玉米穗部性状及其一般配合力的关联分析

穗部性状是影响玉米产量的重要性状,一般配合力是评价玉米自交系利用价值的重要指标。为解析穗部性状及其一般配合力的遗传基础,本研究对248份玉米自交系组成的自然群体和以其中100份自交系为母本按照NCⅡ遗传交配设计与4个测验种(Mo17、昌7-2、E28和郑58)组配的400份F1杂交组合的穗部性状进行研究,并利用分布于全基因组的83057个SNP标记进行穗部性状及其一般配合力的关联分析。结果表明,穗长、穗粗2个穗部性状基因型间、环境间差异达极显著水平,其广义遗传率分别为81.22%和87.70%。母本间、父本间及不同杂交组合间穗长、穗粗差异均达极显著水平,在基因型方差中特殊配合力贡献率较大。利用2年2点4个环境下的数据分别进行关联分析,检测到34个性状SNP关联,利用BLUP值检测到7个性状SNP关联。这些性状SNP关联可解释的表型变异为0.01%～19.42%,其中有5个性状SNP关联的表型贡献率大于10%,未检测到穗部性状本身与一般配合力性状的相同SNP位点。基于该群体的LD衰减距离在显著关联SNP位点上下游各120 kb范围内进行候选基因搜索,共发现158个候选基因,推测可能的候选基因涉及泛素代谢相关基因(GRMZM2G360374、GRMZM2G049568、GRMZM2G178120),β半乳糖苷酶(GRMZM2G178106),丝氨酸苏氨酸蛋白激酶(GRMZM2G127050),赖氨酸和组氨酸特异性转运体(GRMZM2G116004)。研究结果为解析玉米穗长和穗粗及其一般配合力的遗传基础和分子辅助选择育种提供了参考。     

大豆需磷关键时期磷高效利用遗传位点挖掘

土壤有效磷缺乏已成为影响大豆产量和品质的重要因素,深入挖掘大豆需磷关键时期磷高效利用遗传位点成为实现其分子遗传改良的重要前提。鉴于此,本研究利用SoySNP6K(5403个SNP标记)通过全基因组关联分析挖掘大豆需磷关键时期磷高效利用10个相关性状遗传位点,结果发现,T1关键时期(四叶期)适磷条件检测到78个关联SNP,以根系干重与植株总干重SNP较多;低磷条件检测到134个关联SNP,以植株总干重检测SNP最多,并在8号、13号、20号染色体分别检测到同时控制地上部干重和总干重、地上部鲜重和干重与总干重SNP簇;T2关键时期(六叶期)适磷条件检测到83个SNP,以株高和地上部干重检测SNP较多,低磷条件检测到53个SNP,以株高和根冠比SNP较多,并在18号染色体检测到同时控制根干重和总干重SNP簇,在11号、16号、18号染色体分别检测到3个一因多效SNP;上述关联SNP中有9个SNP同时在T1与T2时期被检测到,分别与地上部鲜重、干重、根冠比、株高等关联,为大豆磷素高效利用分子标记辅助育种以及候选基因克隆等提供了依据。     

甘蓝型油菜每角果粒数全基因组关联分析

为挖掘甘蓝型油菜每角果粒数显著关联单核苷酸多态性（SNP）位点及相关候选基因。本研究以300份甘蓝型油菜自交系为试验材料,对甘蓝油菜每角果粒数进行一年两地表型考察,并结合该群体前期开发的201 817个SNPs标记,采用一般线性模型（GLM）和混合线性模型（MLM）进行全基因组关联分析（GWAS）,此外,对性状显著关联SNP位点两侧100 kb区域内相关候选基因进行功能预测。300份甘蓝型油菜每角果粒数在两地均表现出广泛的表型变异,筛选出2份每角果粒数较多的油菜种质资源。基于GLM模型检测到39个与油菜每角果粒数显著关联SNPs,采用MLM分析发现,两地共检测到的3个每角果粒数显著关联SNPs位点均在GLM检测到。8个位点附近找到CIK,ERF022和EDE1等19个拟南芥已报道角果籽粒发育相关的同源基因。研究结果有助于解析甘蓝型油菜每角果粒数的遗传基础,为研究每角果粒数的调控机制、指导每角果粒数的遗传改良奠定基础。     

小麦根部耐盐性状全基因组关联分析

为了解小麦耐盐相关性状的遗传机理,挖掘与小麦耐盐性显著相关的SNP位点及候选基因,本研究利用浓度200 mmol/L的NaCl溶液和正常营养液对全国300份小麦品种(系)进行耐盐性试验,并利用小麦90 K芯片对分布于小麦全基因组的16650个SNP,采用Q+K关联混合模型对小麦最长根长、根干重、根鲜重、根平均直径、根尖数、根表面积、根体积和总根长等8个根部耐盐性相关性状进行全基因组关联分析(GWAS,genome-wide association study)。研究结果表明,小麦根部性状表现出广泛的表型变异,变异系数为24.3%~50.0%,多态性信息含量(PIC,polymorphic information content)为0.170~0.562,全基因组LD衰减距离为6 Mb;群体结构分析表明,试验所用300份小麦品种(系)可分为3个亚群,亚群1包含143个(47.67%)试验材料,主要来自河南、陕西和四川;亚群2包含74个(24.67%)试验材料,主要来自北京;亚群3包含83个(27.67%)试验材料,主要来自河南。GWAS共检测到77个与小麦耐盐相关性状显著关联的SNP位点(P≤0.001),这些位点分布在小麦除6D外的20条染色体上,单个SNP位点可解释3.70%~19.45%的表型变异,其中位于1A、3A、4A、7A、3D和5D染色体上的RAC875_c13169_459等6个位点同时关联到2个或2个以上性状,贡献率为3.78%~19.45%;对77个SNP位点进行发掘,筛选到17个可能与小麦耐盐性有关的候选基因。TraesCS5B01G031800(阳离子反转运蛋白)在Na+等阳离子转运中起重要作用,TraesCS5A01G329000(防御素)可以在阻断Na+等阳离子进入过程中起作用,TraesCS2A01G079000(重复富脯氨酸细胞壁蛋白)在细胞壁的形成中起重要作用,这些候选基因可作为耐盐性重要基因。     

中国人群肝功能检测指标全基因组关联分析研究

肝功能检测(liver function test,LFTs)指标是受遗传和环境影响的复杂性状,具有个体差异性。为系统性研究中国人群全基因组范围内单核苷酸多态性(single nucleotide polymorphism,SNP)与肝功能指标之间的联系,本研究利用英国生物银行(UK Biobank)中1653名中国人的基因分型数据和表型数据为研究对象,利用PLINK软件进行全关联分析研究(genome-wide association study,GWAS),发现229个SNP与中国人群血液中的总胆红素(total bilirubin,TB)相关,27个SNP与中国人群血液中碱性磷酸酶(alkaline phosphatase,ALP)相关,36个SNP与中国人群血液中的γ-谷氨酰转肽酶(γ-glutamyl transpeptidase,GGT)相关,1个SNP与中国人群血液中的门冬氨酸氨基转移酶(aspartate transaminase,AST)相关,最显著的位点中有11个位点是新的LFTs关联位点。通过功能基因组分析,发现这些位点的临床意义(如吉尔伯特综合征),确定了候选基因(UGT1A,ABO,GGT1),为从遗传角度理解中国人群LFTs的个体差异性和肝功能指标临床精准检测提供了前期研究基础。     

普通菜豆镰孢菌枯萎病抗性种质资源筛选及全基因组关联分析

普通菜豆镰孢菌枯萎病是严重制约菜豆(Phaseolus vulgaris)产量的主要病害之一。采用下胚轴双孔注射法对601份普通菜豆种质资源进行枯萎病抗性鉴定, 共筛选出4份高抗材料。在此基础上, 基于分布在全基因组上的3 765 456个单核苷酸多态性(SNP)标记, 进行全基因组关联分析, 以P<1×10^-5为阈值。结果检测到57个显著关联的SNP位点, 分布于1、2、6、8和11号染色体上; 共获得8个显著关联区域, 其中位于1号染色体上的区域1包含SNP最多(48个), 最显著SNP P值为2.18E-07。在8个显著关联区域中, 共检测到186个基因, 其中157个基因有注释信息, 编码过氧化物酶、抗病蛋白、转录因子和蛋白激酶等。结合KEGG富集分析和序列同源性比对, 鉴定出9个候选基因可能与抗性相关。     

生物信息学分析方法I: 全基因组关联分析概述

全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中, 利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间, 而且可揭示表型变异的遗传架构全景图。目前, 人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点, 阐明了这些性状的遗传基础, 并为揭示这些性状背后的分子机理提供候选基因, 也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述, 以期为相关研究提供参考。     

水稻糯性相关基因的全基因组关联分析

利用1 090 071个SNP标记, 对419份广西地方稻种资源核心种质的糯性进行全基因组关联分析。结果表明, 运用混合线性模型, 在P<4.72×10^-8 (4.72E-8)水平下, 检测到45个与糯性显著关联的SNP位点, 均位于第6号染色体上。通过筛选显著关联位点上、下游各150 kb区域内的基因, 共找到305个候选基因, 其中包含2个与淀粉合成酶相关的Wx和SSIIa基因; 同时在第6号染色体5.69-5.89 Mb区域发现4个与糯性显著关联的SNP位点, 该区域可能是影响水稻(Oryza sativa)糯性的重要候选区域。     

SNP芯片数据估计动物个体基因组品种构成的方法及应用

自然和人工选择、地理隔离和遗传漂移等原因使动物基因组中许多位点的等位基因频率在群体间会产生差异。源于不同品种(祖先)杂交(交配)的动物个体,其基因组与这些品种(祖先)的基因频率(基因型)会存在一定的相关性。因此采用合适的统计模型和分析方法,可以估计出每个品种(祖先)对于个体基因组的遗传贡献比例,又称为个体的基因组品种构成(genomic breed composition, GBC)。本文介绍了利用SNP芯片数据估计动物个体GBC的原理、方法及步骤,并且通过对198头待鉴定的日本红毛和牛GBC的评估,演示了用回归模型和混合分布模型估计动物个体GBC的具体步骤,其中包括SNP子集的筛选、参考群体中动物个体选择以及待测定动物GBC的计算。参考动物群体选自日本红毛和牛(Akaushi)、安格斯牛(Angus)、海福特牛(Hereford)、荷斯坦牛(Holstein)和娟珊牛(Jersey) 5个品种共36 574头,每个个体有40K或50K芯片数据。本文在现有商用 SNP芯片基础上筛选用于品种鉴定和估计动物个体GBC的SNP子集,是对现有SNP芯片功能的拓展和深入开发利用。此外,在基因组选择中如何利用SNP基因型估计动物个体GBC的结果,提高纯种和杂种动物的预测准确度,也是值得深入研究的领域。     

中华绒螯蟹MSTN基因SNPs多态性及与生长性状的关联分析

为研究中华绒螯蟹(Eriocheir sinensis)肌肉生长抑制素基因(myostatin, MSTN)的多态性及其与生长性状的相关性, 对中华绒螯蟹3个群体(育种群体、大赛群体、野生群体)共321个个体MSTN基因的多态性进行筛选, 发现该基因的第1外显子存在3个多态性SNP位点(S1: C714T; S2:G729A; S3:G753T), 均为处于Hardy-Weinberg平衡(P>0.05)的中、高度多态性位点。利用一般线性模型分析3个位点及其基因型组合与生长性状的相关性, 发现S1位点对中华绒螯蟹的体重和壳长等生长性状有显著影响(P≤0.05), 而其余2个位点与生长性状无显著关联性。结果表明S1位点的TT基因型对中华绒螯蟹的生长最为有利, 可作为分子标记辅助育种的候选标记。     

GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction

Genome-wide association study (GWAS) and genomic prediction/selection (GP/GS) are the two essential enterprises in genomic research. Due to the great magnitude and complexity of genomic and phenotypic data, analytical methods and their associated software packages are frequently advanced. GAPIT is a widely-used genomic association and prediction integrated tool as an R package. The first version was released to the public in 2012 with the implementation of the general linear model (GLM), mixed linear model (MLM), compressed MLM (CMLM), and genomic best linear unbiased prediction (gBLUP). The second version was released in 2016 with several new implementations, including enriched CMLM (ECMLM) and settlement of MLMs under progressively exclusive relationship (SUPER). All the GWAS methods are based on the single-locus test. For the first time, in the current release of GAPIT, version 3 implemented three multi-locus test methods, including multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). Additionally, two GP/GS methods were implemented based on CMLM (named compressed BLUP; cBLUP) and SUPER (named SUPER BLUP; sBLUP). These new implementations not only boost statistical power for GWAS and prediction accuracy for GP/GS, but also improve computing speed and increase the capacity to analyze big genomic data. Here, we document the current upgrade of GAPIT by describing the selection of the recently developed methods, their implementations, and potential impact. All documents, including source code, user manual, demo data, and tutorials, are freely available at the GAPIT website (http://zzlab.net/GAPIT).     

Genome-wide association study of shank length and diameter at different developmental stages in chicken F2 resource population

In order to find SNPs and genes affecting shank traits, we performed a GWAS in a chicken F2 population of eight half-sib families from five hatches derived from reciprocal crosses between an Arian fast-growing line and an Urmia indigenous slow-growing chicken. A total of 308 birds were genotyped using a 60K chicken SNP chip. Shank traits including shank length and diameter were measured weekly from birth to 12 weeks of age. A generalized linear model and a compressed mixed linear model (CMLM) were applied to achieve the significant regions. The value of the average genomic inflation factor (λ statistic) of the CMLM model (0.99) indicated that the CMLM was more effective than the generalized linear model in controlling the population structure. The genes surrounding significant SNPs and their biological functions were identified from NCBI, Ensembl and UniProt databases. The results indicated that 12 SNPs at 12 different ages passed the LD-adjusted 5% Bonferroni significant threshold. Two SNPs were significant for shank length and nine SNPs were significant for shank diameter. The significant SNPs were located near to or inside 11 candidate genes. The results showed that a number of significant SNPs in the middle ages were higher than the rest. The MXRA8 gene was related to the significant SNP at week 1 that promotes proliferation of growth plate chondrocytes. A unique SNP of Gga_rs16689511 located on chicken Z chromosome within the LOC101747628 gene was related to shank length at three different ages of birds (weeks 8, 9 and 11). The significant SNPs for shank diameter were found at weeks 4 and 7 (four and five SNPs respectively). The identifications of SNPs and genes here could contribute to a better understanding of the genetic control of shank traits in chicken.     

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.     

Genome‐wide association of drought‐related and biomass traits with HapMap SNPs in Medicago truncatula

Improving drought tolerance of crop plants is a major goal of plant breeders. In this study, we characterized biomass and drought‐related traits of 220 Medicago truncatula HapMap accessions. Characterized traits included shoot biomass, maximum leaf size, specific leaf weight, stomatal density, trichome density and shoot carbon‐13 isotope discrimination (δ¹³C) of well‐watered M. truncatula plants, and leaf performance in vitro under dehydration stress. Genome‐wide association analyses were carried out using the general linear model (GLM), the standard mixed linear model (MLM) and compressed MLM (CMLM) in TASSEL, which revealed significant overestimation of P‐values by CMLM. For each trait, candidate genes and chromosome regions containing SNP markers were found that are in significant association with the trait. For plant biomass, a 0.5 Mbp region on chromosome 2 harbouring a plasma membrane intrinsic protein, PIP2, was discovered that could potentially be targeted to increase dry matter yield. A protein disulfide isomerase‐like protein was found to be tightly associated with both shoot biomass and leaf size. A glutamate‐cysteine ligase and an aldehyde dehydrogenase family protein with Arabidopsis homologs strongly expressed in the guard cells were two of the top genes identified by stomata density genome‐wide association studies analysis.     

Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^?6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^?3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize.     

Power analysis for cluster randomized trials with continuous coprimary endpoints

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K ($K\ge 2$) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients among the observations in each cluster, we derive the closed-form joint distribution of K treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the K treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the MLMM are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.     

Genomic loci associated with antibody-mediated immune responses in an F2 chicken population

Immunity-related traits are heritable in chicken, therefore, it is possible to improve the inherent immunity by breeding programs. In this study using the Illumina chicken 60K single nucleotide polymorphisms (SNPs) chip, we performed a set of genome-wide association studies to determine candidate genes and loci responsible for primary and secondary antibody-mediated responses against sheep red blood cell. A F2 population descended from a commercial meat-type breed and an Iranian indigenous chicken was used for this study. Statistical analysis was based on a mixed linear model utilizing genomic relationship matrix to prevent spurious associations. Correction for multiple testing was done by applying 5% and 10% chromosomal false discovery rates (FDRs) for significant and suggestive thresholds, respectively. Nine significant and 17 suggestive associated SNPs were identified. Most of the SNPs that were suggestively associated with the primary response of total plasma immunoglobulins were also significantly associated with this trait in secondary response. Three SNPs were located within a narrow region of 23 kb on chromosome 16. Pathway analysis for the genes surrounding the associated SNPs showed that they are involve in antigen processing and presentation, primary immunodeficiency, vitamin digestion and absorption, cell adhesion molecules, phagosome, influenza A, folding, assembly and peptide loading of class I major histocompatibility complex, lipid digestion, mobilization, and transport (FDR < 0.1). Interestingly, there were common regains associated with multiple immune-related traits.     

A genome‐wide association study suggests several novel candidate genes for carcass traits in Chinese Simmental beef cattle

A genome‐wide association study (GWAS) was conducted for two carcass traits in Chinese Simmental beef cattle. The experimental population consisted of 1301 individuals genotyped with the Illumina BovineHD SNP BeadChip (770K). After quality control, 671 990 SNPs and 1217 individuals were retained for the GWAS. The phenotypic traits included carcass weight and bone weight, which were measured after the cattle were slaughtered at 16 to 18 months of age. Three statistical models—a fixed polygene model, a random polygene model and a composite interval mapping polygene model—were used for the GWAS. The genome‐wide significance threshold after Bonferroni correction was 7.44E‐08 (= 0.05/671 990). In this study, we detected eight and seven SNPs significantly associated with carcass weight and bone weight respectively. In total, 11 candidate genes were identified within or close to these significant SNPs. Of these, we found several novel candidate genes, including PBX1, GCNT4, ALDH1A2, LCORL and WDFY3, to be associated with carcass weight and bone weight in Chinese Simmental beef cattle, and their functional roles need to be verified in further studies.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content

The free moisture in crop kernels after being naturally dried is referred to as kernel moisture content (KMC). Maize KMC reflects grain quality and influences transportation and storage of seeds. We used an IBM Syn10 DH maize population consisting of 249 lines and an association panel comprising 310 maize inbred lines to identify the genetic loci affecting maize KMC in three environments. Using the IBM population detected 13 QTL on seven chromosomes, which were clustered into nine common QTL. Genome-wide association analysis (GWAS) identified 16 significant SNPs across the 3 environments, which were linked to 158 genes across the three environments. Combined QTL mapping and GWAS found two SNPs that were located in two of the mapped QTL, respectively. Twenty-three genes were linked with the loci co-localized in both populations. Of these 181 genes, five have previously been reported to be associated with KMC or to regulate seed development. These associations were verified by candidate gene association analysis. Two superior alleles and one favorable haplotype for Zm00001d007774 and Zm00001d047868 were found to influence KMC. These findings provide insights into molecular mechanisms underlying maize KMC and contribute to the use of marker-assisted selection for breeding low-KMC maize.