Comparison of SSRs and SNPs in assessment of genetic relatedness in maize

Advances in high-throughput SNP genotyping and genome sequencing technologies have enabled genome-wide association mapping in dissecting the genetic basis of complex quantitative traits. In this study, 82 SSRs and 884 SNPs with minor allele frequencies (MAF) over 0.20 were used to compare their ability to assess population structure, principal component analysis (PCA) and relative kinship in a maize association panel consisting of 154 inbred lines. Compared to SNPs, SSRs provided more information on genetic diversity. The expected heterozygosity (He) of SSRs and SNPs averaged 0.65 and 0.44, and the polymorphic information content of these two markers was 0.61 and 0.34 in this panel, respectively. Additionally, SSRs performed better at clustering all lines into groups using STRUCTURE and PCA approaches, and estimating relative kinship. For both marker systems, the same clusters were observed based on PCA and the first two eigenvectors accounted for similar percentage of genetic variations in this panel. The correlation coefficients of each eigenvector from SSRs and SNPs decreased sharply when the eigenvector varied from 1 to 3, but kept around 0 when the eigenvector were over 3. The kinship estimates based on SSRs and SNPs were moderately correlated (r (2)?=?0.69). All these results suggest that SSR markers with moderate density are more informative than SNPs for assessing genetic relatedness in maize association mapping panels.     

Characterization of Sugarcane Mosaic Virus Scmv1 and Scmv2 Resistance Regions by Regional Association Analysis in Maize

Sugarcane Mosaic Virus (SCMV) causes one of the most severe virus diseases in maize worldwide, resulting in reduced grain and forage yield in susceptible cultivars. In this study, two association panels consisting of 94 inbred lines each, from China and the U.S., were characterized for resistance to two isolates: SCMV-Seehausen and SCMV-BJ. The population structure of both association panels was analyzed using 3072 single nucleotide polymorphism (SNP) markers. The Chinese and the U.S. panel were both subdivided into two sub-populations, the latter comprised of Stiff Stalk Synthetic (SS) lines and Non Stiff Stalk Synthetic (NSS). The relative kinships were calculated using informative 2947 SNPs with minor allele frequency ≥ 5% and missing data ≤ 20% for the Chinese panel and 2841 SNPs with the same characteristics were used for the U.S. panel. The Scmv1 region was genotyped using 7 single sequence repeat (SSR) and sequence-tagged site (STS) markers, and 12 SSR markers were used for the Scmv2 region in the U.S. panel, while 5 of them were used for the Chinese panel. For all traits, a MLM (Mix Linear Model) controlling both population structure and relative kinship (Q + K) was used for association analysis. Three markers Trx-1, STS-11, and STS-12 located in the Scmv1 region were strongly associated (P = 0.001) with SCMV resistance, and explained more than 16.0%, 10.6%, and 19.7% of phenotypic variation, respectively. 207FG003 located in the Scmv2 region was significantly associated (P = 0.001) with SCMV resistance, and explained around 18.5% of phenotypic variation.     

Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize

Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A maize (Zea mays L.) association mapping panel including 527 inbred lines with tropical, subtropical and temperate backgrounds, representing the global maize diversity, was genotyped using 1,536 single nucleotide polymorphisms (SNPs). In total, 926 SNPs with minor allele frequencies of ≥0.1 were used to estimate the pattern of genetic diversity and relatedness among individuals. The analysis revealed broad phenotypic diversity and complex genetic relatedness in the maize panel. Two different Bayesian approaches identified three specific subpopulations, which were then reconfirmed by principal component analysis (PCA) and tree-based analyses. Marker–trait associations were performed to assess the suitability of different models for false-positive correction by population structure (Q matrix/PCA) and familial kinship (K matrix) alone or in combination in this panel. The K, Q + K and PCA + K models could reduce the false positives, and the Q + K model performed slightly better for flowering time, ear height and ear diameter. Our findings suggest that this maize panel is suitable for association mapping in order to understand the relationship between genotypic and phenotypic variations for agriculturally complex quantitative traits using optimal statistical methods.     

Identification of Major QTL for Waterlogging Tolerance Using Genome-Wide Association and Linkage Mapping of Maize Seedlings

To investigate the genetic basis of maize seedling response to waterlogging, we performed a genome-wide association study in 144 maize inbred lines, measuring length, fresh and dry weight of roots and shoots under normal and waterlogged conditions using 45,868 SNPs. This panel was divided into three subgroups based on the population structure results and the LD decay distance was 180 kb. A biparental advanced backcross (AB) population was also used to detect quantitative trait loci (QTL). In a comparison of 16 different models, principal components analysis (PCA/top PC3)?+?K was found to be best for reduction of false-positive associations for further analysis. A whole-genome scan detected four strong peak signals (P <?2.18?×?10^?5) significantly associated with the waterlogging response on chromosomes 5, 6 and 9. SNP4784, SNP200, SNP298, and SNP6314 showed significant association with corresponding traits under waterlogging and explained 14.99–19.36 %, 15.75–17.64 %, 16.08 % and 15.44 % of the phenotypic variation, respectively. The identified SNPs were located in GRMZM2G012046, GRMZM2G009808, GRMZM2G137108 and GRMZM2G369629 (AGPV1). SNP4784 (GRMZM2G012046) was colocalized with the major QTL that was identified with the same traits in the AB population. Forty-seven SNPs significantly associated (P?<?2.18?×?10^?4) with six traits in association mapping were identified and, among these, 33 SNPs were already reported in literature as waterlogging-related traits. These results will help elucidate the genetic basis of differential responses and tolerance to waterlogging stress among maize inbred lines, and provide novel loci for improvement of waterlogging tolerance of maize inbred lines using marker-assisted selection.     

Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping

Key message

Using combined linkage and association mapping, 26 stable QTL and six stable SNPs were detected across multiple environments for eight ear and grain morphological traits in maize. One QTL, PKS2, might play an important role in maize yield improvement.

Abstract

In the present study, one bi-parental population and an association panel were used to identify quantitative trait loci (QTL) for eight ear and grain morphological traits. A total of 108 QTL related to these traits were detected across four environments using an ultra-high density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319, and 26 QTL were identified in more than two environments. Furthermore, 64 single nucleotide polymorphisms (SNPs) were found to be significantly associated with the eight ear and grain morphological traits (?log₁₀(P)?>?4) in an association panel of 240 maize inbred lines. Combining the two mapping populations, a total of 17 pleiotropic QTL/SNPs (pQTL/SNPs) were associated with various traits across multiple environments. PKS2, a stable locus influencing kernel shape identified on chromosome 2 in a genome-wide association study (GWAS), was within the QTL confidence interval defined by the RILs. The candidate region harbored a short 13-Kb LD block encompassing four SNPs (SYN11386, PHM14783.16, SYN11392, and SYN11378). In the association panel, 13 lines derived from the hybrid PI78599 possessed the same allele as Qi319 at the PHM14783.16 (GG) locus, with an average value of 0.21 for KS, significantly lower than that of the 34 lines derived from Ye478 that carried a different allele (0.25, P?<?0.05). Therefore, further fine mapping of PKS2 will provide valuable information for understanding the genetic components of grain yield and improving molecular marker-assisted selection (MAS) in maize.

     

Mixed model association scans of multi-environmental trial data reveal major loci controlling yield and yield related traits in Hordeum vulgare in Mediterranean environments

An association panel consisting of 185 accessions representative of the barley germplasm cultivated in the Mediterranean basin was used to localise quantitative trait loci (QTL) controlling grain yield and yield related traits. The germplasm set was genotyped with 1,536 SNP markers and tested for associations with phenotypic data gathered over 2?years for a total of 24?year?×?location combinations under a broad range of environmental conditions. Analysis of multi-environmental trial (MET) data by fitting a mixed model with kinship estimates detected from two to seven QTL for the major components of yield including 1000 kernel weight, grains per spike and spikes per m², as well as heading date, harvest index and plant height. Several of the associations involved SNPs tightly linked to known major genes determining spike morphology in barley (vrs1 and int-c). Similarly, the largest QTL for heading date co-locates with SNPs linked with eam6, a major locus for heading date in barley for autumn sown conditions. Co-localization of several QTL related to yield components traits suggest that major developmental loci may be linked to most of the associations. This study highlights the potential of association genetics to identify genetic variants controlling complex traits.     

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.     

Association mapping of six yield-related traits in rapeseed (Brassica napus L.)

Yield is one of the most important traits for rapeseed (Brassica napus L.) breeding, but its genetic basis remains largely ambiguous. Association mapping has provided a robust approach to understand the genetic basis of complex agronomic traits in crops. In this study, a panel of 192 inbred lines of B. napus from all over the world was genotyped using 451 single-locus microsatellite markers and 740 amplified fragment length polymorphism markers. Six yield-related traits of these inbred lines were investigated in three consecutive years with three replications, and genome-wide association studies were conducted for these six traits. Using the model controlling both population structure and relative kinship (Q + K), a total of 43 associations (P < 0.001) were detected using the means of the six yield-related traits across 3 years, with two to fourteen markers associated with individual traits. Among these, 18 markers were repeatedly detected in at least 2 years, and 12 markers were located within or close to QTLs identified in previous studies. Six markers commonly associated with correlated traits. Conditional association analysis indicated that five of the associations between markers and correlated traits are caused by one QTL with pleiotropic effects, and the remaining association is caused by linked but independent QTLs. The combination of favorable alleles of multiple associated markers significantly enhances trait performance, illustrating a great potential of utilization of the associations in rapeseed breeding programs.     

Genome Wide Association Studies Using a New Nonparametric Model Reveal the Genetic Architecture of 17 Agronomic Traits in an Enlarged Maize Association Panel

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold −log₁₀ (P) >5.74 (α = 1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold −log₁₀ (P) >7.05 (α = 0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding.     

Association Mapping for Fruit,Plant and Leaf Morphology Traits in Eggplant

An eggplant (Solanum melongena) association panel of 191 accessions, comprising a mixture of breeding lines, old varieties and landrace selections was SNP genotyped and phenotyped for key breeding fruit and plant traits at two locations over two seasons. A genome-wide association (GWA) analysis was performed using the mixed linear model, which takes into account both a kinship matrix and the sub-population membership of the accessions. Overall, 194 phenotype/genotype associations were uncovered, relating to 30 of the 33 measured traits. These associations involved 79 SNP loci mapping to 39 distinct chromosomal regions distributed over all 12 eggplant chromosomes. A comparison of the map positions of these SNPs with those of loci derived from conventional linkage mapping showed that GWA analysis both validated many of the known controlling loci and detected a large number of new marker/trait associations. Exploiting established syntenic relationships between eggplant chromosomes and those of tomato and pepper recognized orthologous regions in ten eggplant chromosomes harbouring genes influencing breeders’ traits.     

Identification of kernel iron- and zinc-rich maize inbreds and analysis of genetic diversity using microsatellite markers

Development of micronutrient enriched staple foods is an important breeding goal in view of the extensive problem of ‘hidden hunger’ caused by micronutrient malnutrition. In the present study, kernel iron (Fe) and zinc (Zn) concentrations were evaluated in a set of 31 diverse maize inbred lines in three trials at two locations – Delhi (Kharif 2007 & 2008) and Hyderabad (Rabi 2007–08). The ranges of kernel Fe and Zn concentrations were 13.95–39.31 mg/kg and 21.85–40.91 mg/kg, respectively, across the three environments. Pooled analysis revealed significant genotype × environment (G × E) interaction in the expression of both the micronutrient traits, although kernel Fe was found to be more sensitive to G × E as compared to kernel Zn. Seven inbred lines, viz., BAJIM-06-03, DQPM-6, CM212, BAJIM-06-12, DQPM-7, DQPM-2 and CM129, were found to be the most stable and promising inbred lines for kernel Zn concentration, while for kernel Fe concentration, no promising and stable genotypes could be identified. Analysis of molecular diversity in 24 selected inbred lines with phenotypic contrast for the two kernel micronutrient traits, using 50 SSR markers covering the maize genome, revealed high levels of polymorphisms (214 SSR alleles; mean PIC value?=?0.62). The phenotypically contrasting and genetically diverse maize inbred lines identified in this study could be potentially utilized in further studies on QTL analysis of kernel micronutrient traits in maize, and the stable and most promising kernel micronutrient-rich maize genotypes provide a good foundation for developing micronutrient-enriched maize varieties suitable for the Indian context.     

Maize genetic diversity and association mapping using transposable element insertion polymorphisms

Transposable elements are the major component of the maize genome and presumably highly polymorphic yet they have not been used in population genetics and association analyses. Using the Transposon Display method, we isolated and converted into PCR-based markers 33 Miniature Inverted Repeat Transposable Elements (MITE) polymorphic insertions. These polymorphisms were genotyped on a population-based sample of 26 American landraces for a total of 322 plants. Genetic diversity was high and partitioned within and among landraces. The genetic groups identified using Bayesian clustering were in agreement with published data based on SNPs and SSRs, indicating that MITE polymorphisms reflect maize genetic history. To explore the contribution of MITEs to phenotypic variation, we undertook an association mapping approach in a panel of 367 maize lines phenotyped for 26 traits. We found a highly significant association between the marker ZmV1-9, on chromosome 1, and male flowering time. The variance explained by this association is consistent with a flowering delay of +123 degree-days. This MITE insertion is located at only 289 nucleotides from the 3′ end of a Cytochrome P450-like gene, a region that was never identified in previous association mapping or QTL surveys. Interestingly, we found (i) a non-synonymous mutation located in the exon 2 of the gene in strong linkage disequilibrium with the MITE polymorphism, and (ii) a perfect sequence homology between the MITE sequence and a maize siRNA that could therefore potentially interfere with the expression of the Cytochrome P450-like gene. Those two observations among others offer exciting perspectives to validate functionally the role of this region on phenotypic variation.     

Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels

The maize (Zea mays) kernel plays a critical role in feeding humans and livestock around the world and in a wide array of industrial applications. An understanding of the regulation of kernel starch, protein, and oil is needed in order to manipulate composition to meet future needs. We conducted joint-linkage quantitative trait locus mapping and genome-wide association studies (GWAS) for kernel starch, protein, and oil in the maize nested association mapping population, composed of 25 recombinant inbred line families derived from diverse inbred lines. Joint-linkage mapping revealed that the genetic architecture of kernel composition traits is controlled by 21-26 quantitative trait loci. Numerous GWAS associations were detected, including several oil and starch associations in acyl-CoA:diacylglycerol acyltransferase1-2, a gene that regulates oil composition and quantity. Results from nested association mapping were verified in a 282 inbred association panel using both GWAS and candidate gene association approaches. We identified many beneficial alleles that will be useful for improving kernel starch, protein, and oil content.     

Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due to various reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium high-throughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na⁺/K⁺ ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5–18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na⁺/K⁺ ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.     

Genetic Dissection of Drought and Heat Tolerance in Chickpea through Genome-Wide and Candidate Gene-Based Association Mapping Approaches

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1–7 seasons and 1–3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r²; when r²<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.     

Genotyping-by-sequencing highlights original diversity patterns within a European collection of 1191 maize flint lines,as compared to the maize USDA genebank

Key message

Genotyping by sequencing is suitable for analysis of global diversity in maize. We showed the distinctiveness of flint maize inbred lines of interest to enrich the diversity of breeding programs.

Abstract

Genotyping-by-sequencing (GBS) is a highly cost-effective procedure that permits the analysis of large collections of inbred lines. We used it to characterize diversity in 1191 maize flint inbred lines from the INRA collection, the European Cornfed association panel, and lines recently derived from landraces. We analyzed the properties of GBS data obtained with different imputation methods, through comparison with a 50 K SNP array. We identified seven ancestral groups within the Flint collection (dent, Northern flint, Italy, Pyrenees–Galicia, Argentina, Lacaune, Popcorn) in agreement with breeding knowledge. Analysis highlighted many crosses between different origins and the improvement of flint germplasm with dent germplasm. We performed association studies on different agronomic traits, revealing SNPs associated with cob color, kernel color, and male flowering time variation. We compared the diversity of both our collection and the USDA collection which has been previously analyzed by GBS. The population structure of the 4001 inbred lines confirmed the influence of the historical inbred lines (B73, A632, Oh43, Mo17, W182E, PH207, and Wf9) within the dent group. It showed distinctly different tropical and popcorn groups, a sweet-Northern flint group and a flint group sub-structured in Italian and European flint (Pyrenees–Galicia and Lacaune) groups. Interestingly, we identified several selective sweeps between dent, flint, and tropical inbred lines that co-localized with SNPs associated with flowering time variation. The joint analysis of collections by GBS offers opportunities for a global diversity analysis of maize inbred lines.

     

Nucleotide Diversity of Maize ZmBT1 Gene and Association with Starch Physicochemical Properties

Cereal Brittle1 protein has been demonstrated to be involved in the ADP-Glc transport into endosperm plastids, and plays vital roles in the biosynthesis of starch. In this study, the genomic sequences of the ZmBT1 gene in 80 elite maize inbred lines were obtained, and the nucleotide polymorphisms and haplotype diversity were detected. A total of 30 variants, including 22 SNPs and 8 indels, were detected from the full sequences of this gene. Among these polymorphic sites, 9 SNPs and 2 indels were found to be located in the coding region. The polymorphisms of CDS sequences classified the maize ZmBT1 gene into 6 haplotypes, which encode 6 different ZmBT1 proteins. Neutrality tests revealed a decrease in population size and/or balancing selection on the maize ZmBT1 locus. To detect the association between sequence variations of this gene and the starch physicochemical properties, 7 pasting and 4 gelatinization traits of starch were measured for the tested inbred lines using rapid visco analyzer (RVA) and differential scanning calorimeter (DSC), respectively. The result of association analysis revealed that an indel in the coding region was significantly associated with the phenotypic variation of starch gelatinization enthalpy.     

Identification of functional genetic variations underlying drought tolerance in maize using SNP markers

Single nucleotide polymorphism (SNP) is a common form of genetic variation and popularly exists in maize genome. An Illumina GoldenGate assay with 1 536 SNP markers was used to genotype maize inbred lines and identified the functional genetic variations underlying drought tolerance by association analysis. Across 80 lines, 1 006 polymorphic SNPs (65.5% of the total) in the assay with good call quality were used to estimate the pattern of genetic diversity, population structure, and familial relatedness. The analysis showed the best number of fixed subgroups was six, which was consistent with their original sources and results using only simple sequence repeat markers. Pairwise linkage disequilibrium (LD) and association mapping with phenotypic traits investigated under water-stressed and well-watered regimes showed rapid LD decline within 100-500 kb along the physical distance of each chromosome, and that 29 SNPs were associated with at least two phenotypic traits in one or more environments, which were related to drought-tolerant or drought-responsive genes. These drought-tolerant SNPs could be converted into functional markers and then used for maize improvement by marker-assisted selection.     

Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels

Association mapping of sequence polymorphisms underlying the phenotypic variability of quantitative agronomical traits is now a widely used method in plant genetics. However, due to the common presence of a complex genetic structure within the plant diversity panels, spurious associations are expected to be highly frequent. Several methods have thus been suggested to control for panel structure. They mainly rely on ad hoc criteria for selecting the number of ancestral groups; which is often not evident for the complex panels that are commonly used in maize. It was thus necessary to evaluate the effect of the selected structure models on the association mapping results. A real maize data set (342 maize inbred lines and 12,000 SNPs) was used for this study. The panel structure was estimated using both Bayesian and dimensional reduction methods, considering an increasing number of ancestral groups. Effect on association tests depends in particular on the number of ancestral groups and on the trait analyzed. The results also show that using a high number of ancestral groups leads to an over-corrected model in which all causal loci vanish. Finally the results of all models tested were combined in a meta-analysis approach. In this way, robust associations were highlighted for each analyzed trait.