File S2 (TXT): R-Codes, macros, and terminal command lines used in this study. Annotated text file that describe all the codes used at different analyses performed in this genetic map study.


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Waiker et al (2021). Frieseomelitta varia linkage mapping
'#' or " ' " at the beginning of the sentence represent a comment
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(1) Ubuntu terminal command line

## VCF file filtering
#SNPs filtered for at least 50% missing data, minimum quality score of 30 and Minor allele frequency of 3
vcftools --vcf fv.vcf --max-missing 0.5 --minQ 30 --minDP 6 --mac 3  --recode --recode-INFO-all --out fv_filter1

#Extracting SNP genotype information from filtered VCF file
vcftools --vcf fv_filtered.recode.vcf --extract -FORMAT -info GT

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(2) Example codes for data filtering using Macro VBA

#highlight the rows that have same data in four columns
Sub samedata()						'name of macro code
LastRow = Cells(Rows.Count, "A").End(xlUp).Row  	'define last row containing data in column A
LastCol = Cells(1, Columns.Count).End(xlToLeft).Column  ' define last column containing data for row 1

For yrow = 2 To LastRow					'for loop that runs from row 2 to last row
If Cells(yrow, "B") = Cells(yrow, "C") And Cells(yrow, "C") = Cells(yrow, "D") And Cells(yrow, "D") = Cells(yrow, "E") And Cells(yrow, "B") = Cells(yrow, "E") Then   ' If-then statement to compare values between different cells
    If Cells(yrow, "F") = Cells(yrow, "G") And Cells(yrow, "G") = Cells(yrow, "H") And Cells(yrow, "H") = Cells(yrow, "I") And Cells(yrow, "I") = Cells(yrow, "F") Then
        Cells(yrow, 1).EntireRow.Interior.ColorIndex = 3			'Highlight entire row with a specific color if above criteria meets
    End If
End If					' logical statement to instruct the program to stop if statement
Next yrow				'for loop goes to next row
End Sub					'end macro


#Copy entire rows if a cell has a specific color and paste it to new excel sheet
Sub Sortbycolor()				'macro name
Dim Sourcews As Worksheet			' defining worksheets as variable 'Worksheet'
Dim Destws As Worksheet
LastRow = Cells(Rows.Count, "A").End(xlUp).Row

Set Sourcews = ActiveSheet			'Setting current worksheet as active
Set Destws = Worksheets.Add			'create a new sheet to paste the values

zrow = 2
For yrow = 1 To LastRow
    If Sourcews.Cells(yrow, 1).Interior.Color = vbRed Then	'If then statement to check if a cell has a specific background color
    Sourcews.Cells(yrow, 1).EntireRow.Copy Destws.Cells(zrow, 1) ' If criteria meets then paste the row to destinamtion worksheet
    zrow = zrow + 1						'increasing the value of destination row number to paste next value in empty row
    End If
Next yrow
    
End Sub


#Compare the markers from a list and highlight the row
Sub compareandhighlightmarkers()
x = 1
Do While Cells(x, 1) <> ""      'List of markers that need to be tested
    Sheets(1).Select		'Sheet containing marker number --Markers to delete
    temp = Cells(x, 1)		' temporary variable to hold marker information
    Sheets(2).Select
    Lastrow = Cells(Rows.Count, 1).End(xlUp).Row
    For zrow = 2 To Lastrow
        If Cells(zrow, 1) = temp Then	'compares if value matches
            Cells(zrow, 1).EntireRow.Interior.ColorIndex = 3  'highlight the row if condition meets
        End If
    Next zrow
x = x + 1
Loop
End Sub


#compare a list of SNPs name to a bigger list with map distance and if match then put map distance next to the SNP

Sub Addmapdistance()
x = 2
Do While Cells(x, 1) <> ""    'first column has the SNP list with ID
    temp = Cells(x, 1)
    Lastrow = Cells(Rows.Count, 5).End(xlUp).Row    'row with all markers in col 5
    For zrow = 2 To Lastrow
        If Cells(zrow, 5) = temp Then
            Cells(zrow, 7) = Cells(x, 2)
        End If
    Next zrow
x = x + 1
Loop
End Sub

#check file and highlight if the value of a column is lesser than a threshold value
Sub Highlight_rowbycondition() 'Highlight rows if cell has certain condition (Conditional formatting)
Lastrow = Cells(Rows.Count, 5).End(xlUp).Row    'row with all markers in col 5 - the other list which does not have an ID
    For zrow = 1 To Lastrow
        If Cells(zrow, 6) < 150 Then
            Cells(zrow, 6).EntireRow.Interior.ColorIndex = 3
        End If
    Next zrow
End Sub

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(3) R codes

#Installing RQTL package
install.packages("qtl")

#Loading library in R
library(qtl)

#Setting Working directory
setwd("C:/fakepath")

#Setting parameters to print maximum lines in R output window
options(max.print=1000000)

# Loading the CSV genotype file in R where genotypes are either coded as 'A' or 'H' for homozygous and heterozygous
fv<- read.cross("csvr", "C:/fakepath", "FV_inputfile.csv")

# Plotting missing data to inspect any severe missingness
plotMissing(fv, main="Missing data for FV markers")

#Find duplicate markers in vector dupmar
dupmar <- findDupMarkers(fv, exact.only = F) 

#drop duplicate markers
fv <- drop.markers(fv, unlist(dupmar))

#writenew file with no duplicate marker
write.cross(fv, format='csvr',filestem = "nodupLG1")

#check new numbers of markers (optional confirmatory step)
totmar(fv)

#estimating recombination fraction between each marker pait and calculate LOD score for a test of rf=0.5
fv<- est.rf(fv)

#Inspecting list markers for potentially switched allels
checkAlleles(fv, threshold=5) 

# Plotting marker pairs for potentially switched alleles
rf <- pull.rf(fv)
lod <- pull.rf(fv, what="lod")
plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score")

# Inferring linkage groups
lg <- formLinkageGroups(fv, max.rf=0.25, min.lod= 5)
table(lg[,2])

# reorganizing inferred linkage groups
lg<- formLinkageGroups(fv, max.rf=0.25, min.lod=5, reorgMarkers = T)

#Pull Recombination fraction matrix to inspect potential connections between markers of linkage groups
RFmatrix_endmarkers <- pull.rf(fv, what="lod")
write.table(RFmatrix_endmarkers, 'LODmatrix_endmarkers.csv', sep='\t')

#Ordering markers within linkage groups and saving as a csv file on local computer (repeat code for each linkage group)
fv1<- orderMarkers(lg, chr = 1, error.prob = 0.001)
sink(file = "FV_LG1.csv")
pull.map(fv1,chr = 1)
sink()

#Drop one suspected bad marker at a time to inspect if gaps are being created by that marker
dropone <- droponemarker(lg, error.prob=0.001)
par(mfrow=c(2,1))
plot(dropone, lod=1, ylim=c(-100,0))
plot(dropone, lod=2, ylab="Change in chromosome length")
par(mfrow=c(1,1))


#Tryallpositions() to try an additional marker if it fits in already created linakge map
tryallpositions(lg,"SNPxx", chr = 1, error.prob = 0.001)

#Loop code to try multiple markers for tryallpositions using a list of test markers
markers<- scan(file ="listofextramarkers.csv", character(), quote = "", skip = 1)
output <- vector("list", length(markers))
names(output) <- markers
for(i in seq_along(markers)) {
  output[[i]] <- tryallpositions(lg, markers[i], error.prob = 0.001)    #specify an LG using argument chr=x to avoid intensive never ending computation
}
sink(file="Trymarker_allLG.csv")
output
sink()


#Plotting linkage groups using library LinkageMapView
library(LinkageMapView)
outfile = file.path("C:/fakepath", "fv1.pdf")
lmv.linkage.plot(fv1, outfile)   #repeat for each linkage group


# Plotting all linkage groups in a single plot along with marker density
maxpos <- 310   # draw tickmarks at each cM from 0 to largest position of linkage groups to be drawn
at.axis <- seq(0, maxpos)
axlab <- vector()
for (lab in 0:maxpos) {
  if (!lab %% 50) {
    axlab <- c(axlab, lab)
  }
  else {
    axlab <- c(axlab, NA)
  }
}
outfile = file.path("C:/fakepath", "linkagemapwithdensity.pdf")
lmv.linkage.plot(fv, outfile, denmap = TRUE, cex.axis = 2,cex.lgtitle = 4 ,at.axis = at.axis,labels.axis = axlab, pdf.height = 16)

#Phylogenetic tree - comparison of different species' recmbination rates
library(phytools)   #package to create phylogeny figure
library(ggplot2)
tree2<-read.tree("FileS3.tre")
x2<-read.csv("FileS4.csv",header=TRUE,row.names=1)
x2<-setNames(x2[,1],rownames(x2))
obj<-contMap(tree2,x2,plot=FALSE, method= "fastAnc")
obj<-setMap(obj,invert=TRUE)
plot(obj,fsize=c(1,0.8),outline=FALSE,lwd=c(6,7),leg.txt="Recombination rate (cM/Mb)")


#Correlation analysis Physical length and genetic length for each LG
#q-q plot for normality assumption
ggqqplot(my_data$Contigs_length_Mb, ylab = "Physical length")
ggqqplot(my_data$Avg.RR, ylab = "Rec. rates")

#Normality check statistical test
shapiro.test(my_data$Contigs_length_Mb)
shapiro.test(my_data$Avg.RR)

#plotting correlation graph
library("ggpubr")
my_data <- read.csv("Map_summary.csv")
ggscatter(my_data, x = "Contigs_length_Mb", y = "Avg.RR", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "spearman",
          xlab = "Physical length of LG (Mb)", ylab = "Average Recombination rate of LG (cM/Mb)")

#Rho statistic for spearman correlation test
rho<-cor.test(my_data$Contigs_length_Mb, my_data$Avg.RR,  method = "spearman")
rho

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