diff --git a/docs/17200_ITPR1_mosaic_investigation.md b/docs/17200_ITPR1_mosaic_investigation.md
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+# First look at the one sample
+
+```
+bcftools view -m2 -M2 -v snps 18735_426481-gatk-haplotype-annotated.vcf.gz | bcftools filter -i 'GT[1] = "ref"' | bcftools filter -i 'GT[0] = "het"' | bcftools filter -i 'GT[2] != "ref" & GT[2] != "mis"' | bgzip -c > paternally_inherited_het.vcf.gz
+tabix paternally_inherited_het.vcf.gz
+
+/home/u035/project/software/bcbio/anaconda/bin/vase -i paternally_inherited_het.vcf.gz -o paternally_inherited_het_rare.vcf.gz --freq 0.001 --csq all
+tabix paternally_inherited_het_rare.vcf.gz
+
+header.txt
+chr pos ref alt child_gt child_ref_dp child_alt_dp mom_gt mom_ref_dp mom_alt_dp dad_gt dad_ref_dp dad_alt_dp
+
+cp header.txt paternally_inherited_het_rare.txt
+bcftools query -f "%CHROM\t%POS\t%REF\t%ALT[\t%GT\t%AD]\n" paternally_inherited_het_rare.vcf.gz | sed -e 's/,/\t/g' >> paternally_inherited_het_rare.txt
+```
+
+```
+R
+y = read.table("paternally_inherited_het_rare.txt", header=T, stringsAsFactors=F, sep="\t", na.strings=".")
+y$mom_alt_af = y$mom_alt_dp / (y$mom_alt_dp + y$mom_ref_dp)
+y$dad_alt_af = y$dad_alt_dp / (y$dad_alt_dp + y$dad_ref_dp)
+y$child_alt_af = y$child_alt_dp / (y$child_alt_dp + y$child_ref_dp)
+length(y$chr)
+382
+
+q = subset(y, y$child_alt_af >= 0.2 & y$dad_alt_af >= 0.2)
+length(q$chr)
+225
+
+q = subset(y, y$child_alt_af >= 0.2 & y$dad_alt_af >= 0.2 & y$mom_alt_dp > 0)
+length(q$chr)
+19
+
+z = data.frame(id = c(rep("dad", length(q$chr)), rep("mom", length(q$chr)), rep("child", length(q$chr))), af = c(q$dad_alt_af, q$mom_alt_af, q$child_alt_af))
+library(lattice)
+pdf("paternally_inherited_het_rare_aaf_histogram.pdf", width=6, height=6)
+histogram(~af | id, z, layout=c(1,3), breaks=seq(0,1,0.01), type="c")
+dev.off()
+
+19 of 225 sites have some alternate allele reads in the mother
+
+q = subset(y, y$child_alt_af >= 0.2 & y$dad_alt_af >= 0.2)
+pdf("dad_vs_child_af.pdf", width=6, height=6)
+xyplot(child_alt_af ~ dad_alt_af, q, xlim=c(-0.05,1.05), ylim=c(-0.05,1.05))
+dev.off()
+ 1
+pdf("mom_vs_child_af.pdf", width=6, height=6)
+xyplot(child_alt_af ~ mom_alt_af, q, xlim=c(-0.05,1.05), ylim=c(-0.05,1.05))
+dev.off()
+```
+
+# All families in batch
+
+```
+for ((i = 1; i <= 32; i = i + 1))
+do
+ head -n $i affected_child.ped | tail -n 1 | cut -f 2 > child_id.txt
+ head -n $i affected_child.ped | tail -n 1 | cut -f 3 > father_id.txt
+ head -n $i affected_child.ped | tail -n 1 | cut -f 4 > mother_id.txt
+ family_id=`head -n $i affected_child.ped | tail -n 1 | cut -f 1`
+
+ bcftools view -m2 -M2 -v snps ${family_id}-gatk-haplotype-annotated.vcf.gz | bcftools filter -i 'GT[@mother_id.txt] = "ref" && GT[@child_id.txt] = "het" && GT[@father_id.txt] != "ref" && GT[@father_id.txt] != "mis"' | bgzip -c > ${family_id}_paternally_inherited_het.vcf.gz
+ tabix ${family_id}_paternally_inherited_het.vcf.gz
+
+ /home/u035/project/software/bcbio/anaconda/bin/vase -i ${family_id}_paternally_inherited_het.vcf.gz -o ${family_id}_paternally_inherited_het_rare.vcf.gz --freq 0.001 --csq all
+ tabix ${family_id}_paternally_inherited_het_rare.vcf.gz
+
+ cp header.txt ${family_id}_paternally_inherited_het_rare.txt
+ bcftools query -f "%CHROM\t%POS\t%REF\t%ALT[\t%GT\t%AD]\n" ${family_id}_paternally_inherited_het_rare.vcf.gz | sed -e 's/,/\t/g' >> ${family_id}_paternally_inherited_het_rare.txt
+done
+```
+
+Excluded the one family without VEP annotations
+
+```
+R
+ids = read.table("affected_child.ped", header=F, sep="\t", stringsAsFactors=F)
+x = data.frame()
+for (family_id in ids$V1) {
+ y = read.table(sprintf("%s_paternally_inherited_het_rare.txt", family_id), header=T, stringsAsFactors=F, sep="\t", na.strings=".")
+ y$family_id = family_id
+ x = rbind(x, y)
+}
+
+x$mom_alt_af = x$mom_alt_dp / (x$mom_alt_dp + x$mom_ref_dp)
+x$dad_alt_af = x$dad_alt_dp / (x$dad_alt_dp + x$dad_ref_dp)
+x$child_alt_af = x$child_alt_dp / (x$child_alt_dp + x$child_ref_dp)
+x$count = 1
+
+q = subset(x, x$child_alt_af >= 0.2 & x$dad_alt_af >= 0.2)
+total = aggregate(q$count, by = list(q$family_id), sum)
+
+q = subset(x, x$child_alt_af >= 0.2 & x$dad_alt_af >= 0.2 & x$mom_alt_dp > 0)
+mom_has_reads = aggregate(q$count, by = list(q$family_id), sum)
+
+output = data.frame(family_id = total$Group.1, total = total$x, mreads = mom_has_reads$x)
+output$mprop = output$mreads / output$total
+outsub = subset(output, output$total > 2)
+
+22 of 28 families have a decent number of sites - not sure what's up with the other 6, which only have 1 or 2 each, but these are excluded from analysis.
+
+summary(outsub)
+ family_id total mreads mprop
+ 18735_412876: 1 Min. :136.0 Min. : 6.00 Min. :0.03109
+ 18735_414260: 1 1st Qu.:178.0 1st Qu.:12.25 1st Qu.:0.06782
+ 18735_415251: 1 Median :198.0 Median :16.00 Median :0.07705
+ 18735_421930: 1 Mean :192.6 Mean :15.59 Mean :0.08072
+ 18735_424464: 1 3rd Qu.:204.8 3rd Qu.:18.00 3rd Qu.:0.09609
+ 18735_424521: 1 Max. :230.0 Max. :26.00 Max. :0.13065
+ (Other) :16
+
+quantile(q$mom_alt_dp + q$mom_ref_dp, seq(0,1,0.05))
+ 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
+ 7.0 19.5 29.0 45.5 57.0 69.5 79.0 88.0 101.0 110.0 124.0
+ 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
+ 143.5 168.0 181.0 200.0 229.0 264.0 296.5 360.0 425.0 1023.0
+
+quantile(q$dad_alt_dp + q$dad_ref_dp, seq(0,1,0.05))
+ 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%
+ 2.0 13.0 27.0 47.0 55.0 65.5 78.0 87.0 95.0 111.0 126.0 138.0 149.0
+ 65% 70% 75% 80% 85% 90% 95% 100%
+175.5 201.0 223.0 245.0 281.5 350.0 426.5 829.0
+
+quantile(q$child_alt_dp + q$child_ref_dp, seq(0,1,0.05))
+ 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%
+ 3.0 21.5 30.0 43.0 51.0 61.0 72.0 79.0 92.0 101.5 122.0 141.5 157.0
+ 65% 70% 75% 80% 85% 90% 95% 100%
+174.0 190.0 215.0 238.0 261.0 315.0 402.5 846.0
+```
+
+