explodecomputer

query= 17554300
#run the search
search <- EUtilsSummary(query, type="esearch",db = "pubmed",retmax=30000)
QueryCount(search) #how many hits
summary(search) #number of hits and search terms used
records <- EUtilsGet(search) #get the results from search

PMID(records)
Author(records)[[1]]
a <- Mesh(records)[[1]]

explodecomputer

query= 17554300
#run the search
search <- EUtilsSummary(query, type="esearch",db = "pubmed",retmax=30000)
QueryCount(search) #how many hits
summary(search) #number of hits and search terms used
records <- EUtilsGet(search) #get the results from search

PMID(records)
Author(records)[[1]]
a <- Mesh(records)[[1]]

explodecomputer

shinyApp(
	ui = navbarPage(title="test", id="mainNavbarPage",
		
		tabPanel("Input", value="tabinput",
			numericInput('n', 'Number of obs', 100),
			actionButton(inputId="submit_button", label="Submit")
		),

		tabPanel("Output", value="taboutput",
			verbatimTextOutput("messages"),

explodecomputer

Phenotype   SNP    effect_allele   other_allele   beta   se eaf  samplesize    units
Telomere_length  rs4452212 G    A   0.048547045    0.025368957 0.384488126  20000 mg
Telomere_length  rs621559  G A    0.003061092 0.019672991  0.930963777   20000  mg
Telomere_length  rs755017  G A    0.012278742 0.012858208  0.135563686   20000  mg
Telomere_length  rs7675998 G    A   0.030833397    0.018161069 0.774290377  20000 mg
Telomere_length  rs8105767 G    A   0.041388996    0.011055945 0.290208018  20000 mg
Telomere_length  rs10936599    C   T  0.064543244   0.016425136    0.764278349 20000    mg
Telomere_length  rs10936601    C   T  0.056461006   0.010547034    0.743640342 20000    mg
Telomere_length  rs2736100 C    A   0.055542945    0.013068791 0.51724734   20000  mg
Telomere_length  rs3027234 C    T   0.065856682    0.014545151 0.77323311   20000  mg

explodecomputer

a <- read.table("~/Desktop/analysis1_allstudies.txt", he=T, sep="\t")
b <- subset(a, outcome %in% c("cardiogram", "bladdercancer"))
tel <- subset(b, select=c(snp.exposure, allele1, allele2, effect.exposure, se.exposure, chr_name, chr_pos, outcome, effect_allele, other_allele, beta.outcome, se.outcome, eaf.outcome, eaf.trait))
names(tel) <- c("SNP", "effect_allele.exposure", "other_allele.exposure", "beta.exposure", "se.exposure", "chr_name", "chrom_start", "outcome", "effect_allele.outcome", "other_allele.outcome", "beta.outcome", "se.outcome", "eaf.outcome", "eaf.exposure")
tel$exposure <- "Telomere length"



card <- subset(tel, outcome=="cardiogram", select=c(SNP, effect_allele.outcome, other_allele.outcome, beta.outcome, se.outcome, eaf.outcome))
names(card) <- c("SNP", "effect_allele", "other_allele", "beta", "se", "eaf")

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n <- 1000

# cor_g and cor_e both positive
g <- rnorm(n)
e <- rnorm(n) * 0.5 
g1 <- g + rnorm(n)
g2 <- g + rnorm(n)
cor(g1,g2)
e1 <- e + rnorm(n)
e2 <- e + rnorm(n)

explodecomputer

# Use this library - it handles linear regression instead of assuming balanced ANOVA
library(pwr)

# Just gonna simulate some data but you can obviously use the actual allele frequencies, effect sizes and phenotypic variances


# Allele frequencies:
nsnp <- 29
p <- runif(nsnp)

explodecomputer

import sys
import urllib2
from bs4 import BeautifulSoup, NavigableString, Tag


def get_headfirstbristol():
	web_page = urllib2.urlopen("http://www.headfirstbristol.co.uk/gig-listings").read()
	soup = BeautifulSoup(web_page)
	band = []
	place = []

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#!/bin/bash

cd /panfs/panasas01/shared/alspac/studies/latest/alspac/genetic/variants/arrays/gwas/imputed/1000genomes/released/2015-06-25/data/quality
rm -f ~/out.txt
for i in {1..23}
do
	echo ${i}
	awk -v chr=$i '{if($1 == chr) print $2}' 1000GP_16-06-14_flipped_SNPs.txt > ~/temp
	ii=`printf %02d $i`
	zfgrep -wf ~/temp data_chr${ii}.txt.gz | awk '{ print $2 }' >> ~/out.txt

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test_association(cpg_fixed3, batch)
test_association(cpg_fixed3, slide)
test_association(cpg_fixed3, real_y)
test_association(cpg_fixed3, confounder)