2013;9(5):e1003477. The 247 inbred lines were genotyped using three technologies: a maize Illumina Infinium HD 50 K array [3], a maize Affymetrix Axiom 600 K array [4], and Genotyping-By-Sequencing [2, 37]. Gouesnard B, Negro S, Laffray A, Glaubitz J, Melchinger A, Revilla P, Moreno-Gonzalez J, Madur D, Combes V, Tollon-Cordet C, et al. The distribution of the SNPs along the genome was denser in the telomeres for the GBS and in the peri-centromeric regions for the 600 K, whereas the 50 K exhibited a more uniform distribution (Fig. The challenge to collecting so much information is that it requires a scalable bioinformatics infrastructure for analysis and data storage. GBS5 displayed a slightly better concordance rate than GBS2 (96.25% vs 96.04%) and predicted heterozygotes with a higher quality than GBS4. Remington DL, Thornsberry JM, Matsuoka Y, Wilson LM, Whitt SR, Doebley J, Kresovich S, Goodman MM, Buckler ES. Proportion of low and high recombination regions, recombination rate and percentage of QTLs located in these regions for the three traits. Interestingly, some pairs of loci located on different chromosomes or very distant on a same chromosome remained in high LD despite correction for genetic structure and kinship (Additional file 6: Figure S6). The three first PCoA axes explained 12.9, 15.6 and 16.3% of the variability for the GBS, 50 K and 600 K, respectively (Fig. Butler DG, Cullis BR, Gilmour AR, Gogel BJ. Third, we considered an average LD extent estimated separately in the high and low recombinogenic genomic regions. Comparison of genotyping data between 50K and 600K arrays, and GBS. [32] highlighted the interest of the GBS for (i) deciphering and comparing the genetic diversity of the inbred lines in seedbanks and (ii) identifying QTLs by GWAS for kernel colour, sweet corn and flowering time. Sequencing: Maximum flexibility but expensive. PCR serves as an integral step in many laboratory processes, but it also can be used on its own to reliably genotype individuals (detect particular differences between their DNA codes) for a small number of DNA variants. We use cookies to help provide and enhance our service and tailor content and ads. To further decipher the GWAS differences between 600 K and GBS, we used a resampling approach to explore the interplay between (i) MAF distribution and (ii) SNP distribution along the genome, at different SNP densities. Markers have MAF above 5%. Le Gouis J, Bordes J, Ravel C, Heumez E, Faure S, Praud S, Galic N, Remoué C, Balfourier F, Allard V, et al. Genotype of individual i at marker l (Gi,l) was coded as 0 (the homozygote for an arbitrarily chosen allele), 0.5 (heterozygote), or 1 (the other homozygote). Imputation can produce genotyping errors that can cause false associations and introduce bias in diversity analysis [33]. Boxplot were drawn on 100 sets of 50 000 to 250 000 markers sampled according to different MAF distributions (A, B) and different SNP distributions along the genome (C, D). Human Resistin gene: molecular scanning and evaluation of association with insulin sensitivity and type 2 diabetes in Caucasians. GQE – Le Moulon, INRA, Univ. Nb” indicates the number of environment in which a QTL was detected. Consistent with MAF distribution, the average gene diversity (He) was lower for GBS (0.27) than for arrays (0.35 and 0.34 for the 50 K and 600 K arrays, respectively). SSN performed genotyping data quality control, imputation and genetic analyses. The SNP call rate was higher for the SNP-arrays (average values of 96 and > 99% for the 50 K and 600 K, respectively), than for the GBS (37% for the direct reads). We are grateful to Chris-Carolin Schön (TUM) for providing an early access to the Affymetrix Axiom 600 K array and Edward Buckler (USDA) for providing genotyping using GBS. Interpretation of data and writing the manuscript were done in the framework of AMAIZNG and DROPS projects. Classical LD measurement r2 between loci were represented within triangle below the diagonal. Genetic diversity, linkage disequilibrium and power of a large grapevine (Vitis vinifera L) diversity panel newly designed for association studies. The comparison of the genotyping and imputation quality between the 50 K/GBS, 50 K/600 K and 600 K/GBS was done on 5,336 and 24,286 and 26,154 common markers, respectively. Narrow sense heritability (h2) and variance components (Vg, genetic variance; Ve, residual variance). We selected only markers having MAF above 5%. ZmMADS69 functions as a flowering activator through the ZmRap2.7-ZCN8 regulatory module and contributes to maize flowering time adaptation. Theor Popul Biol. 2008;32(4):361–9. Vertical dotted gray lines indicate limits of centromeric regions. Structure of linkage disequilibrium in plants. 2010;42:348. 5c). ascertainment bias). We used the global LD decay estimated for these large chromosomal regions rather than local LD extent (i) to avoid bias due to SNP sampling within small genomic regions, (ii) to reduce computational time, and (iii) to limit the impact of possible local error in genome assembly. For A, B, C and D, modalities indicated as “Random” in x axis correspond to random sample of SNP. The magnitude of the effect of causal polymorphism in the estimation of these intervals, which is well established for linkage mapping [47], should be explored further. These last two factors are substantial in several cultivated species such as maize [14] and grapevine [15], and their impact on LD can be statistically evaluated [16]. Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. In GBS4, genotype imputation by Beagle was performed on Cornell imputed data after replacing the heterozygous genotypes into missing data. 2014;127(12):2679–93. Darvasi A, Weinreb A, Minke V, Weller JI, Soller M. Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. We hypothesized that significant SNPs with overlapping LD windows at r2K = 0.1 captured the same causal polymorphism and were therefore a single and unique QTL. Surprisingly, increasing the SNP number by combining the markers from the arrays and GBS did not strongly increase the genome coverage as compared to the 600 K, regardless of the threshold for LD extent (Fig. Therefore, higher marker densities are desirable because the maize genome size is large (2.4 Gb), the level of diversity is high (more than one substitution per hundred nucleotides), and LD extent is low [27]. Intraspecific variation of recombination rate in maize. We are grateful to key partners from the field: Pierre Dubreuil, Cécile Richard, Jérémy Lopez (Biogemma), Tamás Spitkó (MTA ATK), Therese Welz (KWS), Franco Tanzi, Ferenc Racz, Vincent Schlegel (Syngenta) and Maria Angela Canè (UNIBO). Genotyping-by-sequencing scored SNPs (GBS-scored SNPs) provides a large number of markers, albeit with high rates of missing data. Hufford MB, Lubinksy P, Pyhäjärvi T, Devengenzo MT, Ellstrand NC, Ross-Ibarra J. These loci were located in centromeric and peri-centromeric regions that displayed low recombination rate, suggesting that this pattern was due to variation of recombination rate along the chromosome. Charlesworth D, Willis JH. Forty-seven percent of the 2Mbp intervals in high recombination regions were better covered by the 600 K than the GBS against only 1%, which were better covered by GBS than 600 K. When exploring smaller window sizes (20, 100, 500 kb), the number of intervals better covered by 600 K than GBS decreased strongly when the intervals were shortened (17.1% of 20 kbp-intervals vs 47.1% of 2 Mbp-intervals). The number of SNPs associated with each QTL varied according to the technology (on average 3.7, 7.6, 3.4 and 6.6 significant SNPs for the 50 K, 600 K, GBS, and the combined technologies, respectively). Moreover, this QTL showed a colocalization with QTL74 for grain yield in 5 environments and QTL30 for plant height in 1 environment suggesting a pleiotropic effect. Linkage disequilibrium based approach to delineate a physical window around each SNP, exemplified with chromosome 3. Scripts to group associated SNPs into QTLs based on two LD approaches (LD_win and LD_adj) are available on request. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, et al. In order to evaluate the effect of kinship and the genetic structure on linkage disequilibrium (LD), we studied genome-wide LD between 29,257 PANZEA markers from the 50 K within and between chromosomes before and after taking into account the kinship (K_Freq estimated from the 50 K), structure (Number of groups = 4) or both (Additional file 6: Figure S6). 1% and ca. Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C. Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Several QTLs identified by LD_win in our study correspond to regions previously identified: in particular, six regions associated with female flowering time [26] and 30 regions associated with different traits in the Cornfed dent panel [11]. As expected, mean IBD was close for the three technologies (K_Freq: − 0.004). “Unpl” after chromosome 10 refers to unplaced SNPs, in an arbitrary order. Lines were ordered according to contributions observed for the 50K. (C) Correlation of IBD between the three technologies after removing the excess of rare alleles in the GBS to have the same distribution of MAF as in the 50K and the 600K.
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