asking how to make chromosome name become easy to distinguish

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amira ndi

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Sep 9, 2021, 11:59:42 AMSep 9
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Dear all,
I need some advice and suggestion due to my problem, I am sorry if this question seems easy but I am beginner here to use r/qtl packages in R software. I have around 1000 markers (genotype) and 1 phenotype (100 data). I tried to run it used scannone with methode "mr"
However the data showed like this,

Capture.JPG


Here is my scripts,
library(qtl)
data_mr <- read.csv("trial2.csv")
data_mr <- read.cross("csvr", file="trial2.csv", estimate.map=FALSE)

data_mr <- jittermap(data_mr, amount= .0001)
data_mr <- calc.genoprob(data_mr, step=0,eror.prob=0.01)
data.scanone <- scanone(data_mr, pheno.col = 1)
permulation.test <- scanone(data_mr, method = "em", 
                            pheno.col = 1, n.perm=10)
plot(data.scanone)

I only have genotype data like "AA/BB" and do not have position of marker in cM. Are there any ways to make the chromosome name could be easy to distinguish? or do I have a mistake to input the data?

Thank you so much before.

Karl Broman

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Sep 9, 2021, 12:09:19 PMSep 9
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R/qtl assumes a set of chromosome IDs for the markers, and really needs marker order and cM locations to handle missing data properly. If you just want to do GWAS with the observed SNPs, you may be better off with more general software like PLINK.

The input file expects that the second row (or 2nd column if you're using the "csvr" format) contain the chromosome IDs.
See chapter 2 of the R/qtl book (https://rqtl.org/book/rqtlbook_ch02.pdf) and the sample data files at https://rqtl.org/sampledata/

karl

amira ndi

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Sep 9, 2021, 12:26:36 PMSep 9
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Dear Karl, 
Thank you for answering.
Yes, my data does not have "cM location". Does it mean that I can not use QTL in Rstudio to estimate the genomic regions in my data?
Because, I want to estimate the location here. Any other suggestion that could be used to still analyze my data in R?
Thanks

Best
Amira 

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Karl Broman

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Sep 9, 2021, 1:48:32 PMSep 9
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If you have a physical map with bp locations, I would start with that, though using Mbp positions.
Otherwise, you could try to estimate a genetic map. See https://rqtl.org/tutorials/geneticmaps.pdf

karl

amira ndi

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Sep 18, 2021, 1:42:13 PMSep 18
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Dear Karl,

Thank you for your suggestion.
I would like to make the visual of my data like this,
pi.jpeg
I am still struggling to look for how to group the data in Excel  so that it can be visualized per-chromosome like the picture above and what is the packages to analyze.
Do you have recommendation? My data consist of 1 phenotype (around 100 data) with 1000 markers (5 chromosomes). 
Please give me your suggestion.

Thank you!

Best,
Amirandi

Karl Broman

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Sep 18, 2021, 3:45:33 PMSep 18
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I don't have anything to add over what I said below. Chapter 2 of the R/qtl book talks about data file format, and it's available online at https://rqtl.org/book/rqtlbook_ch02.pdf

There are example data files online at https://rqtl.org/sampledata

karl
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