GWAS Simulation
comvert hmp to ped1, ped2, map file
SB1.ped, SB2.ped, SB.map
1, choose 20 markers for 30 times
(WD: /share/bioinfo/miaochenyong/GWAS/SB/20Markers-1To5Effect)
python ../choose_multi-markers.py SB.imputed.916.filtered.hmp 20 30 marker pheno
2, combine pheno, ped1, ped2 to intact ped file
python ../genCombine.py phenoPrefix 30 > combine.sh
parallel -j 30 < combine.sh
3, copy SB.map to 30 different SB-*.map
python ../CPmapTOmore.py 30 SB-
4, *map, *ped to *bed, *bim, *fam
python ../generatePLINKcmd.py 30 SB- > PLINK.cmd
chmod 777 PLINK.cmd
parallel -j 6 < PLINK.cmd
5, run gemma
python ../generateGemmaCmd.py 30 SB- > gemma.cmd
chmod 777 gemma.cmd
parallel -j 6 < gemma.cmd
Calculate FDR value:
(WD: /share/bioinfo/miaochenyong/GWAS/SB/20Markers-1To5Effect-FDR)
1, shuffle pheno1.txt to 100 pheno*.txt
python ../shufflePheno.py pheno3.txt 100 pheno-shuffled
2, combine pheno, ped1, ped2 to intact ped file
python ../genCombine.py phenoPrefix 100 > combine.sh
parallel -j 100 < combine.sh
3, copy SB.map to 100 different SB-shuffle*.map
python ../CPmapTOmore.py 100 SB-shuffle-
4, *map, *ped to *bed, *bim, *fam
python ../generatePLINKcmd.py 100 SB-shuffle- > PLINK.cmd
chmod 777 PLINK.cmd
parallel -j 10 < PLINK.cmd
5, run gemma
python ../generateGemmaCmd.py 100 SB-shuffle- > gemma.cmd
chmod 777 gemma.cmd
parallel -j 10 < gemma.cmd
6, calsulate FDR
cd output
python ../../calculateFDR.py SB-shuffle- 100 results.txt
Calculate average Power:
(WD: /share/bioinfo/miaochenyong/GWAS/SB/20Markers-1To5Effect/output)
python ../../calPower.py SB- marker 30 /share/bioinfo/miaochenyong/GWAS/SB/20Markers-1To5Effect-FDR/output/results.txt SB-
python ../../calAveragePower.py SB-
generage new effect 0.9+8
(WD: /share/bioinfo/miaochenyong/GWAS/SB/20Markers-0.9Effect)
ln -s /share/bioinfo/miaochenyong/GWAS/SB/20Markers-1To5Effect/markers-new* .
ln -s ../Imputed/SB.imputed.916.filtered.hmp .
python ../newEffect.py SB.imputed.916.filtered.hmp markers-new 30
事实证明:
平均数取8, 20, 100 模拟结果一样
effect value 取0.9 和0.9*20 结果也一样,
表面结果不同是由于FDR不同导致的。
观察average power in different MAF region:
WD: /share/bioinfo/miaochenyong/GWAS/SB/20Markers-0.9Effect20/output
python ../../DrawHist20Markers.py
WD: /share/bioinfo/miaochenyong/GWAS/SB/5Markers-0.9Effect100/output
$ python ../../DrawHist5Markers.py
可以看到随着MAF增大, power上升。从以上两图也可以推测出整体的MAF分布,多数markers都在0.01-0.1之间。
整体分布:
WD: /share/bioinfo/miaochenyong/GWAS/SB/Imputed
python ../DrawMAFHist.py SB.imputed.916.filtered.hmp
增加遗传率:
WD: /share/bioinfo/miaochenyong/GWAS/SB/5Markers-0.9Effect100
python ../genHeritability.py pheno9.txt 0.7 pheno9-0.7H.txt
上图是5个markers, 发现很多个体有相同的表型,对20个makers的进行作图:
一样的表型很少。
calculate average power of various heritability:
1,generate new phenotype data containing heritability
cd /share/bioinfo/miaochenyong/GWAS/SB/5Markers-1To5Effect100
python ../genHeriPheno.py pheno 30 0.7 phenoH0.7-
cd /share/bioinfo/miaochenyong/GWAS/SB/5Markers-1To5Effect100-0.7H
mv /share/bioinfo/miaochenyong/GWAS/SB/5Markers-1To5Effect100/phenoH0.7-* .
cp /share/bioinfo/miaochenyong/GWAS/SB/5Markers-1To5Effect100/marker* .
python ../genCombine.py phenoPrefix 30 > combine.sh
parallel -j 30 < combine.sh
python ../CPmapTOmore.py 30 SB-
python ../generatePLINKcmd.py 30 SB- > PLINK.cmd
parallel -j 6 < PLINK.cmd
python ../generateGemmaCmd.py 30 SB- > gemma.cmd
parallel -j 6 < gemma.cmd
Statistical results in Sorghum:
统计结果图:
MAF distribution in Seteria Italic:
python DrawMAFHist.py Seteria.imputed.GT.txt
发现小于0.05的基本没有,应该是被过滤掉了。
去除SB和SI中MAF 小于0.05的markers!
Transfer SI GT format to HMP format(SI directory):
python GT2HMP.py Seteria.imputed.GT.txt Seteria.imputed.hmp
SI 有726080 个markers
WD: SB_VS_SI/
python FilterMAF.py SB.imputed.916.filtered.hmp SB.filteredMAF.hmp SB剩余198629 markers
python FilterMAF.py Seteria.imputed.hmp Seteria.filteredMAF.hmp SI剩余725588 markers
重新画MAF分布图 看两者是否相近,相近的话随机选择marker!
SB MAF filtered:
SI MAF filtered:
select 198629 markers randomly from 725588 markers in SI:
python selectMarkers.py SI.filteredMAF.hmp 198629 SI.filteredMAF198629.hmp
重新做分布图:
cmiao
UNL
beadle center
GWAS Simulation的更多相关文章
- causal snps | causal variants | tensorflow | 神经网络实战 | Data Simulation
先读几篇文章: Interpretation of Association Signals and Identification of Causal Variants from Genome-wide ...
- GWAS | 全基因组关联分析 | Linkage disequilibrium (LD)连锁不平衡 | 曼哈顿图 Manhattan_plot | QQ_plot | haplotype phasing
现在GWAS已经属于比较古老的技术了,主要是碰到严重的瓶颈了,单纯的snp与表现的关联已经不够,需要具体的生物学解释,这些snp是如何具体导致疾病的发生的. 而且,大多数病找到的都不是个别显著的snp ...
- GWAS Catalog数据库简介
GWAS Catalog The NHGRI-EBI Catalog of published genome-wide association studies EBI负责维护的一个收集已发表的GWAS ...
- 【GWAS文献】基于GWAS与群体进化分析挖掘大豆相关基因
Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improv ...
- Gate level Simulation(门级仿真)
1 什么是后仿真? 后仿真也成为时序仿真,门级仿真,在芯片布局布线后将时序文件SDF反标到网标文件上,针对带有时序信息的网标仿真称为后仿真. 2 后仿真是用来干嘛的? 检查电路中的timing vio ...
- fdtd simulation, plotting with gnuplot, writting in perl
# 9月13日 于成都黄龙溪 1 #!/usr/bin/perl # Author : Leon Email: yangli0534@gmail.com # fdtd simulation , plo ...
- 【转载】PMC/PEC Boundary Conditions and Plane Wave Simulation
原文链接 PMC/PEC Boundary Conditions and Plane Wave Simulation (FDTD) OptiFDTD now has options to use Pe ...
- dipole antenna simulation by CST
CST偶极子天线仿真,半波振子天线 一.本文使用CST仿真频率为1GHz的偶极子天线,使用2013版本.仿真的步骤为 1.选择一个CST的天线工程模板 2.设置好默认的单位 3.设置背景的材料(空气腔 ...
- Logic and Fault simulation
fault simulation是指对fault circuit的simulation,来locate manufacturing defects并且进行fault diagnosis. logic ...
随机推荐
- hello 漂亮的小靓仔
<form type="text" name="超级" method="post"> <table align=" ...
- 设计模式(3)--抽象工厂模式(Absrtact Factory Pattern)
定义 抽象工厂模式的实质就是提供接口来创建一系列相关或独立的对象而不指定这些对象的具体类. 理解 在软件系统中,经常面临着"一系列相互依赖的对象"的创建工作:同时由于需求的变化,往 ...
- RabbitMQ Step by step(一) 安装
RabbitMQ是一个消息中间件,可以存储转发消息,个人感觉优越于MSMQ RabbitMQ官方网站(http://www.rabbitmq.com)可以获取到安装文件,建议大家详细浏览官方网站,官方 ...
- 支付宝APP支付后台参数生成Java版(一)
一.支付参数组装: String[] parameters={ "service=\"mobile.securitypay.pay\"",//固定值 " ...
- android学习之EdieText组件的使用
界面如下 移通152余继彪 该界面由四个EditText组件和Button按钮还有一个通知Toast完成,首先在xml文件中添加了四个组件和一个按钮还有一个文字显示框,java代码部分为button添 ...
- Shell脚本8种字符串截取方法总结
Linux 的字符串截取很有用.有八种方法. 假设有变量 var=http://www.aaa.com/123.htm. 1. # 号截取,删除左边字符,保留右边字符. 代码如下: echo ${va ...
- Java特性-Collection和Map
创建博客的目的主要帮助自己记忆和复习日常学到和用到的知识:或有纰漏请大家斧正,非常感谢! 之前面试,被问过一个问题:List和Set的区别. 主要区别很明显了,两者都是数组形式存在的,继承了Colle ...
- sql返回两个日期之间的日期_函数实现
-- Description:返回两段日期之间的所有日期 <Description,,>-- ============================================ ...
- Qt之C语言有符号数与无符号数运算
以32位的stm32f4为例: 1. uint32_t t_int_k = 239773, t_int_km1 = 4294859707; 则t_int_k - t_int_km1 > 0; ...
- linux下用eclipse + GDBserver + JLINK 在线调试(ARM11)
(一)环境: 目标版:TINY6410 OS:centOS6.5 IDE:eclipse luna CDT:v8.3 GDB:V7.5 (二)环境监理 1.安装cenntos:参考其他相关文章,这里重 ...