Pre-processing Steps for Genome-wide High-density NARAC Dataset Facilitates its Haplotype Block Partitioning | ||||
| Journal of Advanced Engineering Trends | ||||
| Public Titles, Volume 40, Issue 2, April 2021, Page 61-69 PDF (689.68 K) | ||||
| DOI: 10.21608/jaet.2020.40032.1035 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Fatma Sayed Ibrahim   1; Mohamed ‎ Saad2; Ashraf Said ‎ 3; Hesham Hamed4
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| 1Biomedical Engineering Department,‎ Faculty of Engineering, Minia University, Egypt ‎ | ||||
| 2Biomedical Engineering dept. Minia University ‎ Minia, Egypt | ||||
| 3ashraf.mahroos@mu.edu.eg | ||||
| 4Electrical Engineering Dept., Faculty of Engineering, Minia University, Al-Minia, Egypt. Electrical Engineering Dept., ‎Faculty of Engineering, Egyptian Russian University, Cairo, Egypt | ||||
| Abstract | ||||
| The pre-processing phase is a crucial step to prepare any data for deep considerable analysis. Genome-wide data is considered big data; dealing with such data is not an easy task and still poses a significant challenge. The genome-wide association study (GWAS) is based on enormous high-density data with high throughput. This paper has illustrated the main pre-processing steps on data from North American Rheumatoid Arthritis Consortium (NARAC) for preparing it for haplotype block partitioning using different methods and with different platforms. This paper’s main objective is to summarize the steps of pre-processing the raw genotyped dataset to prepare it for haplotype block partitioning and further analyses. Besides, we present each practical step by clear tables for better visualizing, elucidation, and workflow interpretation. Besides, we aimed to overcome the missing data and normalize the output in a standardized format. Eventually, this will improve the understanding of such data formats and build the foundation stone of critical genome-wide experiments and studies. Thus, this work could a guide for other researchers who use similar data. The pre-processed data will be applied to imputation, BigLD block partitioning under R and Haploview methods. Our sequence of pre-processing steps includes preparing the characters to be in a form that is suitable for imputation. The next step is recording data in 0,1,2 format to be proper for the BigLD. We were finally preparing data for Haploview to provide clear haplotype block partitioning, association analysis, and furthermore. | ||||
| Keywords | ||||
| haplotype blocks; single-nucleotide polymorphism; linkage disequilibrium; interval graph modeling of clusters; rheumatoid arthritis. | ||||
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