeds of two parental cultivars and each RILwere harvested in 35d after heading. The germination percentage of these seeds at 30℃for 7 days were measured as the degree of seed dormancy. QTL analysis was performed withWindows QTL Cartographer 1.13a program by composite interval mapping. A total of four QTLfor seed dormancy were detected on chromosome 2 (two regions), 5 and 11, respectively.Phenotypic variation explained by each QTL ranged from 8.37 to 17.40%. Responses of suchloci to a dormancy-breaking treatment with dry heat were further detected. The resultsshowed that two alleles of qDOR-2-1 and qDOR-5 from DV85 as well as the allele of qDOR-11 from Kinmaze increased the seed dormancy, which seemed to be easily broken by dry heattreatment. Such loci of seed dormancy may be applied to rice genetic improvement. Theallele of qDOR-2-2 from DV85 increased the seed dormancy, which could not be broken bydry heat treatment.

Meta-analysis of soybean amino acid QTLs and candidate gene mining

The composition and quantity of amino acids influence the protein content and nutritional value of soybeans and also have an important impact upon soybean quality. After integrating and proofreading 140 original QTLs associated with amino acid contentfrom soybase（http：//www.soybase.org/）, 138 QTLs were further analyzed to determine high-confidence QTL regions. Meta-analysis was first carried out using the Bio Mercator ver. 2.1 software, yielding 33 consensus QTLs. The consensus QTL confidence intervals（CIs） ranged from 0.07 to 19.85 Mb. Next, the overview method was used to optimize the CIs, and 57 ＂real＂ QTLs were mapped. Candidate genes in the consensus QTL regions were obtained from Phytozome and were annotated using the Gene Ontology（GO）, Kyoto Encyclopedia of Genes and Genomes（KEGG）, Swissprot, and gene annotation databases. Finally, 16 unpublished candidate genes controlling the content of five types of amino acids were identified with Blast. These results laid the foundation for fine mapping of soybean amino acid-related QTLs and marker-assisted selection.

QTLs analysis of rice peduncle vascular bundle and panicle traits

The vascular bundle in plants plays an important role intransportation of photosynthetic products, mineral nutri-ents, water, arid so on. Significant positive correlationswere found between grain yield, panicle traits and the No.of peduncle vascular bundles. So, it is very important tostudy the inheritance of peduncle vascular bundle, whichis a quantitative trait.

Methods for Population-Based eQTL Analysis in Human Genetics

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci（e QTL） analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide e QTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, e QTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for e QTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing e QTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.

QTL mapping of seedling biomass and root traits under different nitrogen conditions in bread wheat(Triticum aestivum L.)

Plant nitrogen assimilation and use efficiency in the seedling's root system are beneficial for adult plants in field condition for yield enhancement.Identification of the genetic basis between root traits and N uptake plays a crucial role in wheat breeding.In the present study,198 doubled haploid lines from the cross of Yangmai 16/Zhongmai 895 were used to identify quantitative trait loci(QTLs)underpinning four seedling biomass traits and five root system architecture(RSA)related traits.The plants were grown under hydroponic conditions with control,low and high N treatments(Ca(NO_(3))_(2)·4H_(2)O at 0,0.05 and 2.0 mmol L^(-1),respectively).Significant variations among the treatments and genotypes,and positive correlations between seedling biomass and RSA traits(r=0.20 to 0.98)were observed.Inclusive composite interval mapping based on a high-density map from the Wheat 660 K single nucleotide polymorphisms(SNP)array identified 51 QTLs from the three N treatments.Twelve new QTLs detected on chromosomes 1 AL(1)in the control,1 DS(2)in high N treatment,4 BL(5)in low and high N treatments,and 7 DS(3)and 7 DL(1)in low N treatments,are first reported in influencing the root and biomass related traits for N uptake.The most stable QTLs(RRS.caas-4 DS)on chromosome 4 DS,which were related to ratio of root to shoot dry weight trait,was in close proximity of the Rht-D1 gene,and it showed high phenotypic effects,explaining 13.1%of the phenotypic variance.Twenty-eight QTLs were clustered in 12 genetic regions.SNP markers tightly linked to two important QTLs clusters C10 and C11 on chromosomes 6 BL and 7 BL were converted to kompetitive allele-specific PCR(KASP)assays that underpin important traits in root development,including root dry weight,root surface area and shoot dry weight.These QTLs,clusters and KASP assays can greatly improve the efficiency of selection for root traits in wheat breeding programmes.

Identification of QTLs Conferring Agronomic and Quality Traits in Hexaploid Wheat

The availability of elite germplasm resources with high yield and quality potentials is very important for development of cultivars in wheat. Thus, seeking such resources has been the continuous effort of breeder community. In this study, genetic analysis of a novel resource, Triticum spelta line CSCR6, from Australia was made by use of a recombination inbred line （RIL） population of 82 individuals from the cross between CSCR6 and another Australian hexaploid wheat cultivar, Lang. Data of a multiple environmental test was employed to genetically dissect quantitative trait loci （QTL） for agronomic traits such as plant height （PH）, spike length （SL）, spikelet per spike （SPI）, grain number per spike （GNS） and thousand grains weight （TGW） and for quality traits including grain protein content （GPC）, gluten content （GC）, grain hardness （GH）, falling number （FN） and sedimentation value （SV）. A 24 QTLs with additive effects were detected for all the investigated traits, and were located on chromosomes 1B, 1D, 2B, 3A, 3B, 3D, 4B, 5A, 5B, 7A, and 7B, respectively. Some QTLs located on 2B and 4B showed higher explanation of phenotypic variances and were not obviously interacted with environment. A QTL in the marker interval of wPT-5334-wPT-4918 （near the locus barc 0199） on 4B gave the highest contribution ratio of 30.76% on PH, while Qgpc-4B and Qgc-4B gave 13.07 and 14.70% contribution ratio on GPC and GC, respectively. Qph-2B, Qgns-2B, and Qgpc-2B showed 13.36, 10.00, and 10.79% contribution ratio on PH, GNS and GPC, respectively. Also, a QTL on 5A, Qsl- 5A, could explain 25.12% of phenotypic variance on SL. For most of agronomic and quality traits, CSCR6 alleles produced increase effects. The fact that a number of loci affecting the investigated traits were detected in T. spelta line CSCR6 revealed that it could offer a new opportunity for the manipulation of these traits in wheat breeding programs.

QTL Mapping for Adult Plant Resistance to Powdery Mildew in Italian Wheat cv. Strampelli

The Italian wheat cv. Strampelli displays high resistance to powdery mildew caused by Blumeria graminis f. sp. tritici. The objective of this study was to map quantitative trait loci （QTLs） for resistance to powdery mildew in a population of 249 F2：3 lines from Strampelli/Huixianhong. Adult plant powdery mildew tests were conducted over 2 yr in Beijing and 1 yr in Anyang and simple sequence repeat （SSR） markers were used for genotyping. QTLs Qpm.caas-3BS, Qpm.eaas-5BL. 1, and Qpm.caas-7DS were consistent across environments whereas, Qpm.caas-2BS. 1 found in two environments, explained 0.4-1.6, 5.5-6.9, 27.1-34.5, and 1.0-3.5% of the phenotypic variation respectively. Qpm.caas-7DS corresponded to the genomic location of Pm38/Lr34/Yr18. Qpm.caas-4BL was identified in Anyang 2010 and Beijing 2011, accounting for 1.9-3.5% of phenotypic variation. Qpm.caas-2BS. 1 and Qpm.caas-5BL. 1 contributed by Strampelli and Qpm.caas-3BS by Huixianhong, seem to be new QTL for powdery mildew resistance. Qpm.caas-4BL, Qpm.caas-5BL.3, and Qpm.caas-7DS contributed by Strampelli appeared to be in the same genomic regions as those mapped previously for stripe rust resistance in the same population, indicating that these loci conferred resistance to both stripe rust and powdery mildew. Strampelli could be a valuable genetic resource for improving durable resistance to both powdery mildew and stripe rust in wheat.

Construction of high-density SNP genetic maps and QTL mapping for dwarf-related traits in Litchi chinensis Sonn

Litchi chinensis Sonn is widely cultivated in subtropical regions and has an important economic value.A high-density genetic map is a valuable tool for mapping quantitative trait loci(QTL)and marker-assisted breeding programs.In this study,a single nucleotide polymorphism(SNP)-based high-density linkage map was constructed by a genotyping-by-sequencing(GBS)protocol using an F1 population of 178 progenies between two commercial litchi cultivars,‘Ziniangxi’(dwarf)and‘Feizixiao’(vigorous).The genetic map consisted of 3027 SNP markers with a total length of 1711.97 cM in 15 linkage groups(LGs)and an average marker distance of 0.57 cM.Based on this high-density linkage map and three years of phenotyping,a total of 37 QTLs were detected for eight dwarf-related traits,including length of new branch(LNB),diameter of new branch(DNB),length of common petiole(LCP),diameter of common petiole(DCP),length of internode(LI),length of single leaf(LSL),width of single leaf(WSL),and plant height(PH).These QTLs could explain 8.0 to 14.7%(mean=9.7%)of the phenotypic variation.Among them,several QTL clusters were observed,particularly on LG04 and LG11,which might show enrichment for genes regulating the dwarf-related traits in litchi.There were 126 candidate genes identified within the QTL regions,55 of which are differentially expressed genes by RNA-seq analysis between‘Ziniangxi’and‘Feizixiao’.These DEGs were found to participate in the regulation of cell development,material transportation,signal transduction,and plant morphogenesis,so they might play important roles in regulating plant dwarf-related traits.The high-density genetic map and QTLs identification related to dwarf traits can provide a valuable genetic resource and a basis for marker-assisted selection and genomic studies of litchi.

Quantitative trait loci detection of E dwardsiella tarda resistance in Japanese flounder Paralichthys olivaceus using bulked segregant analysis

In recent years, Edwardsiella tarda has become one of the most deadly pathogens of Japanese fl ounder( Paralichthys olivaceus), causing serious annual losses in commercial production. In contrast to the rapid advances in the aquaculture of P. o livaceus, the study of E. tarda resistance-related markers has lagged behind, hindering the development of a disease-resistant strain. Thus, a marker-trait association analysis was initiated, combining bulked segregant analysis(BSA) and quantitative trait loci(QTL) mapping. Based on 180 microsatellite loci across all chromosomes, 106 individuals from the F1333(♀: F0768 ×♂: F0915)(Nomenclature rule: F+year+family number) were used to detect simple sequence repeats(SSRs) and QTLs associated with E. tarda resistance. After a genomic scan, three markers(Scaffold 404-21589, Scaffold 404-21594 and Scaffold 270-13812) from the same linkage group(LG)-1 exhibited a signifi cant difference between DNA, pooled/bulked from the resistant and susceptible groups( P <0.001). Therefore, 106 individuals were genotyped using all the SSR markers in LG1 by single marker analysis. Two different analytical models were then employed to detect SSR markers with different levels of signifi cance in LG1, where 17 and 18 SSR markers were identifi ed, respectively. Each model found three resistance-related QTLs by composite interval mapping(CIM). These six QTLs, designated q E1–6, explained 16.0%–89.5% of the phenotypic variance. Two of the QTLs, q E-2 and q E-4, were located at the 66.7 c M region, which was considered a major candidate region for E. tarda resistance. This study will provide valuable data for further investigations of E. tarda resistance genes and facilitate the selective breeding of disease-resistant Japanese fl ounder in the future.

Identification of Quantitative Trait Locus for Overwintering Germinability in Rice(Oryza sativa L.)

312 recombinant inbred lines （RILs） in F9 from a cross between a overwintering cold-tolerant germplasm resource Glutinous rice 89-1（Gr 89-1） and a cold-sensitive variety Shuhui 527 was used for quantitative trait locus （QTL） analysis. The scores of percent ratooning germinability （PRG） and overwintering germinability （POG） were evaluated. The overwintering germination rate of axillary buds was scored to represent the overwintering germinability. Two significant QTLs （qPRG-4 and qPRG-7） on chromosomes 4 and 7 were detected and explained 8.3 and 7.2% of the total phenotypic variation, respectively. Three significant QTLs （qPOG-2, qPOG-3 and qPOG-7） were identified and mapped on chromosomes 2, 3, and 7, respectively. These QTLs contributed 9.6, 6.7, and 17.8% of phenotypic variations, respectively. A comparative analysis using SSR markers closely linked to the three QTLs for the overwintering revealed cold-tolerant individuals, which harbour the Glutinous rice 89-1 alleles at RM7110, RM250, RM418, and RM232, had a high percent overwintering germinability, while cold-sensitive individuals, which carry Shuhui 527 alleles at these loci, had a low percent overwintering germinability in the F2 population of Shuhui 527/Glutinous rice 89-1. This study demonstrated the utility of these SSR markers for selection of overwintering germinability genotypes.

Coherent estimates of genetic effects with missing information

Genetic effect estimates for loci detected in quantitative trait locus (QTL) mapping experiments depend upon two factors. First, they are parameterizations of the genotypic values determined by the model of genetic effects. Second, they are consequently also affected by the regression method used to estimate the genotypic values from the observed marker genotypes and phenotypes. There are two common causes for marker-genotype data to be incomplete in those experiments—missing marker-genotypes and within-interval mapping. Different regression methods tend to differ in how this missing information is represented and handled. In this communication we explain why the estimates of genetic effects of QTL obtained using standard regression methods are not coherent with the model of genetic effects and indeed show intrinsic inconsistencies when there is incomeplete genotype information. We then describe the interval mapping by imputations (IMI) regression method and prove that it overcomes those problems. A numerical example is used to illustrate the use of IMI and the consequences of using current methods of choice. IMI enables researchers to obtain estimates of genetic effects that are coherent with the model of genetic effects used, despite incomplete genotype information. Furthermore, because IMI allows orthogonal estimation of genetic effects, it shows potential performance advantages for being implemented in QTL mapping tools.

Genetic analysis of leaffolder resistance in rice

A double haploid （DH） population, which consists of 120 lines derived from anther culture of a typical indica and japonica hybrid ‘CJ06＇/‘TNI＇, was used to investigate the genetic basis for rice leaffolder resistance. Using a constructed molecular linkage map, five QTLs for rolled leaves were detected on chromosomes 1, 2, 3, 4, and 8. The positive alleles from C J06 on chromosomes 3, 4, and 8 increased the resistance to rice leaffolder, and the alleles from TN1 on chromosomes 1 and 2 also enhanced resistance to leaffolder. The interactions between QTLs were identified and tested, and four conditional interactions were acquired for resistance to rice leaffolder. These loci were located on chromosomes 2, 9, 10, and 11, respectively. QTL pyramiding indicated that the positive alleles affect resistance to leaffolder. The prospective application of this data in rice breeding was also discussed.

A consensus linkage map of common carp (Cyprinus carpio L.) to compare the distribution and variation of QTLs associated with growth traits

The ability to detect and identify quantitative trait loci (QTLs) in a single population is often limited. Analyzing multiple populations in QTL analysis improves the power of detecting QTLs and provides a better understanding of their functional allelic variation and distribution. In this study, a consensus map of the common carp was constructed, based on four populations, to compare the distribution and variation of QTLs. The consensus map spans 2371.6 cM across the 42 linkage groups and comprises 257 microsatellites and 421 SNPs, with a mean marker interval of 3.7 cM/marker. Sixty-seven QTLs affecting four growth traits from the four populations were mapped to the consensus map. Only one QTL was common to three populations, and nine QTLs were detected in two populations. However, no QTL was common to all four populations. The results of the QTL comparison suggest that the QTLs are responsible for the phenotypic variability observed for these traits in a broad array of common carp germplasms. The study also reveals the different genetic performances between major and minor genes in different populations.