An Introduction To geaR

Christopher Ward1*, Alastair Ludington1, James Breen1 and Simon Baxter2

1School of Biological Sciences, University of Adelaide
2School of BioScience, University of Melbourne

*christopher.ward@adelaide.edu.au

Package

geaR 0.1.0

Contents

 knitr::opts_chunk$set(message = FALSE,warning = FALSE)

1 Introduction

The package GDS evolutionary analysis in R or geaR is designed to aid with evolutionary and population genetic analysis across whole genome genotype data in the GDS format.
Analysis can be carried out using classical approaches such as sliding windows, however the main strength of geaR is the ability to include only certain types of features in the analysis.

2 Conversion of VCF to GDS

VCF files can be converted using the R package SeqArray

This command will convert the sample VCF file into GDS format to disk keeping only the Genotype field using 2 threads.

library(SeqArray)
  
packageDir <- system.file("extdata", package = "geaR")

seqVCF2GDS(vcf.fn = paste0(packageDir, "/sampleVCF.vcf.gz"),
           parallel = 2, out.fn = paste0(packageDir, "/sampleVCF.gds"),
           fmt.import = c("GT"), storage.option = "ZIP_RA")

3 Carrying out analysis on different window types

A common analysis in population genetics is to subdivide the genome into equally sized windows before analysis. Below we will construct loci for four different window types.

1. Tiled window
2. Sliding window
3. Tiled SNP windows
4. Sliding SNP windows

Windows of a given size can be generated by using the windowMaker() or SnpWindowMaker() functions. In order to do this, first the package is loaded and scaffold/contig length must be input. If scaffold information is not known the necessary data.frame can be made using the reference genome’s fasta index (.fai)

3.1 Set up scaffold/contig meta data

library(geaR)
library(readr)
library(tibble)


fileDir <- system.file("extdata", package = "geaR")

file <- file.path(fileDir, "/sampleReferenceGenome.fa.fai")

df <- readr::read_tsv(file, col_names = FALSE)
x <- tibble::tibble(ID = df[[1]], length = df[[2]])

x

## # A tibble: 1 x 2
##      ID  length
##   <dbl>   <dbl>
## 1     1 1000000

We can then use the contig/scaffold metadata to construct loci for a tiled or sliding window analysis. This will construct windows using scaffold positions, however the number of genotypes in each window is likely to differ due to missing data. Windows can then be generated by setting arguments windowSize and stepSize. Optionally, nCores can also be set to split the jobs over parallel processors.

3.2 Constructing tiled and sliding windows

### generate 10kb tiled windows
loci <- windowMaker(x = x, windowSize = 10000, stepSize = 0)

loci

## GRangesList object of length 100:
## [[1]] 
## GRanges object with 1 range and 1 metadata column:
##       seqnames    ranges strand |    lociType
##          <Rle> <IRanges>  <Rle> | <character>
##   [1]        1   1-10000      * | tiledWindow
## 
## [[2]] 
## GRanges object with 1 range and 1 metadata column:
##       seqnames      ranges strand |    lociType
##   [1]        1 10001-20000      * | tiledWindow
## 
## [[3]] 
## GRanges object with 1 range and 1 metadata column:
##       seqnames      ranges strand |    lociType
##   [1]        1 20001-30000      * | tiledWindow
## 
## ...
## <97 more elements>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths

### generate 10kb windows sliding by 2kb
loci <- windowMaker(x = x, windowSize = 10000, stepSize = 2000)

loci

## GRangesList object of length 496:
## [[1]] 
## GRanges object with 1 range and 1 metadata column:
##       seqnames    ranges strand |      lociType
##          <Rle> <IRanges>  <Rle> |   <character>
##   [1]        1   1-10000      * | slidingWindow
## 
## [[2]] 
## GRanges object with 1 range and 1 metadata column:
##       seqnames     ranges strand |      lociType
##   [1]        1 2001-12000      * | slidingWindow
## 
## [[3]] 
## GRanges object with 1 range and 1 metadata column:
##       seqnames     ranges strand |      lociType
##   [1]        1 4001-14000      * | slidingWindow
## 
## ...
## <493 more elements>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths

The R package also allows for windows to be constructed based on genotype position in the GDS ensuring the same number of genotypes in each window.

This will require the GDS to be passed to the function so first we must open a connaection to the GDS using seqOpen() from the SeqArray package.

3.3 Constructing tiled and sliding snp windows

library(SeqArray)

# open pointer to GDS on disk
GDS <- seqOpen(paste0(fileDir, "/sampleVCF.gds"), allow.duplicate = TRUE) 

### generate windows with the same number of SNPs in each
loci <- snpWindowMaker(x = x, GDS = GDS, windowSize = 1000, stepSize = 0)

## # of selected variants: 13,436

loci

## GRangesList object of length 13:
## $1 
## GRanges object with 1000 ranges and 1 metadata column:
##          seqnames    ranges strand |       lociType
##             <Rle> <IRanges>  <Rle> |    <character>
##      [1]        1     10177      * | tiledSnpWindow
##      [2]        1     10235      * | tiledSnpWindow
##      [3]        1     10352      * | tiledSnpWindow
##      [4]        1     10505      * | tiledSnpWindow
##      [5]        1     10506      * | tiledSnpWindow
##      ...      ...       ...    ... .            ...
##    [996]        1    138017      * | tiledSnpWindow
##    [997]        1    138041      * | tiledSnpWindow
##    [998]        1    138348      * | tiledSnpWindow
##    [999]        1    138379      * | tiledSnpWindow
##   [1000]        1    138396      * | tiledSnpWindow
## 
## ...
## <12 more elements>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths

### generate windows with the same number of SNPs in each
loci <- snpWindowMaker(x = x, GDS = GDS, windowSize = 1000, stepSize = 200)

## # of selected variants: 13,436

As the analysis will be calculated across a population, we need to define the individuals and which population they belong to. To do this, we first point to the GDS file on disk and extract sample names as a vector. The population data frame can then be constructed by dividing individuals into populaitons. We will do this arbitrarily with 2 individuals in 10 populations.

3.4 Get samples from GDS then construct population metadata

# Extract sample names from GDS object
samples <- seqGetData(gdsfile = GDS, var.name = "sample.id")

# Define populations
pops <- tibble::tibble(Sample = samples[1:20], 
                       Population = rep(paste0("P", 1:10), each = 2))

head(pops)

## # A tibble: 6 x 2
##   Sample  Population
##   <chr>   <chr>     
## 1 HG00096 P1        
## 2 HG00097 P1        
## 3 HG00099 P2        
## 4 HG00100 P2        
## 5 HG00101 P3        
## 6 HG00102 P3

4 Carrying out an analysis

We will then begin constructing cog objects. Cogs form the backbone of an analysis in . For this example we will carry out all analyses: Genetic diversity, Admixture, Loci output and Distance tree output Genetic diversity will calculate

• pi: Neis nucleotide diversity
• dxy: Neis absolute genetic distance
• da: Nei’s ancesteral distance
• dmin: minimum hamming distance between samples
• dmax: maximum hamming distance between samples
• Fst: Nei (1982) st which estimates Fst
• RNDmin: minimum Relative node depth (Requires a outgroup to be set)
• federRND: Relative Node Depth using Feder (2005) (Requites and outgroup)

Admixture will calculate

• f4
• fd

Output Loci will write loci to fasta files in a supplied directory. Output Trees will write distance trees to newick files in a supplied directory

4.1 Calculate default statistics on window GRanges

# Change this to your desired output directory
outDir <- "~/Desktop/setupGear/"

# general arguments for the analysis
argCog <- makeCog(analysisType = "args", 
                  ploidy = 2, nCores = 2, minSites = 0.01, 
                  pairwiseDeletion = TRUE, 
                  removeIndels = TRUE)

# set up diverstity analysis
divCog <- makeCog(analysisType = "diversityFULL", 
                  stats = "all")

# set up admixture cog
admixCog <- makeCog(analysisType = "admixture", 
                    threePop = list(),
                    fourPop = list(c("P1", "P2", "P3", "P10")))

# set up output loci cog
outloci <-  makeCog(analysisType = "outputLoci", 
                    outputDirectory = outDir, 
                    alleles = "seperate", 
                    removeIndels = TRUE)

# set up output tree cog
outTrees <-  makeCog(analysisType = "outputTrees", outputDirectory = outDir, alleles = "seperate", removeIndels = TRUE)

We will then use a list of cogs to build a object for the analysis

4.2 Constructing the geaR object and running the analysis

Now you can initialise a geaR object and run an analysis

gear10kb <- makeGear(loci, 
                     populations = pops, 
                     outgroup = "P10", 
                     cogs = list(argCog,admixCog, divCog,outloci,outTrees))

gear10kb <- analyzeGear(GDS, gear = gear10kb)

4.2.1 Retreiving stats from the geaR object

• Full diversity stats

library(kableExtra)
kable(gear10kb@DiversityStatsFULL) %>%
  kable_styling() %>%
  scroll_box(width = "800px", height = "200px")

SeqName	Start	End	windowMid	snpMid	nSites	P1_pi	P10_pi	P2_pi	P3_pi	P4_pi	P5_pi	P6_pi	P7_pi	P8_pi	P9_pi	P1:P10_dxy	P1:P10_SDdxy	P1:P2_dxy	P1:P2_SDdxy	P1:P3_dxy	P1:P3_SDdxy	P1:P4_dxy	P1:P4_SDdxy	P1:P5_dxy	P1:P5_SDdxy	P1:P6_dxy	P1:P6_SDdxy	P1:P7_dxy	P1:P7_SDdxy	P1:P8_dxy	P1:P8_SDdxy	P1:P9_dxy	P1:P9_SDdxy	P10:P2_dxy	P10:P2_SDdxy	P10:P3_dxy	P10:P3_SDdxy	P10:P4_dxy	P10:P4_SDdxy	P10:P5_dxy	P10:P5_SDdxy	P10:P6_dxy	P10:P6_SDdxy	P10:P7_dxy	P10:P7_SDdxy	P10:P8_dxy	P10:P8_SDdxy	P10:P9_dxy	P10:P9_SDdxy	P2:P3_dxy	P2:P3_SDdxy	P2:P4_dxy	P2:P4_SDdxy	P2:P5_dxy	P2:P5_SDdxy	P2:P6_dxy	P2:P6_SDdxy	P2:P7_dxy	P2:P7_SDdxy	P2:P8_dxy	P2:P8_SDdxy	P2:P9_dxy	P2:P9_SDdxy	P3:P4_dxy	P3:P4_SDdxy	P3:P5_dxy	P3:P5_SDdxy	P3:P6_dxy	P3:P6_SDdxy	P3:P7_dxy	P3:P7_SDdxy	P3:P8_dxy	P3:P8_SDdxy	P3:P9_dxy	P3:P9_SDdxy	P4:P5_dxy	P4:P5_SDdxy	P4:P6_dxy	P4:P6_SDdxy	P4:P7_dxy	P4:P7_SDdxy	P4:P8_dxy	P4:P8_SDdxy	P4:P9_dxy	P4:P9_SDdxy	P5:P6_dxy	P5:P6_SDdxy	P5:P7_dxy	P5:P7_SDdxy	P5:P8_dxy	P5:P8_SDdxy	P5:P9_dxy	P5:P9_SDdxy	P6:P7_dxy	P6:P7_SDdxy	P6:P8_dxy	P6:P8_SDdxy	P6:P9_dxy	P6:P9_SDdxy	P7:P8_dxy	P7:P8_SDdxy	P7:P9_dxy	P7:P9_SDdxy	P8:P9_dxy	P8:P9_SDdxy	P1:P10_da	P1:P2_da	P1:P3_da	P1:P4_da	P1:P5_da	P1:P6_da	P1:P7_da	P1:P8_da	P1:P9_da	P10:P2_da	P10:P3_da	P10:P4_da	P10:P5_da	P10:P6_da	P10:P7_da	P10:P8_da	P10:P9_da	P2:P3_da	P2:P4_da	P2:P5_da	P2:P6_da	P2:P7_da	P2:P8_da	P2:P9_da	P3:P4_da	P3:P5_da	P3:P6_da	P3:P7_da	P3:P8_da	P3:P9_da	P4:P5_da	P4:P6_da	P4:P7_da	P4:P8_da	P4:P9_da	P5:P6_da	P5:P7_da	P5:P8_da	P5:P9_da	P6:P7_da	P6:P8_da	P6:P9_da	P7:P8_da	P7:P9_da	P8:P9_da	P1:P10_dmin	P1:P2_dmin	P1:P3_dmin	P1:P4_dmin	P1:P5_dmin	P1:P6_dmin	P1:P7_dmin	P1:P8_dmin	P1:P9_dmin	P10:P2_dmin	P10:P3_dmin	P10:P4_dmin	P10:P5_dmin	P10:P6_dmin	P10:P7_dmin	P10:P8_dmin	P10:P9_dmin	P2:P3_dmin	P2:P4_dmin	P2:P5_dmin	P2:P6_dmin	P2:P7_dmin	P2:P8_dmin	P2:P9_dmin	P3:P4_dmin	P3:P5_dmin	P3:P6_dmin	P3:P7_dmin	P3:P8_dmin	P3:P9_dmin	P4:P5_dmin	P4:P6_dmin	P4:P7_dmin	P4:P8_dmin	P4:P9_dmin	P5:P6_dmin	P5:P7_dmin	P5:P8_dmin	P5:P9_dmin	P6:P7_dmin	P6:P8_dmin	P6:P9_dmin	P7:P8_dmin	P7:P9_dmin	P8:P9_dmin	P1:P10_dmax	P1:P2_dmax	P1:P3_dmax	P1:P4_dmax	P1:P5_dmax	P1:P6_dmax	P1:P7_dmax	P1:P8_dmax	P1:P9_dmax	P10:P2_dmax	P10:P3_dmax	P10:P4_dmax	P10:P5_dmax	P10:P6_dmax	P10:P7_dmax	P10:P8_dmax	P10:P9_dmax	P2:P3_dmax	P2:P4_dmax	P2:P5_dmax	P2:P6_dmax	P2:P7_dmax	P2:P8_dmax	P2:P9_dmax	P3:P4_dmax	P3:P5_dmax	P3:P6_dmax	P3:P7_dmax	P3:P8_dmax	P3:P9_dmax	P4:P5_dmax	P4:P6_dmax	P4:P7_dmax	P4:P8_dmax	P4:P9_dmax	P5:P6_dmax	P5:P7_dmax	P5:P8_dmax	P5:P9_dmax	P6:P7_dmax	P6:P8_dmax	P6:P9_dmax	P7:P8_dmax	P7:P9_dmax	P8:P9_dmax	P1_vs_P10_Fst	P1_vs_P2_Fst	P1_vs_P3_Fst	P1_vs_P4_Fst	P1_vs_P5_Fst	P1_vs_P6_Fst	P1_vs_P7_Fst	P1_vs_P8_Fst	P1_vs_P9_Fst	P10_vs_P2_Fst	P10_vs_P3_Fst	P10_vs_P4_Fst	P10_vs_P5_Fst	P10_vs_P6_Fst	P10_vs_P7_Fst	P10_vs_P8_Fst	P10_vs_P9_Fst	P2_vs_P3_Fst	P2_vs_P4_Fst	P2_vs_P5_Fst	P2_vs_P6_Fst	P2_vs_P7_Fst	P2_vs_P8_Fst	P2_vs_P9_Fst	P3_vs_P4_Fst	P3_vs_P5_Fst	P3_vs_P6_Fst	P3_vs_P7_Fst	P3_vs_P8_Fst	P3_vs_P9_Fst	P4_vs_P5_Fst	P4_vs_P6_Fst	P4_vs_P7_Fst	P4_vs_P8_Fst	P4_vs_P9_Fst	P5_vs_P6_Fst	P5_vs_P7_Fst	P5_vs_P8_Fst	P5_vs_P9_Fst	P6_vs_P7_Fst	P6_vs_P8_Fst	P6_vs_P9_Fst	P7_vs_P8_Fst	P7_vs_P9_Fst	P8_vs_P9_Fst
1	10177	138397	74287.0	76865	1000	0.011500	0.013625	0.017375	0.016500	0.009125	0.014000	0.016750	0.015125	0.008125	0.011125	0.0167500	0.0050531	0.020500	0.0070238	0.019375	0.0048836	0.012750	0.0037859	0.0155000	0.0033066	0.019125	0.0069845	0.019500	0.0085946	0.0133750	0.0038449	0.0133750	0.0034424	0.022000	0.0074565	0.0186250	0.0047452	0.0186250	0.0034617	0.0186250	0.0040804	0.0205000	0.0070993	0.022125	0.0085703	0.018250	0.0043589	0.017000	0.0051251	0.0218750	0.0064070	0.022250	0.0065371	0.0198750	0.0061305	0.0230000	0.0075719	0.0210000	0.0086948	0.022250	0.0058252	0.0185000	0.0066433	0.0211250	0.0040311	0.0182500	0.0036056	0.0208750	0.0079823	0.020375	0.0074554	0.0208750	0.0035754	0.018375	0.0043646	0.0163750	0.0042720	0.0191250	0.0069077	0.0205000	0.0086333	0.0111250	0.0027049	0.0132500	0.0041069	0.0187500	0.0062769	0.017875	0.0074644	0.0161250	0.0044403	0.015250	0.0045240	0.0211250	0.0078475	0.0183750	0.0072376	0.0187500	0.0057213	0.0205000	0.0084931	0.017250	0.0093417	0.0135000	0.0047469	0.0041875	0.0060625	0.0053750	0.0024375	0.0027500	0.0050000	0.0061875	0.0035625	0.0020625	0.0065000	0.0035625	0.0072500	0.0048125	0.0053125	0.0077500	0.0073750	0.0046250	0.0049375	0.0090000	0.0041875	0.0059375	0.0047500	0.0095000	0.0042500	0.0083125	0.0030000	0.0042500	0.0045625	0.0085625	0.0045625	0.0048125	0.0061875	0.0083750	0.0025000	0.0031250	0.0033750	0.0033125	0.0050625	0.0026875	0.0051875	0.0059375	0.0048125	0.0088750	0.0041250	0.0038750	0.009	0.010	0.011	0.005	0.007	0.009	0.010	0.006	0.007	0.009	0.011	0.013	0.011	0.012	0.010	0.012	0.008	0.011	0.013	0.010	0.011	0.008	0.014	0.007	0.014	0.012	0.007	0.011	0.014	0.012	0.009	0.011	0.011	0.006	0.007	0.011	0.010	0.006	0.004	0.010	0.009	0.010	0.011	0.004	0.004	0.024	0.033	0.030	0.018	0.022	0.033	0.036	0.019	0.019	0.034	0.028	0.024	0.025	0.037	0.037	0.025	0.024	0.032	0.033	0.032	0.038	0.035	0.032	0.030	0.027	0.025	0.033	0.035	0.027	0.027	0.022	0.033	0.036	0.015	0.019	0.034	0.035	0.021	0.021	0.035	0.030	0.030	0.035	0.037	0.020	0.1428571	0.1735242	0.1610487	0.1056911	0.0973451	0.1503759	0.1885714	0.1536388	0.0835443	0.1733333	0.1057514	0.2416667	0.1483622	0.1488616	0.2123288	0.2532189	0.1574468	0.1272142	0.2535211	0.1177504	0.1482059	0.1275168	0.2714286	0.1297710	0.2449355	0.0895522	0.1133333	0.1260794	0.2580038	0.1417476	0.1722595	0.1929825	0.2567050	0.1265823	0.1336898	0.0989011	0.1021195	0.1862069	0.0966292	0.1399663	0.1926978	0.1472275	0.2762646	0.1358025	0.1675676
1	54763	255755	155259.0	88102	1000	0.004500	0.010000	0.012000	0.012875	0.004000	0.009750	0.012750	0.011375	0.005625	0.006250	0.0112500	0.0048374	0.013125	0.0037216	0.014250	0.0053354	0.005875	0.0032634	0.0098750	0.0024732	0.012375	0.0061196	0.014000	0.0082300	0.0077500	0.0026204	0.0065000	0.0033862	0.018125	0.0055603	0.0141250	0.0045589	0.0131250	0.0047028	0.0136250	0.0039137	0.0177500	0.0065473	0.017250	0.0077932	0.014125	0.0045000	0.012000	0.0043512	0.0185000	0.0047610	0.014875	0.0040967	0.0146250	0.0034809	0.0175000	0.0048442	0.0166250	0.0075796	0.016875	0.0027295	0.0125000	0.0034254	0.0163750	0.0048010	0.0137500	0.0035308	0.0183750	0.0076147	0.015875	0.0067811	0.0167500	0.0042661	0.013625	0.0039812	0.0108750	0.0032223	0.0125000	0.0066332	0.0155000	0.0080498	0.0063750	0.0019621	0.0075000	0.0048717	0.0146250	0.0049649	0.013375	0.0066821	0.0120000	0.0040661	0.009500	0.0028284	0.0182500	0.0069809	0.0137500	0.0072065	0.0133750	0.0054022	0.0163750	0.0075531	0.012000	0.0078994	0.0090000	0.0044721	0.0040000	0.0048750	0.0055625	0.0016250	0.0027500	0.0037500	0.0060625	0.0026875	0.0011250	0.0071250	0.0026875	0.0061250	0.0037500	0.0063750	0.0065625	0.0063125	0.0038750	0.0060625	0.0068750	0.0037500	0.0051250	0.0049375	0.0080625	0.0033750	0.0079375	0.0024375	0.0055625	0.0037500	0.0075000	0.0040625	0.0040000	0.0041250	0.0078125	0.0015625	0.0023750	0.0033750	0.0028125	0.0043125	0.0015000	0.0061875	0.0045625	0.0038750	0.0078750	0.0031875	0.0030625	0.004	0.007	0.005	0.002	0.004	0.006	0.004	0.003	0.002	0.008	0.006	0.005	0.006	0.009	0.007	0.007	0.004	0.010	0.009	0.008	0.011	0.005	0.013	0.005	0.010	0.008	0.008	0.009	0.011	0.008	0.006	0.004	0.007	0.003	0.001	0.009	0.005	0.003	0.004	0.010	0.005	0.005	0.009	0.002	0.003	0.018	0.018	0.024	0.013	0.014	0.023	0.029	0.011	0.012	0.027	0.023	0.023	0.020	0.031	0.031	0.021	0.019	0.026	0.022	0.020	0.027	0.031	0.021	0.017	0.024	0.020	0.032	0.030	0.025	0.023	0.017	0.026	0.032	0.010	0.016	0.027	0.027	0.017	0.013	0.030	0.026	0.023	0.030	0.027	0.017	0.2162162	0.2280702	0.2425068	0.1604938	0.1617647	0.1785714	0.2763533	0.2097561	0.0947368	0.2446352	0.1051345	0.3043478	0.1595745	0.2188841	0.2348993	0.2877493	0.1925466	0.1959596	0.3005464	0.1470588	0.1715481	0.1743929	0.3138686	0.1560694	0.3198992	0.0972569	0.1783567	0.1339286	0.2884615	0.1752022	0.2253521	0.1976048	0.3369272	0.1396648	0.1881188	0.1304348	0.1174935	0.2190476	0.0857143	0.2041237	0.1989101	0.1693989	0.3165829	0.1531532	0.2050209
1	66381	536705	301543.0	115742	1000	0.004625	0.010625	0.012375	0.011750	0.004375	0.009125	0.012250	0.008500	0.004750	0.006625	0.0120000	0.0048854	0.013375	0.0037572	0.013500	0.0051897	0.006125	0.0030741	0.0093750	0.0022472	0.011875	0.0045589	0.012375	0.0046314	0.0073750	0.0023910	0.0070000	0.0036697	0.018875	0.0052265	0.0136250	0.0039476	0.0138750	0.0047452	0.0136250	0.0035000	0.0175000	0.0056569	0.015625	0.0043340	0.014375	0.0043340	0.013250	0.0044497	0.0180000	0.0044422	0.015125	0.0038794	0.0143750	0.0037572	0.0171250	0.0038622	0.0152500	0.0049464	0.016625	0.0026552	0.0131250	0.0029411	0.0152500	0.0045534	0.0131250	0.0035754	0.0176250	0.0063757	0.013625	0.0038101	0.0153750	0.0042407	0.013000	0.0040988	0.0102500	0.0027447	0.0118750	0.0053151	0.0135000	0.0044721	0.0061250	0.0022472	0.0081250	0.0052265	0.0142500	0.0038902	0.011625	0.0040146	0.0111250	0.0036492	0.009250	0.0028868	0.0167500	0.0038210	0.0128750	0.0055842	0.0131250	0.0041292	0.0142500	0.0041553	0.010625	0.0047170	0.0088750	0.0050843	0.0043750	0.0048750	0.0053125	0.0016250	0.0025000	0.0034375	0.0058125	0.0026875	0.0013750	0.0073750	0.0024375	0.0063750	0.0037500	0.0060625	0.0060625	0.0066875	0.0046250	0.0059375	0.0067500	0.0036250	0.0048125	0.0048125	0.0080625	0.0036250	0.0071875	0.0026875	0.0056250	0.0035000	0.0071250	0.0038125	0.0035000	0.0035625	0.0070625	0.0015625	0.0026250	0.0035625	0.0028125	0.0041875	0.0013750	0.0063750	0.0043750	0.0036875	0.0076250	0.0030625	0.0031875	0.004	0.007	0.006	0.002	0.005	0.006	0.006	0.003	0.002	0.009	0.007	0.006	0.009	0.008	0.006	0.008	0.005	0.011	0.010	0.006	0.013	0.005	0.013	0.007	0.009	0.006	0.008	0.007	0.008	0.006	0.005	0.007	0.008	0.003	0.001	0.008	0.003	0.002	0.005	0.012	0.004	0.007	0.008	0.003	0.002	0.019	0.020	0.024	0.013	0.014	0.021	0.020	0.010	0.014	0.026	0.022	0.024	0.020	0.028	0.021	0.021	0.021	0.025	0.023	0.020	0.023	0.022	0.022	0.017	0.023	0.018	0.027	0.021	0.024	0.021	0.015	0.024	0.024	0.011	0.017	0.023	0.019	0.015	0.016	0.024	0.023	0.019	0.021	0.019	0.016	0.2229299	0.2228571	0.2449568	0.1529412	0.1538462	0.1692308	0.3069307	0.2227979	0.1089109	0.2427984	0.0982368	0.2982456	0.1595745	0.2095032	0.2406948	0.3031161	0.2114286	0.1975052	0.2872340	0.1442786	0.1634820	0.1873479	0.3200993	0.1602210	0.3083110	0.1140584	0.1898734	0.1473684	0.3015873	0.1718310	0.2058824	0.1764706	0.3542320	0.1461988	0.1926606	0.1428571	0.1376147	0.2318339	0.0802920	0.2350230	0.2046784	0.1634349	0.3652695	0.1683849	0.2188841
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1	955507	982472	968989.5	970906	1000	0.017250	0.016500	0.017000	0.022625	0.029875	0.018000	0.018750	0.016375	0.021375	0.021250	0.0177500	0.0174490	0.026625	0.0182387	0.051125	0.0140089	0.033500	0.0199064	0.0230000	0.0151349	0.021375	0.0175608	0.026000	0.0183811	0.0221250	0.0174428	0.0231250	0.0176253	0.025625	0.0178769	0.0516250	0.0145917	0.0336250	0.0198423	0.0225000	0.0146515	0.0208750	0.0172506	0.025625	0.0177872	0.021875	0.0168992	0.022375	0.0177722	0.0473750	0.0216175	0.028125	0.0227270	0.0196250	0.0148453	0.0207500	0.0173570	0.0175000	0.0171114	0.022500	0.0186190	0.0220000	0.0181475	0.0388750	0.0261607	0.0496250	0.0202809	0.0486250	0.0180993	0.047250	0.0212932	0.0496250	0.0182497	0.049750	0.0191398	0.0297500	0.0218281	0.0291250	0.0219146	0.0276250	0.0227387	0.0310000	0.0219545	0.0313750	0.0213881	0.0198750	0.0146600	0.019000	0.0151526	0.0217500	0.0152206	0.021500	0.0149131	0.0201250	0.0177534	0.0213750	0.0171459	0.0218750	0.0167566	0.0222500	0.0178307	0.021875	0.0177534	0.0227500	0.0174948	0.0008750	0.0095000	0.0311875	0.0099375	0.0053750	0.0033750	0.0091875	0.0028125	0.0038750	0.0088750	0.0320625	0.0104375	0.0052500	0.0032500	0.0091875	0.0029375	0.0035000	0.0275625	0.0046875	0.0021250	0.0028750	0.0008125	0.0033125	0.0028750	0.0126250	0.0293125	0.0279375	0.0277500	0.0276250	0.0278125	0.0058125	0.0048125	0.0045000	0.0053750	0.0058125	0.0015000	0.0018125	0.0020625	0.0018750	0.0025625	0.0013125	0.0018750	0.0033750	0.0030625	0.0014375	0.001	0.001	0.006	0.004	0.003	0.001	0.001	0.004	0.003	0.002	0.006	0.004	0.003	0.002	0.001	0.004	0.002	0.004	0.002	0.003	0.002	0.003	0.002	0.002	0.000	0.005	0.006	0.005	0.005	0.004	0.001	0.000	0.003	0.001	0.004	0.001	0.002	0.003	0.003	0.000	0.002	0.004	0.003	0.003	0.002	0.043	0.044	0.059	0.059	0.042	0.041	0.042	0.043	0.043	0.041	0.060	0.060	0.041	0.040	0.041	0.043	0.042	0.060	0.060	0.040	0.039	0.040	0.044	0.042	0.060	0.064	0.060	0.061	0.060	0.060	0.064	0.060	0.061	0.060	0.060	0.039	0.040	0.044	0.042	0.039	0.043	0.041	0.044	0.042	0.043	0.0252708	0.2171429	0.4388742	0.1741512	0.1323077	0.0857143	0.2145985	0.0678733	0.0914454	0.2094395	0.4503951	0.1837184	0.1320755	0.0844156	0.2184250	0.0719755	0.0848485	0.4102326	0.0909091	0.0572391	0.0744337	0.0237660	0.0794603	0.0699088	0.1938580	0.4191242	0.4030658	0.4157303	0.3856894	0.3879686	0.1082654	0.0900585	0.0886700	0.0949227	0.1020856	0.0392157	0.0500864	0.0497738	0.0455927	0.0679934	0.0316742	0.0447761	0.0820669	0.0752688	0.0326241
1	961366	985645	973505.5	976611	1000	0.012875	0.012000	0.012500	0.023750	0.029000	0.011750	0.011625	0.012000	0.015375	0.015375	0.0132500	0.0116132	0.018750	0.0115557	0.045625	0.0144539	0.026375	0.0198154	0.0180000	0.0108197	0.016500	0.0100995	0.018500	0.0123935	0.0162500	0.0113988	0.0180000	0.0108628	0.017500	0.0112190	0.0462500	0.0154467	0.0262500	0.0201180	0.0172500	0.0098894	0.0157500	0.0095324	0.017875	0.0115116	0.015750	0.0109148	0.016875	0.0108436	0.0470000	0.0218358	0.025250	0.0230290	0.0133750	0.0101119	0.0147500	0.0103376	0.0132500	0.0106050	0.016250	0.0116819	0.0160000	0.0113959	0.0391250	0.0263157	0.0466250	0.0215681	0.0457500	0.0191607	0.046250	0.0213151	0.0465000	0.0185329	0.046750	0.0195670	0.0247500	0.0229652	0.0243750	0.0217466	0.0247500	0.0229594	0.0257500	0.0218403	0.0266250	0.0212630	0.0133750	0.0095280	0.012750	0.0107796	0.0158750	0.0111945	0.015875	0.0103529	0.0137500	0.0104403	0.0155000	0.0101915	0.0157500	0.0095951	0.0161250	0.0115347	0.016000	0.0111714	0.0170000	0.0110574	0.0008125	0.0060625	0.0273125	0.0054375	0.0056875	0.0042500	0.0060625	0.0021250	0.0038750	0.0052500	0.0283750	0.0057500	0.0053750	0.0039375	0.0058750	0.0020625	0.0031875	0.0288750	0.0045000	0.0012500	0.0026875	0.0010000	0.0023125	0.0020625	0.0127500	0.0288750	0.0280625	0.0283750	0.0269375	0.0271875	0.0043750	0.0040625	0.0042500	0.0035625	0.0044375	0.0016875	0.0008750	0.0023125	0.0023125	0.0019375	0.0020000	0.0022500	0.0024375	0.0023125	0.0016250	0.000	0.002	0.007	0.000	0.003	0.002	0.001	0.003	0.003	0.002	0.007	0.000	0.003	0.002	0.001	0.003	0.002	0.005	0.001	0.003	0.001	0.004	0.002	0.002	0.001	0.006	0.006	0.005	0.006	0.004	0.002	0.000	0.001	0.001	0.003	0.002	0.003	0.003	0.003	0.002	0.001	0.003	0.002	0.003	0.002	0.030	0.030	0.060	0.061	0.031	0.029	0.030	0.030	0.031	0.028	0.062	0.063	0.029	0.027	0.029	0.031	0.029	0.063	0.064	0.028	0.029	0.028	0.031	0.029	0.064	0.063	0.061	0.062	0.062	0.062	0.064	0.062	0.063	0.063	0.063	0.027	0.029	0.032	0.030	0.027	0.030	0.028	0.031	0.029	0.031	0.0316302	0.1928429	0.4271750	0.1149273	0.1876289	0.1478261	0.1959596	0.0699588	0.1206226	0.1764706	0.4424951	0.1229947	0.1845494	0.1428571	0.1966527	0.0700637	0.1042945	0.4433781	0.0978261	0.0490196	0.1002331	0.0392157	0.0766046	0.0688935	0.1946565	0.4485437	0.4423645	0.4424951	0.4077578	0.4099906	0.0969529	0.0909091	0.0939227	0.0743155	0.0909091	0.0673317	0.0355330	0.0785563	0.0785563	0.0757946	0.0689655	0.0769231	0.0817610	0.0778947	0.0501931
1	967590	988929	978259.5	980688	1000	0.006250	0.006125	0.004875	0.017375	0.019750	0.005625	0.005250	0.005750	0.007125	0.006625	0.0067500	0.0051316	0.008500	0.0053914	0.033750	0.0133841	0.016875	0.0159243	0.0091250	0.0062597	0.008750	0.0047258	0.008875	0.0061522	0.0077500	0.0059944	0.0086250	0.0051753	0.007750	0.0051962	0.0337500	0.0137623	0.0167500	0.0159562	0.0087500	0.0056391	0.0085000	0.0042895	0.008625	0.0056199	0.007625	0.0054145	0.008000	0.0050200	0.0335000	0.0167849	0.015500	0.0176106	0.0057500	0.0057329	0.0065000	0.0051381	0.0058750	0.0052138	0.007000	0.0057619	0.0066250	0.0052393	0.0282500	0.0196010	0.0335000	0.0168721	0.0331250	0.0161240	0.033000	0.0163870	0.0331250	0.0150505	0.033250	0.0155542	0.0156250	0.0178321	0.0156250	0.0170211	0.0155000	0.0173666	0.0161250	0.0166208	0.0161250	0.0166848	0.0062500	0.0054589	0.006000	0.0063034	0.0075000	0.0067032	0.007125	0.0058977	0.0063750	0.0049917	0.0075000	0.0052915	0.0067500	0.0047258	0.0072500	0.0061482	0.007125	0.0054879	0.0076250	0.0057257	0.0005625	0.0029375	0.0219375	0.0038750	0.0031875	0.0030000	0.0028750	0.0010625	0.0021875	0.0022500	0.0220000	0.0038125	0.0028750	0.0028125	0.0026875	0.0010000	0.0016250	0.0223750	0.0031875	0.0005000	0.0014375	0.0005625	0.0010000	0.0008750	0.0096875	0.0220000	0.0218125	0.0214375	0.0208750	0.0212500	0.0029375	0.0031250	0.0027500	0.0026875	0.0029375	0.0008125	0.0003125	0.0011250	0.0010000	0.0008750	0.0013125	0.0008125	0.0008125	0.0009375	0.0007500	0.000	0.000	0.002	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.002	0.000	0.000	0.000	0.001	0.000	0.000	0.002	0.000	0.000	0.000	0.001	0.000	0.000	0.001	0.002	0.002	0.003	0.002	0.002	0.000	0.000	0.001	0.000	0.000	0.000	0.001	0.000	0.000	0.001	0.000	0.000	0.001	0.001	0.000	0.015	0.015	0.044	0.044	0.016	0.015	0.016	0.015	0.015	0.014	0.044	0.044	0.015	0.014	0.015	0.014	0.014	0.045	0.045	0.014	0.015	0.013	0.014	0.012	0.046	0.045	0.044	0.045	0.044	0.044	0.045	0.044	0.045	0.044	0.044	0.014	0.015	0.015	0.014	0.013	0.014	0.012	0.015	0.013	0.014	0.0434783	0.2088889	0.4814815	0.1297071	0.2116183	0.2068966	0.1932773	0.0735931	0.1452282	0.1698113	0.4835165	0.1284211	0.1965812	0.1982379	0.1845494	0.0701754	0.1130435	0.5014006	0.1146067	0.0454545	0.1243243	0.0502793	0.0769231	0.0707071	0.2069426	0.4888889	0.4908579	0.4810659	0.4600551	0.4696133	0.1037528	0.1111111	0.0973451	0.0909091	0.1002132	0.0695187	0.0267380	0.0810811	0.0754717	0.0736842	0.0958904	0.0640394	0.0593607	0.0704225	0.0517241
1	973519	993104	983311.5	984214	1000	0.011625	0.006375	0.005375	0.019500	0.023375	0.005625	0.004250	0.005750	0.011875	0.006125	0.0106250	0.0080322	0.011500	0.0077717	0.037375	0.0160369	0.020750	0.0169332	0.0118750	0.0080901	0.012125	0.0076757	0.011500	0.0077803	0.0127500	0.0087369	0.0118750	0.0076147	0.008250	0.0053975	0.0403750	0.0175760	0.0197500	0.0190596	0.0088750	0.0054022	0.0090000	0.0042269	0.008750	0.0049464	0.011625	0.0073564	0.008500	0.0049396	0.0387500	0.0195056	0.018125	0.0198423	0.0061250	0.0052010	0.0062500	0.0048097	0.0062500	0.0043742	0.010375	0.0082775	0.0065000	0.0043970	0.0332500	0.0233109	0.0386250	0.0198154	0.0382500	0.0199850	0.038125	0.0193559	0.0366250	0.0174619	0.038500	0.0191276	0.0180000	0.0201362	0.0177500	0.0199650	0.0178750	0.0196431	0.0201250	0.0175533	0.0183750	0.0193903	0.0055000	0.0055136	0.006000	0.0058195	0.0105000	0.0089815	0.006625	0.0053401	0.0056250	0.0053276	0.0103750	0.0091278	0.0060000	0.0047046	0.0101250	0.0087168	0.006625	0.0050712	0.0106250	0.0086246	0.0016250	0.0030000	0.0218125	0.0032500	0.0032500	0.0041875	0.0028125	0.0010000	0.0030000	0.0023750	0.0274375	0.0048750	0.0028750	0.0036875	0.0026875	0.0025000	0.0022500	0.0263125	0.0037500	0.0006250	0.0014375	0.0006875	0.0017500	0.0007500	0.0118125	0.0260625	0.0263750	0.0255000	0.0209375	0.0256875	0.0035000	0.0039375	0.0033125	0.0025000	0.0036250	0.0005625	0.0003125	0.0017500	0.0007500	0.0006250	0.0023125	0.0008125	0.0013125	0.0006875	0.0016250	0.001	0.001	0.004	0.002	0.001	0.002	0.001	0.000	0.002	0.000	0.003	0.001	0.000	0.001	0.001	0.001	0.000	0.003	0.001	0.000	0.001	0.001	0.001	0.001	0.001	0.002	0.002	0.002	0.002	0.002	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.023	0.024	0.051	0.051	0.025	0.024	0.024	0.025	0.024	0.015	0.052	0.052	0.014	0.013	0.015	0.023	0.013	0.051	0.051	0.015	0.014	0.013	0.024	0.013	0.053	0.051	0.051	0.050	0.051	0.051	0.051	0.051	0.050	0.051	0.051	0.013	0.015	0.025	0.013	0.013	0.024	0.011	0.024	0.013	0.024	0.0828025	0.1500000	0.4120425	0.0849673	0.1585366	0.2087227	0.1393189	0.0408163	0.1445783	0.1681416	0.5146542	0.1407942	0.1932773	0.2576419	0.1814346	0.1204819	0.1525424	0.5140415	0.1153846	0.0537634	0.1299435	0.0582011	0.0921053	0.0612245	0.2160000	0.5091575	0.5261845	0.5024631	0.4002389	0.5006090	0.1076923	0.1247525	0.1021195	0.0662252	0.1094340	0.0538922	0.0267380	0.0909091	0.0600000	0.0588235	0.1254237	0.0726257	0.0693069	0.0547264	0.0828025
1	978846	999198	989022.0	987203	1000	0.014375	0.009250	0.007625	0.015000	0.019375	0.007250	0.005625	0.006875	0.014625	0.008250	0.0138750	0.0090028	0.015000	0.0092448	0.027250	0.0118519	0.019375	0.0117636	0.0148750	0.0090323	0.015125	0.0090545	0.014250	0.0091688	0.0157500	0.0102274	0.0155000	0.0090480	0.011750	0.0074072	0.0316250	0.0109476	0.0191250	0.0127325	0.0122500	0.0070000	0.0125000	0.0061319	0.011875	0.0064692	0.014625	0.0089209	0.012000	0.0070427	0.0305000	0.0138948	0.017125	0.0145734	0.0086250	0.0069174	0.0083750	0.0069077	0.0082500	0.0058935	0.013125	0.0101382	0.0088750	0.0064692	0.0252500	0.0167153	0.0290000	0.0136821	0.0288750	0.0142449	0.028750	0.0137865	0.0273750	0.0141745	0.030375	0.0138028	0.0161250	0.0145046	0.0158750	0.0146828	0.0158750	0.0143614	0.0187500	0.0134387	0.0173750	0.0143800	0.0071250	0.0074822	0.007500	0.0072847	0.0130000	0.0103923	0.009000	0.0073575	0.0067500	0.0070475	0.0127500	0.0109453	0.0081250	0.0070605	0.0125000	0.0105135	0.008500	0.0068118	0.0132500	0.0107920	0.0020625	0.0040000	0.0125625	0.0025000	0.0040625	0.0051250	0.0036250	0.0012500	0.0041875	0.0033125	0.0195000	0.0048125	0.0040000	0.0050625	0.0038125	0.0026875	0.0032500	0.0191875	0.0036250	0.0011875	0.0017500	0.0010000	0.0020000	0.0009375	0.0080625	0.0178750	0.0185625	0.0178125	0.0125625	0.0187500	0.0028125	0.0033750	0.0027500	0.0017500	0.0035625	0.0006875	0.0004375	0.0020625	0.0012500	0.0005000	0.0026250	0.0011875	0.0017500	0.0009375	0.0018125	0.001	0.001	0.004	0.002	0.002	0.002	0.001	0.000	0.002	0.000	0.003	0.001	0.001	0.001	0.002	0.000	0.000	0.003	0.001	0.001	0.001	0.002	0.001	0.001	0.001	0.002	0.002	0.003	0.002	0.002	0.000	0.000	0.001	0.000	0.000	0.000	0.001	0.000	0.000	0.001	0.000	0.000	0.000	0.001	0.000	0.027	0.030	0.039	0.039	0.029	0.028	0.029	0.029	0.028	0.021	0.040	0.040	0.018	0.017	0.018	0.027	0.019	0.040	0.040	0.021	0.019	0.018	0.030	0.020	0.040	0.039	0.038	0.039	0.040	0.040	0.039	0.038	0.039	0.040	0.040	0.017	0.018	0.029	0.019	0.016	0.028	0.016	0.029	0.017	0.028	0.0802920	0.1538462	0.2995529	0.0689655	0.1581509	0.2039801	0.1457286	0.0413223	0.1561772	0.1640867	0.4457143	0.1439252	0.1951220	0.2539185	0.1912226	0.1011765	0.1566265	0.4588939	0.1183673	0.0739300	0.1166667	0.0645161	0.0824742	0.0557621	0.1899853	0.4454829	0.4736842	0.4488189	0.2977778	0.4464286	0.0955414	0.1189427	0.0948276	0.0489510	0.1142285	0.0506912	0.0300429	0.0861619	0.0746269	0.0384615	0.1147541	0.0788382	0.0752688	0.0583658	0.0734177

• admixture stats

kable(gear10kb@AdmixtureStats) %>%
  kable_styling() %>%
  scroll_box(width = "800px", height = "200px")

SeqName	Start	End	windowMid	snpMid	nSites	f4(P1,P2,P3;P10)	fd(P1,P2,P3;P10)
1	10177	11177	10677	77090.0	927	0.0012810	0.0012855
1	54763	55763	55263	87983.0	942	0.0013933	0.0013955
1	66381	67381	66881	115714.5	932	0.0013412	0.0013441
1	83094	84094	83594	235106.5	928	0.0012123	0.0012156
1	94476	95476	94976	526927.5	928	0.0001347	0.0001350
1	138430	139430	138930	564769.0	931	0.0002014	0.0002019
1	255806	256806	256306	566875.0	929	0.0002018	0.0002023
1	536816	537816	537316	569290.0	926	0.0001350	0.0001353
1	565470	566470	565970	592543.5	920	0.0001359	0.0001361
1	568308	569308	568808	669746.0	915	-0.0001366	0.0001368
1	569767	570767	570267	713050.0	909	-0.0001375	0.0001377
1	636991	637991	637491	721237.0	909	-0.0002063	0.0002067
1	702142	703142	702642	727317.0	920	-0.0001359	0.0001361
1	715977	716977	716477	734952.5	914	-0.0001368	0.0001371
1	724721	725721	725221	741761.5	898	0.0000000	0.0000000
1	731716	732716	732216	750802.0	898	0.0000000	0.0000000
1	738445	739445	738945	758860.0	896	0.0000000	0.0000000
1	747966	748966	748466	766371.5	890	0.0000000	0.0000000
1	755162	756162	755662	772303.0	905	0.0000000	0.0000000
1	762398	763398	762898	776776.5	906	0.0000000	0.0000000
1	769197	770197	769697	783027.0	907	0.0000000	0.0000000
1	774667	775667	775167	789929.5	908	0.0000000	0.0000000
1	780198	781198	780698	794856.0	912	0.0000000	0.0000000
1	786704	787704	787204	800831.0	903	0.0000000	0.0000000
1	792161	793161	792661	807167.5	920	0.0000000	0.0000000
1	797506	798506	798006	812755.5	918	0.0000000	0.0000000
1	803550	804550	804050	821453.5	906	0.0000000	0.0000000
1	810013	811013	810513	828539.0	899	0.0000000	0.0000000
1	817656	818656	818156	834746.0	884	0.0000000	0.0000000
1	824607	825607	825107	839824.5	872	0.0000000	0.0000000
1	832448	833448	832948	846842.0	873	0.0000000	0.0000000
1	837456	838456	837956	851763.0	881	0.0000000	0.0000000
1	843502	844502	844002	855919.0	887	0.0000000	0.0000000
1	849253	850253	849753	860551.5	904	0.0000000	0.0000000
1	853596	854596	854096	863754.5	912	0.0000000	0.0000000
1	857410	858410	857910	867428.0	916	0.0000000	0.0000000
1	862708	863708	863208	871737.0	919	0.0000000	0.0000000
1	865459	866459	865959	875470.0	921	0.0000000	0.0000000
1	869387	870387	869887	879324.5	920	0.0000000	0.0000000
1	873394	874394	873894	882573.0	924	0.0000000	0.0000000
1	877822	878822	878322	886379.5	924	0.0000000	0.0000000
1	880601	881601	881101	890195.0	927	0.0000000	0.0000000
1	884635	885635	885135	893239.0	909	0.0000000	0.0000000
1	888239	889239	888739	897654.0	913	0.0000000	0.0000000
1	891689	892689	892189	901116.0	915	0.0000000	0.0000000
1	895268	896268	895768	905389.5	906	0.0000000	0.0000000
1	899557	900557	900057	908698.0	896	0.0000000	0.0000000
1	903258	904258	903758	913465.5	908	0.0000000	0.0000000
1	907140	908140	907640	918068.5	908	0.0000000	0.0000000
1	910174	911174	910674	923411.0	893	0.0000000	0.0000000
1	915528	916528	916028	928288.0	891	0.0000000	0.0000000
1	920605	921605	921105	933055.0	899	0.0001390	0.0001392
1	925917	926917	926417	938206.5	906	0.0001380	0.0001381
1	930705	931705	931205	943388.5	902	0.0001386	0.0001387
1	935398	936398	935898	948100.0	905	0.0001381	0.0001383
1	940517	941517	941017	952550.0	896	-0.0002790	0.0002795
1	945257	946257	945757	958236.0	899	-0.0004171	0.0004177
1	950599	951599	951099	963986.0	903	-0.0004153	0.0004159
1	955507	956507	956007	971601.0	921	-0.0004072	0.0004077
1	961366	962366	961866	977200.0	935	-0.0004011	0.0004015
1	967590	968590	968090	980651.5	954	0.0000000	0.0000000
1	973519	974519	974019	983869.0	947	-0.0019799	0.0019862
1	978846	979846	979346	986994.5	938	-0.0021988	0.0022065

• retreive output directory for fasta and tree files

gear10kb@OutputLoci

## An object of class "cog.outputLoci"
## Slot "outputDirectory":
## [1] "~/Desktop/setupGear/"
## 
## Slot "alleles":
## [1] "seperate"
## 
## Slot "removeIndels":
## logical(0)

gear10kb@OutputTrees

## An object of class "cog.outputTrees"
## Slot "outputDirectory":
## [1] "~/Desktop/setupGear/"
## 
## Slot "alleles":
## [1] "seperate"
## 
## Slot "removeIndels":
## logical(0)

5 Constructing loci using a gff

Users may wish to investigate different features across the genome. Below we will read in coding sequence from a gff file, select 4-fold degenerate codons, and incorperate into our windows.

5.1 Setting up loci for cds using a GDS

cds <- makeFeatures(gffName = paste0(fileDir, "/sampleGFF.gff3"), 
                    feature = "gene:cds", 
                    longestIsoform = TRUE, 
                    geneIDField = "Name")

kable(head(as.data.frame(cds))) %>%
  kable_styling() %>%
  scroll_box(width = "800px", height = "200px")

group	group_name	seqnames	start	end	width	strand	gene
1	AGRN	1	955553	955753	201		AGRN
1	AGRN	1	957581	957842	262		AGRN
1	AGRN	1	970657	970704	48		AGRN
1	AGRN	1	976045	976260	216		AGRN
1	AGRN	1	976553	976777	225		AGRN
1	AGRN	1	976858	977082	225		AGRN

We can then use the extracted coding sequence to generate a database containing codon positions and residues. After this is generated we will then extract the 3rd position of 4-fold degenerate codons. However, it is not sufficient just to filter the data as there may be variation within the test dataset causing an amino acid substitution. To counter this we will validate the presence of 4-fold degenerate codons and return only those that match.

5.2 Build codon database then validate 4-fold degenerate codons

library(Biostrings)

# Import genome and build the codon database
genome <- readDNAStringSet(paste0(fileDir, "/sampleReferenceGenome.fa"))
codons <- buildCodonDB(genome, exons = cds)

# Calidate codons
fourFold <- validate4FoldCodons(GDS, input = codons, pops = pops, nCores = 1)

## # of selected samples: 20
## # of selected variants: 131
## # of selected variants: 9
## # of selected variants: 8
## # of selected variants: 1
## # of selected variants: 0
## # of selected variants: 54
## # of selected variants: 12
## # of selected variants: 8
## # of selected variants: 30
## # of selected variants: 48
## # of selected variants: 0
## # of selected variants: 0
## # of selected variants: 8
## # of selected variants: 63
## # of selected variants: 65

kable(head(as.data.frame(codons))) %>%
  kable_styling() %>%
  scroll_box(width = "800px", height = "200px")

group	group_name	seqnames	start	end	width	strand	gene	residue	codon	number	codonPosition
1	AGRN	1	955553	955553	1		AGRN	Met	atg	1	first
1	AGRN	1	955554	955554	1		AGRN	Met	atg	1	second
1	AGRN	1	955555	955555	1		AGRN	Met	atg	1	third
1	AGRN	1	955556	955556	1		AGRN	Ala	gcc	2	first
1	AGRN	1	955557	955557	1		AGRN	Ala	gcc	2	second
1	AGRN	1	955558	955558	1		AGRN	Ala	gcc	2	third

These ranges can then be incorperated into out 10kb tiled windows. In order to do this we use the mergeLoci function specifying our windows from before and the cds we just generated.

5.3 Combining multiple loci

# additive merge the two loci 
cdsLoci <- mergeLoci(x = loci, y = cds, overlap = "+", nCores = 1)