The aim of this study was to examine the relationship between genetic polymorphisms in DNA ligase 1 (LIG1) and non-small cell lung cancer (NSCLC) susceptibility and radiosensitivity in a Chinese population. This was a case-control study that included 352 NSCLC patients and 448 healthy controls. Polymerase chain reaction-restriction fragment length polymorphism analysis was conducted to detect HaeIII polymorphisms in exon 6 of the LIG1 gene in this popula-tion. This information was used to observe the effects of radiation in pa-tients with different genotypes in order to determine the genotypes as-sociated with radiosensitivity. The CC genotype and C allele frequency were significantly higher in the NSCLC group than in the control group (P = 0.012 and P = 0.023, respectively). The relative risk of experienc-ing NSCLC was 2.55 [95% confidence interval (CI), 1.12-3.98] for CC homozygous patients and 0.87 (95%CI, 0.46-1.88) for AA homozygous patients. Analysis of LIG1 genetic polymorphisms and radiosensitiv-ity of NSCLC patients showed that AA homozygous patients were sig-nificantly more radiosensitive than the control group (AA vs AC, P = 0.014; AA vs CC, P < 0.001; AC vs CC, P = 0.023). Therefore, the LIG1 CC genotype was associated with susceptibility to NSCLC, and the AA genotype demonstrated increased radiosensitivity compared to the AC and CC genotypes.
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http://dx.doi.org/10.4238/2015.June.26.14 | DOI Listing |
Respir Res
January 2025
Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Center for Chronic Disease Prevention and Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, influenced by both environmental and genetic factors. Single nucleotide polymorphism (SNP) in the human genome may influence the risk of developing COPD and the response to treatment. We assessed the effects of gene polymorphism of inflammatory and immune-active factors and gene-environment interaction on risk of COPD in middle-aged and older Chinese individuals.
View Article and Find Full Text PDFNat Genet
January 2025
Frontiers Science Center for Molecular Design Breeding (MOE); State Key Laboratory of Animal Biotech Breeding; College of Animal Science and Technology, China Agricultural University, Beijing, China.
Ongoing efforts to improve sheep reference genome assemblies still leave many gaps and incomplete regions, resulting in a few common failures and errors in genomic studies. Here, we report a 2.85-Gb gap-free telomere-to-telomere genome of a ram (T2T-sheep1.
View Article and Find Full Text PDFSci Rep
January 2025
Plant Science Research Unit, USDA-ARS, St. Paul, MN, USA.
Plant genebanks contain large numbers of germplasm accessions that likely harbor useful alleles or genes absent in commercial plant breeding programs. Broadening the genetic base of commercial alfalfa germplasm with these valuable genetic variations can be achieved by screening the extensive genetic diversity in germplasm collections and enabling maximal recombination among selected genotypes. In this study, we assessed the genetic diversity and differentiation of germplasm pools selected in northern U.
View Article and Find Full Text PDFNat Commun
January 2025
Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, MD, USA.
The sex chromosomes contain complex, important genes impacting medical phenotypes, but differ from the autosomes in their ploidy and large repetitive regions. To enable technology developers along with research and clinical laboratories to evaluate variant detection on male sex chromosomes X and Y, we create a small variant benchmark set with 111,725 variants for the Genome in a Bottle HG002 reference material. We develop an active evaluation approach to demonstrate the benchmark set reliably identifies errors in challenging genomic regions and across short and long read callsets.
View Article and Find Full Text PDFPlant Genome
March 2025
USDA-ARS Southeast Area, Plant Science Research, Raleigh, North Carolina, USA.
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions.
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