A Novel Prediction Method for Tag SNP Selection using Genetic Algorithm based on KNN



Abstract—Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords—Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

I. INTRODUCTION

INGLE nucleotide polymorphisms (SNPs) are the most common variants amongst species. The number of identified SNPs is very high and is currently estimated to be about 10 million [1].With the genome-wide SNP discovery, many genome-wide association (GWA) studies are likely to identify multiple genetic variants that are associated with complicated diseases [2], [3]. However, genotyping all existing SNPs for a large number of samples remains a challenge. Therefore, it is essential to select informative SNPs representing the original SNP distributions in the genome (tag SNP selection) for genome-wide association studies. These SNPs are usually chosen from haplotype data and are thus called haplotype tag SNPs (htSNPs). Accordingly, the scale and cost of genotyping can be significantly decreased. Recently, some

Li-Yeh Chuang is with the Department of Chemical Engineering, I-Shou University, Kaohsiung, Taiwan (email: chuang@isu.edu.tw)

Yu-Jen Hou is with the Department of Electronic Engineering, National

Kaohsiung University of Applied Sciences, Taiwan (e-mail:

1096305143@cc.kuas.edu.tw).

Cheng-Hong Yang is with the Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Taiwan (e-mail: chyang@cc.kuas.edu.tw)

hybrid algorithms, such as HAPLO-IHP [4] and ISHAPE [5], have been developed which are capable of improving the performance of haplotyping.

Many algorithms have been developed to select the most informative tag SNPs. Tag SNP selection can follow two different strategies: the block-based and the block-free methods. Numerous block-free methods are also available [13]–[18]. A block-based method is based on the haplotype block structure of the human genome. The rationale is that the human genome can be partitioned into discrete blocks [6] and that most of the population share a very small subset of common haplotypes within each block. Haplotype diversity is limited and conserved in the haplotype block of the whole genome [7], [8]. Many algorithms first partition genomes into haplotype blocks [8]–[11] and then select the tag SNP subset within each block. This method focuses on finding a set of tag SNPs to distinguish all the common haplotypes [6], [12]. The main problem with the block-based method is that the definition of the blocks is not always straightforward and there is no consensus how the blocks are formed. Moreover, tag SNP selection based only on the local correlations between markers of each block ignores inter-block correlations [13].

In a block-free method, the tag SNPs is regarded as a subset of all SNPs, from which the remaining SNPs can be reconstructed with minimal error [14], [15]. Black-free methods do not assume prior block partitioning or limit the diversity of haplotypes. Block-free tagging SNP methods are based on weak correlations that occur across nearby blocks [15]. They make use of the proximity of potentially predictive SNPs and are less limiting than methods involving rigid notation of haplotype blocks. A natural measure for evaluating the prediction accuracy of a set of tag SNPs was developed for these methods [16]. Researchers developed a novel algorithm called STAMPA (selection of tag SNPs to maximize prediction accuracy) to find a minimum set of tag SNPs and minimize their prediction error. Dynamic programming was applied in STAMPA to select tag SNPs and maximize prediction accuracy. STAMPA was found to provide higher prediction accuracy than ldSelect [19] and HapBlock [20] tested on a variety of data sets [16]. He and Zelikovsky have introduced two novel approaches for informative SNP prediction based on multiple linear regression (MLR-tagging) [21] and support vector machines (SVM/STSA) [22]. These prediction algorithms combined were with a

A Novel Prediction Method for Tag SNP

Selection using Genetic Algorithm based on

KNN

Li-Yeh Chuang, Yu-Jen Hou, Jr., and Cheng-Hong Yang

S

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stepwise tag selection algorithm (STSA) to select a tag SNP set of minimal size. In a direct comparison of MLR-tagging and SVM/STSA, SVM/STSA was proved more effective than MLR-tagging, but also more time-consuming.

Yet another method of tag SNP selection is through the calculation of correlation between each pair of SNPs (such as linkage disequilibrium, LD). Linkage disequilibrium describes the correlation between genotypes at a pair of polymorphic sites and is usually higher when pairwise SNPs are closer. Two statistical values are used to describe LD, named D’ and r2 [26]. r2is most frequently used for pairwise SNP correlation, because it is directly related to statistical power to detect disease associations [19]. However, some studies try to identify a minimum set of LD bin set in existing SNPs with high-LD (r2 0.8) [19]. To do this, SNPs will be partitioned into different regions according to the relevance of SNPs [19], [23]–[25]. SNPs within a bin are denoted tag SNP, and only one tag would be genotyped per bin. However, the disadvantage of this method is that it can not exclude the possibility that SNPs with a low-LD also enhance prediction accuracy.

In this paper, a genetic algorithm (GA) is applied to the tag SNPs problem and the K-nearest neighbor (K-NN) methods serves as an evaluator of the GA; it is used to evaluate the prediction accuracy of a set of tag SNPs. GAs are a randomized search and optimization techniques that derive their working principles from natural genetics; they have been successfully applied to the optimization of a variety of problems. The results of our study were compared to state-of-the-art studies and indicate that the proposed method can effectively select a minimum number of tag SNPs with higher prediction accuracy.

II. PROBLEM FORMULATION

In a haplotype sequence, SNPs are generally bi-allelic, meaning that there are only two alleles in a single SNP: a major type and a minor. In bi-allelic SNPs, each haplotype can be represented by a binary string set. The allele information value is formed by a sequence of base pairs {A, T, C, G}. Each haplotype can be formalized by binary strings 0 and 1 where 0 represents the major allele and 1 represents the minor allele. Thus, we can represent a haplotype h with m SNPs as h = {h1, h2,

…, hm}, hi



In a genotype sequence, the allele information value is formed by {A/A, A/T, A/C, A/G…G/C, G/T}. In order to present our method, If a genotype g has m SNPs, it can be represented by g = {g1, g2, …, gm}, gi



1 to represent the homozygous types ({0,0} or {1,1}), and 2 to represent the heterozygous types ({0,1} or {1,0}).

°¯ ° ® ­ us heterozygo are SNP ith of alleles two : 2 homozygous minor are SNP ith of alleles two : 1 homozygous major are SNP ith of alleles two : 0 gi (2)

A sample S of a population P of genotype (or haplotype) individuals on m SNPs was given. Our goal then was to find a

minimum set of tag SNPs T = {t1, t2, …, tk}, where k represents

the number of tag SNPs (k < m), which consists of selected SNPs of the genotypes, and can predict the remaining unselected SNPs with minimum error. In order to achieve this goal, we need to find the minimum number of tag SNPs. The two major processes involved are the tag selection algorithm and the SNP prediction algorithm.

III. METHODS FOR TAG SNP SELECTION The purpose of tag SNP selection is to find a small subset of informative SNPs (tag SNP), which accurately represents the rest of the genome sequence. In this paper, a GA was applied to the tag SNP selection problem, and the K-nearest neighbor (K-NN) method served as an evaluator of the GA.

A. Genetic Algorithm (GA)

Genetic Algorithms (GAs) were developed by Alan Turing in 1950, and further required by John Holland in 1970 [26]. The main components of the GA used in our study are the encoding schemes, population initialization, fitness evaluation, selection, crossover operator, mutation operator, and the amendment chromosome. The flowchart of the proposed method is shown Figure 1. The components are explained in detail below.

Fig. 1 Flowchart of the proposed method

B. Encoding schemes

Fundamental to the GA’s structure is the encoding scheme. In this paper, the binary encoding method used in a chromosome corresponds to the tag SNP selection problem, as shown in Figure 2. Given are p chromosomes of a population, with each chromosome containing m SNPs (dimension). Each chromosome of the length m is a sequence over {0, 1}m (0 represents a non-selected SNP and 1 represents a selected SNP). The binary encoding method used can be described by:

Ci= {ci1, ci2, ... , cim} and cij = {0, 1}, i = 1, 2, …, p, j = 1, 2,

…, m, where p represents the size of population. cij= 1 means

that the jth SNP on the ith chromosome was selected. For example, assume there is a chromosome represented by Ci = {1,

0, 1, 0, 0, 1, 0}. In this encoding scheme SNP1, SNP3 and SNP6

are predicted to be tag SNPs.

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ACKNOWLEDGMENT

This work is partly supported by the National Science Council in Taiwan under grant NSC96-2622-E-151-019-CC3, NSC96-2221-E-214-050-MY3, NSC95-2221-E-214-087.

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