SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs

Abstract

Genome-wide analysis of single nucleotide polymorphism (SNP) markers is an extremely efficient means for genetic mapping of mutations or traits in mice. However, this approach often defines a relatively large recombinant interval. To facilitate the refinement of this interval, we developed the program SNP2RFLP. This program can be used to identify region-specific SNPs in which the polymorphic nucleotide creates a restriction fragment length polymorphism (RFLP) that can be readily assayed at the benchtop using restriction enzyme digestion of SNP-containing PCR products. The program permits user-defined queries that maximize the informative markers for a particular application. This facilitates fine-mapping in a region containing a mutation of interest, which should prove valuable to the mouse genetics community. SNP2RFLP and further details are publicly available at http://genetics.bwh.harvard.edu/snp2rflp/

Introduction

The positional cloning and characterization of mutations in the mouse is a powerful means for functional annotation of the mammalian genome. Many mouse gene mutations cause phenotypes that serve as models of human genetic disorders. Mapping and positional cloning of these potentially accelerate our understanding of the mouse gene, its human ortholog, and the underlying etiology of the disorder. The utilization of single nucleotide polymorphism (SNP) markers has markedly facilitated genetic mapping because they are abundant throughout the genome and can be analyzed in a high-throughput manner using automated technology (Wang et al. 1998). However, mutation mapping analysis using a genome-wide SNP panel does not generally yield high-resolution localization (Moran et al. 2006), and “benchtop” technologies for fine-mapping using SNPs and microsatellite markers are often inefficient. We have developed a web-based tool we call SNP2RFLP, which can extract region-specific SNPs from the dbSNP database (Sherry et al. 1999) and identify those SNPs that would create restriction fragment length polymorphisms (RFLPs) when assayed by restriction enzyme digestion of SNP-containing PCR products. The input to SNP2RFLP is the two mouse strains used in the cross, the chromosomal region, and a user-defined set of restriction endonucleases. SNP2RFLP extracts the SNPs from dbSNP that are polymorphic between the two strains in the region in question. The program simulates a restriction digest of the SNP-containing sequences with each enzyme to determine whether the SNP creates an RFLP. Informative markers are then analyzed using Primer3 (Rozen and Skaletsky 2000), which finds suitable PCR primers surrounding the SNP. The output of SNP2RFLP is the informative SNPs that create RFLPs and the forward and reverse PCR primers. This information can then be used to readily perform the RFLP assays and further refine the region containing the mutation of interest.

Methods

A local PostgreSQL database was constructed to hold all mouse SNPs from the NCBI dbSNP (Mouse Build 126) along with their flanking sequences. The database contains 8 million unique mouse SNPs, with 200–400 bp of flanking sequence for each SNP. SNP-containing flanking sequences were analyzed by Primer3, which identifies optimal PCR primers surrounding each SNP that meet standardized criteria for product size, primer melting temperature (T_m) (~60°C), and GC content (~50%) (Rozen and Skaletsky 2000). These forward and reverse primers are stored in the database along with each SNP.

There are 68 million known strain genotypes for the SNPs in the database, which holds genotype data for 99 different mouse strains. Seventeen strains, including A/J, DBA/2 J, 129S1/SvlmJ, C3H/HeJ, BALB/cByJ, AKR/J, NZW/LacJ, CAST/EiJ, BTBR T + tf/J, WSB/EiJ, FVB/NJ, NOD/LTJ, KK/HIJ, PWD/PhJ, MOLF/EiJ, C57BL/6 J, and 129X1/SvJ, were interrogated using a high-density array and each has approximately 2–6 million SNP genotypes (Sherry et al. 1999). The other 82 strains have only on the order of hundreds or thousands of SNP genotypes.

Restriction digest simulation is done by scanning each SNP-containing sequence for the recognition sites of select restriction enzymes. A SNP is considered to result in an informative RFLP assay if an enzyme site is found in the sequence of one strain but not in the other strain due to the alteration of the restriction site by the polymorphism. The default enzymes are AluI, AflII, ClaI, DdeI, EcoRV, Fnu4HI, HaeIII, HhaI, HinfI, KpnI, MboI, MseI, MspI, PstI, PvuI, PvuII, RsaI, SacII, SalI, ScaI, ScrFI, and Sau96I. This list comprises efficient, frequently cutting restriction enzymes that have a high probability of providing a robust RFLP assay for any given SNP. In addition, the user can select an option that includes all the enzymes in the simulated restriction digest. Analysis of the number of restriction enzyme sites within a given amplicon is performed to avoid assays with very high complexity or very small size differences of restriction fragments. All the restriction enzymes and recognition sequences used by SNP2RFLP were obtained from the restriction enzyme database (REBASE) (Roberts et al. 2003).

To avoid nonspecific amplification for a given SNP, the surrounding sequence for each SNP was queried for the presence of known repetitive elements and simple and complex repeats using RepeatMasker, which “masks” these sequences with “N”s (http://www.repeatmasker.org/). The genomic locations of these premasked sequences are stored with each SNP so the user can decide whether to discard SNPs that fall in repeat regions.

The program SNP2RFLP was written in the programming language PERL. PERL was chosen because of its database connections and pattern-matching capabilities. The program was then incorporated into a CGI script that is called from a web interface. This interface was written with the HTML and JavaScript languages.

Results

The input to SNP2RFLP is the two mouse strains used in the cross, the chromosomal region (as defined by base pairs), and a set of restriction endonucleases. A default list of 22 commonly used restriction endonucleases with frequently occurring recognition sites is used by SNP2RFLP to simulate restriction digestion, but additional enzymes can be selected from a list of 1300 endonucleases.

SNP2RFLP extracts the SNPs from dbSNP that are polymorphic between the two strains in the region in question. SNP2RFLP then simulates a restriction digest on the SNP-containing sequences with each enzyme that was selected to determine if the SNP is contained within one or more enzyme recognition sites and creates an RFLP. That is, a SNP-containing sequence is scanned to see if the recognition sequence for any particular enzyme contains the SNP and is found for one strain but not the other due to the alteration of the recognition sequence by the SNP. If this is the case, the SNP is considered informative because the alleles can be distinguished by amplifying the region with PCR, digesting the products with the enzyme, and examining the resulting restriction pattern after agarose gel electrophoresis of the digested product (Fig. 1). Informative SNPs are listed and are accompanied by suggested oligonucleotide primer sequences for PCR amplification of the SNP (extracted from data stored in the database for each SNP), the position of the primers with respect to the SNP, and the number of enzyme recognition sites present in the amplified sequence. The entirety of these data can be visualized as a web-based display (Fig. 2) or can be exported as a spreadsheet document.

Fig. 1.

A SNP2FRLP-identified RFLP assay used to identify mice carrying a mapped ENU-induced mutation. PCR products of 195 bp encompassing SNP rs37311177 on chromosome 13 were amplified from tail DNA isolated from individual mice and digested with the restriction enzyme MseI. Samples included AJ, FVB strain controls (underlined), and five experimental samples. AJ polymorphism at this SNP creates an RFLP that is not present in the FVB genome

Fig. 2.

A screen shot of three informative SNPs returned by SNP2RFLP. The restriction enzyme recognition sites (bold) cut at the SNP position (bold, blue) in the sequence. The genotype of the SNP for each strain is shown. The suggested primers found by Primer3 are highlighted in red along the sequence

We have used the SNP2RFLP service to assist in developing markers in our mapping of mutants in an ongoing ENU mutagenesis screen. In the process of mapping seven different recessive mutations, we have utilized 32 different RFLPs which are summarized in Table 1. We used primarily SNP2RFLPs identified with the default enzyme set. Twenty-three of these yielded easily interpreted results when digested with the prescribed enzyme, although three required additional optimization. Nine assays were not usable for mapping purposes: One did not give a discrete PCR product and eight assays failed to detect the RFLP as predicted by this program. Overall, this program yielded easily implemented assays with 72% reliability.

Table 1.

Analysis of SNP2RFLP-designed RFLP assays for positional cloning

Primer No.	Position (chr_Mb)	Strains tested	SNP	Enzyme	Success
217/218	13_32.5	A/J v FVB	rs29904172	AluI	Yes
241/242	13_33.5	A/J v FVB	rs29239961	BbsI	Yes
233/234	13_34.2	A/J v FVB	rs37311177	MseI	Yes
243/244	13_37.2	A/J v FVB	rs6259014	HinfI	Yes
211/212	2_61.9	A/J v FVB	rs28002307	RsaI	Yes
BB1207/1208	7_102.96	A/J v FVB	rs37343086	MseI	No: no RFLP by digestion
BB1211/1212	7_122.5	A/J v FVB	rs37274506	Fnu4HI	Yes
BB1203/1204	7_67.7	A/J v FVB	rs36590391	HaeIII	Yes
BB1205/1206	7_88.1	A/J v FVB	rs36897851	DdeI	No: no RFLP by digestion
BB1213/1214	1_128.56	A/J v FVB	rs33427936	PstI	Yes
BB1215/1216	1_130.6	A/J v FVB	rs13476106	RsaI	Yes
249/350	1_195	A/J v FVB	rs13476313	MseI	Yes
237/238	15_5.14	A/J v FVB	rs32664631	MseI	Yes
231/232	15_66.0	A/J v FVB	rs36757821	BglII	Yes
641/642	15_63.3	A/J v FVB	rs37879829	AluI	Yes
367/368	7_22.0	A/J v FVB	rs36238918	NcoI	No: no RFLP by digestion
371/372	7_28.2	A/J v FVB	rs37765358	PvuI	Yes
403/404	7_31.2	A/J v FVB	rs36772588	RsaI	No: no discrete PCR bands
369/370	7_35.3	A/J v FVB	rs38119160	NruI	Yes: optimize for robustness
BB1137/1138	11_82.8	B6 v FVB	rs28191426	RsaI	No: no RFLP by digestion
BB1227/1228	11_82.83	B6 v FvB	rs28191333	HaeIII	Yes
BB1229/1230	11_82.83	B6 v FvB	rs28191306	MspI	Yes
BB1231/1232	11_82.85	B6 v FvB	rs28191239	MspI	No: no RFLP by digestion
BB1235/1236	11_83.01	B6 v FvB	rs28210166	AluI	No: no RFLP by digestion
BB1139/1140	11_83.6	B6 v FvB	rs28209292	HaeIII	No: no RFLP by digestion
BB1237/1238	11_83.60	B6 v FvB	rs28209292	HaeIII	No: no RFLP by digestion
BB1133/1134	11_87.8	B6 v FvB	rs28241207	BssHII	Yes
BB1143/1144	11_88.2	B6 v FvB	rs26953277	Fnu4HI	Yes: optimize for robustness
BB1141/1142	11_88.3	B6 v FvB	rs29407170	AluI	Yes
BB1145/1146	11_88.5	B6 v FvB	rs27065145	Sau96I	Yes: optimize for robustness
BB1135/1136	11_89.1	B6 v FvB	rs29401408	HaeIII	Yes
BB1131/1132	11_89.6	B6 v FvB	rs27083857	HincII	Yes

Discussion

Because the number of characterized SNPs and their distribution across the genome are highly variable, we have incorporated multiple options to control the output returned by SNP2RFLP to produce a useful number of informative markers. First, each SNP in the database has a validation status. NCBI’s dbSNP defines many different ways that a SNP can be validated. For simplicity, if the “display validated SNPs only” option is selected, SNPs that have no validation information are excluded. This reduces the number of informative SNPs in many cases, but gives higher confidence in the utility of those reported.

Second, there are occasions when no informative SNPs are reported between two strains in a specific region. It may be that there are indeed informative SNPs in the region but the genotype may be recorded in only one strain. If the “display SNPs recorded in only one strain” option is selected, then the restriction digest is simulated on the SNPs from the strain where the genotype is known and compared with that for the alternate allele. For those identified as potentially polymorphic, additional methods can be applied by the user to infer the genotype of each SNP in the other strain (such as comparison to haplotypes of well-characterized strains), or they can simply be empirically tested.

Third, as previously noted, SNPs in a repeat region of the genome are often difficult to amplify. The user can select an option for SNP2RFLP to discard SNPs that fall within repeats.

Finally, the desired density of SNP markers returned can be set. SNP2RFLP can be instructed to return all of the informative markers or a subset (e.g., 1 of every 5, 1 of every 10, etc.). This is an extremely valuable option that allows the user to retrieve an adequate and manageable number of markers. As an example, suppose a genome-wide SNP scan, crossing A/J and FVB/NJ, reveals a candidate region on chromosome 13, 14.8–46.7 Mb, in which a particular mutation of interest may be located. Table 2 gives the different numbers of informative SNPs in this region returned by SNP2RFLP by selecting different options in the program. When selecting all of the enzymes and instructing SNP2RFLP to keep all SNPs, SNP2RFLP returns a large and perhaps unmanageable number of SNP markers. By restricting the type and number of enzymes and directing SNP2RFLP to report SNPs at a desired density, a manageable and adequate number of SNP markers can be considered that will facilitate fine-mapping of this candidate region that contains the phenotype-causing mutation of interest.

Table 2.

SNP2RFLP analysis of a recombinant interval from 14.8 to 46.7 Mb on chromosome 13 derived from an A/J × FVB/NJ cross

No SNPs to output	No SNPs returned (all enzymes)	No suitable primers found	Total SNP2RFLP assays	No SNPs returned (default enzymes)	No suitable primers found	Total SNP2RFLP assays
All	856	29	827	306	10	296
1 every 5	172	2	170	62	2	60
1 every 10	86	0	86	31	1	30
1 every 20	43	0	43	16	1	15

The SNP2RFLP interface can be accessed through the web at http://genetics.bwh.harvard.edu/snp2rflp. An instruction manual is available on the website or as supplementary data.