A comprehensive in silico analysis of the functional and structural impact of nonsynonymous SNPs in the ABCA1 transporter gene.
Nonsynonymous single nucleotide polymorphisms (nsSNPs) are single base changes in coding regions that cause an amino acid substitution in the correspondent proteins. These mis-sense variants constitute the most identifiable group of SNPs represented by a small (<1%) proportion . The nsSNPs might alter structure, stability, and function of proteins and produce the least conservative substitutions with drastic phenotypic consequences 2-5]. Studies suggest that about 60% of Mendelian diseases are caused by amino acid exchanges . Thousands of associations between Mendelian and complex diseases reveal a phenotypic code that links each complex disorder to a unique set of Mendelian loci . Discriminating disease-associated from neutral variants would help to understand the genotype/phenotype relation and to develop diagnosis and treatment strategies for diseases. Nonetheless, the most important application is the evaluation of functional effect and impact of genomic variation, relating interactions with phenotypes translating the finding into medical practices.
ATP-binding cassette transporter ABCA1 gene also known as the cholesterol efflux regulatory protein (CERP) encodes a 220 kDa protein . This protein is crucial for reverse cholesterol transport and is considered as an important target in antiatherosclerosis treatment. ABCA1 mediates the efflux of cholesterol and phospholipids to lipid-poor apolipoproteins (apoA1 and apoE), which form nascent high-density lipoproteins (HDL). ABCA1 resides on the cell membrane and has an extensive intracellular pathway, with rapid movement of the transporter between the cell membrane and intracellular vesicles . ABCA1 is present in higher quantities in tissues that transfer or are involved in the turnover of lipids such as the liver, the small intestine, and adipose tissue [10-12]. As well, lipid export activity of ABCA1 improves the function of pancreatic cells and ameliorates insulin release , reduces biliary cholesterol content protecting against gallstone , and plays a key role in lipid homeostasis in the lung . Besides, evidence suggest a causal link-between ABCA1 as cholesterol transporter and its antitumor activity [16,17], as well as its implication in brain cholesterol homeostasis [15-20] founding lipid and myelin abnormalities in schizophrenia and Alzheimer's disease [18-20].
Although theentireABCA1 protein 3D-structure remains unknown electron microscopic studies suggest a structural model consisting of a transmembrane domain (TMD) and a nucleotide-binding domain (NBD) (Figure 1) [14, 21, 22], where an NBD-TMD dimer is the minimum unit required for transport function [22, 23]. Also, X-ray structures are available for different domains in the C-terminus protein essential for lipid efflux activity [24, 25]. Many variants disrupting the normal ABCA1 protein function result in modest or no circulating HDL [26-32]. Cholesterol accumulated within cells produces a toxicity that impairs cell function leading to a diversity of phenotypes, from severe disease states to mild impacts on health. In fact, the ABCA1 variability is associated with myocardial infarction, cancer, type 2 diabetes, and metabolic syndrome . Heterozygous states, nearly one-third of them, are associated with hypoalphalipoproteinemia, known as familial HDL deficiency syndrome (FHA). Two copies cause a more severe syndrome Tangier disease (TD) [34-38] described by reduced HDL-c plasma level (<5%), impaired cholesterol efflux, and a trend to accumulate intracellular cholesterol [34-43]. Indeed, loss of function of ABCA1 mutations in TD patients has a major impact on lipoprotein metabolism. A failure to acquire apolipoproteins leads to a rapid catabolism of lipid-poor apoA1 and accumulation of lipids in macrophages, intestinal cells, platelet, and hepatocytes [34-38, 44]. Compared with unaffected family members, heterozygotes and homozygotes have a more prevalent, premature, and severe atherosclerosis .
Because high levels of HDL-c are atheroprotective there is considerable interest in developing agents that act to increase ABCA1 expression and thereby raise plasma HDL-c levels. The nsSNPs are important indicators of action sites and effective potential therapeutic approaches. Therefore, it is crucial to identify deleterious nsSNPs to characterize the genetic basis of diseases, assess individual susceptibility to these diseases, determinate molecular and therapeutic targets, and predict clinical phenotypes. Beyond the genetic level, a disease depends on the sequence and the structural location of the nsSNPs of the protein. While the nsSNPs occur all through the ABCA1 gene, they tend to cluster in the extracellular loops, the NBD, and the COOH-terminal region (Figure 1). In fact, three structural motifs have been functionally associated with disease: the ARA motif, an interface between NBD and TMD that forms a partially buried a-helix able to interact with the transmembrane helices, the conserved-loop 1, a allosteric loop between the membrane and globular domains, and the conserved-loop 2, an interaction surface for intracellular partners, critical in ATP-binding.
Even though many of nsSNP (rare or common) found in human ABCA1 have been identified, mainly in the HapMap project (http://hapmap.ncbi.nlm.nih.gov/), the molecular bases relating these variants and the caused phenotypes have not been studied in detail. To explore the effect of the large number of nsSNPs ABCA1 by experimental approaches would be extremely time-consuming and with low statistical chance. Alternatively, bioinformatic approaches, based on the biophysical severity of the amino acid exchange and the protein sequence and structural information, can offer a more feasible phenotype prediction. As such, MutPred (mutation prediction)  andPolyPhen2 (polymorphism phenotyping 2) algorithms , were used in this study to investigate the impact of all known nsSNPs on ABCA1 protein function. Besides, based on the results of in vitro, in vivo, and human studies of this gene in the literature we validated the predictions made by reviewing the effect of the most critical nsSNPs in ABCA1 gene and its pathological consequences.
Figure 2 shows the workflow designed to predict the nsSNPs effect on ABCA1 protein. ABCA1 human gene variants including SNPs, short insertions, and deletions were retrieved from Ensemble Variation 72 database 3141. Mutations were annotated using the SnpEff v3.2 toolbox  based on the human genome assembly GRCh37.68. Only variants found on the canonical transcript were considered for functional effect prediction, for what we used two different algorithms. PolyPhen2 (http://genetics.bwh.harvard.edu/pph2/) algorithm uses a naive Bayesian classifier to predict allele function based on a combination of sequence and structure-based attributes (if available) . It calculates the probability for a given mutation to be benign, possibly damaging, or probably damaging. Then, we used MutPred (http://mutpred.mutdb.org/)  based upon SIFT algorithm  and a gain/loss of 14 predicted structural and functional properties. The predicted mutation outcome is based on a random forest (RF) classifier. The MutPred output includes the top 5 property scores and a general score (RF) equal to the probability of amino acid exchange is either deleterious or disease-associated. The ROC curves for both methods were generated using R programming language and the ROCR package (Figure 3) by a variation dataset obtained from VariBench (http://structure.bmc.lu.se/VariBench/) that contains mutations affecting protein tolerance including a neutral set of mutations comprising 17393 human coding nsSNPs and a pathogenic set of 14610 missense mutations obtained by manual curation from the PhenCode database.
Prediction accuracy accomplished by MutPred and PolyPhen2 depends on their specific criterion. Twelve structural and six sequence-based properties were used in this study (Table 1). About 28% of validated nsSNPs in the Human Genome Variation Database are predicted to affect protein function . Similarly, about 25% of nsSNPs affecting protein activity was predicted by PolyPhen2 . MutPred offers classification accuracy with respect to human disease mutations. Considering conservative thresholds on the predicted disruption of molecular function, MutPred generates accurate and reliable hypotheses on the molecular basis of disease for about 11% of known inherited disease-causing mutations .
Our MutPred and PolyPhen2 predictions were validated by comparing them with previously obtained results from in vitro, in vivo, and human studies of ABCA1 gene in the databases and literature. When a given nsSNP found experimentally to be associated with a remarkable change of phenotype such as altered transporting activity or a disease was predicted by in silico methods as deleterious, it was considered that the prediction on this nsSNPs was correct. The prediction was defined as an error if such a deleterious nsSNP was predicted as tolerant.
3. Results and Discussion
The importance of ABCA1 in cholesterol efflux was demonstrated by the identification of ABCA1 mutations in TD and FHA families [34-38]. This has produced extensive research into the possibility to provide protection from atherosclerosis by increasing ABCA1 expression and thereby to raise plasma HDL-c levels. The identification of the large number of alleles for this transporter gene as target directly involved in HDL-c regulation constitutes a significant therapeutic strategy in reducing the risk for atherosclerosis.
3.1. Accuracy of the Prediction of the Functional Impact of nsSNPs. Out of a total of 3141 SNPs in ABCA1 gene retrieved from dbSNP, we found 233 nsSNPs, 126 sSNPs, 59 mRNA 37UTR SNPs, 12 mRNA 5'-UTR SNPs, and 2543 intronic SNPs (Figure 4). Among the 233 nsSNPs, MutPred (RF score > 0.5) predicted 122 (52.36%) as deleterious whereas PolyPhen2 (pph2_prob > 0.5) identified 97 (41.63%) as potentially damaging and damaging. Then, once that MutPred was used to predict the nsSNP disease-association probability, the damaging probability of nsSNPs was validated by PolyPhen2. A total of 80 (34.33%) nsSNPs were found to be deleterious by both methods. Among these 80 deleterious nsSNPs, the 29 (12.44%) targeted (MAF/NA) that resulted with high pathological phenotype (probability > 0.8) are C1477R, W590L, W590S, A1046D, N1611D, M1091T, F2009S, N935H, R2081W, R1068H, N935S, R1068C, D1099Y, D1099N, W1699C, W840R, A937V, I1517R, C1660R, R1680W, P1065S, R1615P, T929I, Y2206D, L1379F, T940M, G1216V, Y2178H, and R1680Q. As shown in Table 2, a good correlation index was obtained between the scores observed from the evolutionary-based approach MutPred and the structural-based approach PolyPhen2. As shown in Figure 5, the overall correlation of the predictions made by both methods is high (~0.57). The majority of mutations classified as pathogenic by PolyPhen2 with the highest score (=1) are also classified as pathogenic by MutPred but within a score range between 0.51 and 1. The prediction accuracy depends not only by limitations of the in silico algorithms such as false positive error and interference of redundant motifs but also by the phenotype data from experimental studies .
Equally important is to consider the incorrect predictions in order to know the limitations of both algorithms and to suggest how they might be improved. Where MutPred predicts P2150L variant as deleterious, PolyPhen2 indicates a benign amino acid exchange. Conversely, MutPred predicted P85L to be probably damaging, while PolyPhen2 indicates it as neutral. Conflicting results were observed for a few other nsSNPs included in Table 2. A total of seventeen deleterious nsSNPs predicted by PolyPhen2 resulted neutral by MutPred. In contrast, forty two deleterious nsSNPs by MutPred result neutral by PolyPhen2. We have observed (Figure 5, Table 2) that some mutational characteristics of nsSNPs such as C1477F, R666Q, P1475S, G616V, Q2210H, V1806M, and V304M show high PolyPhen2 values but very low MutPred scores due, at least in part, to loss or gain of catalytic residues and disorder and gain of ubiquitination and phosphorylation to the protein. On the other hand, some mutational characteristics of nsSNPs included T459P, A2028V, T774P, Q1279K, N1185K, D917N, E1005K, C887F, D1289N, Q188K, D462G, M1012I, R965C, S1181F, A255T, D457E, R496W, R1341T, R1925Q, R230C, L184S, R999L, and K1974R with low PolyPhen2 values but high MutPred scores produce, however loss of solvent accessibility and of disorder, gain of phosphorylation, and both loss and gain of molecular recognition features (MoRFs) binding, loss and gain of methylation, and loss and gain of helix structure. Both, loss and gain of catalytic residues are actively involved in human inherited disease. Also, the small ubiquitin--a 76 residue [beta]-grasp protein--is about 95% conserved from yeast to human. Overall, both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancer-causing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations. Changes in secondary structure impair large functional alterations, as well as the solvent accessibility degree. Therefore, inaccurate predictions occurred at these sites could be explicated not only for the limited effects of genetic variant but also for gene-environment interactions. Since the MutPred is based on a predicted structure of the protein under study rather than a solved structure as PolyPhen2 and considering the fact that nowadays the ABCA1 protein structure is only partially solved, it makes sense to prioritize the MutPred predictions. This fact was confirmed after evaluating the performance of both methods using a curated nsSNPs dataset with known outcome as a benchmark as shown in Figure 3. We have also observed that mostly, nsSNPs with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, controversial experimental data are obtained for those nsSNPs predicted as neutral by both of these methods.
3.2. Functional Assessment of ABCA1 Variants. Disease-causing variants are under strong selective constraints, which determines if mutation frequency will increase, decrease, or change randomly during evolution. Most alterations are delseterious and so are finally removed during purifying selection. Benign mutations can sweep through the population and become fixed contributing to species differentiation. The ABCA1 gene is highly conserved between species. Human ABCA1 is 95.2% identical to mouse, 85.3% to chicken, 25.5% to drosophila, 21.6% to C. elegans, and 10.2% identical to fugu. In humans, there is an abundance of common nsSNPs that disrupt sites highly conserved across species and likely to be deleterious . The information of nsSNPs can be used to outline the migration patterns of ancient humans and the ancestry of modern humans. Causal nsSNPs in single gene disorders are sufficient to impart large effects. Instead, complex traits are due to a much more complicated system of causative mechanisms that in aggregate increase the probability of disease. Genome-wide association studies reveal common genetic variants effects (common disease/common variant hypothesis) in complex traits. However, where common nsSNPs account for a relatively slight heritability of the traits, rare variants might produce large effects on the phenotype (rare variant/common disease hypothesis). The frequency range includes alleles that are exceptionally rare and even unique to an individual genome to be extremely common. Most deleterious nsSNPs are retained at low-population frequencies due to negative selection. Thus, variants with large effect tend to be rare and those that exert weak effects are more common. It is worthy to note that rare alleles can also have weak effect or no effect. A specific locus may contain numerous rare alleles, so there may be many rare variants with large effect and a few common variants with weak effects. Although it has not yet been possible to determine whether other variables are associated with specific nsSNPs frequencies, variants within metabolic genes are not randomly distributed along the human population but follow diverse ethnic and/or geographic-specific patterns. It has been reported  that a significant proportion (~16%) of individuals with low HDL-c from the general population has the rare sequence of 25 variants in ABCA1 gene (Table 2, MAF [less than or equal to] 0.01). However, consistent with MutPred and PolyPhen2 only nine of them, N1800H, W590L, S1731C, C1477R, D1706N, R1615P, R638Q, T2073A, and A1670T, are predicted as functionally impaired. Some deleterious mutations from some other genes have reached intermediate to high frequencies. Specifically, the ancestral APOE4 allele, remains higher in populations like Pygmies (0.41), Khoi San (0.37), Papuans (0.37), some Native Americans (0.28), Lapps (0.31) and aborigines of Malaysia (0.24), and Australia (0.26) . The exposure of APOE4 to the current environmental conditions could have rendered it a susceptibility allele for cardiovascular and Alzheimer diseases. However, the prediction for variant within ABCA1 gene indicates lack of harmful alleles to MAF [greater than or equal to] 0.01. Therefore we have evaluated and contrasted the predictions made for nsSNPs (rare/common) most widely studied for their role in cholesterol pathway by reviewing the effect of the most significant nsSNPs in ABCA1 gene and its pathological consequences.
3.2.1. Accurate Prediction of the Functionally Deleterious nsSNPs in the ABCA1 Gene. The N1800H ABCA1 has been fully characterized showing a complete lack of protein function in terms of cholesterol efflux and HDL production [53, 54]. Unlike the WT (wild-type), which is found at the endoplasmic reticulum and plasma membrane, N1800H is accumulated intracellularly . Even similar physicochemical properties (polar, medium size) of exchanged residues the N1800H nsSNP, located between transmembrane domains , is a critical site for protein function. Scores from in silico methods predict the N1800H variant as highly deleterious.
The W590L was never studied, but the W590S ABCA1 variant affecting the same position is extensively known . Distribution of W590S is identical to WT  as well as apoA1 binding activity [54-57]; however it shows defective lipid transport [54, 56, 58, 59]. Since multiple alignments often show a leucine residue in this position, it could be assumed that W590L had a similar behavior or even a lower impact than W590S on the protein function. Both W590S and W590L were predicted as deleterious nsSNPs with loss of functionality.
Studies indicate that S1731C variant alters the activity of ABCA1 protein [27, 51, 60]. This allele is present in French-Canadian families with low HDL-c levels  but not in subjects with normal  or high 51] HDL-c levels. Compared with WT, heterozygous show decreased ~60% the cholesterol efflux activity [27, 51, 61]. Interestingly, some but not all families harboring S1731C also carried the 2144X stop mutation  able to produce the most severe effects on HDL-c levels and on cholesterol efflux . These data along with our in silico predictions indicate that conserved S1731C is highly likely to affect protein function.
S1506L, Q597R, are linked to TD variants are linked to TD and FHA and found in tumor cancer . Normal function of ABCA1 inhibits tumor growth in human cancer cells . However, although expressed to similar levels as WT, these alleles show deficient cellular cholesterol efflux and HDL production and do not decrease tumor growth . The three are located intracellularly but C1477R is also found in membrane , which indicates that membrane localization is essential but not sufficient for apoA1 binding [54, 63]. In fact, ApoA1 binds to ABCA1 protein oligomers but not with monomers . Thus, conformation changes in binding sites might be produced by these nsSNPs found as deleterious by our in silico analysis.
The R587W reaches the cell surface but reduces the apoA1 binding efficiency ~50% . Others studies indicate that this allele is mainly retained intracellularly decreasing cholesterol efflux and apoA1 binding ~75% . Severe HDL deficiency  and premature CVD is caused by R587W . This variant is highly conserved during evolution, and the in silico analysis predicts it as strongly damaging and disease-associated. Besides to be related with TD, the R587W as well as W590S variants are linked to AD. As the WT, these mutants significantly reduce A[beta]-peptide synthesis ~45% , but increase by ~2-fold (R587W) and by 25% (W590S) amyloid precursor protein intracellular domain, a major cytotoxic of AD .
The A1046D, localized between conserved motifs [54, 67], shows an intermediate phenotype caused by its limited presence in the plasma membrane. This variant shows reduced apoA1 binding efficacy, poor HDL-c, and folding protein alteration. Both in silico methods predict A1046D as a functional residue with a probability to be deleterious very close to 1.
According to literature, N1611D is associated to probable atherosclerosis [62, 68]. The mutated protein expression was comparable to WT although cholesterol efflux from the cells was markedly reduced. Our theoretical results indicate very high probabilities of this nsSNP being deleterious, which indicate an adverse and potential harmful effect on ABCA1 function.
The M1091T variant exerts a dominant-negative impact on ABCA1 function with severe phenotype observed in subjects carrying this variant [42, 54, 62]. It is retained intracellularly preventing the protein from reaching the membrane . In heterozygous, M1091T is lowered by 50% HDL and inhibits apoA1 binding and cholesterol efflux . From evolutionary path, the inherited residue at this position has been methionine. Among related homologues ABCA2 and ABCA4 share a methionine at this position, while ABCA7 substitutes a leucine. Consistent with this fact, ABCA1 and ABCA7 are functionally divergent, with ABCA7 easing the efflux of phospholipids but not cholesterol . Despite the modest conservation at this position, located in a critical cluster at the C-terminal region, in silico data suggest a severe-negative impact.
The F2009S is conserved between human and mouse, that along with the exchange from large size and aromatic (F) to small size and polar (S) explicates its reduced cholesterol efflux, low HDL-c, andapoA1 levels . The functional effect produced by F2009S variant is consistent with our prediction made by PolyPhen2 and MutPred indicating a deleterious mutant.
The N935S variant is found intracellularly  in subjects without risk of premature atherosclerosis but with extremely low levels of HDL and signs of severe dementia and amyloid depositions in the brain [71,72]. This variant was predicted as deleterious by the used methods.
R1068H mutation is located within the first ATP-binding domain. It is identified in TD homozygous . Since the R1068H mutation is likely to produce a dysfunctional protein, one would expect it to be associated with FHA in the heterozygous state . Residue R1068 is located in an [alpha]-helix of the Walker B motif in the NBD, vulnerable to interaction with D1092 and E1093 . Homology modeling of the ABCA1 protein showed that the R1068H mutation disrupts the conformation of NBD. Functional studies of R1068H showed a lack of cholesterol efflux activity due to defective transference to the plasma membrane, mainly caused by impaired oligomerization . The in silico analysis predicts a high possibility for R1068H to be damaging. Besides, a different mutation of this position, R1068C, predicted as a deleterious by our methods, has been reported in a compound heterozygote with almost no HDL .
D1099Y is located at possible interaction site and exchanges the medium size and acidic residue to the large and aromatic tyrosine. Surface residues not at defined interfaces are usually preserved. Still, a moderate to highly conserved domain on the surface of the structure includes this nsSNP, which is associated to familial HDL deficiency [70, 75] and predicted as deleterious in our analysis.
The W1699C, located within the transmembrane domain, is accumulated within the cytoplasm and a small proportion reaches the plasma membrane . It introduces a cysteine residue, which stimulates the formation of a new disulphide bridge able to disrupt the ABCA1 protein structure preventing its oligomerization and transference to the plasma membrane. Probably, W1699C retains some residual functions, as shown by the plasma HDL-c levels found in members carrying this mutation which were not as low as might be expected in carriers . In silico analysis with PolyPhen2 and MutPred indicate a deleterious effect of this nsSNP on ABCA1 function.
3.2.2. Controversial Results for Prediction of the Neutral nsSNPs in the ABCA1 Gene. The mutant R1897W that induces a change from basic (R) to aromatic (W) is predicted functionally neutral in this analysis. This variant was identified in the mother and the brother of an FHA patient, who had plasma HDL levels in the lower range of the normal values 77].
Both the D1289N and P2150L variants identified in TD patients are considered as disease causative [42, 78, 79]. Further experimental evidences disagreeing with these results suggest that both could be nonfunctional variants [51,54,80]. Indeed, they showed a lipid transport activity, apoA1 binding, and distribution similar to WT [54,80]. Interestingly, P2150L is only found in patients who also harbor a second variant, the deleterious R587W described above . Besides, TD patients with D1289N variant were homozygous for a second mutation R2081W that could cause the shown pathological phenotype . R2081W is missed at the plasma membrane and instead accumulated intracellularly . Our results suggest that mutations R2081W and R587W are highly deleterious. For D1289N and P2150L variants, PolyPhen2 predicts a neutral impact on protein function contrary to MutPred predictions that indicates a high probability for these mutants to be deleterious. The positions 1289 and 2150 are conserved among all ABCA1 orthologs but with the close-related ABCA7 and ABCA4. Since conservation patterns in ABCA1 protein endure for a relatively short time in evolutionary path, it is hard to determine if the conservation at these positions is due to functional constraint or simply reflects random chance. Along with experimental data, this suggests that R2081W is a major responsible of ABCA1 protein dysfunction found in TD patients.
The rare R219K polymorphism is located on an N-terminal extracellular loop, which mediates ABCA1 protein interaction with apoA1 [39, 56, 58, 59]. Despite high number of case-control studies conducted to investigate the functionality of R219K variant the results have been inconclusive [60, 81-83]. While some reports suggest an association of R219K is with risk of CVD , other research indicates a decreased atherosclerosis progression in general population [60, 85]. Conversely, large prospective studies found no association with HDL-c levels or atherosclerosis susceptibility [82,86]. A meta-analysis indicates that R219K polymorphism is protective against CVD in Asians but not in Caucasians . Unexpectedly, the K219 allele was associated with a decreased risk of myocardial infarction [18, 60, 84]. Also, this variant has effect on triglycerides  but not with HDL-c  or with apoAl levels . Otherwise, a study indicates that blood lipid levels do not seem to be R219K dependent . Whether this variant confers major susceptibility to CVD is for clarification. The association of R219K variant to risk of AD has been studied in diverse ethnic groups [18-20, 89, 90]. Although conflicting results were noted, a study observed a protective dependence in delaying the risk of late-onset AD . Equal to other cases, experimental results inconclusive and contradictories result prediction of the R219K polymorphism was predicted to be neutral in our in silico analysis.
Some studies [60, 91-93] but not all 39, 94] indicate that I883M variant severely increases the risk of atherosclerosis and AD . The I883M has been reported as a milder phenotype with a significant reduction of HDL-c and cholesterol efflux (~70% of WT) . In contrast, others studies [28, 60, 82, 83, 88] did not find any difference in lipid levels in I883M carriers. Studies among different healthy people [95, 96] as well as population with T2D  correlated the I883M variant with higher HDL-c concentration. Also, a stepwise regression approach identified I883M as one the key predictors of ischemic heart disease, whereas additive effects were found for V771M/I883M and I883M/E1172D pairs [82, 98]. As well, several studies have reported associations between V825I/I883M and increased plasma HDL-c levels [39,67,99]. Despite the controversial experimental results on the influence on cholesterol efflux activity observed of this polymorphism, our data predict that the I883M variant is functionally neutral. Interestingly, both alleles are found in the human population and the minor allele, methionine, is likely to be the ancestral allele at this position. Along with the human ABCA1orthologs, murine aligns valine at this position and the chimpanzee sequence aligns methionine. This divergence could explain why a simple conservation-based approach predicts the I883M change as neutral.
The R1851Q variant exchanges the large size and basic arginine (R) residue to medium size and polar glutamine predicted deleterious by MutPred and neutral by PolyPhen2. R1851Q occurs within the extracellular loop proximal to the transmembrane [68, 100]. Heterozygotes states show low HDL-c and apoA1 levels compared with those related to WT protein.
The R230C variant, found in Native American groups but not in European, Asian, or African individuals, has been associated with low levels of HDL-c and apoA1 . These results are confirmed after adjusting for gender, BMI, and waist circumference . Besides, the C230 allele is associated with obesity, metabolic syndrome, and T2D in Mexican population . Still, R230C may have conferred resistance against certain infectious diseases . R230C has been reported as a rare variant causing FHA in an Oji-Cree individual . MutPred predicts a high probability of functional impairment of R230C, while that the PolyPhen2 program predicts the variant as neutral. Other facts that suggest functionally damage are (1) R230C occurs at the first extracellular loop, where TD and FHA mutations are clustered; (2) the arginine at position 230 is conserved between species; and (3) very different nature of residues involved; whereas arginine is basic and hydrophilic, the hydrophobic cysteine is vulnerable to disulfide bond.
The variants, R1901S that induces a change from large size and basic (R) to small size and polar (S); Q2196H that exchanges residues with similar physicochemical property (medium size, polar); and E284K that exchanges a medium size and acidic (E) to large size and basic (K), are predicted to be deleterious by MutPred and neutral by PolyPhen2. The R1901S and Q2196H variants occur within the C-terminal domain, close to the NBD, and E284K was located in the first extracellular loop, all of them associated to FHA . The A594T, I659V, T1512M, and R2004K polymorphisms display different degrees of mislocalization to the plasma membrane and slight impacts on cholesterol efflux . These nsSNPs were identified in low-HDL subjects . The A594T, I659V, and T1512M were predicted to be functionally neutral and the R2004K mutation possibly damaging . Finally, the novel mutation (P85L) in ABCA1 was identified in one family with low HDL but was not detected in over 400 chromosomes of healthy subjects . Our in silico prediction indicated this variant as possible damaging by MutPred and neutral by PolyPhen2.
4. Conclusion and Future Directions
The practice of medicine, including health promotion and disease prevention, is primarily based on phenotype-based approaches. Most of them are proximal phenotypes achieved through biochemical markers. Finding genetic determinants of the phenotypes could not only clarify biological and functional consequence of variants but also might translate and extend to clinical phenotype. This focus would consider the large locus heterogeneity and numerous nongenetic factors to contribute to the phenotype. Since high levels of HDL-c are atheroprotective, there is extensive interest in developing agents that enhance ABCA1 expression and thereby raise plasma HDL-c levels. Amino acid exchange variants are crucial indicators of action sites and effective potential therapeutic approaches. In fact, nsSNPs represent disease modifiers capable of altering drug/nutrient response and potential targets vulnerable to environmental factors.
Evaluation of 233 nsSNPs (rare or common) found in ABCA1 transporter indicates that the rare 29 (12.44%) of them resulted to be highly deleterious with a probability >0.8. From 20 sequence variants found in about 16% of individuals with low HDL cholesterol only nine of them, D1706N, R1615P, W590L, C1477R, N1800H, R638Q, T2073A, A1670T, and S1731C, were predicted by MutPred and PolyPhen2 as functionally impaired. We have observed that mostly nsSNPs with verified functional effect in all experimental studies made are correctly predicted as damage variants by MutPred and PolyPhen2 tools. However, controversial experimental data are obtained for those nsSNPs predicted as neutral by both methods. Presumably clinical phenotype is the result of the additive effects and interactions among multiple alleles with different effect degree. Multiple rare alleles in ABCA1 contribute to plasma HDL-c levels in the general population.
Predicting the phenotypic consequence of nsSNPs using computational algorithms provides a better understanding of genetic differences in susceptibility to diseases and drug/ nutrient response. These methods predict whether an amino acid altering mutation is deleterious or disease-causing based on physicochemical properties, population frequency, protein structure, and cross-species conservation. However, computational prediction tools are generally based on machine learning algorithms, which need to be trained before classifying a mutation as either neutral or deleterious. A major obstacle of these approaches is the lack of experimentally validated and impartial data sets. A further complication is that mutations in highly conserved sequences do not always produce phenotypes that are easily noticeable. Besides, knowledge of protein structure is crucial to accurately predict functional nsSNPs and understand their linkage with disease. Severe limitation arises thus when protein 3D-structure is not available as the ABCA1 case. Thus, an accurate, efficient, and generally applicable approach is needed to establish a genotype/phenotype correlation. Whole genome sequencing is likely to become a commodity that could be readily available at a reasonable cost and be easily accommodated into the decision making tree of health care of every individual. The challenging task will be to identify variants that are disease-causing or likely disease-causing and develop strategies to prevent and attenuate the evolving phenotype. Likewise, various complementary studies, genetic and biological, would be necessary to discern the associated alleles from the true disease-causing variants. Moreover a better understanding of genome components, such as functional, large intergenic noncoding RNAs, small non-coding RNAs, and primary transcripts, would be essential. An integrated approach that utilizes genomics, transcriptomic, proteomics, and metabolomic would be expected to facilitate identification and characterization of the mechanisms involved in the pathogenesis of the phenotype.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publishing of this paper.
This work has been supported by European Union Structural Funds.
 E. Pennisi, "ENCODE project writes eulogy for junk DNA," Science, vol. 337, no. 6099, pp. 1159-1161, 2012.
 C. M. Yates and M. J. E. Sternberg, "The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on proteinprotein interactions," Journal of Molecular Biology, vol. 425, no. 21, pp. 3949-3969, 2013.
 P. C. Ng and S. Henikoff, "Predicting the effects of amino acid substitutions on protein function," Annual Review of Genomics and Human Genetics, vol. 7, pp. 61-80, 2006.
 M. Gonzalez-Castejon, F. Marin, C. Soler-Rivas, G. Reglero, F. Visioli, and A. Rodriguez-Casado, "Functional non-synonymous polymorphisms prediction methods: current approaches and future developments," Current Medicinal Chemistry, vol. 18, no. 33, pp. 5095-5103, 2011.
 A. Rodriguez-Casado, "In silico investigation of functional nsSNPs an approach to rational drug design," Research and Reports in Medicinal Chemistry, vol. 2, pp. 31-42, 2012.
 D. Botstein and N. Risch, "Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease," Nature Genetics, vol. 33, pp. 228-237, 2003.
 D. R. Blair, C. S. Lyttle, and J. M. Mortensen, "A non-degenerate code of deleterious variants in Mendelian loci contributes to complex disease risk," Cell, vol. 155, no. 1, pp. 70-80, 2013.
 J. F. Oram, "HDL apolipoproteins and ABCA1 partners in the removal of excess cellular cholesterol," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 23, no. 5, pp. 720-727, 2003.
 S. Santamarina-Fojo, A. T. Remaley, E. B. Neufeld, and H.B. Brewer Jr., "Regulation and intracellular trafficking of the ABCA1 transporter," Journal of Lipid Research, vol. 42, no. 9, pp. 1339-1345, 2001.
 L. R. Brunham, J. K. Kruit, J. Iqbal et al., "Intestinal ABCA1 directly contributes to HDL biogenesis in vivo," Journal of Clinical Investigation, vol. 116, no. 4, pp. 1052-1062, 2006.
 J. M. Timmins, J. Lee, E. Boudyguina et al., "Targeted inactivation of hepatic Abca1 causes profound hypoalphalipoproteinemia and kidney hypercatabolism of apoA-I," Journal of Clinical Investigation, vol. 115, no. 5, pp. 1333-1342, 2005.
 L. R. Brunham, R. R. Singaraja, M. Duong et al., "Tissue-specific roles of ABCA1 influence susceptibility to atherosclerosis," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 29, no. 4, pp. 548-554, 2009.
 L. R. Brunham, J. K. Kruit, T. D. Pape et al., "[beta]-cell ABCA1 influences insulin secretion, glucose homeostasis and response to thiazolidinedione treatment," Nature Medicine, vol. 13, no. 3, pp. 340-347, 2007.
 J. Lee, A. Shirk, J. F. Oram, S. P. Lee, and R. Kuver, "Polarized cholesterol and phospholipid efflux in cultured gall-bladder epithelial cells: evidence for an ABCA1-mediated pathway," Biochemical Journal, vol. 364, no. 2, pp. 475-484, 2002.
 J. McNeish, R. J. Aiello, D. Guyot et al., "High density-lipoprotein deficiency and foam cell accumulation in mice with targeted disruption of ATp-binding cassette transporter-1," Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 8, pp. 4245-4250, 2000.
 T. Sjoblom, S. Jones, and L. D. Wood, "The consensus coding sequences of human breast and colorectal cancers," Science, vol. 314, no. 5797, pp. 268-274, 2006.
 B. Smith and H. Land, "Anticancer activity of the cholesterol exporter ABCA1 gene," Cell Reports, vol. 2, no. 3, pp. 580-590, 2012.
 M. A. Wollmer, J. R. Streffer, D. LUtjohann et al., "ABCA1 modulates CSF cholesterol levels and influences the age at onset of Alzheimer's disease," Neurobiology of Aging, vol. 24, no. 3, pp. 421-426, 2003.
 P. D. Sundar, E. Feingold, R. L. Minster, S. T. DeKosky, and M. I. Kamboh, "Gender-specific association of ATP-binding cassette transporter 1 (ABCA1) polymorphisms with the risk of late-onset Alzheimer's disease," Neurobiology of Aging, vol. 28, no. 6, pp. 856-862, 2007.
 M. Ota, T. Fujii, K. Nemoto et al., "A polymorphism of the ABCA1 gene confers susceptibility to schizophrenia and related brain changes," Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 35, no. 8, pp. 1877-1883, 2011.
 M. F. Rosenberg, R. Callaghan, R. C. Ford, and C. F. Higgins, "Structure of the multidrug resistance p-glycoprotein to 2.5 nm resolution determined by electron microscopy and image analysis," The Journal of Biological Chemistry, vol. 272, no. 16, pp. 10685-10694,1997.
 M. Dean, Y. Hamon, and G. Chimini, "The human ATP-binding cassette (ABC) transporter superfamily," Journal of Lipid Research, vol. 42, no. 7, pp. 1007-1017, 2001.
 M. L. Fitzgerald, A. J. Mendez, K. J. Moore, L. P. Andersson, H. A. Panjeton, and M. W. Freeman, "ATP-binding cassette transporter A1 Contains an NH2-terminal signal anchor sequence that translocates the protein's first hydrophilic domain to the exoplasmic space," Journal of Biological Chemistry, vol. 276, no. 18, pp. 15137-15145, 2001.
 F. Scheffel, U. Demmer, E. Warkentin, A. Hulsmann, E. Schneider, and U. Ermler, "Structure of the ATPase subunit CysA of the putative sulfate ATP-binding cassette (ABC) transporter from Alicyclobacillus acidocaldarius," FEBS Letters, vol. 579, no. 13, pp. 2953-2958, 2005.
 C. L. Reyes and G. Chang, "Structure of the ABC transporter MsbA in complex with ADP.vanadate and lipopolysaccharide," Science, vol. 308, no. 5724, pp. 1028-1031, 2005.
 R. S. Kiss, N. Kavaslar, K. Okuhira et al., "Genetic etiology of isolated low HDL syndrome: incidence and heterogeneity of efflux defects," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 27, no. 5, pp. 1139-1145, 2007.
 K. Alrasadi, I. L. Ruel, M. Marcil, and J. Genest, "Functional mutations of the ABCA1 gene in subjects of French-Canadian descent with HDL deficiency," Atherosclerosis, vol. 188, no. 2, pp. 281-291, 2006.
 M. Mantaring, J. Rhyne, S. Ho Hong, and M. Miller, "Genotypic variation in ATP-binding cassette transporter-1 (ABCA1) as contributors to the high and low high-density lipoprotein-cholesterol (HDL-C) phenotype," Translational Research, vol. 149, no. 4, pp. 205-210, 2007
 T. L. Slatter, G. T. Jones, M. J. A. Williams, A. M. van Rij, and S. P. A. McCormick, "Novel rare mutations and promoter haplotypes in ABCA1 contribute to low-HDL-C levels," Clinical Genetics, vol. 73, no. 2, pp. 179-184, 2008.
 M. T. Chhabria, B. N. Suhagia, and S. B. Pathik, "HDL elevation and lipid lowering therapy: current scenario and future perspectives," Frontiers in Cardiovascular Drug Discovery, vol. 1, pp. 32-60, 2010.
 M. C. Probst, H. Thumann, C. Aslanidis et al., "Screening for functional sequence variations and mutations in ABCA1," Atherosclerosis, vol. 175, no. 2, pp. 269-279, 2004.
 C. Albrecht, K. Baynes, A. Sardini et al., "Two novel missense mutations in ABCA1 result in altered trafficking and cause severe autosomal recessive HDL deficiency," Biochimica et Biophysica Acta, vol. 1689, no. 1, pp. 47-57, 2004.
 M. Daimon, T. Kido, M. Baba et al., "Association of the ABCA1 gene polymorphisms with type 2 DM in a Japanese population," Biochemical and Biophysical Research Communications, vol. 329, no. 1, pp. 205-210, 2005.
 R. M. Lawn, D. P. Wade, M. R. Garvin et al., "The Tangier disease gene product ABC1 controls the cellular apolipoprotein-mediated lipid removal pathway," Journal of Clinical Investigation, vol. 104, no. 8, pp. R25-R31, 1999.
 A. von Eckardstein, C. Langer, T. Engel et al., "ATP binding cassette transporter ABCA1 modulates the secretion of apolipoprotein E from human monocyte-derived macrophages," The FASEB Journal, vol. 15, no. 9, pp. 1555-1561, 2001.
 M. R. Hayden, S. M. Clee, A. Brooks-Wilson, J. Genest Jr., A. Attie, and J. J. P. Kastelein, "Cholesterol efflux regulatory protein, Tangier disease and familial high density lipoprotein deficiency," Current Opinion in Lipidology, vol. 11, no. 2, pp. 117-122, 2000.
 M. Marcil, A. Brooks-Wilson, S. M. Clee et al., "Mutations in the ABC1 gene in familial HDL deficiency with defective cholesterol efflux," The Lancet, vol. 354, no. 9187, pp. 1341-1346, 1999.
 H. H. Hobbs and D. J. Rader, "ABC1: connecting yellow tonsils, neuropathy, and very low HDL," The Journal of Clinical Investigation, vol. 104, no. 8, pp. 1015-1017, 1999.
 R. Frikke-Schmidt, B. G. Nordestgaard, G. B. Jensen, and A. Tybjaerg-Hansen, "Genetic variation in ABC transporter A1 contributes to HDL cholesterol in the general population," The Journal of Clinical Investigation, vol. 114, no. 9, pp. 1343-1353, 2004.
 M. Marcil, L. Yu, L. Krimbou et al., "Cellular cholesterol transport and efflux in fibroblasts are abnormal in subjects with familial HDL deficiency," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 19, no. 1, pp. 159-169, 1999.
 M. E. Brousseau, G. P. Eberhart, J. Dupuis et al., "Cellular cholesterol efflux in heterozygotes for Tangier disease is markedly reduced and correlates with high density lipoprotein cholesterol concentration and particle size," Journal of Lipid Research, vol. 41, no. 7, pp. 1125-1135, 2000.
 S. M. Clee, J. J. P. Kastelein, M. van Dam et al., "Age and residual cholesterol efflux affect HDL cholesterol levels and coronary artery disease in ABCA1 heterozygotes," The Journal of Clinical Investigation, vol. 106, no. 10, pp. 1263-1270, 2000.
 N. F. Fitz, A. A. Cronican, M. Saleem et al., "Abca1 deficiency affects Alzheimer's disease-like phenotype in human ApoE4 but not in ApoE3-targeted replacement mice," Journal of Neuroscience, vol. 32, no. 38, pp. 13125-13136, 2012.
 E. J. Schaefer, L. A. Zech, D. E. Schwartz, and H. B. Brewer Jr., "Coronary heart disease prevalence and other clinical features in familial high-density lipoprotein deficiency (Tangier disease)," Annals of Internal Medicine, vol. 93, no. 2, pp. 261-266, 1980.
 B. Li, V. G. Krishnan, M. E. Mort et al., "Automated inference of molecular mechanisms of disease from amino acid substitutions," Bioinformatics, vol. 25, no. 21, pp. 2744-2750, 2009.
 I. A. Adzhubei, S. Schmidt, L. Peshkin et al., "A method and server for predicting damaging missense mutations," Nature Methods, vol. 7, no. 4, pp. 248-249, 2010.
 P. Cingolani, A. Platts, L. L. Wang et al., "A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain [w.sup.1118]; iso-2; iso-3," Fly, vol. 6, no. 2, pp. 80-92, 2012.
 P. Kumar, S. Henikoff, and P. C. Ng, "Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm," Nature Protocols, vol. 4, no. 7, pp. 1073-1081, 2009.
 V. Ramensky, P. Bork, and S. Sunyaev, "Human non-synonymous SNPs: server and survey," Nucleic Acids Research, vol. 30, no. 17, pp. 3894-3900, 2002.
 S. Chun and J. C. Fay, "Identification of deleterious mutations within three human genomes," Genome Research, vol. 19, no. 9, pp. 1553-1561, 2009.
 J. C. Cohen, R. S. Kiss, A. Pertsemlidis, Y. L. Marcel, R. McPherson, and H. H. Hobbs, "Multiple rare alleles contribute to low plasma levels of HDL cholesterol," Science, vol. 305, no. 5685, pp. 869-872, 2004.
 R. M. Corbo and R. Scacchi, "Apolipoprotein E (APOE) allele distribution in the world. Is APOE*4 a "thrifty" allele?" Annals of Human Genetics, vol. 63, part 4, pp. 301-310, 1999.
 D. Weissglas-Volkov and P. Pajukanta, "Genetic causes of high and low serum HDL-cholesterol," Journal of Lipid Research, vol. 51, no. 8, pp. 2032-2057, 2010.
 R. R. Singaraja, H. Visscher, E. R. James et al., "Specific mutations in ABCA1 have discrete effects on ABCA1 function and lipid phenotypes both in vivo and in vitro," Circulation Research, vol. 99, no. 4, pp. 389-397, 2006.
 K. Nagao, M. Tomioka, and K. Ueda, "Function and regulation of ABCA1--membrane meso-domain organization and reorganization," The FEBS Journal, vol. 278, no. 18,pp. 3190-3203, 2011.
 M. L. Fitzgerald, A. L. Morris, J. S. Rhee, L. P. Andersson, A. J. Mendez, and M. W. Freeman, "Naturally occurring mutations in the largest extracellular loops of ABCA1 can disrupt its direct interaction with apolipoprotein A-I," The Journal of Biological Chemistry, vol. 277, no. 36, pp. 33178-33187, 2002.
 A. M. Vaughan, C. Tang, and J. F. Oram, "ABCA1 mutants reveal an interdependency between lipid export function, apoA-I binding activity, and Janus kinase 2 activation," Journal of Lipid Research, vol. 50, no. 2, pp. 285-292, 2009.
 R. Frikke-Schmidt, C. F. Sing, B. G. Nordestgaard, and A. Tybjaerg-Hansen, "Gender- and age-specific contributions of additional DNA sequence variation in the 5' regulatory region of the APOE gene to prediction of measures of lipid metabolism," Human Genetics, vol. 115, no. 4, pp. 331-345, 2004.
 R. Frikke-Schmidt, B. G. Nordestgaard, P. Schnohr, and A. Tybjaerg-Hansen, "Single nucleotide polymorphism in the low-density lipoprotein receptor is associated with a threefold risk ofstroke: a case-control and prospective study," European Heart Journal, vol. 25, no. 11, pp. 943-951, 2004.
 S. M. Clee, A. H. Zwinderman, J. C. Engert et al., "Common genetic variation in ABCA1 is associated with altered lipoprotein levels and a modified risk for coronary artery disease," Circulation, vol. 103, no. 9, pp. 1198-1205, 2001.
 M. V. Reddy, I. Iatan, D. Weissglas-Volkov et al., "Exome sequencing identifies 2 rare variants for low high-density lipoprotein cholesterol in an extended family," Circulation: Cardiovascular Genetics, vol. 5, no. 5, pp. 538-546, 2012.
 C. L. Wellington, Y. Yang, S. Zhou et al., "Truncation mutations in ABCA1 suppress normal upregulation of full-length ABCA1 by 9-cis-retinoic acid and 22-R-hydroxycholesterol," Journal of Lipid Research, vol. 43, no. 11, pp. 1939-1949, 2002.
 S. Wang, K. Gulshan, G. Brubaker, S. L. Hazen, and J. D. Smith, "ABCA1 mediates unfolding of apolipoprotein AI N terminus on the cell surface before lipidation and release of nascent high-density lipoprotein," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 33, no. 6, pp. 1197-1205, 2013.
 M. Denis, B. Haidar, M. Marcil, M. Bouvier, L. Krimbou, and J. Genest, "Characterization of oligomeric human ATP binding cassette transporter A1: potential implications for determining the structure of nascent high density lipoprotein particles," The Journal of Biological Chemistry, vol. 279, no. 40, pp. 41529-41536, 2004.
 S. Bertolini, L. Pisciotta, M. Seri et al., "A point mutation in ABC1 gene in a patient with severe premature coronary heart disease and mild clinical phenotype of Tangier disease," Atherosclerosis, vol. 154, no. 3, pp. 599-605, 2001.
 W. S. Kim, A. F. Hill, M. L. Fitzgerald, M. W. Freeman, G. Evin, and B. Garner, "Wild type and tangier disease ABCA1 mutants modulate cellular amyloid-[beta] production independent of cholesterol efflux activity," Journal of Alzheimer's Disease, vol. 27, no. 2, pp. 441-452, 2011.
 J. Wang, J. R. Burnett, S. Near et al., "Common and rare ABCA1 variants affecting plasma HDL cholesterol," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 20, no. 8, pp. 1983-1989, 2000.
 Y. Nishida, K. Hirano, K. Tsukamoto et al., "Expression and functional analyses of novel mutations of ATP-binding cassette transporter-1 in Japanese patients with high-density lipoprotein deficiency," Biochemical and Biophysical Research Communications, vol. 290, no. 2, pp. 713-721, 2002.
 N. Wang, D. Lan, M. Gerbod-Giannone et al., "ATP-binding cassette transporter A7 (ABCA7) binds apolipoprotein A-I and mediates cellular phospholipid but not cholesterol efflux," The Journal of Biological Chemistry, vol. 278, no. 44, pp. 42906-42912, 2003.
 S. Ho Hong, J. Rhyne, K. Zeller, and M. Miller, "Novel ABCA1 compound variant associated with HDL cholesterol deficiency," Biochimica et Biophysica Acta--Molecular Basis of Disease, vol. 1587, no. 1, pp. 60-64, 2002.
 M. Walter, S. Kerber, C. Fechtrup, U. Seedorf, G. Breithardt, and G. Assmann, "Characterization of atherosclerosis in a patient with familial high-density lipoprotein deficiency," Atherosclerosis, vol. 110, no. 2, pp. 203-208, 1994.
 R. P. Koldamova, I. M. Lefterov, M. Staufenbiel et al., "The liver X receptor ligaun T0901317 decreases amyloid [beta] production in vitro and in a mouse model of Alzheimer's disease," The Journal ofBiological Chemistry, vol. 280, no. 6, pp. 4079-4088, 2005.
 T. L. Slatter, M. J. A. Williams, R. Frikke-Schmidt, A. Tybjaerg-Hansen, I. M. Morison, and S. P. A. McCormick, "Promoter haplotype of a new ABCA1 mutant influences expression of familial hypoalphalipoproteinemia," Atherosclerosis, vol. 187, no. 2, pp. 393-400, 2006.
 R. J. Suetani, B. Sorrenson, J. D. A. Tyndall, M. J. A. Williams, and S. P. A. McCormick, "Homology modeling and functional testing of an ABCA1 mutation causing Tangier disease," Atherosclerosis, vol. 218, no. 2, pp. 404-410, 2011.
 L. Kelly, R. Karchin, and A. Sali, "Protein interactions and disease phenotypes in the ABC transporter superfamily," in Proceedings of the Pacific Symposium on Biocomputing, pp. 51-63, 2007.
 L. Pisciotta, L. Bocchi, C. Candini et al., "Severe HDL deficiency due to novel defects in the ABCA1 transporter," Journal of Internal Medicine, vol. 265, no. 3, pp. 359-372, 2009.
 T. Fasano, L. Bocchi, L. Pisciotta, S. Bertolini, and S. Calandra, "Denaturing high-performance liquid chromatography in the detection of ABCA1 gene mutations in familial HDL deficiency," Journal of Lipid Research, vol. 46, no. 4, pp. 817-822, 2005.
 M. E. Brousseau, E. J. Schaefer, J. Dupuis et al., "Novel mutations in the gene encoding ATP-binding cassette 1 in four tangier disease kindreds," Journal of Lipid Research, vol. 41, no. 3, pp. 433-441, 2000.
 W. Huang, K. Moriyama, T. Koga et al., "Novel mutations in ABCA1 gene in Japanese patients with Tangier disease and familial high density lipoprotein deficiency with coronary heart disease," Biochimica et Biophysica Acta, vol. 1537, no. 1, pp. 71-78, 2001.
 F. Quazi and R. S. Molday, "Differential phospholipid substrates and directional transport by ATP binding cassette proteins ABCA1, ABCA7, and ABCA4 and disease-causing mutants," The Journal of Biological Chemistry, vol. 288, no. 48, pp. 34414-34426, 2013.
 V. Kolovou, G. Kolovou, A. Marvaki et al., "ATP-binding-cassette transporter A1 gene polymorphisms and serum lipid levels in young Greek nurses," Lipids in Health and Disease, vol. 10, article 56, 2011.
 R. Frikke-Schmidt, B. G. Nordestgaard, G. B. Jensen, R. Steffensen, and A. Tybjaerg-Hansen, "Genetic variation in ABCA1 predicts ischemic heart disease in the general population," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 28, no. 1, pp. 180-186, 2008.
 A. Kitjaroentham, H. Hananantachai, A. Tungtrongchitr, S. Pooudong, and R. Tungtrongchitr, "R219K polymorphism of ATP binding cassette transporter A1 related with low HDL in overweight/obese Thai males," Archives of Medical Research, vol. 38, no. 8, pp. 834-838, 2007.
 A. Cenarro, M. Artieda, S. Castillo et al., "A common variant in the ABCA1 gene is associated with a lower risk for premature coronary heart disease in familial hypercholesterolaemia," Journal of Medical Genetics, vol. 40, no. 3, pp. 163-168, 2003.
 D. Tregouet, S. Ricard, V. Nicaud et al., "In-depth haplotype analysis of ABCA1 gene polymorphisms in relation to plasma ApoA1 levels and myocardial infarction," Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 24, no. 4, pp. 775-781, 2004.
 T. M. Morgan, H. M. Krumholz, R. P. Lifton, and J. A. Spertus, "Nonvalidation of reported genetic risk factors for acute coronary syndrome in a large-scale replication study," Journal of the American Medical Association, vol. 297, no. 14, pp. 1551-1561, 2007.
 Y. Li, K. Tang, K. Zhou et al., "Quantitative assessment of the effect of ABCA1 R219K polymorphism on the risk of coronary heart disease," Molecular Biology Reports, vol. 39, no. 2, pp. 1809-1813, 2012.
 V. Kolovou, A. Marvaki, A. Karakosta et al., "Association of gender, ABCA1 gene polymorphisms and lipid profile in Greek young nurses," Lipids in Health and Disease, vol. 11, article 62, 2012.
 Z. Xiao, J. Wang, W. Chen, P. Wang, and H. Zeng, "Association studies of several cholesterol-related genes (ABCA1, CETP and LIPC) with serum lipids and risk of Alzheimer's disease," Lipids in Health and Disease, vol. 11, article 163, 2012.
 F. Wavrant-De Vrieze, D. Compton, M. Womick et al., "ABCA1 polymorphisms and Alzheimer's disease," Neuroscience Letters, vol. 416, no. 2, pp. 180-183, 2007.
 E. B. Neufeld, J. A. Stonik, S. J. Demosky Jr. et al., "The ABCA1 transporter modulates late endocytic trafficking: insights from the correction of the genetic defect in Tangier disease," The Journal of Biological Chemistry, vol. 279, no. 15, pp. 15571-15578, 2004.
 E. J. Schaefer, C. B. Blum, R. I. Levy et al., "Metabolism of high density lipoprotein apolipoproteins in Tangier disease," The New England Journal of Medicine, vol. 299, no. 17, pp. 905-910, 1978.
 J. Huang, W. Huang, H. Li et al., "Relationship between the I883M polymorphism of ATP-binding cassette transporter 1 gene and cardiovascular disease," Shandong Medical Journal, vol. 9, 2009.
 A. Sandhofer, B. Iglseder, S. Kaser, E. More, B. Paulweber, and J. R. Patsch, "The influence of two variants in the adenosine triphosphate-binding cassette transporter 1 gene on plasma lipids and carotid atherosclerosis," Metabolism, vol. 57, no. 10, pp. 1398-1404, 2008.
 U. Hodoglugil, D. W. Williamson, Y. Huang, and R. W. Mahley, "Common polymorphisms of ATP binding cassette transporter A1, including a functional promoter polymorphism, associated with plasma high density lipoprotein cholesterol levels in Turks," Atherosclerosis, vol. 183, no. 2, pp. 199-212, 2005.
 M. K. Jensen, J. K. Pai, K. J. Mukamal, K. Overvad, and E. B. Rimm, "Common genetic variation in the ATP-binding cassette transporter A1, plasma lipids, and risk of coronary heart disease," Atherosclerosis, vol. 195, no. 1, pp. e172-e180, 2007
 I. Porchay-Balderelli, F. Pean, N. Emery et al., "Relationships between common polymorphisms of adenosine triphosphate-binding cassette transporter A1 and high-density lipoprotein cholesterol and coronary heart disease in a population with type 2 diabetes mellitus," Metabolism: Clinical and Experimental, vol. 58, no. 1, pp. 74-79, 2009.
 M. E. Brousseau, M. Bodzioch, E. J. Schaefer et al., "Common variants in the gene encoding ATP-binding cassette transporter 1 in men with low HDL cholesterol levels and coronary heart disease," Atherosclerosis, vol. 154, no. 3, pp. 607-611, 2001.
 T. Harada, Y. Imai, T. Nojiri et al., "A common Ile 823 Met variant of ATP-binding cassette transporter A1 gene (ABCA1) alters high density lipoprotein cholesterol level in Japanese population," Atherosclerosis, vol. 169, no. 1, pp. 105-112, 2003.
 E. J. Schaefer, D. W. Anderson, L. A. Zech et al., "Metabolism of high density lipoprotein subfractions and constituents in Tangier disease following the infusion of high density lipoproteins," Journal of Lipid Research, vol. 22, no. 2, pp. 217-228, 1981.
 M. T. Villarreal-Molina, C. A. Aguilar-Salinas, M. Rodriguez-Cruz et al., "The ATP-binding cassette transporter A1 R230C variant affects HDL cholesterol levels and BMI in the Mexican population: association with obesity and obesity-related comorbidities," Diabetes, vol. 56, no. 7, pp. 1881-1887, 2007.
 C. A. Aguilar-Salinas, S. Canizales-Quinteros, R. Rojas-Martinez et al., "The non-synonymous Arg230Cys variant (R230C) of the ATP-binding cassette transporter A1 is associated with low HDL cholesterol concentrations in Mexican adults: a population based nation wide study," Atherosclerosis, vol. 216, no. 1, pp. 146-150, 2011.
 B. Sorrenson, R. J. Suetani, M. J. A. Williams et al., "Functional rescue of mutant ABCA1 proteins by sodium 4-phenylbutyrate," Journal of Lipid Research, vol. 54, no. 1, pp. 55-62, 2013.
 S. H. Hong, J. Rhyne, K. Zeller, and M. Miller, "ABCAlAlabama: a novel variant associated with HDL deficiency and premature coronary artery disease," Atherosclerosis, vol. 164, no. 2, pp. 245-250, 2002.
 S. Bungert, L. L. Molday, and R. S. Molday, "Membrane topology of the ATP binding cassette transporter ABCR and its relationship to ABC1 and related ABCA transporters: identification of N-linked glycosylation sites," The Journal of Biological Chemistry, vol. 276, no. 26, pp. 23539-23546, 2001.
Francisco R. Marin-Martin (1), Cristina Soler-Rivas (1), Roberto Martin-Hernandez (2), and Arantxa Rodriguez-Casado (2,3)
(1) Department of Production and Characterization of New Foods, Institute of Food Science Research (CIAL), UAM-CSIC, Campus de Cantoblanco, 28049 Madrid, Spain
(2) IMDEA Food Institute, Campus de Cantoblanco, 28049 Madrid, Spain
(3) Nutritional Genomics of the Cardiovascular Disease and Obesity, IMDEA Food Institute, Carretera Cantoblanco 8, 28049 Madrid, Spain
Correspondence should be addressed to Arantxa Rodriguez-Casado; email@example.com
Received 30 April 2014; Revised 7 July 2014; Accepted 24 July 2014; Published 19 August 2014
Academic Editor: Akihiro Inazu
TABLE 1: Structural and functional properties used by MutPred and PolyPhen2. PolyPhen2 MutPred Sequence based properties Bond annotation X Functional site annotation X X Region annotation X X PHAT score X PSIC score X SIFT score X Evolutionary attributes X X Structural properties Secondary structure X X Solvent-accessible X X surface area Phi-psi dihedral angles X Normalized accessible X surface area Change in accessible X surface propensity Change in residue side X chain volume Region of the phi-psi map X (Ramachandran map) Normalized B-factor X X Ligand contacts X Interchain contacts X Functional site contacts X Molecular Recognition X Fragments (MoRFs) TABLE 2: Functional effects of nsSNPs for ABCA1 gene (UniProt: 095477) predicted by PolyPhen2 and MutPred. A score > 0.5 means that the mutation is classified as damaging by the algorithms. In addition, MutPred algorithm formulates hypotheses about structural and functional impact of the mutation; the most statistically significant hypothesis is reported in this table. The MAP column includes Minor Allele Frequency. The deviation column represents how close the two predicted scores are; a value of 0 corresponding to identical values. Mutation pph2_prob Polyphen RFscore Mutpred class hypothesis A1010V 0,104 neutral 0,816 Loss of ubiquitination at K1009 A1046D 0,999 deleterious 0,938 Loss of MoRF binding A1182T 0,001 neutral 0,425 Loss of sheet A1407T 0,119 neutral 0,277 Gain of helix A1631V 0,401 neutral 0,321 Loss of sheet A1670T 0,997 deleterious 0,566 Gain of glycosylation at A1670 A1756T 0,81 deleterious 0,418 Gain of glycosylation at A1756 A2028V 0,146 neutral 0,564 Gain of methylation at K2023 A255T 0 neutral 0,755 Loss of helix A343V 0,001 neutral 0,227 Loss of disorder A391S 0,318 neutral 0,34 Gain of disorder A594T 0,02 neutral 0,406 Loss of catalytic residue at A594 A697T 0,416 neutral 0,508 Gain of sheet A746G 1 deleterious 0,608 Loss of stability A779T 0,411 neutral 0,468 Loss of helix A795S 0,07 neutral 0,494 Loss of helix A937V 0,991 deleterious 0,921 Loss of disorder C1477F 1 deleterious 0,496 Loss of catalytic residue at C1477 C1477R 1 deleterious 0,852 Gain of methylation at C1477 C1660R 0,954 deleterious 0,917 Gain of methylation at C1660 CSS 0,005 neutral 0,48 Gain of disorder C887F 0,002 neutral 0,604 Gain of phosphorylation at S884 D1018G 0,992 deleterious 0,535 Loss of catalytic residue at D1018 D1099N 1 deleterious 0,906 Gain of sheet D1099Y 1 deleterious 0,946 Loss of disorder D1263E 0,003 neutral 0,214 Gain of helix D1289N 0,339 neutral 0,573 Gain of sheet D1553E 0,001 neutral 0,273 Loss of helix D1706N 1 deleterious 0,611 Loss of helix D2243E 0,078 neutral 0,491 Gain of relative solvent accessibility D434E 0,005 neutral 0,413 Gain of disorder D441N 0 neutral 0,348 Loss of helix D444E 0 neutral 0,338 Loss of helix D446E 0 neutral 0,301 Loss of helix D457E 0,001 neutral 0,566 Loss of helix D462G 0,071 neutral 0,555 Loss of catalytic residue at D462 D581E 0,38 neutral 0,346 Loss of helix D677E 0,158 neutral 0,846 Loss of loop D831N 0,999 deleterious 0,589 Gain of helix D917N 0,1 neutral 0,577 Gain of MoRF binding E1005K 0,026 neutral 0,542 Gain of MoRF binding E1172D 0,003 neutral 0,253 Loss of sheet E1172G 0,003 neutral 0,281 Loss of sheet E1253K 0,048 neutral 0,453 Gain of ubiquitination at E1253 E1916A 0,027 neutral 0,34 Gain of MoRF binding E226G 0,019 neutral 0,47 Loss of stability E284K 0,206 neutral 0,842 Gain of methylation at E284 E815G 0,072 neutral 0,361 Loss of disorder E868K 0,051 neutral 0,69 Gain of methylation at E868 F1285L 0,001 neutral 0,235 Loss of loop F1573S 0,065 neutral 0,532 Gain of disorder F2009S 0,811 deleterious 0,932 Gain of disorder F2082V 0,266 neutral 0,442 Gain of MoRF binding F2163S 0,939 deleterious 0,727 Loss of sheet F346L 0 neutral 0,239 Gain of disorder F426L 0,94 deleterious 0,549 Gain of helix F632C 0,998 deleterious 0,694 Loss of stability F950S 0,995 deleterious 0,664 Gain of disorder G1049R 1 deleterious 0,638 Gain of MoRF binding G1216V 0,951 deleterious 0,844 Loss of ubiquitination at K1214 G1321A 0,936 deleterious 0,715 Gain of catalytic residue at G1321 G156V 0,017 neutral 0,389 Loss of helix G2147E 0,49 neutral 0,397 Loss of catalytic residue at S2148 G315W 0,997 deleterious 0,526 Loss of disorder G616V 0,998 deleterious 0,444 Loss of disorder G788W 1 deleterious 0,664 Loss of catalytic residue at P784 G851R 0,998 deleterious 0,71 Gain of catalytic residue at G851 H263D 0 neutral 0,399 Loss of helix H551D 0,613 deleterious 0,468 Gain of sheet I1084V 0,241 neutral 0,511 Gain of MoRF binding I1239V 0,078 neutral 0,376 Loss of catalytic residue at L1244 I1517R 0,962 deleterious 0,917 Gain of catalytic residue at 11517 I1555T 0,439 neutral 0,484 Loss of stability I1911T 0,006 neutral 0,437 Loss of stability I35V 0,066 neutral 0,31 Loss of helix I546V 0 neutral 0,31 Gain of MoRF binding I560T 0,829 deleterious 0,496 Loss of stability I649F 0,97 deleterious 0,669 Loss of catalytic residue at 1649 1659V 0,034 neutral 0,405 Loss of methylation at K663 I883M 0,002 neutral 0,296 Gain of disorder K1250R 0,118 neutral 0,377 Foss of methylation at K1250 K1587R 0,034 neutral 0,377 Gain of helix K166R 0 neutral 0,251 Foss of ubiquitination at K166 K1761T 0,038 neutral 0,545 Foss of methylation at K1761 K1974R 0,158 neutral 0,679 Foss of ubiquitination at K1974 K2040E 0,243 neutral 0,459 Foss of MoRF binding K401Q 0,007 neutral 0,322 Foss of ubiquitination at K401 K613E 0,002 neutral 0,428 Foss of ubiquitination at K613 K776N 0,985 deleterious 0,568 Foss of methylation at K776 L1041V 0,999 deleterious 0,625 Foss of ubiquitination at K1040 L1379F 0,987 deleterious 0,866 Foss of catalytic residue at F1379 L1408F 0,443 neutral 0,418 Foss of stability F1648P 0,961 deleterious 0,749 Foss of stability F184S 0,007 neutral 0,576 Foss of sheet F184W 0,928 deleterious 0,54 Gain of helix F2032M 0,897 deleterious 0,688 Gain of methylation at K2036 F2168P 0,984 deleterious 0,693 Gain of disorder F2187Q 0,995 deleterious 0,698 Gain of disorder M1012I 0,008 neutral 0,595 Foss of catalytic residue at V1008 M1091T 0,98 deleterious 0,955 Gain of disorder M233V 0 neutral 0,417 Foss of stability M415F 0,002 neutral 0,423 Gain of relative solvent accessibility M674F 0,534 deleterious 0,599 Foss of catalytic residue at M674 N1185K 0,01 neutral 0,583 Gain of MoRF binding N1185S 0,002 neutral 0,358 Gain of helix N1406K 0,006 neutral 0,408 Gain of ubiquitination at N1406 N1611D 0,968 deleterious 0,921 Foss of MoRF binding N1800H 0,758 deleterious 0,826 Foss of stability N1800S 0,241 neutral 0,84 Foss of stability N2119Y 0,96 deleterious 0,727 Foss of MoRF binding N820S 0,001 neutral 0,348 Foss of catalytic residue at N820 N935H 0,996 deleterious 0,966 Gain of disorder N935S 0,831 deleterious 0,969 Gain of disorder P1065S 0,998 deleterious 0,906 Gain of MoRF binding P1475S 1 deleterious 0,235 Gain of phosphorylation at P1475 P1878T 0,019 neutral 0,482 Gain of helix P2150F 0,068 neutral 0,826 Foss of disorder P248A 0 neutral 0,207 Foss of helix P250F 0 neutral 0,244 Foss of glycosylation at P250 P641L 1 deleterious 0,603 Gain of MoRF binding P855S 0,97 deleterious 0,59 Loss of catalytic residue at W856 P85L 0,394 neutral 0,618 Loss of disorder Q1279K 0,032 neutral 0,715 Gain of MoRF binding Q188K 0,001 neutral 0,543 Gain of ubiquitination at Q188 Q205E 0,01 neutral 0,255 Loss of loop Q205R 0 neutral 0,279 Gain of helix Q2196H 0,011 neutral 0,798 Loss of MoRF binding Q2210H 0,984 deleterious 0,388 Gain of catalytic residue at D2214 Q597R 1 deleterious 0,71 Gain of MoRF binding Q849R 0,689 deleterious 0,662 Gain of methylation at Q849 R104C 0,985 deleterious 0,589 Loss of MoRF binding R1068C 1 deleterious 0,962 Loss of MoRF binding R1068H 1 deleterious 0,972 Loss of MoRF binding R1082C 1 deleterious 0,694 Loss of MoRF binding R1195Q 0,044 neutral 0,364 Loss of loop R1195W 0,952 deleterious 0,589 Gain of catalytic residue at A1194 R126H 0 neutral 0,467 Loss of MoRF binding R1273L 0,007 neutral 0,35 Loss of loop R1283C 0,001 neutral 0,449 Gain of ubiquitination at K1278 R1341T 0,016 neutral 0,604 Loss of methylation at R1341 R1344W 0,993 deleterious 0,64 Loss of methylation at K1345 R1417H 0,837 deleterious 0,457 Loss of phosphorylation at T1416 R1615P 0,947 deleterious 0,895 Loss of methylation at R1615 R1615Q 0,187 neutral 0,679 Gain of helix R1680Q 0,997 deleterious 0,807 Gain of ubiquitination at K1683 R1680W 1 deleterious 0,911 Loss of disorder R1839H 0,006 neutral 0,48 Loss of phosphorylation at S1842 R1851Q 0,015 neutral 0,897 Gain of catalytic residue at R1851 R1897W 0,009 neutral 0,408 Probably damaging R1901S 0,091 neutral 0,81 Gain of phosphorylation at R1901 R1925Q 0,006 neutral 0,69 Loss of MoRF binding R2004K 0,911 deleterious 0,699 Gain of ubiquitination at R2004 R2030Q 0,032 neutral 0,442 Loss of MoRF binding R2081W 0,998 deleterious 0,936 Loss of MoRF binding R2173Q 0,008 neutral 0,382 Gain of sheet R2189G 0,142 neutral 0,443 Loss of MoRF binding R219K 0 neutral 0,41 Gain of ubiquitination at R219 R230C 0 neutral 0,68 Loss of MoRF binding R306G 0,04 neutral 0,463 Loss of helix R306H 0,899 deleterious 0,431 Loss of helix R369H 0,937 deleterious 0,433 Loss of MoRF binding R437Q 0,088 neutral 0,48 Loss of helix R437W 0,991 deleterious 0,562 Gain of catalytic residue at L435 R443K 0 neutral 0,44 Loss of stability R496W 0,209 neutral 0,576 Loss of loop R500H 0,013 neutral 0,519 Loss of helix R587W 1 deleterious 0,688 Loss of disorder R638Q 0,957 deleterious 0,664 Loss of methylation at R638 R638W 0,999 deleterious 0,671 Loss of methylation at R638 R666Q 1 deleterious 0,437 Gain of ubiquitination at K668 R666W 1 deleterious 0,56 Gain of catalytic residue at R666 R672Q 0,832 deleterious 0,501 Loss of MoRF binding R965C 0,302 neutral 0,608 Loss of disorder R999C 0,127 neutral 0,766 Loss of solvent accessibility R999L 0,208 neutral 0,748 Loss of solvent accessibility S107A 0,002 neutral 0,342 Gain of helix S1141Y 0,805 deleterious 0,306 Loss of phosphorylation at S1141 S1157N 0,022 neutral 0,355 Gain of sheet S116N 0,196 neutral 0,241 Loss of phosphorylation at S116 S1181F 0,042 neutral 0,542 Loss of disorder S1255R 0,027 neutral 0,326 Loss of glicosilation at S1255 S1280R 0,009 neutral 0,351 Gain of sheet S1376G 0,007 neutral 0,407 Gain of methylation at K1373 S139G 0 neutral 0,284 Gain of helix S1506L 0,996 deleterious 0,733 Loss of helix S1536F 0,998 deleterious 0,594 Loss of disorder S1731C 0,893 deleterious 0,526 Loss of helix S212T 0,028 neutral 0,375 Gain of helix S2182F 0,578 deleterious 0,498 Loss of disorder S2186F 0,862 deleterious 0,458 Loss of disorder S442R 0,001 neutral 0,449 Loss of helix S713G 1 deleterious 0,576 Loss of stability S780N 0,996 deleterious 0,559 Loss of stability T1175M 0,432 neutral 0,331 Loss of sheet T1242M 0,993 deleterious 0,62 Loss of catalytic residue at T1242 T1399M 0,003 neutral 0,279 Loss of glycosylation at T1399 T1401I 0,003 neutral 0,368 Loss of glycosylation at T1401 T1427M 0,005 neutral 0,352 Gain of catalytic residue at T1427 T2073A 0,852 deleterious 0,657 Loss of phosphorylation at T2073 T459P 0,03 neutral 0,534 Gain of helix T515A 0,04 neutral 0,449 Gain of helix T774P 0,003 neutral 0,61 Gain of methylation at K776 T929I 0,946 deleterious 0,889 Loss of disorder T940M 1 deleterious 0,844 Loss of methylation at K939 V1054I 0,951 deleterious 0,51 Loss of ubiquitination at K1052 V1096I 0 neutral 0,467 Gain of helix V1158I 0,065 neutral 0,226 Loss of sheet V1674I 0,053 neutral 0,376 Loss of catalytic residue at V1674 V1704D 0,752 deleterious 0,876 Loss of helix V1806M 0,98 deleterious 0,417 Gain of ubiquitination at K1804 V1858M 0,305 neutral 0,472 Loss of catalytic residue at V1858 V2035M 0,042 neutral 0,38 Loss of methylation at K2036 V2244I 0,002 neutral 0,386 Gain of sheet V304M 0,823 deleterious 0,387 Gain of disorder V380I 0,001 neutral 0,353 Gain of methylation at K376 V399A 0,071 neutral 0,563 Gain of disorder V408G 0,101 neutral 0,431 Loss of stability V481L 0,001 neutral 0,374 Gain of sheet V589I 0,004 neutral 0,334 Gain of catalytic residue at V589 V654G 0,433 neutral 0,438 Loss of stability V702A 0,002 neutral 0,408 Loss of stability V724M 0,833 deleterious 0,462 Loss of catalytic residue at V724 V771M 0,034 neutral 0,421 Loss of ubiquitination at K776 V825I 0,001 neutral 0,401 Loss of catalytic residue at V825 W1699C 1 deleterious 0,915 Gain of catalytic residue at L1700 W590L 0,841 deleterious 0,888 Loss of helix W590S 0,889 deleterious 0,857 Gain of disorder W840R 0,998 deleterious 0,923 Gain of methylation at W840 Y1921H 0,986 deleterious 0,752 Gain of disorder Y2178H 0,982 deleterious 0,826 Gain of disorder Y2206D 0,992 deleterious 0,878 Gain of sheet Y482C 0,03 neutral 0,826 Gain of loop Y835H 0,967 deleterious 0,653 Loss of stability Mutation MAF Deviation A1010V NA 0,50346003 A1046D NA 0,04313351 A1182T 0,00045914 0,29981328 A1407T 0,00045914 0,11172287 A1631V NA 0,05656854 A1670T NA 0,30476302 A1756T NA 0,27718586 A2028V 0 0,29557064 A255T NA 0,53386562 A343V NA 0,15980613 A391S NA 0,01555635 A594T NA 0,27294322 A697T 0,00045914 0,06505382 A746G 0,00045914 0,27718586 A779T NA 0,04030509 A795S NA 0,29981328 A937V NA 0,04949748 C1477F NA 0,35638182 C1477R NA 0,10465181 C1660R NA 0,02616295 CSS NA 0,33587572 C887F 0,00137741 0,42567828 D1018G NA 0,3231478 D1099N NA 0,06646804 D1099Y NA 0,03818377 D1263E NA 0,14919953 D1289N NA 0,16546299 D1553E NA 0,19233304 D1706N NA 0,27506454 D2243E 0,00459137 0,2920351 D434E NA 0,28849957 D441N NA 0,24607316 D444E NA 0,23900209 D446E NA 0,21283914 D457E NA 0,39951533 D462G NA 0,34223968 D581E NA 0,02404163 D677E NA 0,48648947 D831N NA 0,28991378 D917N NA 0,33728994 E1005K NA 0,3648671 E1172D 0,0509642 0,1767767 E1172G 0,00091827 0,19657569 E1253K NA 0,28637825 E1916A 0,00183655 0,22132442 E226G NA 0,31890516 E284K NA 0,44971991 E815G NA 0,20435386 E868K 0,00275482 0,45184123 F1285L NA 0,16546299 F1573S NA 0,33021887 F2009S NA 0,08555992 F2082V NA 0,12445079 F2163S NA 0,14990664 F346L NA 0,16899852 F426L 0,00045914 0,27647875 F632C 0,00045914 0,21496046 F950S NA 0,23405235 G1049R NA 0,25597266 G1216V NA 0,07566043 G1321A NA 0,15627061 G156V NA 0,26304372 G2147E NA 0,06576093 G315W NA 0,33304729 G616V NA 0,39173716 G788W NA 0,23758788 G851R NA 0,20364675 H263D NA 0,28213561 H551D NA 0,10253048 I1084V NA 0,19091883 I1239V NA 0,21071782 I1517R NA 0,03181981 I1555T NA 0,03181981 I1911T 0,00045914 0,30476302 I35V 0,00045914 0,17253406 I546V NA 0,2192031 I560T NA 0,23546656 I649F NA 0,21283914 1659V NA 0,26233662 I883M 0,365473 0,20788939 K1250R 0,00045914 0,18314066 K1587R 0,410927 0,24253763 K166R NA 0,1774838 K1761T 0,00045914 0,35850314 K1974R NA 0,36840263 K2040E 0,00045914 0,15273507 K401Q NA 0,22273864 K613E 0,00045914 0,30122749 K776N 0,00183655 0,29486353 L1041V 0,00137741 0,26445794 L1379F NA 0,08555992 L1408F 0,00045914 0,01767767 F1648P NA 0,14990664 F184S 0,00045914 0,40234376 F184W 0,00045914 0,27435743 F2032M NA 0,14778532 F2168P NA 0,20576807 F2187Q 0,00045914 0,21001071 M1012I NA 0,41507168 M1091T NA 0,01767767 M233V NA 0,29486353 M415F NA 0,29769196 M674F 0,00045914 0,04596194 N1185K NA 0,40517219 N1185S NA 0,25173001 N1406K NA 0,28425693 N1611D NA 0,03323402 N1800H NA 0,04808326 N1800S NA 0,42355696 N2119Y NA 0,16475588 N820S NA 0,24536605 N935H NA 0,02121321 N935S NA 0,09758074 P1065S NA 0,06505382 P1475S NA 0,54093669 P1878T NA 0,32739044 P2150F NA 0,53598694 P248A NA 0,1463711 P250F NA 0,17253406 P641L NA 0,28072139 P855S NA 0,26870058 P85L 0,00045914 0,15839192 Q1279K NA 0,48295393 Q188K 0 0,38325188 Q205E NA 0,17324116 Q205R NA 0,19728279 Q2196H NA 0,55649304 Q2210H NA 0,42143564 Q597R NA 0,20506097 Q849R NA 0,01909188 R104C NA 0,28001429 R1068C NA 0,02687006 R1068H NA 0,01979899 R1082C NA 0,21637468 R1195Q NA 0,22627417 R1195W NA 0,25667976 R126H NA 0,33021887 R1273L NA 0,24253763 R1283C NA 0,31678384 R1341T [less than or equal to] 0,001 0,41577879 R1344W 0,00045914 0,24960869 R1417H 0,00183655 0,26870058 R1615P [less than or equal to] 0,001 0,03676955 R1615Q NA 0,34789654 R1680Q NA 0,13435029 R1680W NA 0,06293251 R1839H 0,00045914 0,33516861 R1851Q NA 0,62366818 R1897W NA 0,28213561 R1901S NA 0,50840978 R1925Q 0,00229568 0,48366104 R2004K NA 0,14990664 R2030Q NA 0,28991378 R2081W NA 0,04384062 R2173Q NA 0,26445794 R2189G NA 0,21283914 R219K 0,419192 0,28991378 R230C 0,00734619 0,48083261 R306G [less than or equal to] 0,001 0,29910617 R306H NA 0,33092597 R369H NA 0,35638182 R437Q NA 0,27718586 R437W NA 0,30334881 R443K 0,00045914 0,31112698 R496W NA 0,25950819 R500H NA 0,35779603 R587W NA 0,22061732 R638Q NA 0,20718229 R638W 0,00045914 0,23193102 R666Q NA 0,39810112 R666W NA 0,31112698 R672Q NA 0,23405235 R965C [less than or equal to] 0,001 0,21637468 R999C NA 0,45184123 R999L NA 0,38183766 S107A NA 0,24041631 S1141Y NA 0,35284628 S1157N 0,00045914 0,23546656 S116N NA 0,03181981 S1181F 0,00045914 0,35355339 S1255R 0,00183655 0,21142493 S1280R 0,00045914 0,24183052 S1376G [less than or equal to] 0,001 0,28284271 S139G NA 0,20081833 S1506L NA 0,18596908 S1536F NA 0,28567114 S1731C [less than or equal to] 0,001 0,2595081 S212T 0,00459137 0,24536605 S2182F NA 0,05656854 S2186F NA 0,28567114 S442R NA 0,31678384 S713G NA 0,29981328 S780N NA 0,30900566 T1175M 0,00091827 0,07141779 T1242M NA 0,26375083 T1399M NA 0,19516147 T1401I NA 0,25809398 T1427M 0,00091827 0,24536605 T2073A [less than or equal to] 0,001 0,13788582 T459P [less than or equal to] 0,001 0,35638182 T515A NA 0,28920667 T774P 0,00183655 0,42921382 T929I NA 0,04030509 T940M NA 0,11030866 V1054I NA 0,31183409 V1096I NA 0,33021887 V1158I NA 0,11384419 V1674I 0,00137741 0,22839549 V1704D NA 0,08768124 V1806M NA 0,39810112 V1858M NA 0,11808683 V2035M NA 0,23900209 V2244I NA 0,271529 V304M NA 0,30829856 V380I NA 0,24890159 V399A 0,00183655 0,34789654 V408G NA 0,23334524 V481L 0,00045914 0,26375083 V589I 0,00091827 0,23334524 V654G NA 0,00353553 V702A NA 0,28708535 V724M 0,00045914 0,26233662 V771M 0,0606061 0,27365032 V825I 0,128099 0,28284271 W1699C NA 0,06010408 W590L [less than or equal to] 0,001 0,03323402 W590S [less than or equal to] 0,001 0,02262742 W840R NA 0,05303301 Y1921H NA 0,16546299 Y2178H NA 0,11030866 Y2206D NA 0,08061017 Y482C NA 0,56285691 Y835H NA 0,22203153
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Research Article; single nucleotide polymorphisms; ATP-binding cassette transporter|
|Author:||Marin-Martin, Francisco R.; Soler-Rivas, Cristina; Martin-Hernandez, Roberto; Rodriguez-Casado, Aran|
|Date:||Jan 1, 2014|
|Previous Article:||Effects of Securigera securidaca extract on lipolysis and adipogenesis in diabetic rats.|
|Next Article:||Association of the total cholesterol content of erythrocyte membranes with the severity of disease in stable coronary artery disease.|