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Algorithm Implementation of Genetic Association Analysis for Rheumatoid Arthritis Data Based on Haplotype Blocks
- 1. 1 Master of science dissertation defense
- 2. ﴿ ْ مَلَأ َْرَت ْ نَأ ْ َّللا َْلَزنَأ َْنِم ِْاءَمالس ْ اءَم َانجَرخَأَف ْ ِهِب ْ اتَرَمَث ْ خُم افِلَت اَهُنا َولَأ َْنِم َو ِْلاَب ِجال ْ دَدُج ْ يضِب ْ رمُح َو ْ فِلَتخُم اَهُنا َولَأ ُْيبِباََرغ َو ْ ودُس ❁ َْنِم َو ِْ اسالن ْ ِاب َوالد َو ِْامَعاألن َو ْ فِلَتخُم ْ ُهُنا َولَأ َْكِلَذَك اَمنِإ ْ َي ََخ ْ َّللا ْ نِم ْ ِهِداَبِع ْ ُءاَمَلُعال ْ نِإ ْ َّللا ْ يز ِ زَع ْ ورُفَغ ﴾ [ فاطر :35-36] 2
- 3. 3 Minia University Faculty of Engineering Biomedical Engineering Department Fatma Sayed Ibrahim Master of science thesis defense Wednesday, January 27, 2021 Algorithm Implementation of Genetic Association Analysis for Rheumatoid Arthritis Data Based on Haplotype Blocks
- 5. 5 Supervisors Prof. Dr. Hesham Fathy A. Hamed Former Dean of Faculty of Engineering, Minia University Professor at Egyptian-Russian University Dr. Ashraf Mahroos Said Associate Professor Biomedical Engineering Department, Minia University, Dr. Mohamed Nagy Saad Assistant Professor Biomedical Engineering Department, Minia University
- 6. 6 Dr Muhammad Ali M. Rushdi Biomedical Engineering and Systems Department Faculty of Engineering, Cairo university Dr Essam Halim Houssein Vice-dean for Postgraduate studies and research affairs Faculty of Computers and Information, Minia University Thesis committee members Prof. Dr. Hesham Fathy A. Hamed Former Dean of Faculty of Engineering, Minia University Professor at Egyptian- Russian University Dr. Ashraf Mahroos Said Associate Professor Biomedical Engineering Department Minia University, Minia
- 7. 7 1. Introduction 2. Literature review 3. Data description 4. Pre-processing 5. Methods 6. Results 7. Conclusion Outlines
- 8. 8 Introduction Literature review Data description pre-processing Methods Results Conclusion Background Motivation Research Objectives Introduction
- 9. 9 Introduction Literature review Data description pre-processing Methods Results Conclusion Background
- 12. 12
- 13. 13
- 14. 14 All human DNA is 99.9% identical and, 0.1 % is difference
- 15. A A A T T T A G A T T C SNP 15 A SNP is a mutation at a single nucleotide position, where a possible nucleotide type is called an allele. SNP
- 16. Chromosome Chromosome Allele 1 Allele 2 A T T T A A T A Major homozygous genotype Minor homozygous genotype Heterozygous genotype 16
- 19. The minor allele frequency (MAF) 19
- 20. 20 The minor allele frequency (MAF) ...ATGTCACACGTACTT... ...ATGTCACACGTACTT... ...ATGACACAGGTACTT... ...ATGTCACAGGTACTT... ...ATGTCACAGGTACTT... ...ATGACACAGGTACTT... ...ATGTCACAGGTACTT... ...ATGTCACAGGTACTT... ...ATGACACACGTACTT... ...ATGACACAGGTACTT... SNP1 SNP2 SNP1 SNP2 Allele 1 Allele 2 Allele 1 frequency Allele 2 frequency Allele 1 Allele 2 Major Minor T C A G 6 3 4 7 60% 30% 40% 70% T G A C
- 21. Linkage Disequilibrium (LD) 21 LD is the nonrandom association of alleles at different sites.
- 22. Ab Ab Ab aB Ab aB aB aB AB AB ab AB AB ab ab ab ab Equilibrium AB AB ab AB AB ab ab ab ab Disequilibrium AB AB AB AB ab ab ab ab 22
- 23. 23
- 24. 24 Low LD High LD A C A T A C A T G C G T G C G T Individuals SNP 1 SNP 2 A C A C A C A C G T G T G T G T Individuals SNP 1 SNP 2 24
- 25. | D’ | 1.0 0.8 0.6 0.2 0.0 SNP SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 1 2 3 5 6 4 25 Measuring the LD
- 26. Pairwise LD 1 2 3 5 6 4 r 2 Color Key 0.2 0 0.6 0.8 1 0.4 26 Measuring the LD
- 27. The interpretation of LD 27
- 28. 28
- 29. 29
- 33. 33 Haplotype blocks partitioning and visualization
- 35. SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 1 2 3 4 5 6 7 8 Block 1 Block 2 35
- 36. 36 The role of SNPs and haplotype blocks in diseases
- 37. Environmental component Genetic component Infectious diseases Complex diseases Hereditary disease 37
- 39. 39 Samples from Population Cases and controls Data acquisition Genotyping Data SNP array dataset
- 40. 40 Single SNP-based association studies approach Haplotype block- based association studies approach
- 41. 41 Single SNP-based association studies approach Single point mutation (Single SNP) Single point mutation result in a disease
- 42. 42 Haplotype block-based association studies approach Multiple SNPs Multiple SNPs contributes to disease susciptability
- 43. Why haplotype blocks? • Dimensional reduction • Act better in complex diseases • Multiple SNPs could have moderate effect • Considers the interrelationship between linked • Evolutionary studies 43 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 44. 44 Introduction Literature review Data description pre-processing Methods Results Conclusion Background Motivation Research Objectives Introduction
- 45. Motivations
- 46. Why this point ? • The genetic variations influence our predisposition to diseases and any disease has a genetic component, even infectious diseases. • Complex diseases are very common in societies. In particular, chronic condition hugely affects the productivity of a person and its quality of life. • Since the haplotype blocks are much more effective and powerful in such case 46 Motivations Introduction Literature review Data description pre-processing Methods Results Conclusion
- 47. Why this point ? • The gap in knowledge in this field (especially MAF). There are many remains questions that are unanswered • No so many Arabs in this field especially in Minia. 47 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 48. 48 Research Objectives Introduction Literature review Data description pre-processing Methods Results Conclusion • Practically implement computational algorithms to partition genotyped data based on the haplotype blocks. • Find the best haplotype partitioning method applied for the whole-genome case-control dataset to reduce the number of SNPs in the association study.
- 49. 49 . . . G_G A_C . . . . A_G A_A . . . . . . . . . . . . . . . . . . . . . . . . . G_G A_C . . . A_A SNPs
- 50. 50 Samples Cases and controls Data acquisition Genotyping Data SNP array dataset
- 51. 51 The input The output
- 52. 52 The Input 52 . . . G_G A_C . . . . A_G A_A . . . . . . . . . . . . . . . . . . . . . . . . . G_G A_C . . . A_A SNPs
- 53. 53 . . . . G_G A_C G_G . . . . . A_G A_A A_G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G_G A_C G_G . . . . A_A SNPs
- 54. 54 . G_G A_C G_G A_C G_G G_G . . A_G A_A A_G A_A A_G A_G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G_G A_C G_G A_C G_G G_G . A_A SNPs Block 1 Block 2 Individuals IDs
- 55. 55 Start index End index Start rsID End rsID rs22572xxx rs225722xxx rs74307xxx rs198574xxx The output
- 56. 56 Major finding Introduction Literature review Data description pre-processing Methods Results Conclusion • Practically data exploration and uncovering interesting detections • Investigating partitioning method from literature review and empirical comparative study (Biomarker reduction with high SNP correlation) • Sequence of data preprocessing 🡪 R (Hope to make it in R package) • The role of MAF on haplotype block partitioning
- 59. 59 Biomarker reduction percentage 13.5 18.7 • The value is varying from one method to another • MAF has an impact on the results
- 60. 60 Outlines 1. Introduction 2. Literature review 3. Data description 4. Pre-processing 5. Methods 6. Results 7. Conclusion
- 61. Literature review 61 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 62. Literature review 62 Introduction Literature review Data description pre-processing Methods Results Conclusion The main projects in genomics Haplotype bock partitioning methods
- 63. 2001 2002 2005 2007 2009 Human Genome Project International HapMap Project The 100,000 Genomes Project The 1000 Genome Project The announcement of the HGP The completion of the HGP The initial HGP sequencing The announcement of the International HapMap Project HapMap Phase I completion HapMap Phase II completion HapMap Phase III completion 1990 2003 2008 2010 2012 2015 2012 2015 2019 The announcement of 1KGP The completion of pilot phase The 1KGP fulfill its goal The 1KGP’s completion Northern Ireland and Scotland joint the project Beginning the initiative to involve the public in genomic research The 100, 000 Genome Project’s announcement 63
- 64. Literature review haplotype block partitioning Survey in from 2001 to 2021 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 66. Hidden Markov model (HMM) Mark Daly 2001 66
- 67. 67
- 68. 68 Hidden Markov model (HMM) • 2001 Greedy algorithm (GA) • 2002 Dynamic programming (DP) • 2002 Confidence interval (CI) • 2002 Four-gamete test (FGT) • 2002 The minimum description length (MDL) • 2003 Ning Wang Kui Zhang Stacey Gabriel Nila Patil Mark Daly Mikko Koivisto
- 69. 69 Solid spine of LD (SSLD) • 2005 Markov Chain Monte Carlo (MCMC) algorithm • 2008 Xor-genotypes • 2009 Wavelet transforms • 2011 GA-SVM algorithm • 2013 From 2005 to 2013 Jeffrey Barrett Pattaro
- 70. 70 MIG++ • 2014 S-MIG ++ • 2015 Big-LD • 2018 on neutrosophic c-means (NCM) algorithm • 2020 LDBlockShow • 2020 From 2014 to 2020 Daniel Talian Sunah Kim
- 71. 71 Introduction Literature review Data description pre-processing Methods Results Conclusion Data description and exploration
- 72. 72
- 73. 73 Introduction Literature review Data description pre-processing Methods Results Conclusion Data description and exploration NARAC dataset Description Map file SNP array data file Missing data Participants % Cases Very rare SNPs Alleles distribution Genotype distribution % Male % Female Rare SNPs SNP annotation % Controls Visualization based on MAF viewpoint MAF for each SNP Low-frequency SNPs Common SNPs % Male % Female
- 74. 74 Participants ID Af fe ct io n Se x DRB1 _1 DRB1 _2 SE Num SE Status Anti- CCP RFUW rs104 39884 D0024949 0 F 0101 0401 SS yes ? ? G_G D0024302 0 F 0101 7 SN yes ? ? G_G D0023151 0 F 0101 11 SN yes ? ? G_G D0022042 0 F 0101 2 SN yes ? ? G_G D0021275 0 F 0101 7 SN yes ? ? G_G D0021163 0 F 0101 0403 SN yes ? ? G_G D0020795 0 F 0101 3 SN yes ? ? G_G 6045201 1 F 0101 7 SN yes 31.3 142 G_G D0023027 0 M 0101 3 SN yes ? ? G_G 1015200 1 M 0101 0403 SN yes 112.9 405 ?_? D0015941 0 F 2 7 NN no ? ? A_G D0016405 0 F 0101 7 SN yes ? ? ?_? KNH763243 1 M 0404 0301 SN yes 99 ? G_G Sample of the SNPs’ array data
- 75. 75 Chromosome rsID Position 1 rs3094315 792429 1 rs12562034 808311 11 rs3802985 188510 11 rs3741411 189256 21 rs2821850 13693682 21 rs2257226 13695103 Sample of the map file from
- 76. North American Rheumatoid Arthritis Consortium (NARAC) dataset 76 Cases (RA) Controls Total Male 227 342 569 Female 641 852 1493 Total 868 1194 2,062
- 77. SNP array data file 77
- 78. 78 The percentage of the missing data
- 80. 80
- 81. 81
- 82. 82 Common SNPs Low frequency Rare SNPs Very rare SNPs MAF>0.05 0.05<MAF<0.01 0.001<MAF<0.01 MAF<0.001
- 83. 83 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 84. 84 84 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 85. ■ Very rare ■ Rare ■ Low frequency ■ Common MAF distribution for the data from chromosome 1 to chromosome 22
- 86. ■ Very rare ■ Rare ■ Low frequency ■ Common The common SNPs are 90.5%. The histogram of the MAF distribution
- 87. The SNPs effect (Annotation) 87 87 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 90. Non-coding transcript exon variant TF-binding inside variant 3-prime UTR variant Missense variant Synonymous variant Others Intron variant Non-coding transcript variant Downstream Gene variant Upstream Gene variant Intergenic variant NMD transcript variant Regulatory region variant All consequence’s regions Very rare SNPs Rare SNPs 90 Low frequency SNPs Common SNPs 90 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 91. Coding consequence’s regions Missense variants Synonymous variant Others (Stop gained, Start lost, Stop lost, Coding sequence variant, Incomplete terminal codon variant) Very rare SNPs Rare SNPs Low frequency SNPs Common SNPs 91 Introduction Literature review Data description pre-processing Methods Results Conclusion 91
- 92. 92
- 93. 93
- 94. 94 Outlines 1. Introduction 2. Literature review 3. Data description 4. Pre-processing 5. Methods 6. Results 7. Conclusion 9
- 95. 95 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 96. Introduction Literature review Data description pre-processing Methods Results Conclusion
- 97. Our Data consists of about 545,080 SNPs for about 2062 individuals (Cases and controls) Matrix size = 2062*545080 = 1,123,954,960 about 5,619,774,800 letters (it taking in acount homozygous and homozygous in a string format) Introduction Literature review Data description pre-processing Methods Results Conclusion
- 98. SNPs from chromosome 1 to chromosome 22 (531,689 SNPs ×2,062 participants) 1,096,342,718 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 99. Reading and cropping Starting with reading genotyped data and removing the first 9 columns Introduction Literature review Data description pre-processing Methods Results Conclusion
- 100. 100 rs10439884 rs2260810 rs1296971 rs2257224 G_G A_A A_A G_G G_G A_A A_A G_G G_G A_A A_A G_G G_G A_A A_A A_A G_G A_A A_A G_G G_G A_A A_A G_G G_G A_A A_A G_G G_G G_G C_C A_G A_G A_G A_C A_G A_G A_G A_C G_G ?_? A_G A_C A_G G_G A_G A_C G_G Introduction Literature review Data description pre-processing Methods Results Conclusion
- 101. 101 rs10439884 rs2260810 rs1296971 rs2257224 GG AA AA GG GG AA AA GG GG AA AA GG GG AA AA AA GG AA AA GG GG AA AA GG GG AA AA GG GG GG CC AG AG AG AC AG AG AG AC GG NA AG AC AG GG AA AA GG Introduction Literature review Data description pre-processing Methods Results Conclusion
- 102. Convert the genotyped matrices and its map file into gp.data form as preparation to codeGeno function Introduction Literature review Data description pre-processing Methods Results Conclusion
- 103. Imputation using marginal allele distribution Introduction Literature review Data description pre-processing Methods Results Conclusion
- 104. Introduction Literature review Data description pre-processing Methods Results Conclusion
- 105. Imputed data in bi-allelic format Introduction Literature review Data description pre-processing Methods Results Conclusion
- 106. After Imputation and recoding using Synbreed R package Introduction Literature review Data description pre-processing Methods Results Conclusion 0==Reference allele= Major allele 1==heterozygous 2==major allele
- 107. The output of imputation and recoding Pre-processed dataset Introduction Literature review Data description pre-processing Methods Results Conclusion
- 108. From bi-allelic format to 1,2,3,4 format A🡪1 C🡪2 G🡪3 T🡪4 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 109. Family ID ID P ID M ID sex aff SNPs SNP 1 Preparing data for Haploview Introduction Literature review Data description Methods Results pre-processing Conclusion
- 110. 110 Introduction Literature review Data description Methods Results pre-processing Conclusion Minor allele frequency (MAF) quality control
- 111. Why studying the effect of MAF (8 SNPs) in the CIT Same size LD block (12 SNPs) in SSLD In 2019, Saad et al. different MAF threshold discard significant SNPs while the size of block was the same
- 113. 113 Outlines 1. Introduction 2. Literature review 3. Data description 4. Pre-processing 5. Methods 6. Results 7. Conclusion 1 3
- 114. 114 Introduction Literature review Data description pre-processing Methods Results Conclusion Methods and workflow The proposed methods for haplotype block partitioning
- 115. Applied haplotype partitioning methods FGT CIT SSLD 2002 2005 BigLD 2018 2002 115 Introduction Literature review Data description pre-processing Methods Results Conclusion
- 116. start NARAC genotype dataset ch21 NARAC map file Position ch21 Reformatting Dataset Imputation using ImputR MAF=0.01 Biomarker check MAF=0.02 MAF=0.05 MAF=0.1 MAF=0.001 Haploview R (BigLD) Comparison and calculations Haploview R (BigLD) Haploview R (BigLD) Haploview R (BigLD) Haploview R (BigLD) Flowchart and system description
- 117. NARAC 22 chromosomes input files NARAC genomic data (2,062 individuals) Perl Ch1 to ch22 data and map file Haploview NARAC map file (545,080 SNPs) R Haplotype blocks for 22 chromosome using 4 methods using 5 MAF thresholds R Pre-processing Imputation and recoding Reformatting Reformatting for Haploview MAF 0.001 MAF 0.01 MAF 0.05 MAF 0.02 MAF 0.1 FGT CIT SSLD BigLD Reformatting for BigLD Chromosomes separated map file Chromosomes separated data Haplotype block partitioning 117 Flowchart and system description
- 118. Ch1 to ch22 data and map file Haploview Haplotype blocks for 22 chromosome using 4 methods using 5 MAF thresholds R Pre- processing Imputation and recoding Reformatting Reformatting for Haploview MAF 0.001 MAF 0.01 MAF 0.05 MAF 0.02 MAF 0.1 FGT CIT SSLD BigLD Reformatting for BigLD Haplotype block partitioning 118
- 120. Introduction Literature review Data description pre-processing Methods Results Conclusion The measured evaluated parameters
- 121. NARAC 22 chromosomes input files NARAC genomic data (2,062 individuals) Perl Ch1 to ch22 data and map file Haploview NARAC map file (545,080 SNPs) R Haplotype blocks R Pre-processing Imputation and recoding Reformatting Reformatting for Haploview MAF 0.001 MAF 0.01 MAF 0.05 MAF 0.02 MAF 0.1 FGT CIT SSLD BigLD Reformatting for BigLD Chromosomes separated map file Chromosomes separated data Haplotype block partitioning 121 Flowchart and system description Introduction Literature review Data description pre-processing Methods Results Conclusion
- 122. Proposed Method Based on Interval Graph Modeling (BigLD)
- 123. 123
- 124. Heatmap for the haplotype blocks detected by interval graph modeling of clusters for a portion of chromosome 21 from 9,993,822 bp to 14,137,685 bp. 12 4
- 125. • Confidence interval test (CIT) • Four-gamete test (FGT) • Solid spine of linkage disequilibrium (SSLD) Haploview
- 126. Solid spine of linkage disequilibrium (SSLD) 126
- 127. Introduction Literature review Data description pre-processing Methods Results Conclusion Implementation on Haploview
- 128. 128 BLOCK 1. MARKERS: 4 5 6 132 (0.473) |0.277 0.124 0.074| 311 (0.467) |0.262 0.114 0.092| 112 (0.036) |0.011 0.017 0.008| Multiallelic D prime: 0.054 BLOCK 2. MARKERS: 22 23 33 (0.564) |0.365 0.122 0.076| 13 (0.259) |0.103 0.052 0.104| 31 (0.175) |0.083 0.075 0.017| Multiallelic D prime: 0.238 BLOCK 3. MARKERS: 26 27 33 (0.551) |0.242 0.174 0.136| 13 (0.252) |0.124 0.078 0.049| 11 (0.197) |0.081 0.077 0.038| Multiallelic D prime: 0.068 BLOCK 4. MARKERS: 34 35 33 (0.446) |0.313 0.040 0.093| 31 (0.328) |0.209 0.064 0.055| 11 (0.223) |0.120 0.096 0.006| Multiallelic D prime: 0.189 BLOCK 5. MARKERS: 39 40 31 (0.644) |0.322 0.323 0.000| 33 (0.201) |0.015 0.003 0.183| 13 (0.155) |0.155 0.000 0.000| Multiallelic D prime: 0.669
- 130. Post-processing for a standardization output
- 131. Chromosome 1 Chromosome 2 Post-processing of Haploview output
- 133. 133 Introduction Literature review Data description pre-processing Methods Results Conclusion 1) The total number of haplotype blocks FGT CIT SSLD BigLD
- 134. 134 Introduction Literature review Data description pre-processing Methods Results Conclusion 1) The total number of haplotype blocks The smaller is the number of haplotypes blocks the greater is the reduction rate.
- 135. 135 Introduction Literature review Data description pre-processing Methods Results Conclusion The MAF and total number of haplotype blocks
- 136. 136 Introduction Literature review Data description pre-processing Methods Results Conclusion 1) The total number of haplotype blocks 0.1 0.05 0.02 0.01 0.001
- 137. 137 Introduction Literature review Data description pre-processing Methods Results Conclusion 2) Total number of blocks with considering the singletons CIT FGT BigLD SSLD
- 138. 138 Introduction Literature review Data description pre-processing Methods Results Conclusion 2) Total number of blocks with considering the singletons
- 139. 139 Introduction Literature review Data description pre-processing Methods Results Conclusion 3) Total number of SNPs in all blocks SSLD BigLD FGT CIT
- 140. 140 Introduction Literature review Data description pre-processing Methods Results Conclusion 3) Total number of SNPs in all blocks
- 141. 141 Introduction Literature review Data description pre-processing Methods Results Conclusion 4) The total length of all blocks (bp) SSLD
- 142. 142 Introduction Literature review Data description pre-processing Methods Results Conclusion 4) The total length of all blocks (bp) 0.1 0.05 0.02 0.01 0.001
- 143. 143 Introduction Literature review Data description pre-processing Methods Results Conclusion 5) Mean number of SNPs in blocks The BigLD and SSLD has higher mean number of SNPs in blocks than FIG and CIT
- 144. 144 Introduction Literature review Data description pre-processing Methods Results Conclusion 5) Mean number of SNPs in blocks • MAF does not affect the mean number of SNPs in blocks so much. • The highest mean number of SNPs in blocks is in chromosome 6 using SSLD with MAF=0.1 which equals to 6.637.
- 145. 145 Introduction Literature review Data description pre-processing Methods Results Conclusion 5) Mean number of SNPs in blocks The BigLD has almost the same mean number of SNPs in blocks in the range from 0.001 to 0.05 and a higher mean number of SNPs in blocks at MAF=0.1. The SSLD’s mean number of SNPs in blocks increases with the MAF threshold increases
- 146. 146 Introduction Literature review Data description pre-processing Methods Results Conclusion 6) The mean block length in base pair SSLD BigLD CIT FG
- 147. 147 Introduction Literature review Data description pre-processing Methods Results Conclusion 6) The mean block length in base pair 0.1 0.05 0.02 0.01 0.001
- 148. 148 Introduction Literature review Data description pre-processing Methods Results Conclusion 7) The mean r2 within blocks The correlation mean r2 within the blocks is higher in BigLD in general BigLD CIT FGT SSLD
- 149. 149 Introduction Literature review Data description pre-processing Methods Results Conclusion 7) The mean r2 within blocks 0.1 0.05 0.02 0.01 0.001 Contrastingly, in the BigLD method, the mean r2 within a block decrease with the MAF threshold increases
- 150. 150 Introduction Literature review Data description pre-processing Methods Results Conclusion 8) The mean r2 between consecutive blocks (without considering the singleton blocks) FGT CIT BigLD SSLD 0.1 0.05 0.02 0.01 0.001
- 151. 151 Introduction Literature review Data description pre-processing Methods Results Conclusion 8) The mean r2 between consecutive blocks (without considering the singleton blocks)
- 152. 152 Introduction Literature review Data description pre-processing Methods Results Conclusion 8) The mean r2 between consecutive blocks (without considering the singleton blocks) FGT CIT BigLD SSLD
- 153. 153 Introduction Literature review Data description pre-processing Methods Results Conclusion 9) The mean r2 between consecutive all blocks (with singleton) FGT CIT SSLD BigLD
- 154. 154 Introduction Literature review Data description pre-processing Methods Results Conclusion 9) The mean r2 between consecutive all blocks (with singleton)
- 155. 155 Introduction Literature review Data description pre-processing Methods Results Conclusion 10) The intersection percentage
- 156. Methods Matching percentage FGT, CIT and, SSLD 67% FGT, CIT, SSLD and, Big-LD 57.45% FGT and Big-LD 78.6% CIT and Big-LD 76.7% SSLD and Big-LD 71.92% The results of agreement in percentage of haplotype blocks produced by compared methods 15 6
- 157. Index bp FGT CIT SSLD Big_LD 1 9993822 2 13562271 ✓ ✓ 3 13609442 ✓ 1 4 13690214 ✓ ✓ ✓ ✓ 5 13693682 ✓ ✓ ✓ ✓ 6 13695103 ✓ ✓ ✓ ✓ 7 13707489 ✓ 8 13729007 9 13769165 ✓ 10 13823791 ✓ ✓ 11 13823972 ✓ ✓ 12 13865210 ✓ 13 13866986 ✓ ✓ ✓ 14 13879844 ✓ ✓ 15 13895773 ✓ 16 13950406 17 14027356 ✓ 18 14059449 ✓ 19 14070504 20 14087640 ✓ ✓ 21 14088675 ✓ ✓ 22 14092050 ✓ ✓ ✓ ✓ 23 14095717 ✓ ✓ ✓ 24 14121682 25 14128555 ✓ 26 14136579 ✓ ✓ ✓ ✓ 27 14137685 ✓ ✓ ✓ ✓ 28 14191954 29 14197852 30 14258290 ✓ ✓ 31 14261969 ✓ ✓ 32 14292734 33 14297378 34 14322489 ✓ ✓ ✓ 35 14334270 ✓ ✓ ✓ 36 14334702 ✓ ✓ ✓ 37 14367339 Intersection among the methods
- 158. Plot of a sample of chromosome 21 haplotype blocks produced by FGT, CIT, SSLD, and Big-LD. 15 8
- 160. Compared parameters Big-LD FGT CIT SSLD Max. No. of SNPs in each block 26 17 20 27 Total No. of Blocks 1,182 1,562 1,464 1,378 Max. block size (in bp) 190,708 140,491 178,064 218,644 Min. block size (in bp) 34 4 2 12 Percentage of uncovered SNPs 14.5% 12.8% 22.1% 4.9% Median No. of SNPs within each block 4 4 3 4 Median block size (in bp) 9,830 7,551 6,783 10,870 Total block size (in bp) 23932662 23452817 23696256 23696256 The comparison between Big-LD, FGT, CIT, and SSLD haplotype block partitioning methods in chromosome 21 16 0
- 161. Max. No. SNPs in each block 161
- 162. Init. SNPs No. SNPs No. blocks Max Block size (bp) Min Block size (bp) Max Block size (SNPs) Min Block size (SNPs) Median block size (bp) Median No. SNPs within each block % uncovered SNPs Ch 1 40929 39000 6586 192010 10 41 2 13040 4 12.54103 Ch 2 44090 42033 6586 192010 10 41 2 13040 4 12.57583 Ch 3 36690 35078 5502 214260 13 62 2 12425 4 13.22766 Ch 4 32628 31123 5018 222084 14 37 2 13234 4 13.33098 Ch 5 33612 32220 4992 314333 13 49 2 12636.5 4 13.48852 Ch 6 35574 34140 5020 216439 11 55 2 12732 4 11.90393 Ch 7 29244 28120 4315 260965 7 44 2 12220 4 13.58464 Ch 8 30990 28120 4315 260965 7 44 2 12220 4 13.58464 Ch 9 26128 25095 3741 190762 17 44 2 9934 4 12.38892 Ch 10 28331 27070 3968 206622 8 57 2 11668.5 4 12.60805 Ch 11 26477 25333 3901 214818 11 61 2 11601 4 12.91596 Ch 12 26365 25229 3879 190743 15 37 2 11500 4 13.68267 Ch 13 20242 19380 2842 191785 12 39 2 12827.5 4 12.52322 Ch 14 17951 17243 2648 201362 22 32 2 11740 4 13.50693 Ch 15 16166 15470 2648 201362 22 32 2 11740 4 13.23206 Ch 16 16460 15780 2442 237308 10 37 2 9532.5 4 14.02408 Ch 17 14027 13538 2273 247297 10 30 2 10013 4 16.49431 Ch 18 16450 15708 2406 250370 12 61 2 10374 4 13.38172 Ch 19 9236 8973 1596 134312 15 44 2 9875 3 19.20205 Ch 20 13843 13310 2056 144803 11 36 2 10054 4 13.78663 Ch 21 8051 7786 1185 190709 34 32 2 9587 4 13.52427 Ch 22 8205 7887 1240 176594 7 44 2 7769 4 14.42881 Total 531689 507636 79159 4651913 291 959 44 249763 87 299.9369
- 163. Conclusion 16 3
- 164. 164 Introduction Literature review Data description pre-processing Methods Results Conclusion 164 • The alleles distribution and description. • The percentage of SNP appearance in physical location in chromosomes affect by SNP’s MAF. • The genotype imputation and preprocessing are crucial steps in HBP and we produce a sequence of preprocessing that facilitates several any genetic analysis. Conclusion
- 165. 165 Introduction Literature review Data description pre-processing Methods Results Conclusion 165 • HBP reduce the biomarker to about 13% • Big-LD method provided robust blocks partitioning in terms of the block size and genomic coverage. Conclusion
- 166. 166 • There is a 70% intersection agreement among most HBP methods, Big-LD matched more with FGT. • FGT produce modest results in term of correlation and in term of biomarker reduction. • BigLD produced large haplotype blocks and show high r2 between blocks and the lowest r2 between blocks considering the singleton blocks. • In term of computation, BigLD takes less than half computational time of Haploview methods. 166 Introduction Literature review Data description pre-processing Methods Results Conclusion 166 Conclusion
- 167. 167 • MAF quality control has a high effect of haplotype block partitioning • We recommend taking the MAF in consideration when applying a haplotype block partition. However, it is a tradeoff, higher MAF produces a higher correlation within the blocks but its trunk a portion of data that may could be significant. • In term of correlation, we recommend using high MAF while using Haploview methods, low or moderate MAF in BigLD method. 167 Introduction Literature review Data description pre-processing Methods Results Conclusion 167 Conclusion
- 168. 168 • We could answer the question related to MAF 🡪 the number of blocks not necessarily affect the number of SNPs within blocks. • The SNPs within blocks is the highest in SSLD at the same MAF due to its size and is lowest in CIT • At the same block size, the SNPs within blocks is decrease with MAF increase. 168 Introduction Literature review Data description pre-processing Methods Results Conclusion 168 Conclusion
- 169. Publications and Future work 169
- 170. 170