Microsoft excel

In the summer of 2018 and 2019, 289 ILs were planted in Dangtu, Anhui province (E1) and Shangqiu, Henan province (E2), respectively, based on a randomized complete block design with two replications. Then a randomized complete block design with three replicates was applied, 289 ILs were planted in Sanya of Hainan province (2019 winter) (E3), Shangqiu of Henan province (2020 summer) (E4), and Shihezi of Xinjiang province (2020 summer) (E5). For field experiments under five environments, the recipient TM-1 and donor SXY 1 were used as control. Twenty-five bolls from each ILs in the middle of each row were hand-harvested from the internal middle parts of the plants. The yield-related traits, i.e., boll weight (BW), lint percent (LP), seed index (SI), number of bolls per plant (BN), number of fruit branches (FBN), and plant height (PH) were tested. All fiber samples from the five different environments were ginned by a roller. The fiber qualities were evaluated by high volume instrument for 2.5% fiber length (FL, mm), fiber strength (FS, cN/tex), micronaire (MIC), fiber elongation (FE, %), and fiber uniformity (FU, %). Basic statistical parameters, correlation coefficients, and phenotypic variation were performed using Microsoft Excel and SPSS 20.0 (SPSS, Chicago, IL, USA). The heritability of all the traits was calculated using QTL IciMapping 4.2 software.

All statistical analysis was conducted using Microsoft Excel and SPSS 16.0 (SPSS, Chicago IL USA). Allele frequency of each SNP in the control subjects was analyzed using the exact test to determine whether the four SNPs departed from Hardy-Weinberg equilibrium (HWE). We used Chi-square test/Fisher's exact test to compare the differences in SNP allele and genotype distribution between GC cases and controls [27 (link)]. Then the association between each SNP and GC was assessed under three genetic models: dominant, recessive and additive model using PLINK software, a web-based program available at http://pngu.mgh.harvard.edu/purcell/plink/. Finally, the SHEsis software platform (http://www.nhgg.org/analysis) and Haploview software package (version 4.2) were used to analyze and visualize patterns of linkage disequilibrium (LD) and haplotype construction [28 (link)]. The odd ratio (OR) and 95% confidence intervals (CI), calculated by using unconditional logistic regression analysis with adjustments for age and gender, were used to assess the association between each SNP and the risk of GC [29 ]. Two-sided P≤0.05 was considered statistically significant for all statistical tests.

For cases and controls, we analyzed the gender distribution using Pearson's χ² test and the age distribution by Welch's t test. We analyzed the genotype frequencies in the controls with Fisher's exact test to determine whether the five SNPs departed from Hardy-Weinberg equilibrium. The differences in the SNP allele and genotype distributions between patients and controls were evaluated with Chi-squared test/Fisher's exact tests. Genetic model analyses (Dominant, Recessive and Additive) were performed with PLINK software to assess the significance of the SNPs. The associations of the SNPs with the risk of esophageal cancer were estimated from the odds ratios (ORs) and 95% confidence intervals (CIs), which were determined by unconditional logistic regression and adjusted for age and gender [32 (link)]. The P-values reported are two-sided, and values of P < 0.05 were considered to be statistically significant. We used the Haploview software package (version 4.2) platform for analyses of pairwise linkage disequilibrium (LD) and haplotype structure [10 (link)]. All statistical analyses were performed with Microsoft Excel and SPSS 19.0 (SPSS, Chicago, IL, USA).