Introduction

After extracting data for the common variants, the last cleaning step is to extract genomic data for NIES individuals only. Unique identifiers are used for individuals and a unique list of UUIDs need to be identified to extract the relevant data.

Methods and Results

1. Load .fam file from the merged data set. (.fam file contains UUIDs of individuals with genomic data)

merged_fam_data <- read.table('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/plink_output/common_id_merge.fam', header = F)

head(merged_fam_data)

##   V1     V2   V3     V4 V5 V6
## 1  1 110050  764 368910  1 -9
## 2  1 110110 2621 400796  1 -9
## 3  1 110130 2994   2330  1 -9
## 4  1 110150    0      0  2 -9
## 5  1 110160 2070   2071  1 -9
## 6  1 110320 4462   6317  1 -9

2.Load .csv file that contains UUIDs of NIES individuals that provided DNA samples, either from NIES, NIHS, or both.

nies_with_dna <- read.csv('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/NIES_with_dna.csv', header = T)

head(nies_with_dna)

##     UUID IN_NIES   NIES DNA_NIES DNA_NIHS
## 1 110150     Yes ES_715       NA        1
## 2 110160     Yes ES_695       NA        1
## 3 110460     Yes ES_006       NA        1
## 4 110490     Yes ES_591       NA        1
## 5 110500     Yes ES_555       NA        1
## 6 110590     Yes ES_686       NA        1

Note: prior to loading this data, the original ID file contained UUIDs for ALL NIES and/or NIHS individuals. I selected the UUIDs for NIES individuals only, and then filtered ONLY for individuals that provided DNA. I.e. there were 801 individuals in the NIES, of which 632 individuals provided DNA, so I only obtained the UUIDs for the 632 individuals.

3.Assign column names/labels to data from the .fam file. This is important so that the two lists can be easily merged.

colnames(merged_fam_data) <- c("Fam ID", "UUID", "Pat ID", "Mat ID", "Sex", "Affection")

head(merged_fam_data)

##   Fam ID   UUID Pat ID Mat ID Sex Affection
## 1      1 110050    764 368910   1        -9
## 2      1 110110   2621 400796   1        -9
## 3      1 110130   2994   2330   1        -9
## 4      1 110150      0      0   2        -9
## 5      1 110160   2070   2071   1        -9
## 6      1 110320   4462   6317   1        -9

4.Merge two lists to obtain unique UUIDs.

nies_with_geno <- merge(nies_with_dna, merged_fam_data, by="UUID")

head(nies_with_geno)

##     UUID IN_NIES   NIES DNA_NIES DNA_NIHS Fam ID Pat ID Mat ID Sex
## 1 110150     Yes ES_715       NA        1      1      0      0   2
## 2 110160     Yes ES_695       NA        1      1   2070   2071   1
## 3 110500     Yes ES_555       NA        1      1   3185 400162   2
## 4 110650     Yes ES_225       NA        1      1 400226 362911   2
## 5 110820     Yes ES_629       NA        1      1 400184   1910   2
## 6 111280     Yes ES_257        1        1      1    933     59   2
##   Affection
## 1        -9
## 2        -9
## 3        -9
## 4        -9
## 5        -9
## 6        -9

The output from this step indicates that there is an overlap of 362 individuals, suggesting that there are 362 individuals within the NIES with both phenotypic and genomic data.

5.Extract Family IDs and UUIDs only because PLINK requires both to extract data based on indiv IDs

nies_geno_id <- cbind(nies_with_geno$`Fam ID`, nies_with_geno$UUID)
head(nies_geno_id)

##      [,1]   [,2]
## [1,]    1 110150
## [2,]    1 110160
## [3,]    1 110500
## [4,]    1 110650
## [5,]    1 110820
## [6,]    1 111280

6.Export to .txt file for use in PLINK

write.table(nies_geno_id, 'C:/Users/Martha/Documents/Honours/Project/honours.project/nies_geno_id.txt', col.names = F, row.names = F)

7.Extract relevant genomic data in PLINK

plink1.9 --bfile plink_output/common_id_merge --keep nies_geno_id.txt --make-bed --out plink_output/nies_only_geno

This last line in PLINK extracted the genomic data (5, 789,188 variants) for the 362 NIES individuals. The total genotyping rate for this data set is 96.38%.

This extracted data is the final data set that will be used for subsequent analyses. The phenotypic data will also be restricted to these individuals only.

8.Generate allele frequency statistic for the NIES only genomic data

plink1.9 --bfile plink_output/nies_only_geno --freq --out plink_output/nies_allele_freq

9.Load allele frequency file from PLINK

nies_only_geno <- read.table('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/plink_output/nies_allele_freq.frq', header = T)
head(nies_only_geno)

##   CHR         SNP A1 A2     MAF NCHROBS
## 1   1  rs62637813  C  G 0.00000      50
## 2   1 rs141149254  A  G 0.00000      22
## 3   1   rs2462492  T  C 0.07143      14
## 4   1  rs76735897  G  A 0.37500       8
## 5   1  rs77573425  C  G 0.37500       8
## 6   1  rs28850140  A  G 0.00000      32

10.Visualise allele frequency distributions in this data set.

hist(nies_only_geno$MAF, main = "Allele Frequency Distributions of Genomic Data for NIES", xlab = "Minor Allele Frequency", xlim = c(0.0, 0.5), col = "plum")

11.Generate missingness statistics for NIES only data set

plink1.9 --bfile plink_output/nies_only_geno --missing --out plink_output/nies_missing

This exercise concludes the cleaning step for the genomic data. I have extracted the relevant information by including only the common variants across both WGS and SNP-array data sets, and only including NIES individuals. This has resulted in a final data set that includes 5,789,188 variants and 362 individuals. More detailed analysis of basic statistics generated at different points will follow.

Note: 362 individuals was more than I expected. I thought that of the NIES individuals, 238 had SNP-array data and 86 had WGS data, which is a total of 324, including overlaps. After exclusion of these overlaps, I was expecting 301 individuals.