Mapping QTLs and meta-QTLs for two inflorescence architecture traits in multiple maize populations under different watering environments

Drought significantly affects the architectural development of maize inflorescence, which leads to massive losses in grain yield. However, the genetic mechanism for traits involved in inflorescence architecture in different watering environments, remains poorly understood in maize. In this study, 19 QTLs for tassel primary branch number (TBN) and ear number per plant (EN) were detected in 2 F_2:3 populations under both well-watered and water-stressed environments by single environment mapping with composite interval mapping (CIM); 11/19 QTLs were detected under water-stressed environments. Moreover, 21 QTLs were identified in the 2 F_2:3 populations by joint analysis of all environments with a mixed linear model based on composite interval mapping (MCIM), 11 QTLs were involved in QTL × environment interactions, seven epistatic interactions were identified with additive by additive/dominance effects. Remarkably, 12 stable QTLs (sQTLs) were simultaneously detected by single environment mapping with CIM and joint analysis through MCIM, which were concentrated in ten bins across the chromosomes: 1.05_1.07, 1.08_1.10, 2.01_2.04, 3.01, 4.06, 4.09, 5.06_5.07, 6.05, 7.00, and 7.04 regions. Twenty meta-QTLs (mQTLs) were detected across 19 populations under 51 watering environments using a meta-analysis, and 34 candidate genes were predicted in corresponding mQTLs regions to be involved in the regulation of inflorescence development and drought resistance. Therefore, these results provide valuable information for finding quantitative trait genes and to reveal the genetic mechanisms responsible for TBN and EN under different watering environments. Furthermore, alleles for TBN and EN provide useful targets for marker-assisted selection to generate high-yielding maize varieties.     

Methylation of KvDMR1 involved in regulating the imprinting of CDKN1C gene in cattle

The CDKN1C gene encodes a cyclin‐dependent kinase inhibitor and is one of the key genes involved in the development of Beckwith–Wiedemann syndrome and cancer. In this study, using a direct sequencing approach based on a single nucleotide polymorphism (SNP) at genomic DNA and cDNA levels, we show that CDKN1C exhibits monoallelic expression in all seven studied organs (heart, liver, spleen, lung, kidney, muscle and subcutaneous fat) in cattle. To investigate how methylation regulates imprinting of CDKN1C in cattle, allele‐specific methylation patterns in two putative differential methylation regions (DMRs), the CDKN1C DMR and KvDMR1, were analyzed in three tissues (liver, spleen and lung) using bisulfite sequencing PCR. Our results show that in the CDKN1C DMR both parental alleles were unmethylated in all three analyzed tissues. In contrast, KvDMR1 was differentially methylated between the two parental alleles in the same tissues. Statistical analysis showed that there is a significant difference in the methylation level between the two parental alleles (P < 0.01), confirming that this region is the DMR of KvDMR1 and that it may be correlated with CDKN1C imprinting.     

Genetic Variants Associated with Lipid Profiles in Chinese Patients with Type 2 Diabetes

Dyslipidemia is a strong risk factor for cardiovascular disease among patients with type 2 diabetes (T2D). The aim of this study was to identify lipid-related genetic variants in T2D patients of Han Chinese ancestry. Among 4,908 Chinese T2D patients who were not taking lipid-lowering medications, single nucleotide polymorphisms (SNPs) in seven genes previously found to be associated with lipid traits in genome-wide association studies conducted in populations of European ancestry (ABCA1, GCKR, BAZ1B, TOMM40, DOCK7, HNF1A, and HNF4A) were genotyped. After adjusting for multiple covariates, SNPs in ABCA1, GCKR, BAZ1B, TOMM40, and HNF1A were identified as significantly associated with triglyceride levels in T2D patients (P < 0.05). The associations between the SNPs in ABCA1 (rs3890182), GCKR (rs780094), and BAZ1B (rs2240466) remained significant even after correction for multiple testing (P = 8.85×10⁻³, 7.88×10⁻⁷, and 2.03×10⁻⁶, respectively). BAZ1B (rs2240466) also was associated with the total cholesterol level (P = 4.75×10⁻²). In addition, SNP rs157580 in TOMM40 was associated with the low-density lipoprotein cholesterol level (P = 6.94×10⁻³). Our findings confirm that lipid-related genetic loci are associated with lipid profiles in Chinese patients with type 2 diabetes.