(Luận văn thạc sĩ) detecting bad SNPs from illumina beadchips using jeffreys distance, phát hiện các SNP xấu từ illumina beadchips sử dụng khoảng cách jeffreys

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY  NGUYEN HOANG SON Detecting bad SNPs from Illumina BeadChips using Jeffreys distance MASTER THESIS OF INFORMATION TECHNOLOGY Hanoi - 2012 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY  NGUYEN HOANG SON DETECTING BAD SNPS FROM ILLUMINA BEADCHIPS USING JEFFREYS DISTANCE MASTER THESIS Sector: Information Technology Major: Computer Science Code : 60 48 01 Supervised by: Dr Le Sy Vinh Hanoi - 2012 ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UET or any other educational institution, except where due acknowledgement is made in the thesis Any contribution made to the research by others is explicitly acknowledged in the thesis I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed i Abstract Current microarray technologies are able to assay thousands of samples over million of SNPs simultaneously Computational approaches have been developed to analyse a huge amount of data from microarray chips to understand sophisticated human genomes The data from microarray chips might contain errors due to bad samples or bad SNPs In this thesis, a novel method is proposed to detect bad SNPs from the probe intensities data of Illumina Beadchips This approach measures the difference among results determined by three software Illuminus, GenoSNP and Gencall to detect the unstable SNPs Experiment with SNP data in chromosome 20 of Kenyan people demonstrates the usefulness of our method This approach reduces the number of SNPs that are needed to check manually Furthermore, it has the ability in detecting bad SNPs that have not been recognized by other criteria Acknowledgements Apart from the efforts of myself, the success of any project depends largely on the encouragement and guidelines of many others First and foremost, I would like to thank to my supervisor Dr Sy Vinh Le for the valuable guidance and advice This research project would not have been finished successfully without his continuously support and assistance His enthusiastic supervision helped me in all the time of research and writing of this thesis I also would like to gratefully acknowledge the tremendous encouragement and insightful comments of Dr Si Quang Le during the time of research of this thesis His brilliant ideas helped me so much to overcome numerous problems and difficulties Any word is inadequate for his helpful aids The author would also like to convey thanks to the Department of Computer Science for providing the useful preferences and laboratory facilities I also wish to express love and gratitude to my beloved families; for their understanding and endless love, through the duration of my studies My thanks and appreciations also go to my colleague in developing the project and people who have willingly helped me out with their abilities i Table of Contents Overview 1 Introduction 1.1 Biological Background 1.2 SNP Genotyping 1.3 Quality Control and Quality Assurance Related Work 2.1 Naive method for SNP genotyping 2.2 GenCall 2.3 Illuminus 2.4 GenoSNP 2.5 Discussion 3 10 10 11 12 12 13 15 15 16 16 17 19 Experimental Results 4.1 Data description 4.2 Parameter estimation 4.3 Evaluation 23 23 24 26 Method 3.1 Kullback-Leibler divergence 3.2 Approximate relative entropy between two Student distributions 3.2.1 Approximate Student distribution 3.2.2 The matched bound approximation 3.3 Estimate conflict degree between three callers Conclusion 34 ii List of Figures 1.1 1.2 An example of a SNP detected by an alignment of two DNA sequences BeadChip work flow 3.1 3.2 Approximate t-distribution by a mixture of Gaussians 18 An example of Matched Bound Approximate method with two mixtures of three components 19 4.1 4.2 4.3 4.4 Input file format Histograms of three metrics in term of conflict score Erroneous loci filtered by applied two functions but minimum HWE, MAF and missing rate histogram of three callers, with original and filtered data iii 23 26 27 29 List of Tables 4.1 4.2 4.3 4.4 Result Result Result Result of of of of missing rate filter MAF filter HWE exact test filter synthesis criteria iv 28 28 30 30 Overview Recently, Genome-Wide Association Study (GWAS), also known as Whole Genome Association Study (WGAS), proved to be an successful strategy in identifying genetic variants associated with common diseases or complicated phenotypes This method focuses on associations between Single Nucleotide Polymorphisms (SNP) and traits Because GWAS investigates the entire genome rather than just testing one or a few genetic regions, there will be a large extreme amount of SNP genotype data needed to be called As the consequence, different impressive methods have been developed to deal with the problem of genotype calling automatically and efficiently In general, these programs try to translate probe hybridization intensities outputed from microarray chips into SNP genotypes In the ideal case, a call at a SNP loci generates three clusters of signals, so that SNP genotype for a certain sample could be determined according to which cluster it belongs to However, in fact, the ambiguous data and errors happen frequently due to many reasons Quality Control (QC), as an indispensable step, is included in every studies to remove these error data from the dataset as much as possible Unfortunately, this kind of work requires significant time and effort because it is hardly automated completely It is also difficult to find the bad data among the good ones when the applied criteria are not clear for these cases Although many statistical methods have been proposed, expert-guided evaluations are usually needed to determine the ultimate results On top of that, the statistical variables used in considering SNP genotype quality have thresholds that depending on conditions, making obstacles on the way of finding a automatic solution based on them Therefore, an novel approach beside the traditional QC process is necessary to make this important step self-regulating as much as possible In our work, we developed a new method to detect bad SNPs Our method takes advantage of three available genotype callers for Illumina BeadChip, namely GenCall, GenoSNP and Illuminus The general idea comes from the fact that data LIST OF TABLES of bad SNPs tend to confuse the callers, so the calling results of different methods are usually not consistent That is, by comparing visually the cluster clouds of three callers SNP by SNP, we could recognize these bad loci The distance in informatics theory (relative entropy) is applied to examine these dissimilarities so that bad SNPs could be detected automatically We applied our method with real-life data and it proved to be a good protocol with satisfactory results Despite of the fact that there still a lot of work to do, this method really has the ability to help experts in confirming the filtered data, also suggests potential bad SNPs that hard to find by traditional QC process The thesis is organized in five chapters An introduction to the definitions and background knowledge is given in Chapter At first, this chapter will provide the very first images in bioinformatics by explaining briefly some terminologies in molecular biology, such as human genome, DNA, SNP and SNP genotyping It also offer further information relating to our work, about solutions for genotype calling problem and quality control process Attending these sections are necessary for better understanding the later content of the thesis Chapter will describe in more details about the available genotype callers The general idea for a simple caller will be given at the beginning to give the most basic solution for the genotype calling problem After that, three callers used in our algorithm are explained thoroughly Information provided in this chapter will help reader get the first step further toward our solution The proposed method is presented in the third chapter We will review the definitions about relative entropy (also known as Kullback-Leibler divergence) and its application with Normal distribution The next section will state an available method to calculate relative entropy between Student distributions, which is required to deal with our problem Finally, an estimate technique is proposed to measure the dissimilarities between outputs of three callers The experiment process of applying our protocol with real data is offered in Chapter At first, input data description is given After that, there is a step of try-and-error experiment to find the most appropriate parameters that should be used in our program Finally, the result of bad SNPs detected by our method is evaluated by compared with other traditional QC criteria The final chapter will give you the overall conclusion and further discussion about the thesis Chapter Experimental Results 4.1 Data description The SNP calling of three callers for 4473 Kenyan people in 28410 SNP loci is synthesised and used as input data in VCF format Each call will consist of confident scores of each callers: Gencall score and with each of the two remainings, a set of three probabilities represented how likely it belong to three genotype clusters The recommended cut-off for GenoSNP and Illuminus are both 0, 95, while it is 0.2 with Gencall Every call that have confidence below the cut-off are considered as no call The VCF file that we used as input is described as below: Figure 4.1: Input file format The file consist of three parts: Meta-information lines This field is stated after the ## string, usually contain key = value pairs The information in this field are optional, but including them 23 4.2 Parameter estimation 24 make the data files more well-formed and readable They provide us first glance, also the description for the data stated lately Header line The first eight columns are mandatory, which means they appear in every VCF file namely #CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO The FORMAT column header are also included because our file is to store genotype data Starting from the tenth column are an arbitrary number of sample IDs, which is 4473 in our case Data lines These lines contain detail information, each line for each locus The first eight fixed fields show the information about every SNPs included in data file The ninth column points out the format used to stored genotype information, which we could understand by looking at the meta-information lines There are seven subfields in each of every genotype data columns, which is separated by the delimiter : We just focus on five of them • GLI (the second sub-field) shows the confident scores for three genotypes 00, 01, 11 respectively generated by Illuminus • GLG (the third sub-field) illustrates genotype probabilities of GenoSNP • GC (the fourth sub-field) is the genotype call by GenCal, follow by its confident score in the fifth subfield (GCS) • XY (the sixth sub-field) show the density value in pair for each sample locus 4.2 Parameter estimation The degree of freedom parameter υ is fixed to according to the estimation of GenoSNP (Giannoulatou et al., 2008) However, these parameters is assigned the values of at least (up to 20), in Illuminus’ model (Teo et al., 2007) In our experiments, the minimum among them is chosen so that it could work with the most flexible Student distribution model The next parameter should be taken into account is P because it highly depends on the fixed degree of freedom υ Based on the work in (El Attar et al., 2009), 10 normal components for a t-distribution are sufficient for accuracy, and a Monte Carlo loop of 20 iterations is executed and then get the average (expected) value of 4.2 Parameter estimation 25 this process as the last result This selection is our decision in all experiment after already considered the trade-off between accuracy and computational cost together Next, we have to work out the most suitable metric function to determine what value should be returned from the triad (ig, ag, ia) We test three candidates: minimum, maximum or average of the three values Each of them is used in our examinations and then compared to find out the most suitable one In fact, the clustered results from three callers are very similar, and the relative entropy of two similar distribution is very small (often less than 0.1) The approximation method works well with these stable results but in some cases tends to overestimate the discrepancy acceptable dissimilarities For that reason, we assigned the distance of two callers as the minimum among three pairs of genotype clusters in Equation 3.7 However we have to thoroughly consider whether the option is used one more time as metric function to find the consensus distance of three, because the minimum alone could not provide the whole characteristics of the triple Nevertheless, maximum itself has the problem of failing some SNPs which are successfully called by two but removed just in one caller accidentally Figure 4.2 demonstrates conflict scores among SNPs when each of three approach are applied The first one is chosen, the dotted line show the cut-off at the recommended 0.1 The graph shows that using maximum and average results in similar distributions of discrepancies, this agrees with the fact that scores are usually very small The scatter plots of removed SNPs in each circumstance are generated for being assessed visually Figure 4.3 points out some mistaken SNPs if we not use minimum metric 4.3a is loci that failed by Gencall (GTA) but called successfully in the two remaining (GTI and GTG); 4.3c and 4.3b are of the cases when the distance is overestimated In reality, all above are considered as meaningful SNPs by experts In general, the three metrics work quite well However, referred to the behaviour of consensus call in which the failure of one caller (Gencall in most of the case) could be tolerated, the minimum is the most suitable option Furthermore, maximum (and so average) metric sometimes over-estimates the unstabilities of SNPs and assigned acceptable ones as the bad accidentally With each of three methods, the scatter plots of ten worse, ten best and ten middle cases are examined and compared with each others manually There appears a number of misinterpreted loci when it comes to maximum or average metric, some of them are shown in Figure 4.3 With all the reasons discussed before and the histogram 4.2, it is concluded that a SNP with minimum conflict score among three greater than 0.1 will be filtered 4.3 Evaluation 26 25 15 percentage Histogram of minimum metric 0.0 0.2 0.4 0.6 0.8 1.0 0.8 1.0 0.8 1.0 conflict degree percentage Histogram of maximum metric 0.0 0.2 0.4 0.6 conflict degree percentage Histogram of average metric 0.0 0.2 0.4 0.6 conflict degree Figure 4.2: Histograms of three metrics in term of conflict score out There are 2360 out of 28410 (∼ 8.3%) being removed in this protocol 4.3 Evaluation Because this is a new protocol that worked in the proprietary dataset, there exist no such efficient evaluation for the results due to the lack of completed expert-guided data However, we could compare the results with the baseline of several traditional QC criteria namely missing rate, minor allele frequency and Hardy-Weinberg 4.3 Evaluation 27 GTA GTG ● ● ● ● ● ● ● ● 0 ● ● ● ● ● 1 ●● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● 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● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ● X intensity ● X intensity GTI ● ● ● ● ● Y intensity ● ● ● 0/0 0/1 1/1 −/− ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●●● ●●●●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● 0/0 0/1 1/1 −/− ● ● ● ● ● ● ● ● ● ● ● ● ● Y intensity ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ●●●●●●● ● ●● ●● ● ●● ●● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● 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●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ●● ●● ● ● ●● ●●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ●● ●●● ● X intensity (b) rs6081232 (c) rs6137567 Figure 4.3: Erroneous loci filtered by applied two functions but minimum equilibrium Figure 4.4 provides histograms describing the distribution of those three specifications in the whole SNP data set of Kenyan, before and after running our filtering procedure The red areas show the amount of SNPs being removed As shown in these graphs, SNPs with very low p-value of HWE exact test (Wigginton et al., 2005) and MAF are very susceptible to be removed In contrast, SNPs with smaller ● ● ●● ●● ●● ● 4.3 Evaluation 28 missing rate are likely maintained in the dataset These parameters are only used as an aid phase to help experts at the very beginning to identify possible bad SNPs, further considerations have to made manually As stated before, finding appropriate thresholds for QC variables from the beginning is impossible These values are just initialized only when an overall observation of the dataset has been done already After that, it is necessary to have a thorough process of re-estimated these parameters toward the reasonable values that varies from study to study For that reason, we run the experiment with several possible values of thresholds that could happens in an arbitrary raw dataset For missing rate (MSP), there are five boundaries of interest: 2%, 5%, 10%, 15% and 20% The MAF thresholds are 0.01, 0.02, 0, 03, 0.04, 0.05 continuously And finally, p-values for HWE exact test are limited by also five exponentiation of base 10 with negative exponents ranging from −8 to −4 Table 4.1: Result of missing rate filter ❳❳ ❳❳❳ Callers Rate ❳Miss ❳❳❳ ❳❳❳ 2% 5% 10% 15% 20% Gencall 27957/2345 13721/1547 2589/753 1276/565 Illuminus 14761/1943 GenoSNP 28410/2360 20894/1545 4272/691 2165/554 1454/476 2245/590 248/137 79/56 891/474 34/25 Table 4.2: Result of MAF filter PP PP MAF P Callers PP 0.01 0.02 0.03 0.04 0.05 PP Gencall 1544/271 2668/556 3887/813 4798/966 5585/1127 Illuminus 481/189 1926/346 2932/547 4188/811 GenoSNP 3070/560 4217/875 4886/1051 5519/1190 6142/1308 1211/247 The three tables 4.1, 4.2 and 4.3 illustrate different criteria used to help experts evaluate SNP quality Each cell of the tables include two numbers: the first one show how many SNPs that break the corresponding threshold and need to be reconsidered manually, the second one is the number of SNPs among them also filtered by our protocol For instance, in Table 4.3, with missing call rate threshold is set to 2%, there are 14761 SNP calling result being removed in Illuminus Among them, 1943 4.3 Evaluation 29 count 2000 6000 Histogram of HWE 10 −log10(HWE) 1000 500 count Histogram of MAF 0.0 0.1 0.2 0.3 0.4 0.5 MAF count 1000 3000 Histogram of missing rate 10 15 20 missing rate(%) (a) Gencall Histogram of HWE 8000 count 10 −log10(HWE) Histogram of MAF Histogram of MAF 2500 10 count 1000 800 400 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 MAF MAF Histogram of missing rate Histogram of missing rate 0.4 0.5 0 2000 count 6000 4000 0.0 2000 count −log10(HWE) 1200 count 4000 4000 count 8000 Histogram of HWE 10 missing rate(%) (b) Illuminus 15 20 10 15 20 missing rate(%) (c) GenoSNP Figure 4.4: HWE, MAF and missing rate histogram of three callers, with original and filtered data SNPs are also filtered out by our program This mean that our protocol helps reducing the overall SNPs that have to be checked in this case by 1943 Beside, we also detect other 417 potential bad SNPs that are missed in missing rate criteria alone Visual check shows that these SNPs are really problematic The similar observations are shown in the other two tables However, the num- 4.3 Evaluation 30 Table 4.3: Result of HWE exact test filter ❳❳❳ ❳❳❳ HWE ❳❳❳ Callers ❳❳❳ 10−8 10−7 10−6 10−5 10−4 Gencall 6868/1570 7290/1603 7792/1666 8424/1741 9221/1815 Illuminus 1658/280 2036/323 GenoSNP 4540/975 4935/1039 5386/1130 5906/1221 6567/1332 2540/372 3230/442 4255/551 Table 4.4: Result of synthesis criteria ❤❤❤ ❤❤❤❤ ❤❤❤ ❤❤ Number of SNPs Callers ❤❤❤❤ Gencall Illuminus GenoSNP ❤ ❤ Detected SNPs by synthesis criteria 4497 478 4777 Intersected with our method 1070 89 1037 bers in these tables increase along with the threshold because lower bounds are used In all three tables, Illuminus is the caller with highest call rate in this case since it has the least SNP filtered out among three The SNPs that violate the criteria are the ones that need to be checked carefully There are quite significant of them also assigned as bad SNPs in our protocol This help experts to narrow the range of SNPs that need to focus on In fact, SNP data is filtered hierarchically, combining all those metrics in one completed QC process For this reason, the most powerful criteria among three tables are used simultaneously to identify bad SNPs: 2% missing rate, 0.05 MAF threshold and HWE value of 10−4 Table 4.4 shows the number of bad SNPs for each caller using this synthesis criteria, together with overlapped amount of bad SNPs also detected by our method For instance, with regard to Gencall, there are 4497 SNPs that cannot pass the synthesis condition, 1070 among them also filtered by our measurement Looking back to the three Tables 4.1, 4.2, and 4.3 , the numbers of bad SNPs recognized by applying three separated conditions are 27957, 5585 and 9221 respectively The intersection of these three sets is 4497 bad SNPs in Table 4.4 stated above Similarly, the overlap of three counterparts which have 2345, 1127 and 1815 SNPs in the first three tables forms the set of 1070 overall bad SNPs in Table 4.4 These are the common bad SNPs between the corresponding QC criteria and our criteria The second criteria (minor allele frequency) seem to be the most reasonable filter since its result (5585 SNPs) is smallest and closest to the synthesis 4.3 Evaluation 31 outcome (4497 SNPs in this case) We could see that the agreed results between our method and MAF filter (1127 bad SNPs) are also more stable than the other two criteria since it is closest to the combined set of 1070 intersected bad SNPs This reflects the high reliability of our method results Beside these agreed SNPs, we also detect 2360 − 1070 = 1290 potential bad ones but pass the synthesis condition Manual check shows that these latent bad SNPs are confirmed with high probability To sum up, it could be seen that our novel approach not only helps other QC process to reduce the SNPs needed to check out, but also able to recognize the latent erroneous SNPs To understand better the performance of our protocol, the plots of worse SNPs suggested by our algorithm are offered as in figure 4.5 In fact, this is the final safeguard step which implemented by experts manually to confirm the quality of candidate bad SNPs Our filtering procedure requires about 20 minutes to work out the Kenyan dataset automatically, using platform with one processor Intel Xeon X5355 2.66 GHz and Linux Ubuntu operating system 4.3 Evaluation 32 ● ● X intensity ● ● ● ● ● ● ● X intensity ● ● ● ● ● X intensity ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●●● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ●● ●●●●● ● ● ●● ●● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ●● ●● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●● ● ●● ●● ●● ●● ●● ● ● ● ● ● ●● ● ●● ● ●●● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ●●● ●● ● ● ● ●● ● 1 ● ● ● ● ● ● ● ● ● ● 0/0 0/1 1/1 −/− ● ● ● ● ● ● ● ● 0/0 0/1 1/1 −/− ● ● ● ● ● ● X intensity ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ●●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●●●● ● ● ● ● ● ● ● ●● ●●●● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●●●● ● ● ●●●● ● ● ● ● ● X intensity ● 0/0 0/1 1/1 −/− ● ● ● ● Y intensity ● GTI GTI ● ● ● X intensity ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ●●● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●●●●● ● ● ● ● ● ● ● ●● ●●●● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●●●● ● ● ●●●● ● ● ● ● ● ● 0/0 0/1 1/1 −/− ● ● GTG ● ● ● ● Y intensity ● ● 0/0 0/1 1/1 −/− ● ● Y intensity ● ● GTA 5 GTG ● ●● X intensity GTA ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●●● ●●● ●●● ● ● ● ● ●● ● Y intensity ● 0 X intensity ● ● 0/0 0/1 1/1 −/− ● Y intensity ● 5 ● Y intensity ● ● GTI ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●●● ●●● ● ● ● ●● ● ● ● ● ● ● ● 0/0 0/1 1/1 −/− ● ● ● GTG ● Y intensity ● 3 ● 0/0 0/1 1/1 −/− ● Y intensity ● ● Y intensity ● GTA GTG GTA ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ●● ●● ●● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● 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our criteria In which, the three clustering results are very different 4.3 Evaluation 33 ● ●● ● ● ● ● ●● ● ● ●●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●●●● ●●● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●● ● ●●● ● ● ● ● ●●●● ● ●● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ●●● ● ●●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ●●●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ●● ● ● ●● ● ● ● ● ● ● ● GTI ● ● 0/0 0/1 1/1 −/− ● ● ● ●● ● ● X intensity ● ● ● ● ● ● ● ● Y intensity ● ● ● ● ● ● ● ● ● ● X intensity ● Y intensity ● ● 0/0 0/1 1/1 −/− ● ● ● ● ● ● GTI ●● ● ● ● ● ●●● ●●● ● ● ●●●● ● ● ● ● ● ●● ● ● ●●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ●●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● GTA ● ● 0/0 0/1 1/1 −/− Y intensity ● ●● 0/0 0/1 1/1 −/− ● ● ● GTG ● ● ● ●● ●●●● ●● ● ●● ● ● ●● ●● ● ● ● ● ●●● ●●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ●●●● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ●●●●●●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● X intensity ● ●● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ●● ● ● ●● ● ● ● ● ● ● GTA ● ● ● ● X intensity ● GTG X intensity ● Y intensity ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ●● X intensity GTI ● ● ● ● ● ●● ●● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●●●● ● ● ●●● ● ● ●● ● ● ● ●●●● ● ●● ● ● ● ● ● ●●● ●● ● ●● ● ●● ● ● ● 0 ● ● ● ● ● ● ● ●● ● ● ● 0/0 0/1 1/1 −/− Y intensity ● ● ● ● ● Y intensity ● ● ● Y intensity ● 0/0 0/1 1/1 −/− 5 ● ● ● ●● ● ● ● ● ●● ● ● ●●● ●● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●●●● ●●● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ●●● ● ●● ● ●● ● GTA ● ● ● Conclusion Ascertaining the quality of data is the primary task in the whole genotyping process In this work, we already introduced a novel method to filter out bad SNPs It does not require expert’s knowledge based on QC variables such as MAF, missing rate, HWE (Hardy-Weinberg equilibrium), etc Instead, it measures called results available from three callers Illuminus, Gencall and GenoSNP The underlying rationale of our algorithm is to estimate the differences between the classification of these three methods and then finding the useless loci based on these informations By applying this approach as a data post-processing step, a number of worse SNPs would be filtered out of the dataset This helps decreasing the times and efforts for later procedures In addition, our procedure is able to find potential failed SNPs that could be ignored by other constraints Furthermore, thanks to its simplicity and computational effectiveness, this procedure might be reused or combined with other methods of quality assurance independently to improve their performances We would like to emphasize that our procedure should be carried out in the very first step of the whole quality control process as the primary filter that could help all three callers In many other quality control or quality assurance methods, the parameters highly depend on the characteristics of the dataset (platforms, chips, callers that are used in genotyping) In our program, the only unstable factor that should be considered is the conflict threshold, which is set to 0.1 as default In fact, this evaluation is made not only via various experiments, but also based on the fact about the theoretic distances of closed distributions which are usually fall in concrete and small set of values Our recommendation for this parameter is 0.1 for raw dataset and 0.05 for higher quality ones Considering all together, this method of using Jeffreys distance between three available genotype callers is another reliable and helpful detector in finding the badquality SNPs, which usually takes many time and effort to deal with 34 Publications Hoang Son Nguyen, Sy Vinh Le, Si Quang Le Detecting bad SNPs from Illumina BeadChips using Jeffreys distance 4th International Conference on Knowledge and Systems Engineering (KSE 2012), in Proceedings 35 Bibliography Anderson Carl A, Pettersson Fredrik H, C G M C L R M A P Z K T Collins, F S., & McKusick, V A (2001) Implications of the human genome project for medical science JAMA: The Journal of the American Medical Association, 285, 540–544 El Attar, A., Pigeau, A., & Gelgon, M (2009) Fast aggregation of student mixture models European Signal Processing Conference (Eusipco’2009) (pp 312–216) Glasgow, Royaume-Uni Miles (Pays-de-la-Loire) Giannoulatou, E., Yau, C., Colella, S., Ragoussis, J., & Holmes, C C (2008) Genosnp: a variational bayes within-sample snp genotyping algorithm that does not require a reference population Bioinformatics, 24, 2209–2214 Goldberger, J., Gordon, S., & Greenspan, H (2003) An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures In Proc ICCV (pp 487–493) Group, G C R (2007) New models of collaboration in genome-wide association studies: the genetic association information network Nat Genet, 39, 1045–1051 Hershey, J., & Olsen, P (2007) Approximating the kullback leibler divergence between gaussian mixture models Acoustics, Speech and Signal Processing, 2007 ICASSP 2007 IEEE International Conference on (pp IV–317 –IV–320) Illumina Inc (2005) Spotlight, Illumina Gencall Data Analysis Software Laurie, C C., Doheny, K F., Mirel, D B., Pugh, E W., Bierut, L J., Bhangale, T., Boehm, F., Caporaso, N E., Cornelis, M C., Edenberg, H J., Gabriel, S B., Harris, E L., Hu, F B., Jacobs, K B., Kraft, P., Landi, M T., Lumley, T., Manolio, T A., McHugh, C., Painter, I., Paschall, J., Rice, J P., Rice, K M., Zheng, 36 References 37 X., Weir, B S., & for the GENEVA Investigators (2010) Quality control and quality assurance in genotypic data for genome-wide association studies Genetic Epidemiology, 34, 591–602 Peiffer, D A., Le, J M., Steemers, F J., Chang, W., Jenniges, T., Garcia, F., Haden, K., Li, J., Shaw, C A., Belmont, J., Cheung, S W., Shen, R M., Barker, D L., & Gunderson, K L (2006) High-resolution genomic profiling of chromosomal aberrations using infinium whole-genome genotyping Genome Research, 16, 1136– 1148 Ritchie, M E., Liu, R., Carvalho, B S A., & New Zealand Multiple Sclerosis Genetics Consortium (ANZgene), Irizarry, R A (2011) Comparing genotyping algorithms for Illumina’s Infinium whole-genome SNP BeadChips BMC Bioinformatics, 12, 68 Steemers, F J., Chang, W., Lee, G., Barker, D L., Shen, R., & Gunderson, K L (2006) Whole-genome genotyping with the single-base extension assay Nature Methods, 3, 31–33 Teo, Y Y., Inouye, M., Small, K S., Gwilliam, R., Deloukas, P., Kwiatkowski, D P., & Clark, T G (2007) A genotype calling algorithm for the illumina beadarray platform Bioinformatics, 23, 2741–2746 Wigginton, J E., Cutler, D J., & Abecasis, G R (2005) A note on exact tests of hardy-weinberg equilibrium The American Journal of Human Genetics, 76, 887 – 893 ... chips might contain errors due to bad samples or bad SNPs In this thesis, a novel method is proposed to detect bad SNPs from the probe intensities data of Illumina Beadchips This approach measures... HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY  NGUYEN HOANG SON DETECTING BAD SNPS FROM ILLUMINA BEADCHIPS USING JEFFREYS DISTANCE MASTER THESIS Sector: Information Technology Major: Computer... and SNPs filter We just focus on the later in this article In which, low-quality SNPs are called bad SNPs and should be filtered out The most common and widely protocol used in evaluating SNPs