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The root‐knot nematode (RKN) species Meloidogyne javanica(MJ) causes substantial root damage and yield loss in susceptible soybean [Glycine max(L.) Merr.] cultivars. In this study, a genome wide association study (GWAS) was undertaken to identify genomic regions controlling RKN resistance in a soybean association‐mapping panel, using single nucleotide polymorphism (SNP) markers and haplotype information. This study was carried out with a sample (n= 124) from the Coodetec soybean gene bank, including some commercial Brazilian varieties. For the phenotypic evaluations MJ inoculum was obtained from roots of cultivar CD 206, a highly susceptible soybean used to maintain MJ for studies at Coodetec. The cultivars CD 208 and BRS CONQUISTA were used as resistant checks while CD 206, R7, and Nidera NA 5909 RG were used as susceptible checks and all 124 lines were genotyped with 6,000 SNP markers. MJ resistance determinants in the soybean panel were linked to SNPs with significant effects, with nine SNPs distributed along chromosomes 7, 13, and 20. The observed average MJ resistance in the group of soybean entries with haplotype GCT, ACC, ACT, and GCC in the markers Gm20_44446670_G_A, Gm13_29739984_C_A and Gm07_8166605_T_C, respectively, represents moderately resistant genotypes. Gm13_29739984_C_A and Gm07_8166605_T_C discriminated resistant and susceptible cultivars better than the marker Gm20_44446670_G_A. These results are particularly promising for soybean‐breeding programs, by not only identifying new resistance‐associated haplotypes, but also suggesting new genetic mechanisms in RKN‐resistance sources.

The sporophytic homomorphic self‐incompatibility system advanced the greatest diversity within populations.The phenotypic correlation network facilitates rapid observation of the relationships among groups of variables related to vegetative vigor, incidence of leaf diseases, and physicochemical properties of passion fruit.The correlation networks associated with the relative contribution of the variable can help in the selection of important variables. Sour passion fruit (Passiflora edulisSims) is a highly suitable crop for small farmers because of its high market value and short time to maturity. However, passion fruit breeding programs need to provide cultivars to producers with quality, disease resistance, and production improvements. The objective of this study was to assess the genetic diversity among and within improved populations of sour passion fruit (SPF) using predicted genetic values, select the most divergent accessions, and evaluate the relationships among groups of traits of vegetative vigor, incidence of leaf diseases, and physicochemical properties of fruits. Twenty‐three genotypes were selected and divided into five populations with variable numbers of accessions. Phenotypic data were transformed into genotypic values using mixed model restricted maximum likelihood‐best linear unbiased prediction. We tested the hypothesis that there is differentiation among populations and between their components and the accessions. Graphic dispersion of the genotypes around the centroids of five populations was performed. The Tocher method was used to group the accessions. The relative importance of the variables and correlation networks was evaluated. Populations showed genetic variability; the hybrid combinations involving the population I accessions 1 (B1 29 PL1), 2 (B1 41 PL3), 4 (B2 9 PL 3), 5 (B2 37 PL3), 10 (G1 B1 9), and 19 (BRS GA1) can be exploited in breeding programs to improve the quality of SPF. The correlation network facilitates the observation of the relationships among groups of traits and helps in the selection of variables when associated with the relative contribution.