Confronting two-pair primer design for enzyme-free SNP genotyping based on a genetic algorithm
© Yang et al; licensee BioMed Central Ltd. 2010
Received: 9 January 2010
Accepted: 13 October 2010
Published: 13 October 2010
Polymerase chain reaction with confronting two-pair primers (PCR-CTPP) method produces allele-specific DNA bands of different lengths by adding four designed primers and it achieves the single nucleotide polymorphism (SNP) genotyping by electrophoresis without further steps. It is a time- and cost-effective SNP genotyping method that has the advantage of simplicity. However, computation of feasible CTPP primers is still challenging.
In this study, we propose a GA (genetic algorithm)-based method to design a feasible CTPP primer set to perform a reliable PCR experiment. The SLC6A4 gene was tested with 288 SNPs for dry dock experiments which indicated that the proposed algorithm provides CTPP primers satisfied most primer constraints. One SNP rs12449783 in the SLC6A4 gene was taken as an example for the genotyping experiments using electrophoresis which validated the GA-based design method as providing reliable CTPP primer sets for SNP genotyping.
The GA-based CTPP primer design method provides all forms of estimation for the common primer constraints of PCR-CTPP. The GA-CTPP program is implemented in JAVA and a user-friendly input interface is freely available at http://bio.kuas.edu.tw/ga-ctpp/.
Genotyping is a common technique used in association studies of diseases and cancers. Although many high-throughput platforms of single nucleotide polymorphism (SNP) genotyping, such as SNP array  and real-time PCR using TaqMan probes , have been introduced, most laboratories still validate SNP or novel mutation by PCR-restriction fragment length polymorphism (RFLP) genotyping [3–6] because this method is inexpensive for small-scale genotyping. One shortcoming of PCR-RFLP is its long digestion time (usually in 2-3 hours) for restriction enzymes [7, 8].
Recently, a restriction enzyme-free SNP genotyping technique called "PCR with confronting two-pair primers (PCR-CTPP)" was developed [9–12]. It has been applied successfully to at least 30 different SNP genotypings. For example, interleukin-1B (IL-1B) C-31T, interleukin-2 (IL-2) -330G, beta2-adrenergic receptor (beta2-AR) Gln27Glu, and aldehyde dehydrogenase 2 (ALDH2) were genotyped by PCR-CTPP for association studies with smoking behavior , pylori-induced gastric atrophy , severe coronary artery disease , and esophageal cancer risk , respectively. There is no doubt that the PCR-CTPP method is suitable and reliable for most cases of SNPs. This method considerably lowers the need to consume restriction enzymes. However, the criteria for the PCR-CTPP primers are only tolerant of a small difference in melting temperature (Tm-diff) between the four primers in the PCR-CTPP method . Moreover, typical primer design constraints also need to be considered, such as primer length, primer length difference, PCR product length, GC proportion, melting temperature (Tm), GC clamp, dimer (including cross-dimers and self-dimers), hairpin structure, and specificity. Hence, the computational requirements needed to improve the primer design with PCR-CTTP are rather high.
To design CTPP primers with many corresponding constraints, we introduce a genetic algorithm (GA) [17, 18] to improve the design of CTPP primer sets. GA is a stochastic search algorithm modeled on the process of natural selection underlying biological evolution . It constitutes a randomized search and an optimization technique that derives its working principle from natural genetics. Since GA computation is similar in nature to the evolution of the species, it best fits DNA behavior associated with SNP interaction  and general primer design . The evolutionary computations involved, such as selection, crossover and mutation, are effective in achieving optimal solutions for many CTPP primer sets. After each run, chromosomes in a GA share information with each other and the superior solutions based on a fitness rule are refined from generation to generation. Therefore, CTPP primers obeying the typical primer design constraints described above can be mined.
where B i is the regular nucleotide code (A, T, C, or G) mixed with a single IUPAC code of SNP (M, R, W, S, Y, K, V, H, D, B or N) (is the existence and uniqueness). For the target SNP, we focused only on true SNPs described in dbSNP  of NCBI, i.e., deletion/insertion polymorphisms (DIPs) and multi-nucleotide polymorphisms (MNPs) are not included.
where both Pf 1/Pr 1and Pf 2/Pr 2are two primer pairs of PCR-CTPP. Fs 1vs. Fe 1and Rs 1vs. Re 1indicate the start index vs. the end index of Pf 1and P r1 in T D , respectively. Fs 2vs. Fe 2and Rs 2vs. Re 2indicate the start index vs. the end index of Pf 2and P r2 in T D , respectively. is the complementary nucleotide of B i , which is described in formula (1). For example, if B i = 'A', then = ' T'; if B i = 'C', then = ' G', and vice versa.
Fl 1, Pl 1, Rl 1, Fl 2, Pl 2and Rl 2represent the lengths of forward primer 1, PCR product between Pf 1and P r1 , reverse primer 1, forward primer 2, PCR product between Pf 2and P r2 and reverse primer 2, respectively. Consequently, the forward and reverse primers can be acquired from P v , which is the prototype of a chromosome in GA and is used to perform evolutionary computations as described in the following sections.
Definition of the fitness function
The weights (3, 10, 50, 60 and 100) of the fitness function are applied to estimate the importance of the primer constraints. These weights are set according to the experiential conditions for PCR-CTPP. They also accept adjustment based on the user experimental requirements.
The feasible primer length for a PCR experiment is set between 16 and 28 bp. For longer primers, the Tm is increased whereas the Tm of relatively short primers is decreased. Accordingly, primers which are neither too long nor too short are suitable. We have limited the random values of Fl 1, Rl 1, Fl 2and Rl 2in an appropriate range; therefore, the primer length estimation is not considered to be joined to the fitness function.
where Len diff (P v ) has a maximal fitness value of 3; the fitness value is decreased when the length difference between a primer pair is less than or equal to 3 bp. ABS represents the absolute value.
GC content and GC clamp
where P represents a primer and | P | represents the length of primer P; G number (P) and C number (P) represent the numbers of the nucleotides G and C, respectively.
where P represents a primer and | P | represents the length of primer P; Na+ is the molar salt concentration. The suffix BM represents the formula which was proposed by Bolton and McCarthy .
Dimer and hairpin
Subsequently, the function specificity(P v ) is proposed to check for repetition of each CTPP primer in the template DNA sequence to ensure its specificity. The PCR experiment may fail when a designed primer is not sequence-specific (i.e. it reappears in the genome). The fitness value of the primers (Pf 1, Pr 1, Pf 2or Pr 2) appearing in T D is evaluated using a binary value, i.e., when the primers repeatedly appear in T D , the specificity(P v ) is defined as 1; or else the specificity(P v ) is defined as 0.
PCR product length
(1) Random initial population
To start the algorithm, chromosomes P v = (Fl 1, Pl 1, Rl 1, Fl 2, Pl 2, Rl 2) of particular number are randomly generated for an initial population without duplicates. Fl 1, Rl 1, Fl 2and Rl 2are randomly generated between the minimum and the maximum of the primer length constraint. The minimum and maximum lengths of the primer length constraint are set to 16 and 28 bp, respectively. The PCR product lengths, Pl 1and Pl 2are randomly generated between 100 bp and δ1, and between 100 bp and δ2, respectively. (δ1 and δ2 are the maximum tolerant PCR product lengths of Pl 1and Pl 2shown in Figure 1)
(2) Fitness evaluation
The fitness value in the fitness function is used to ascertain that an individual chromosome is either good or bad. We use formula (7) to evaluate the fitness values of all chromosomes in the population for related chromosomal operations later.
(3) Selection, crossover, and mutation
After the evolutionary computation processes have been implemented, the two worst chromosomes in a population are replaced by the new offsprings, and the process is repeated in the next generation.
(5) Judgment on termination conditions
Once an optimal solution of chromosomes (i.e. the fitness value is 0) or the maximum number of iterations is achieved, the GA is terminated.
Other important criteria for CTPP primer design
There is already one alternative nucleotide in the defined SNP for the CTPP primers Pf 2and Pr 1. If a further SNP exists in any other CTPP primers, such as Pf 1and Pr 2, the Tm between all primers is more dynamic and is difficult to optimize. Accordingly, we avoid designing primers Pf 1and Pr 2with extra SNPs, i.e., all the designed primers for Pf 1and Pr 2including SNPs are eliminated without further processing.
Dry dock experiments
The proposed algorithm was run using Xeon(TM) CPU 3.20 GHz × 2 and 2 GB RAM under the Microsoft Windows XP SP2 and JAVA 5.0 platforms.
Four main parameters are set for the proposed algorithm, i.e. the number of iterations (generations), the population size, the probability of crossover and the probability of mutation. The respective values were 1000, 50, 0.6 and 0.001; the values are based on DeJong and Spears' parameter settings . The population size was then set at 1000 and the other parameters were held constant; only the population size was increased (see Discussion for detail). The PCR product length was set to three ratios (ratios 1, 2, and 3) at 8, 13, and 20, respectively, which allowed for the distinct separation of PCR bands via gel electrophoresis. These ratios were chosen based on our previously conducted PCR experiments.
Results for GA-based CTPP primer design in the example of the SLC6A4 gene
A point mutation in the SLC6A4 gene was recently identified and shown to be associated with autism spectrum disorders , psychosis , and bipolar  patients. The SLC6A4 gene is the member 4 for a solute carrier family 6 (neurotransmitter transporter, serotonin). The common constraints for CTPP primer design were used, including a flanking length of 500 bp, primer length of between 16~28 bp, GC% between 20~80%, primer Tm between 45 and 62°C, difference of CTPP primer Tm of less than 1°C, product length larger than 100 bp, and clearly separated PCR bands in gel electrophoresis. The SNPs for SLC6A4 gene (288 SNPs) are used as an example in this study which excluded the deletion/insertion polymorphisms (DIPs) and multi-nucleotide polymorphisms (MNPs). These SNPs were retrieved with 500 bp flanking length (at both sides of SNP) from SNP-Flankplus (http://bio.kuas.edu.tw/snp-flankplus/) ; the reference cluster IDs of these SNPs are shown in http://bio.kuas.edu.tw/ga-ctpp/dataset.jsp.
The statistics of the designed CTPP primers showing how many primers satisfied the common constraints for SNPs of the SLC6A4 gene*
T m difference
One SNP rs12449783 in the SLC6A4 gene was taken as an example for the genotyping test. Three DNA samples with three known different SNP genotypes for rs12449783 were used to demonstrate the effectiveness of the GA-based CTPP primer design.
Validation of SNP genotyping by GA-based PCR-CTPP and TaqMan probe
The information for the designed CTPP primers for rs12449783 of the SLC6A4 gene
CTPP primer set for rs12449783
Product size (bp)
Pf 1/Pr 1: 0.41
Pf 1/Pr 1: 228
Pf 2/Pr 2: 0.41
Pf 2/Pr 2: 105
Pf 1/Pr 2: 0
Pf 1/Pr 2: 294
As in Figure 7A for either the four CTPP primers or three different sets of two combined CTPP primers, the samples with AA genotype showed AA-negative for 228-bp (P f1 P r1 ) and AA-positive for 105- (P f2 P r2 ) and 294-bp (P f1 P r2 ); the samples with CC genotype showed CC-negative for 105-bp and CC-positive for 228- and 294-bp; and the samples with AC genotype showed AC-positive for 228-, 105- and 294-bp. Accordingly, the bands of the A- and C-alleles-specific PCR amplifications were successfully demonstrated for AA/AC (105-bp) and CC/AC (228-bp), respectively. The internal positive PCR controls for all alleles (i.e., A and C) were confirmed. Therefore, it is clearly demonstrated that our proposed algorithm is able to provide the validated primers for PCR-CTPP.
Using the same samples in Figure 7A, the CTPP results were examined further using the TaqMan probes which were ABI no. hcv_7911133, VIC-/FAM-labels for ACACATAGAAAGTTACAGACTAGCA[A/C]GTCTGGTATTCATAAAGAATTGTGA, respectively. The TaqMan probe program was performed by a 2 step-protocol built-in the ABI real-time system (50°C, 2 min (stage 1, 1 cycle), 95°C, 10 min (stage 2, 1 cycle), 95°C, 15 sec (stage 3, 40 cycles), and 60°C, 1 min.). As shown in Figure 7B, the samples with AA, AC, and CC genotypes for rs12449783 in PCR-CTPP results (Figure 7A) were confirmed to be the same using the TaqMan probe assay. Therefore, the primer information for PCR-CTPP designed by our proposed algorithm was well proved.
To date, many primer design approaches have been proposed, such as dynamic programming , parthenogenetic algorithm MG-PGA , greedy algorithm , heuristic algorithm , genetic algorithm [21, 33], memetic algorithm  and any others. However, most of these methods do not provide the SNP genotyping function. In contrast, we reported the brief idea of the GA-CTPP method for primer design of SNP genotyping in the IEEE BIBE 2009 conference . The differences between them and the improvements of the algorithm proposed in this study are described in the additional file 1. In this study, we present an improved GA-based algorithm which has been shown to be a robust search and optimization method for a number of practical problems, especially for highly complex problems for SNP genotyping with the CTPP primers design function. We had used electrophoresis to validate the reliability of the GA-based CTPP primer design method.
Influence of annealing temperatures
In PCR-CTPP, the designed annealing temperatures of primers are more important than in PCR-RFLP. When the Tm value is similar among the four primers of PCR-CTPP, the PCR competition between all possible DNA products is balanced . When the annealing temperature is low, the PCR reactions are non-specific, leading to incorrect heterozygous genotyping. Therefore, a competitive or specific amplification is important to correctly genotype SNPs using PCR-CTPP. This problem is resolved by computationally finding similar Tm values for the four CTPP primers and by experimentally adjusting the annealing temperature for the PCR [10, 35]. The GA used in this study to design the PCR-CTPP primers improves the efficiency by finding almost identical Tm values for the four primers. The in silico testing of the proposed GA-based PCR-CTPP primer design showed it to fit the Tm constraint to the primers reliably (Table 1).
Typical primer design constraints concerned
Since the Tm is important to our proposed GA-based PCR-CTPP method, further basic research is required to determine other factors to improve this automated PCR-CTPP system. This study is also concerned with the typical primer design constraints, such as primer length, primer length difference, GC proportion, GC clamp, dimer of primer pair (including cross-dimers and self-dimers), hairpin, PCR product length and specificity etc. as described in the Methods section.
Effect of population size
Dejong and Spears' parameter settings are the standard for most GAs, and for this reason, they were used in the present study. Typically, crossover is usually applied at more than or equal to the rate of 0.6, and the mutation rate is equal to 0.001 . However, the population size 50 of Dejong and Spears's parameter settings is too small to provide the necessary sampling accuracy for the design of CTPP primer sets. Consequently, we tested the population size for 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 to evaluate the primer design performance. When the population size was increased to 1000, it provides the more accurate sampling (as shown in the additional file 2). As shown in Table 1 ID2, the number of primers that satisfy the constraints was increased to 9.11% and 35.65% for the Tm and the Tm difference constraint, respectively. For other constraints, the numbers of satisfied constraints were almost similar. The results demonstrate that the increased population size can aid in the search for more feasible CTPP primer sets.
Available for GA-CTPP
The GA-CTPP can be accessed at http://bio.kuas.edu.tw/ga-ctpp/. GA-CTPP designs appropriate two-pair primers to genotype a defined SNP based on the parameter settings of DeJong and Spears. Parameter settings or the primer design conditions can be changed individually by users based on their requirements. When the input sequence contains multiple SNPs, the first SNP will be taken as the defined SNP to design CTPP primer sets. GA-CTPP reports an optimal set of confronting two-pair primers through a text file that records all information of the CTPP primer set for genotyping of the defined SNP.
PCR-CTPP may replace PCR-RFLP because the restriction enzyme digestion step can be omitted, resulting in lower costs and shorter genotyping times ; however, the PCR-CTPP is less developed for its computational tool providing PCR-CTPP primer design. A novel strategy for PCR-CTPP primer design has been introduced in this paper and the freely available web server implemented with this method was also constructed. With experimental validation, our proposed GA-based method is a useful algorithm to design feasible CTPP primers and it conforms to most of the PCR-CTPP constraints.
Availability and requirements
Project name: GA-CTPP: Confronting two-pair primer design using genetic algorithm.
Project home page: http://bio.kuas.edu.tw/ga-ctpp/
Operating system(s): Operating systems free with web browser.
Programming language: Java
Other requirements: Java 1.5
License: none for academic users. For any restrictions regarding the use by non-academics please contact the corresponding author.
This work is partly supported by the National Science Council in Taiwan under grant NSC97-2622-E-151-008-CC2, NSC96-2221-E-214-050-MY3, NSC96-2311-B037-002, NSC96-2622-E214-004-CC3, NSC98-2221-E-151-040-, NSC 98-2622-E-151-001-CC2 and the funds KMU-EM-99-1.4 and DOH99-TD-C-111-002. We also thank for the technical support from Mr. Ming-Tz Tsai.
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