Owing to the high throughput and low cost, next generation sequencing has attracted much attention for SNP genotyping application for researchers. Here, we introduce a new method based on three-round multiplex PCR to precisely genotype SNPs with next generation sequencing. This method can as much as possible consume the equivalent amount of each pair of specific primers to largely eliminate the amplification discrepancy between different loci. After the PCR amplification, the products can be directly subjected to next generation sequencing platform. We simultaneously amplified 37 SNP loci of 757 samples and sequenced all amplicons on ion torrent PGM platform; 90.5 % of the target SNP loci were accurately genotyped (at least 15×) and 90.4 % amplicons had uniform coverage with a variation less than 50-fold. Ligase detection reaction (LDR) was performed to genotype the 19 SNP loci (as part of the 37 SNP loci) with 91 samples randomly selected from the 757 samples, and 99.5 % genotyping data were consistent with the next generation sequencing results. Our results demonstrate that three-round PCR coupled with next generation sequencing is an efficient and economical genotyping approach. Graphical Abstract The schematic diagram of three-round PCR.

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There are currently only two methods (the within-growing season method and the inter-growing season method) used to analyse the normalized difference vegetation index (NDVI)–climate relationship at the monthly time scale. What are the differences between the two methods, and why do they exist? Which method is more suitable for the analysis of the relationship between them? In this study, after obtaining NDVI values (GIMMS NDVI3g) near meteorological stations and meteorological data of Inner Mongolian grasslands from 1982 to 2015, we analysed temporal changes in NDVI and climate factors, and explored the difference in Pearson correlation coefficients (R) between them via the above two analysis methods and analysed the change in R between them at multiple time scales. The research results indicated that: (1) NDVI was affected by temperature and precipitation in the area, showing periodic changes, (2) NDVI had a high value of R with climate factors in the within-growing season, while the significant correlation between them was different in different months in the inter-growing season, (3) with the increase in time series, the value of R between NDVI and climate factors showed a trend of increase in the within-growing season, while the value of R between NDVI and precipitation decreased, but then tended toward stability in the inter-growing season, and (4) when exploring the NDVI–climate relationship, we should first analyse the types of climate in the region to avoid the impacts of rain and heat occurring during the same period, and the inter-growing season method is more suitable for the analysis of the relationship between them.