【广东会GDH基因检测】临床科研服务:GWAS课题中的统计分析
1、GWAS分析中的加法模型Statistical analysis. GWAS analysis was performed using the additive model by logistic regression analysis. Population structure was evalsuated by PCA in the software package EIGENSTRAT 3.0 (ref. 20). We used PLINK 1.07 for general
statistical analysis. SNPs for the first stage of replication were selected based on a separate meta-analysis for every SNP from the Nanjing and the Beijing GWAS.
Our default meta-analysis used a fixed-effect model with inverse variance weighting and a calculation of Cochran’s Q statistic and the I2 statistic for heterogeneity22. When there was no indication of heterogeneity for a SNP (P for Q >0.05), the fixed-effect model was kept in place. When heterogeneity was present (P for Q ≤ 0.05), we adopted a random-effects model (DerSimonian-Laird) for that SNP. The Manhattan plot of −log10 P was generated using R 2.11.1. Weused MACH 1.0 software to impute ungenotyped SNPs using the LD information from the HapMap 3 database (CHB and JPT as reference set, released Feb.2009). The chromosome region was plotted using an online tool, LocusZoom 1.1. PCA identified one significant (P < 0.05) eigenvector, which we included in the logistic regression analysis with the other covariates of age, gender, and smoking and drinking status for both the GWAS and the combined analysis.
The chi-squared (χ2)-based Cochran’s Q statistic was also calculated to test for heterogeneity between groups in stratified analysis. Gene-gene and geneenvironment multiplicative interactions were tested by a general logistic regression model using the equation
Y b = + 0 1b A × + b B 2 3 × + b A × × ( B e ) + where Y is the logit of case-control status, A and B are factors (SNP or environmental), b0 is constant, b1 and b2 are the main effects of factor A and B,respectively, and b3 is the interaction term. P values are two sided, and the ORs reported in the manuscript are from an additive model by logistic regression analyses unless otherwise specified. The analyses were also performed using SAS version 9.1.3 (SAS Institute) or Stata version 9.2 (StataCorp LP).
(责任编辑:广东会GDH基因)
顶一下
(0)
0%
踩一下
(0)
0%
推荐内容:
- 【广东会GDH基因检测】什么是MLPA基因检测?有什么优点?...
- 【广东会GDH基因检测】如何将全基因组测序(WGS)基因检测数据定位到人的标准基因组上?...
- 【广东会GDH基因检测】FISH基因检测中的探针类型选择...
- 【广东会GDH基因检测】肿瘤基因检测生物信息分析注意事项...
- 【广东会GDH基因检测】癌症基因组检测要点:一定要知道!...
- 【广东会GDH基因检测】什么是基因组检测?...
- 【广东会GDH基因检测】TP53突变基因检测...
- 【广东会GDH基因检测】基因解码对Y染色体的进一步解密...
- 【广东会GDH基因检测】肿瘤基因检测需要包括重复或反复区域的分析吗?...
- 【广东会GDH基因检测】如何采用液体活检检进行细胞学检测与NGS测序...
- 【广东会GDH基因检测】临床科研服务:GWAS课题中的统计分析...
- 【广东会GDH基因检测】肿瘤靶向药物Regorafenib (Stivarga) 及其在结直肠癌治疗中的作用...
- 【广东会GDH基因检测】ALDOA的群体遗传学结果对基因检测正确性的影响...
- 【广东会GDH基因检测】SLC25A4的双生子遗传学分析结果简介...
- 【广东会GDH基因检测】ASIC1的分子遗传学分析成果...
- 【广东会GDH基因检测】ANXA6分子病理学成果概要...
- 【广东会GDH基因检测】检验科医师晋升考试关于ADRA2C的知识...
- 【广东会GDH基因检测】医学院硕士研究考试关于ACVR2A基因检测的知识要点...
- 【广东会GDH基因检测】医学博士ANK1基因检测的知识结构准备...
- 【广东会GDH基因检测】医学院专升本关于ADCYAP1R1基因检测的基本技能...
- 【广东会GDH基因检测】病例分析会中需要知道的关于ACLY基因的知识...
- 【广东会GDH基因检测】病案讨论中需要知道的关于AIF1的知识...
- 【广东会GDH基因检测】质谱基因检测AGTR2基因存在基因突变该怎么理解?...
- 【广东会GDH基因检测】飞行质谱基因检测发现ADRA2A有突变,严重吗?...
- 【广东会GDH基因检测】核型分析发现NAT1突变了,是什么意思?...
- 【广东会GDH基因检测】遗传学检测结果指出ALOX15突变,该找谁咨询?...
- 【广东会GDH基因检测】高精度基因检测为什么包含ADD1基因?...
- 【广东会GDH基因检测】基因检测包中为什么一定要有ACTA2基因?...
- 【广东会GDH基因检测】基因检测时查看是否包含ADH1C重要吗?...
- 【广东会GDH基因检测】NR0B1基因间序列存在突变是否需要阻断遗传?...
- 来了,就说两句!
-
- 贼新评论 进入详细评论页>>