Genotype imputation for Han Chinese population using Haplotype Reference Consortium as reference

2018 ◽  
Vol 137 (6-7) ◽  
pp. 431-436 ◽  
Author(s):  
Yuan Lin ◽  
Lu Liu ◽  
Sen Yang ◽  
Yun Li ◽  
Dongxin Lin ◽  
...  
2019 ◽  
Vol 21 (5) ◽  
pp. 1806-1817 ◽  
Author(s):  
Wei-Yang Bai ◽  
Xiao-Wei Zhu ◽  
Pei-Kuan Cong ◽  
Xue-Jun Zhang ◽  
J Brent Richards ◽  
...  

Abstract Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that imputation accuracy could always benefit from the increased panel size when the reference panel was population specific, the results brought two new thoughts. First, when the haplotype size of the reference panel was fixed, the imputation accuracy of common and low-frequency variants (Minor Allele Frequency (MAF) > 0.5%) decreased while the population diversity of the reference panel increased, but for rare variants (MAF < 0.5%), a small fraction of diversity in panel could improve imputation accuracy. Second, when the haplotype size of the reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF > 5%) for the European population could always benefit from the expanding sample size. However, for the Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of an extra diverse sample (8–21%). In addition, we evaluated the imputation performances in the existing reference panels, such as the Haplotype Reference Consortium (HRC), 1000 Genomes Project Phase 3 and the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE). For the European population, the HRC panel showed the best performance in our analysis. For the Han Chinese population, we proposed an optimum imputation reference panel constituent ratio if researchers would like to customize their own sequenced reference panel, but a high-quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome; a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).


2019 ◽  
Vol 48 (D1) ◽  
pp. D971-D976 ◽  
Author(s):  
Yang Gao ◽  
Chao Zhang ◽  
Liyun Yuan ◽  
YunChao Ling ◽  
Xiaoji Wang ◽  
...  

Abstract As the largest ethnic group in the world, the Han Chinese population is nonetheless underrepresented in global efforts to catalogue the genomic variability of natural populations. Here, we developed the PGG.Han, a population genome database to serve as the central repository for the genomic data of the Han Chinese Genome Initiative (Phase I). In its current version, the PGG.Han archives whole-genome sequences or high-density genome-wide single-nucleotide variants (SNVs) of 114 783 Han Chinese individuals (a.k.a. the Han100K), representing geographical sub-populations covering 33 of the 34 administrative divisions of China, as well as Singapore. The PGG.Han provides: (i) an interactive interface for visualization of the fine-scale genetic structure of the Han Chinese population; (ii) genome-wide allele frequencies of hierarchical sub-populations; (iii) ancestry inference for individual samples and controlling population stratification based on nested ancestry informative markers (AIMs) panels; (iv) population-structure-aware shared control data for genotype-phenotype association studies (e.g. GWASs) and (v) a Han-Chinese-specific reference panel for genotype imputation. Computational tools are implemented into the PGG.Han, and an online user-friendly interface is provided for data analysis and results visualization. The PGG.Han database is freely accessible via http://www.pgghan.org or https://www.hanchinesegenomes.org.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Juan Xia ◽  
Chunyue Guo ◽  
Kuo Liu ◽  
Yunyi Xie ◽  
Han Cao ◽  
...  

Abstract Background There is a well-documented empirical relationship between lipoprotein (a) [Lp(a)] and cardiovascular disease (CVD); however, causal evidence, especially from the Chinese population, is lacking. Therefore, this study aims to estimate the causal association between variants in genes affecting Lp(a) concentrations and CVD in people of Han Chinese ethnicity. Methods Two-sample Mendelian randomization analysis was used to assess the causal effect of Lp(a) concentrations on the risk of CVD. Summary statistics for Lp(a) variants were obtained from 1256 individuals in the Cohort Study on Chronic Disease of Communities Natural Population in Beijing, Tianjin and Hebei. Data on associations between single-nucleotide polymorphisms (SNPs) and CVD were obtained from recently published genome-wide association studies. Results Thirteen SNPs associated with Lp(a) levels in the Han Chinese population were used as instrumental variables. Genetically elevated Lp(a) was inversely associated with the risk of atrial fibrillation [odds ratio (OR), 0.94; 95% confidence interval (95%CI), 0.901–0.987; P = 0.012)], the risk of arrhythmia (OR, 0.96; 95%CI, 0.941–0.990; P = 0.005), the left ventricular mass index (OR, 0.97; 95%CI, 0.949–1.000; P = 0.048), and the left ventricular internal dimension in diastole (OR, 0.97; 95%CI, 0.950–0.997; P = 0.028) according to the inverse-variance weighted method. No significant association was observed for congestive heart failure (OR, 0.99; 95% CI, 0.950–1.038; P = 0.766), ischemic stroke (OR, 1.01; 95%CI, 0.981–1.046; P = 0.422), and left ventricular internal dimension in systole (OR, 0.98; 95%CI, 0.960–1.009; P = 0.214). Conclusions This study provided evidence that genetically elevated Lp(a) was inversely associated with atrial fibrillation, arrhythmia, the left ventricular mass index and the left ventricular internal dimension in diastole, but not with congestive heart failure, ischemic stroke, and the left ventricular internal dimension in systole in the Han Chinese population. Further research is needed to identify the mechanism underlying these results and determine whether genetically elevated Lp(a) increases the risk of coronary heart disease or other CVD subtypes.


2020 ◽  
Vol 23 (8) ◽  
pp. 1050-1056
Author(s):  
Tianyun Zhao ◽  
Chi Ma ◽  
Wei Wang ◽  
Bin Zhao ◽  
Baopin Xie ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanmei Ruan ◽  
Jinwei Zhang ◽  
Shiqi Mai ◽  
Wenfeng Zeng ◽  
Lili Huang ◽  
...  

AbstractGenetic factors and gene-environment interaction may play an important role in the development of noise induced hearing loss (NIHL). 191 cases and 191 controls were selected by case–control study. Among them, case groups were screened from workers exposed to noise in binaural high-frequency hearing thresholds greater than 25 dB (A). Workers with hearing thresholds ≤ 25 dB (A) in any binaural frequency band were selected to the control group, based on matching factors such as age, exposure time to noise, and operating position. The blood samples from two groups of workers were subjected to DNA extraction and SNP sequencing of CASP3 and CASP7 genes using the polymerase chain reaction ligase detection reaction method. Conditional logistic regression correction was used to analyze the genetic variation associated with susceptibility to NIHL. There was an association between rs2227310 and rs4353229 of the CASP7 gene and the risk of NIHL. Compared with the GG genotype, the CC genotype of rs2227310 reduced the risk of NIHL. Compared with CC genotype, the TT genotype of rs4353229 reduced the risk of NIHL. Workers carrying the rs2227310GG and rs4353229CC genotype had an increased risk of NIHL compared to workers without any high-risk genotype. There were additive interaction and multiplication interaction between CASP7rs2227310 and CNE, and the same interaction between CASP7rs4353229 and CNE. The interaction between the CASP7 gene and CNE significantly increased the risk of NIHL. The genetic polymorphisms of CASP7rs2227310GG and CASP7rs4353229CC were associated with an increased risk of NIHL in Han Chinese population and have the potential to act as biomarkers for noise-exposed workers.


2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guangsen Hou ◽  
Yong Tang ◽  
Luping Ren ◽  
Yunpeng Guan ◽  
Xiaoyu Hou ◽  
...  

Background. Our aim was to investigate the association between the genetics of the angiopoietin protein-like 8 (ANGPTL8) rs2278426 (C/T) polymorphism with prediabetes (pre-DM) and type 2 diabetes (T2DM) in a Han Chinese population in Hebei Province, China. Methods. We enrolled 1,460 participants into this case-control study: healthy controls, n = 524; pre-DM, n = 460; and T2DM: n = 460. Ligase assays on blood samples from all participants were used to identify polymorphisms. Differences in genotype and allele distributions were compared by the chi-square test and one-way analysis of variance, and a post hoc pairwise analysis was performed using the Bonferroni test. The logistic regression technique was adjusted for age, sex, and body mass index. Results. The frequency of the TT (10.9%) genotype was significantly higher in pre-DM patients than in controls (odds ratio [OR] = 1.696, 95% confidence interval [CI] = 1.026–2.802, P = 0.039 ). In the T2DM group, the CT (48%) and TT (15%) genotypes were significantly higher compared with those in the control group (CT : OR = 1.384, 95% CI = 1.013–1.890, P = 0.041 ; TT : OR = 2.530, 95% CI = 1.476–4.334, P = 0.001 ). The frequency of the T allele was significantly higher in the pre-DM (32.8%) and T2DM (39%) groups compared with the control group (26.9%) and was significantly associated with an increased risk of pre-DM (OR = 1.253, 95% CI = 1.017–1.544, P = 0.034 ) and T2DM (OR = 1.518, 95% CI = 1.214–1.897, P = 0.001 ). Furthermore, insulin levels in the pre-DM and T2DM groups were significantly decreased in those with the TT genotype compared with the CC and CT genotypes. Conclusion. ANGPTL8 rs2278426 may be involved in the mechanism of insulin secretion and could lead to an increased risk of pre-DM and T2DM.


2013 ◽  
Vol 15 (2) ◽  
pp. 279-287 ◽  
Author(s):  
Xiao-Ying Ma ◽  
Jin-Tai Yu ◽  
Wei Wang ◽  
Hui-Fu Wang ◽  
Qiu-Yan Liu ◽  
...  

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