To facilitate selection of single-nucleotide polymorphisms (SNP) for molecular epidemiologic studies investigating the hormonal carcinogenesis hypothesis, we used two sequence homology–based tools [Sort Intolerant from Tolerant (SIFT) and Polymorphism Phenotype (PolyPhen)] to predict the potential impact a nonsynonymous SNP (nsSNP), which results in an amino acid substitution, may have on the activity of proteins encoded by genes involved in the steroid hormone metabolism and response pathway. We screened 137 variants. Of these, 28% were predicted by SIFT and PolyPhen as having a potentially damaging effect on protein function. Investigation into the association of these variant alleles with hormone-related cancers may prove to be fruitful.

Epidemiologic association studies focus a great amount of effort into identifying single-nucleotide polymorphisms (SNP) in genes that may have an association with disease risk. Often, the SNPs that have an association with disease are those that are known as nonsynonymous SNPs (nsSNP), which result in an amino acid substitution. These types of polymorphisms are believed to be more likely to cause a change in structure and as such compromise the function of a protein. The structure of a protein can change in various ways due to the biochemical differences of the amino acid variant (acidic, basic, or hydrophobic) and by the location of the variant in the protein sequence (by affecting tertiary or quaternary structure or the active site where substrate binds). Many molecular epidemiologic studies focus on studying SNPs found in coding regions in hopes of finding significant association between SNPs and disease susceptibility, but often find little or no association (1-3). Our ability to better select a nsSNP for an association study can be enhanced by first examining the potential impact an amino acid variant may have on the function of the encoded protein with the use of two innovative sequence homology–based programs, Sort Intolerant from Tolerant (SIFT; http://blocks.fhcrc.org/sift/SIFT.html) and Polymorphism Phenotype (PolyPhen; http://www.bork.embl-heidelberg.de/PolyPhen/).

SIFT uses sequence homology among related genes and domains across species to predict the impact of all 20 possible amino acids at a given position, allowing users to determine which nsSNPs would be of most interest to study by sorting variants by this prediction score (1, 2, 4-9). The SIFT algorithm has been shown to predict a phenotype for a nsSNP more accurately than previously used substitution scoring matrices, such as BLOSUM62, as these matrices do not incorporate information specific to the protein of interest (4, 10). Another advantage to using SIFT is the potential to analyze a larger number of nsSNPs than methods that are dependent on the availability of protein structure alone (4, 11).

The PolyPhen algorithm, like SIFT, also takes an evolutionary approach in distinguishing deleterious nsSNPs from functionally neutral ones. PolyPhen differs from SIFT in that it predicts how damaging a particular variant may be by using a set of empirical rules based on sequence, phylogenetic, and structural information characterizing a particular variant. In addition to using sequence alignments, PolyPhen utilizes protein structure databases, such as PDB (Protein Data Bank) or PQS (Protein Quarternary Structure), DSSP (Dictionary of Secondary Structure in Proteins), and three-dimensional structure databases to determine if a variant may have an effect on the protein's secondary structure, interchain contacts, functional sites, and binding sites (3, 7-9).

These two algorithms have great potential in their use to screen for potentially damaging nsSNPs in genes that may be associated with disease risk, such as hormone-related cancers like breast, prostate, and endometrial cancer. Interindividual variation in the biosynthesis and metabolism of sex hormones has been shown in population studies and may be related to risk of these types of cancers. For instance, Dunning et al. (12) found that circulating levels of several steroid hormones, such as estrogen, androgen, and their precursors, are directly related to the risk of breast cancer. There is evidence that variation in circulating levels of steroid hormones may be associated with polymorphisms of genes involved in the steroid hormone metabolism and response pathways (13-15). In this study, we screened 137 nsSNPs identified in genes in these pathways, using the SIFT and PolyPhen algorithms, to predict which of these nsSNPs would most likely have a damaging effect on the function of the encoded proteins.

nsSNP Screening Process

Two public SNP databases, dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) and SNP500cancer (http://snp500cancer.nci.nih.gov/home.cfm), were used to identify nsSNPs for the 25 genes of interest. From dbSNP, we identified 113 nsSNPs. An additional 24 nsSNPs were identified from the SNP500cancer database.

Obtaining nsSNP Tolerance Scores with SIFT and PolyPhen

For each gene of interest, we analyzed the protein sequence identified from dbSNP using the SIFT-version 2 database. In general, for the peptide sequence, SIFT performs multiple alignments of a number of sequences until a median conservation for the peptide sequence is reached at the default of 3.0 and predicts whether substitution with any of the other amino acids is tolerated or deleterious for every position in the submitted sequence. The SIFT prediction is given as a tolerance index (TI) score ranging from 0.0 to 1.0, which is the normalized probability that the amino acid change is tolerated. A nsSNP with a TI score of ≤0.05 is considered to be deleterious. The confidence measure of this result is given as the median sequence conservation score (MSCS) for the given codon. Deleterious predictions with a MSCS ≥3.25 were made with low confidence because the sequences used to determine the score were not diverse enough.

We adjusted the PolyPhen algorithm settings to include all sequences homologous to the peptide sequence of interest for the calculation of the difference in structural parameters instead of using the single most homologous sequence as a default. All other settings of PolyPhen were set as the default. Predictions of how a particular nsSNP may affect protein structure by PolyPhen are assigned as “probably damaging,” a score made with high confidence that the nsSNP should affect protein structure and/or function; “possibly damaging,” where it may affect protein function and/or structure; and “benign,” as most likely having no phenotypic effect (3). It arrives at these predictions by determining the position-specific independent count difference of the two allelic variants in the polymorphic position and also measures the degree of damaging effect a variant may have on structural parameters of a protein. The position-specific independent count is a logarithmic ratio of the likelihood of a given amino acid occurring at a particular position to the likelihood of the same amino acid occurring at any position in the sequence, also known as background frequency. Thus, PolyPhen, as mentioned before, uses a set of empirical rules based on sequence, phylogenetic, and structural information to determine if protein structure may be compromised.

The screen for nsSNPs of the two online databases, dbSNP and SNP500cancer, yielded 137 nsSNPs in 25 genes associated with sex steroid biosynthesis, catabolism, and response. Of the 25 genes selected, only UGT1A1 lacked any submitted nsSNPs in the two databases. The results of the SIFT and PolyPhen analyses are listed in Table 1. SIFT scores ≤0.05 are considered to be deleterious (TI ≤ 0.05) and SIFT scores with low confidence (MSCS ≥ 3.25) are in parentheses. Of the 137 nsSNPs screened with SIFT and PolyPhen, the 111 nsSNPs that were scored with confidence by SIFT (MSCS < 3.25) were used in determining concordance between SIFT and PolyPhen.

Table 1.

SIFT and PolyPhen results for 137 amino acid variants of genes involved in steroid hormone metabolism and response

Gene (protein ID)SNP identifierAA variant*SIFT score†SIFT prediction‡PolyPhen predictionGene (protein ID)SNP identifierAA variantSIFT scoreSIFT predictionPolyPhen prediction
AR (NP_000035) rs1800053 A646D 0.19 Tolerant Benign CYP19A1 (NP_000094) rs2236722 W39R (0.11) (Tolerant) POS 
 rs9332968 I665T 0.06 Tolerant Benign  cyp19a1-44 T201M 0.06 Tolerant Benign 
 rs9332969 R841H 0.07 Tolerant POS  rs700519 R264C 0.03 INTOL Benign 
 rs9332970 I843T 0.03 INTOL POS  rs2304462 R264H 0.21 Tolerant Benign 
 rs9332971 R856H 0.01 INTOL POS       
      ESR1 (NP_000116) rs9340773 G77S 1.00 Tolerant Benign 
COMT(NP_000745) comt-11 G32S (0.00) (INTOL) Benign  esr1-22 M264I 1.00 Tolerant Benign 
 rs6270 C34S (0.04) (INTOL) Benign       
 rs6267 A72S 0.31 Tolerant Benign GSTM1 (NP_000552) rs1065411 K173N 0.33 Tolerant Benign 
 rs13306281 V92M 0.00 INTOL Benign  rs449856 S210T 0.90 Tolerant Benign 
 rs5031015 A102T 0.56 Tolerant Benign       
 rs4986871 A146V 0.03 INTOL Benign GSTT1 (NP_000844) rs2266635 A21T 0.00 INTOL Benign 
 rs4680 V158M 0.02 INTOL Benign  rs11550605 T104P 0.10 Tolerant POS 
 rs13306279 P199L 0.01 INTOL PRB  rs2266633 D141N 0.75 Tolerant Benign 
       rs2266637 V169I 0.08 Tolerant Benign 
CYP1A1 (NP_000490) rs4646422 G45D 0.06 Tolerant Benign  rs2234953 E173K 0.06 Tolerant PRB 
 cyp1a1-58 M66V 0.01 INTOL POS       
 cyp1a1-96 I78T 0.00 INTOL PRB HSD3B1 (NP_000853) rs17023677 T54I 0.53 Tolerant Benign 
 rs2229150 R93W 0.00 INTOL PRB  rs4986952 R71I 0.01 INTOL PRB 
 cyp1a1-98 A132P 0.09 Tolerant Benign  rs6201 I79V 1.00 Tolerant Benign 
 cyp1a1-57 I171M 0.01 INTOL POS  rs6684974 G90S 0.02 INTOL POS 
 cyp1a1-56 T173R 0.68 Tolerant Benign  rs6205 F286L 1.00 Tolerant POS 
 cyp1a1-21 P238S 0.01 INTOL POS  rs1047303 T367N 0.30 Tolerant Benign 
 cyp1a1-23 R279W 0.00 INTOL PRB       
 rs4987133 I286T 0.00 INTOL PRB HSD3B2 (NP_000189) rs4986954 D74N 0.02 INTOL Benign 
 cyp1a1-26 M331I 0.12 Tolerant Benign  rs6211 E94Q 1.00 Tolerant Benign 
 rs2856833 F381L 0.03 INTOL Benign       
 cyp1a1-28 R431G 0.00 INTOL PRB HSD17B1 (NP_000404) rs605059 S313G (0.85) (Tolerant) Benign 
 rs1799814 T461N 0.49 Tolerant Benign       
 rs1048943 I462V 0.13 Tolerant Benign       
 rs2278970 A463G 0.52 Tolerant Benign LHB (NP_000885) rs1800447 W28R 0.40 Tolerant POS 
       lhb-02 I35T 1.00 Tolerant Benign 
CYP1A2 (NP_000752) cyp1a2-20 D104N 0.00 INTOL POS  rs5030773 Q74R 0.00 INTOL PRB 
 cyp1a2-31 S298R 0.06 Tolerant POS  rs5030774 G122S 0.64 Tolerant Benign 
 cyp1a2-32 G299S 0.11 Tolerant Benign       
 cyp1a2-44 R457W 0.00 INTOL PRB NQO1 (NP_000894) rs11555215 R119P 0.00 INTOL PRB 
       rs4986998 R139W 0.01 INTOL Benign 
CYP1B1 (NP_000095) rs10012 R48G (0.27) (Tolerant) POS  rs1800566 P187S 0.11 Tolerant PRB 
 rs9282670 Q68R 0.34 Tolerant Benign  nqo1-04 I193T 0.33 Tolerant Benign 
 rs9282671 Y81N 0.00 INTOL PRB       
 rs1056827 A119S 0.02 INTOL Benign PGR (NP_000917) rs11571143 A50T (0.01) (INTOL) Benign 
 rs9341248 S206N 0.03 INTOL Benign  rs3740754 G57R (0.44) (Tolerant) Benign 
 rs9341250 R266L 0.26 Tolerant Benign  rs11571144 A120V (0.00) (INTOL) Benign 
 rs4398252 M372V 0.01 INTOL PRB  rs11571145 P186L (0.00) (INTOL) PRB 
 rs1056836 V432L 0.22 Tolerant Benign  rs11571146 M301R (0.00) (INTOL) PRB 
 rs4986887 D441H 0.16 Tolerant Benign  rs3740753 T344S (0.68) (Tolerant) Benign 
 rs4986888 A443G 0.05 INTOL Benign  rs11571147 C347S (1.00) (Tolerant) Benign 
 rs1056837 D449E 0.05 INTOL Benign  rs11571148 P352L (0.00) (INTOL) PRB 
 rs1800440 N453S 0.03 INTOL Benign  rs11571149 S378R (0.00) (INTOL) Benign 
       rs11571150 A444S (0.33) (Tolerant) Benign 
CYP3A4 (NP_059488) rs12721634 L15P (0.00) (INTOL) PRB  rs11571151 V529L (0.02) (INTOL) Benign 
 cyp3a4-05 G56D 0.00 INTOL Benign  rs11571152 Q536P (0.03) (INTOL) PRB 
 rs3091339 K96E 0.00 INTOL Benign  rs2020874 R625I (0.00) (INTOL) POS 
 cyp3a4-06 I118V 0.57 Tolerant Benign  rs11571222 L651V (0.11) (Tolerant) Benign 
 rs4986907 R162Q 0.64 Tolerant Benign  rs1042838 L660V (1.00) (Tolerant) Benign 
 cyp3a4-09 V170I 0.46 Tolerant Benign  rs2020880 S865L (0.06) (Tolerant) Benign 
 rs4986908 D174H 0.06 Tolerant Benign       
 rs4986908 D174N 0.02 INTOL Benign SULT1A1 (NP_001046) rs1042008 H149Y 1.00 Tolerant Benign 
 rs12721627 T185S 0.01 INTOL Benign  rs1042011 E151Q 0.21 Tolerant Benign 
 rs4987161 F189S 0.00 INTOL PRB  rs1042014 E151D 1.00 Tolerant Benign 
 cyp3a4-13 P218R 0.01 INTOL Benign  rs9282861 R213H 0.00 INTOL POS 
 cyp3a4-14 S222P 0.13 Tolerant POS  rs1801030 V223M 0.00 INTOL Benign 
 rs3208363 S252A 1.00 Tolerant Benign       
 rs10250778 T349N 0.02 INTOL Benign SULT1A2 (NP_001045) rs1136703 I7T 0.47 Tolerant Benign 
 cyp3a4-17 T363M 0.00 INTOL PRB  rs10797300 P19L 0.34 Tolerant Benign 
 rs12721629 L373F 0.12 Tolerant Benign  rs4987024 Y62F 0.33 Tolerant Benign 
 rs4986909 P416L 0.00 INTOL PRB  rs1126451 V86A 0.68 Tolerant Benign 
 rs1041988 I431T 0.31 Tolerant Benign  rs11569763 H226D 0.01 INTOL PRB 
 rs4986910 M445T 0.00 INTOL PRB  rs1059491 N235T 0.00 INTOL Benign 
 rs4986913 P467S 0.42 Tolerant POS  rs11569766 N239S 0.00 INTOL POS 
       rs27742 E282K 1.00 Tolerant Benign 
CYP3A5 (NP_000768) rs13220949 R439K 0.00 INTOL PRB       
 rs13233803 R495T 0.00 INTOL PRB UGT2B4 (NP_066962) rs13119049 D458E 0.00 INTOL POS 
CYP11A1 (NP_000772) rs11544450 G15C (0.01) (INTOL) Benign       
 rs1130841 Y16C (1.00) (Tolerant) PRB UGT2B7 (NP_001065) rs12233719 A71S 0.67 Tolerant Benign 
 rs1130843 F274L 0.00 INTOL PRB  rs7439366 H268Y 0.00 INTOL PRB 
 rs1049968 M301I 0.82 Tolerant Benign       
 rs6161 E314K 0.31 Tolerant Benign UGT2B15 (NP_001067) rs1902023 D85Y 0.06 Tolerant Benign 
       rs4148269 T523K (0.04) (INTOL) POS 
CYP17A1 (NP_000093) rs762563 C22W (0.19) (Tolerant) PRB       
Gene (protein ID)SNP identifierAA variant*SIFT score†SIFT prediction‡PolyPhen predictionGene (protein ID)SNP identifierAA variantSIFT scoreSIFT predictionPolyPhen prediction
AR (NP_000035) rs1800053 A646D 0.19 Tolerant Benign CYP19A1 (NP_000094) rs2236722 W39R (0.11) (Tolerant) POS 
 rs9332968 I665T 0.06 Tolerant Benign  cyp19a1-44 T201M 0.06 Tolerant Benign 
 rs9332969 R841H 0.07 Tolerant POS  rs700519 R264C 0.03 INTOL Benign 
 rs9332970 I843T 0.03 INTOL POS  rs2304462 R264H 0.21 Tolerant Benign 
 rs9332971 R856H 0.01 INTOL POS       
      ESR1 (NP_000116) rs9340773 G77S 1.00 Tolerant Benign 
COMT(NP_000745) comt-11 G32S (0.00) (INTOL) Benign  esr1-22 M264I 1.00 Tolerant Benign 
 rs6270 C34S (0.04) (INTOL) Benign       
 rs6267 A72S 0.31 Tolerant Benign GSTM1 (NP_000552) rs1065411 K173N 0.33 Tolerant Benign 
 rs13306281 V92M 0.00 INTOL Benign  rs449856 S210T 0.90 Tolerant Benign 
 rs5031015 A102T 0.56 Tolerant Benign       
 rs4986871 A146V 0.03 INTOL Benign GSTT1 (NP_000844) rs2266635 A21T 0.00 INTOL Benign 
 rs4680 V158M 0.02 INTOL Benign  rs11550605 T104P 0.10 Tolerant POS 
 rs13306279 P199L 0.01 INTOL PRB  rs2266633 D141N 0.75 Tolerant Benign 
       rs2266637 V169I 0.08 Tolerant Benign 
CYP1A1 (NP_000490) rs4646422 G45D 0.06 Tolerant Benign  rs2234953 E173K 0.06 Tolerant PRB 
 cyp1a1-58 M66V 0.01 INTOL POS       
 cyp1a1-96 I78T 0.00 INTOL PRB HSD3B1 (NP_000853) rs17023677 T54I 0.53 Tolerant Benign 
 rs2229150 R93W 0.00 INTOL PRB  rs4986952 R71I 0.01 INTOL PRB 
 cyp1a1-98 A132P 0.09 Tolerant Benign  rs6201 I79V 1.00 Tolerant Benign 
 cyp1a1-57 I171M 0.01 INTOL POS  rs6684974 G90S 0.02 INTOL POS 
 cyp1a1-56 T173R 0.68 Tolerant Benign  rs6205 F286L 1.00 Tolerant POS 
 cyp1a1-21 P238S 0.01 INTOL POS  rs1047303 T367N 0.30 Tolerant Benign 
 cyp1a1-23 R279W 0.00 INTOL PRB       
 rs4987133 I286T 0.00 INTOL PRB HSD3B2 (NP_000189) rs4986954 D74N 0.02 INTOL Benign 
 cyp1a1-26 M331I 0.12 Tolerant Benign  rs6211 E94Q 1.00 Tolerant Benign 
 rs2856833 F381L 0.03 INTOL Benign       
 cyp1a1-28 R431G 0.00 INTOL PRB HSD17B1 (NP_000404) rs605059 S313G (0.85) (Tolerant) Benign 
 rs1799814 T461N 0.49 Tolerant Benign       
 rs1048943 I462V 0.13 Tolerant Benign       
 rs2278970 A463G 0.52 Tolerant Benign LHB (NP_000885) rs1800447 W28R 0.40 Tolerant POS 
       lhb-02 I35T 1.00 Tolerant Benign 
CYP1A2 (NP_000752) cyp1a2-20 D104N 0.00 INTOL POS  rs5030773 Q74R 0.00 INTOL PRB 
 cyp1a2-31 S298R 0.06 Tolerant POS  rs5030774 G122S 0.64 Tolerant Benign 
 cyp1a2-32 G299S 0.11 Tolerant Benign       
 cyp1a2-44 R457W 0.00 INTOL PRB NQO1 (NP_000894) rs11555215 R119P 0.00 INTOL PRB 
       rs4986998 R139W 0.01 INTOL Benign 
CYP1B1 (NP_000095) rs10012 R48G (0.27) (Tolerant) POS  rs1800566 P187S 0.11 Tolerant PRB 
 rs9282670 Q68R 0.34 Tolerant Benign  nqo1-04 I193T 0.33 Tolerant Benign 
 rs9282671 Y81N 0.00 INTOL PRB       
 rs1056827 A119S 0.02 INTOL Benign PGR (NP_000917) rs11571143 A50T (0.01) (INTOL) Benign 
 rs9341248 S206N 0.03 INTOL Benign  rs3740754 G57R (0.44) (Tolerant) Benign 
 rs9341250 R266L 0.26 Tolerant Benign  rs11571144 A120V (0.00) (INTOL) Benign 
 rs4398252 M372V 0.01 INTOL PRB  rs11571145 P186L (0.00) (INTOL) PRB 
 rs1056836 V432L 0.22 Tolerant Benign  rs11571146 M301R (0.00) (INTOL) PRB 
 rs4986887 D441H 0.16 Tolerant Benign  rs3740753 T344S (0.68) (Tolerant) Benign 
 rs4986888 A443G 0.05 INTOL Benign  rs11571147 C347S (1.00) (Tolerant) Benign 
 rs1056837 D449E 0.05 INTOL Benign  rs11571148 P352L (0.00) (INTOL) PRB 
 rs1800440 N453S 0.03 INTOL Benign  rs11571149 S378R (0.00) (INTOL) Benign 
       rs11571150 A444S (0.33) (Tolerant) Benign 
CYP3A4 (NP_059488) rs12721634 L15P (0.00) (INTOL) PRB  rs11571151 V529L (0.02) (INTOL) Benign 
 cyp3a4-05 G56D 0.00 INTOL Benign  rs11571152 Q536P (0.03) (INTOL) PRB 
 rs3091339 K96E 0.00 INTOL Benign  rs2020874 R625I (0.00) (INTOL) POS 
 cyp3a4-06 I118V 0.57 Tolerant Benign  rs11571222 L651V (0.11) (Tolerant) Benign 
 rs4986907 R162Q 0.64 Tolerant Benign  rs1042838 L660V (1.00) (Tolerant) Benign 
 cyp3a4-09 V170I 0.46 Tolerant Benign  rs2020880 S865L (0.06) (Tolerant) Benign 
 rs4986908 D174H 0.06 Tolerant Benign       
 rs4986908 D174N 0.02 INTOL Benign SULT1A1 (NP_001046) rs1042008 H149Y 1.00 Tolerant Benign 
 rs12721627 T185S 0.01 INTOL Benign  rs1042011 E151Q 0.21 Tolerant Benign 
 rs4987161 F189S 0.00 INTOL PRB  rs1042014 E151D 1.00 Tolerant Benign 
 cyp3a4-13 P218R 0.01 INTOL Benign  rs9282861 R213H 0.00 INTOL POS 
 cyp3a4-14 S222P 0.13 Tolerant POS  rs1801030 V223M 0.00 INTOL Benign 
 rs3208363 S252A 1.00 Tolerant Benign       
 rs10250778 T349N 0.02 INTOL Benign SULT1A2 (NP_001045) rs1136703 I7T 0.47 Tolerant Benign 
 cyp3a4-17 T363M 0.00 INTOL PRB  rs10797300 P19L 0.34 Tolerant Benign 
 rs12721629 L373F 0.12 Tolerant Benign  rs4987024 Y62F 0.33 Tolerant Benign 
 rs4986909 P416L 0.00 INTOL PRB  rs1126451 V86A 0.68 Tolerant Benign 
 rs1041988 I431T 0.31 Tolerant Benign  rs11569763 H226D 0.01 INTOL PRB 
 rs4986910 M445T 0.00 INTOL PRB  rs1059491 N235T 0.00 INTOL Benign 
 rs4986913 P467S 0.42 Tolerant POS  rs11569766 N239S 0.00 INTOL POS 
       rs27742 E282K 1.00 Tolerant Benign 
CYP3A5 (NP_000768) rs13220949 R439K 0.00 INTOL PRB       
 rs13233803 R495T 0.00 INTOL PRB UGT2B4 (NP_066962) rs13119049 D458E 0.00 INTOL POS 
CYP11A1 (NP_000772) rs11544450 G15C (0.01) (INTOL) Benign       
 rs1130841 Y16C (1.00) (Tolerant) PRB UGT2B7 (NP_001065) rs12233719 A71S 0.67 Tolerant Benign 
 rs1130843 F274L 0.00 INTOL PRB  rs7439366 H268Y 0.00 INTOL PRB 
 rs1049968 M301I 0.82 Tolerant Benign       
 rs6161 E314K 0.31 Tolerant Benign UGT2B15 (NP_001067) rs1902023 D85Y 0.06 Tolerant Benign 
       rs4148269 T523K (0.04) (INTOL) POS 
CYP17A1 (NP_000093) rs762563 C22W (0.19) (Tolerant) PRB       

Abbreviations: AA, amino acid; INTOL, intolerant; PRB, probably damaging; POS, possibly damaging.

*

Variants listed were obtained from dbSNP (rs_ID number) and SIFT500cancer (gene name_ID number).

†

TI scores ≤0.05 are predicted to be intolerant, whereas TI scores >0.05 are tolerant variants. TI scores in parenthesis have a MSCS ≥3.25 and should be interpreted with caution.

‡

Predictions in parenthesis were made in low confidence (MSCS ≥ 3.25) and should be interpreted with caution.

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Table 2 gives the concordance of SIFT and PolyPhen predictions for only the 111 variants scored with confidence by SIFT. Of these 111 nsSNPs, 31 (28%) were predicted to have a negative effect on protein function (SIFT: TI ≤ 0.05 with confidence; PolyPhen: probably damaging/possibly damaging) and 50 (45%) were predicted to be tolerated variants (TI > 0.05 with confidence; benign). The remaining 30 nsSNPs (27%) of these variants had conflicting results between the SIFT and PolyPhen algorithms.

Table 2.

Concordance of SIFT and PolyPhen predictions for the 111 variants scored with confidence by SIFT of genes involved in steroid hormone metabolism and response

PolyPhen predictionSIFT prediction and score
Most intolerant (TI score, 0.00)Intolerant (TI score, 0.01-0.05)Borderline tolerant (TI score, 0.06-0.10)Tolerant (TI score, 0.11-1.00)
Probably damaging 17 (15.3)* 4 (3.6) 1 (0.9) 1 (0.9) 
Possibly damaging 4 (3.6) 6 (5.4) 3 (2.7) 4 (3.6) 
Benign 6 (5.4) 15 (13.5) 7 (6.3) 43 (38.7) 
PolyPhen predictionSIFT prediction and score
Most intolerant (TI score, 0.00)Intolerant (TI score, 0.01-0.05)Borderline tolerant (TI score, 0.06-0.10)Tolerant (TI score, 0.11-1.00)
Probably damaging 17 (15.3)* 4 (3.6) 1 (0.9) 1 (0.9) 
Possibly damaging 4 (3.6) 6 (5.4) 3 (2.7) 4 (3.6) 
Benign 6 (5.4) 15 (13.5) 7 (6.3) 43 (38.7) 
*

Number (%) of variants. These numbers are representative of only the 111 nsSNPs that were scored with confidence by SIFT (MSCS < 3.25) of the 137 nsSNPs screened by SIFT and PolyPhen.

View Large

We identified a total of 66 of 137 nsSNPs that were scored as intolerant by SIFT; 14 of these were scored with low confidence. A total of 30 nsSNPs were scored as “probably damaging” and 21 as “possibly damaging” by PolyPhen. Out of the 66 nsSNPs classified as intolerant by SIFT, 17 variants are distributed among a number of genes: one in LHB, one in CYP11A1, five in CYP1A1, one in CYP1A2, one in CYP1B1, four in CYP3A4, two in CYP3A5, one in NQO1, and one in UGT2B7, and were scored as “most intolerant” by SIFT (TI = 0.0 with confidence) and probably damaging by PolyPhen. Thirty-one variants were scored as having some damaging effect on protein function (TI ≤ 0.05; probably damaging/possibly damaging). It would be interesting to study these particular variants in an epidemiologic study to assess possible associations with the risk of hormone-related cancers. Of the 111 nsSNPs scored with confidence by SIFT, 30 nsSNPs (27%) were found to have conflicting results. A possible explanation as to why SIFT and PolyPhen may differ in their prediction scores could be that PolyPhen, in addition to using sequence information, incorporates structural information of the protein (obtained from PQS and DSSP).

Several studies have used SIFT and/or PolyPhen to assess function of nsSNPs in candidate genes for association studies, but none have reported such assessment of the steroid hormone metabolism pathway (2, 9). Based on an evaluation of published association of nsSNPs and cancer risk, Zhu et al. (1) concluded that the risk of a number of cancers were significantly inversely correlated with SIFT predictions. Their results also suggest that variants that occur in conserved sequences are more likely to be associated with cancer susceptibility. Two studies thus far have combined both the SIFT and PolyPhen algorithms to screen for deleterious nsSNPs. In one paper, Xi et al. (7) examined the DNA repair pathways and found that the predictions made by the two programs are highly correlated, with ∼62% concordance in the predictions of deleterious or potentially damaging nsSNPs. We observed a similar concordance of 73% between SIFT and PolyPhen predictions, but in genes involved in steroid hormone metabolism and response. In a second, more recent, study, Livingston et al. (8) used SIFT and PolyPhen to identify 57 potentially deleterious nsSNPs involved in DNA repair, cell cycle regulation, apoptosis, and drug metabolism.

Whereas useful, SIFT and PolyPhen are nonetheless somewhat limited. This is because both require available sequence data. However, as more whole genome sequences become publicly available, prediction of functionality of more nsSNPs will become possible and the results more reliable (5, 9). When there are too few sequences available on a particular gene for comparison, for example, the PGR gene, or when the sequences seem to be too homologous to one another, a nonfunctional variant may be predicted to be “intolerant” (6, 9). When the MSCS is >3.25, Savas et al. (2) interpreted the SIFT predictions as “possibly affecting” or “possibly tolerated.” However, like Ng and Henikoff (4, 5), we chose to disregard nsSNPs that were scored with low confidence by SIFT until more sequence data become publicly available to allow more reliable predictions to be made. Another limitation of these algorithms is that the impacts of a combination of variants are not assessed and the dependence of functional impact of a variant on genotype of other genes or on exposure risk is not addressed (9). One final restriction of SIFT and PolyPhen is that the algorithms are unable to predict the impact of SNPs that occur outside of the coding region, such as promoter and enhancer regions, and splice sites that may affect protein levels or protein function.

To those conducting large-scale population-based epidemiologic studies, the idea of prioritizing nsSNPs in the investigation of association of SNPs with disease risk is of great interest. The use of SIFT and PolyPhen to select potentially intolerant nsSNPs for epidemiology studies can be an efficient way to explore the role of genetic variation in disease risk and to contain cost. Furthermore, predicted impact of these nsSNPs can be tested with the use of animal models and/or cell line systems to determine if functionality of the protein has indeed been altered.

Grant support: National Cancer Institute, NIH, grant R01CA84141.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1
Zhu Y, Spitz MR, Amos CI, Lin J, Schabath MB, Wu X. An evolutionary perspective on single-nucleotide polymorphism screening in molecular cancer epidemiology.
Cancer Res
 
2004
;
64
:
2251
–7.
2
Savas S, Kim DY, Ahmad MF, Shariff M, Ozcelik H. Identifying functional genetic variants in DNA repair pathway using protein conservation analysis.
Cancer Epidemiol Biomarkers Prev
 
2004
;
13
:
801
–7.
3
Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey.
Nucleic Acids Res
 
2002
;
30
:
3894
–900.
4
Ng PC, Henikoff S. Predicting deleterious amino acid substitutions.
Genome Res
 
2001
;
11
:
863
–74.
5
Ng PC, Henikoff S. Accounting for human polymorphisms predicted to affect protein function.
Genome Res
 
2002
;
12
:
436
–46.
6
Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function.
Nucleic Acids Res
 
2003
;
31
:
3812
–4.
7
Xi T, Jones IM, Mohrenweiser HW. Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function.
Genomics
 
2004
;
83
:
970
–9.
8
Livingston RJ, von Niederhausern A, Jegga AG, et al. Pattern of sequence variation across 213 environmental response genes.
Genome Res
 
2004
;
14
:
1821
–31. Epub 2004 Sep 13.
9
Rebbeck TR, Spitz M, Wu X. Assessing the function of genetic variants in candidate gene association studies.
Nat Rev Genet
 
2004
;
5
:
589
–97.
10
Henikoff S, Henikoff JG. Automated assembly of protein blocks for database searching.
Nucleic Acids Res
 
1991
;
19
:
6565
–72.
11
Sunyaev S, Ramensky V, Bork P. Towards a structural basis of human non-synonymous single nucleotide polymorphisms.
Trends Genet
 
2000
;
16
:
198
–200.
12
Dunning AM, Dowsett M, Healey CS, et al. Polymorphisms associated with circulating sex hormone levels in postmenopausal women.
J Natl Cancer Inst
 
2004
;
96
:
936
–45.
13
Kristensen VN, Borresen-Dale AL. Molecular epidemiology of breast cancer: genetic variation in steroid hormone metabolism.
Mutat Res
 
2000
;
462
:
323
–33.
14
Henderson BE, Feigelson HS. Hormonal carcinogenesis.
Carcinogenesis
 
2000
;
21
:
427
–33.
15
Mitrunen K, Hirvonen A. Molecular epidemiology of sporadic breast cancer. The role of polymorphic genes involved in oestrogen biosynthesis and metabolism.
Mutat Res
 
2003
;
544
:
9
–41.
American Association for Cancer Research
2005