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.

Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R 2 = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R 2 >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement.

SummaryImprovement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear.We used 10 different recombination inbred line (RIL) populations genotyped with highdensity markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits.Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor-or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation.The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.

Highlights CXCR5 + CD8 + T cells produce higher levels of HBV-specific IFN-c and IL-21 than CXCR5 À CD8 + T cells. CXCR5 + CD8 + T cells retain functional capacity in inhibiting HBV replication and supporting B cell antibody production. PD1 blockade and exogenous IL-21 enhance production of IFN-c from CXCR5 + CD8 + T cells. High expression of intrahepatic CXCL13 facilitates CXCR5 +-CD8 + T cell recruitment and promotes immune control of HBV.