【佳學基因檢測】臨床科研服務:GWAS課題中的統(tǒng)計分析
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 evaluated 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).
(責任編輯:佳學基因)
頂一下
(0)
0%
踩一下
(0)
0%
推薦內容:
- 【佳學基因檢測】什么是MLPA基因檢測?有什么優(yōu)點?...
- 【佳學基因檢測】如何將全基因組測序(WGS)基因檢測數(shù)據(jù)定位到人的標準基因組上?...
- 【佳學基因檢測】FISH基因檢測中的探針類型選擇...
- 【佳學基因檢測】腫瘤基因檢測生物信息分析注意事項...
- 【佳學基因檢測】癌癥基因組檢測要點:一定要知道!...
- 【佳學基因檢測】什么是基因組檢測?...
- 【佳學基因檢測】TP53突變基因檢測...
- 【佳學基因檢測】基因解碼對Y染色體的進一步解密...
- 【佳學基因檢測】腫瘤基因檢測需要包括重復或反復區(qū)域的分析嗎?...
- 【佳學基因檢測】如何采用液體活檢檢進行細胞學檢測與NGS測序...
- 【佳學基因檢測】臨床科研服務:GWAS課題中的統(tǒng)計分析...
- 【佳學基因檢測】腫瘤靶向藥物Regorafenib (Stivarga) 及其在結直腸癌治療中的作用...
- 【佳學基因檢測】ALDOA的群體遺傳學結果對基因檢測正確性的影響...
- 【佳學基因檢測】SLC25A4的雙生子遺傳學分析結果簡介...
- 【佳學基因檢測】ASIC1的分子遺傳學分析成果...
- 【佳學基因檢測】ANXA6分子病理學成果概要...
- 【佳學基因檢測】檢驗科醫(yī)師晉升考試關于ADRA2C的知識...
- 【佳學基因檢測】醫(yī)學院碩士研究考試關于ACVR2A基因檢測的知識要點...
- 【佳學基因檢測】醫(yī)學博士ANK1基因檢測的知識結構準備...
- 【佳學基因檢測】醫(yī)學院專升本關于ADCYAP1R1基因檢測的基本技能...
- 【佳學基因檢測】病例分析會中需要知道的關于ACLY基因的知識...
- 【佳學基因檢測】病案討論中需要知道的關于AIF1的知識...
- 【佳學基因檢測】質譜基因檢測AGTR2基因存在基因突變該怎么理解?...
- 【佳學基因檢測】飛行質譜基因檢測發(fā)現(xiàn)ADRA2A有突變,嚴重嗎?...
- 【佳學基因檢測】核型分析發(fā)現(xiàn)NAT1突變了,是什么意思?...
- 【佳學基因檢測】遺傳學檢測結果指出ALOX15突變,該找誰咨詢?...
- 【佳學基因檢測】高精度基因檢測為什么包含ADD1基因?...
- 【佳學基因檢測】基因檢測包中為什么一定要有ACTA2基因?...
- 【佳學基因檢測】基因檢測時查看是否包含ADH1C重要嗎?...
- 【佳學基因檢測】NR0B1基因間序列存在突變是否需要阻斷遺傳?...
- 來了,就說兩句!
-
- 賊新評論 進入詳細評論頁>>