Cholesterol / 2014 / Article

Research Article | Open Access

Volume 2014 |Article ID 639751 |

Francisco R. Marín-Martín, Cristina Soler-Rivas, Roberto Martín-Hernández, Arantxa Rodriguez-Casado, "A Comprehensive In Silico Analysis of the Functional and Structural Impact of Nonsynonymous SNPs in the ABCA1 Transporter Gene", Cholesterol, vol. 2014, Article ID 639751, 19 pages, 2014.

A Comprehensive In Silico Analysis of the Functional and Structural Impact of Nonsynonymous SNPs in the ABCA1 Transporter Gene

Academic Editor: Akihiro Inazu
Received30 Apr 2014
Revised07 Jul 2014
Accepted24 Jul 2014
Published19 Aug 2014


Disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs), which are important indicators of action sites and effective potential therapeutic approaches. Identification of deleterious nsSNPs is crucial to characterize the genetic basis of diseases, assess individual susceptibility to disease, determinate molecular and therapeutic targets, and predict clinical phenotypes. In this study using PolyPhen2 and MutPred in silico algorithms, we analyzed the genetic variations that can alter the expression and function of the ABCA1 gene that causes the allelic disorders familial hypoalphalipoproteinemia and Tangier disease. Predictions were validated with published results from in vitro, in vivo, and human studies. Out of a total of 233 nsSNPs, 80 (34.33%) were found deleterious by both methods. Among these 80 deleterious nsSNPs found, 29 (12.44%) rare variants resulted highly deleterious with a probability >0.8. We have observed that mostly variants with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, the controversial results of experimental approaches correspond to nsSNPs predicted as neutral by both methods, or contradictory predictions are obtained for them. A total of seventeen nsSNPs were predicted as deleterious by PolyPhen2, which resulted neutral by MutPred. Otherwise, forty two nsSNPs were predicted as deleterious by MutPred, which resulted neutral by PolyPhen2.

1. Introduction

Nonsynonymous single nucleotide polymorphisms (nsSNPs) are single base changes in coding regions that cause an amino acid substitution in the correspondent proteins. These missense variants constitute the most identifiable group of SNPs represented by a small (<1%) proportion [1]. The nsSNPs might alter structure, stability, and function of proteins and produce the least conservative substitutions with drastic phenotypic consequences [25]. Studies suggest that about 60% of Mendelian diseases are caused by amino acid exchanges [6]. Thousands of associations between Mendelian and complex diseases reveal a phenotypic code that links each complex disorder to a unique set of Mendelian loci [7]. 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 [8]. 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 [9]. 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 [1012]. As well, lipid export activity of ABCA1 improves the function of pancreatic cells and ameliorates insulin release [13], reduces biliary cholesterol content protecting against gallstone [14], and plays a key role in lipid homeostasis in the lung [15]. 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 [1520] founding lipid and myelin abnormalities in schizophrenia and Alzheimer’s disease [1820].

Although the entire ABCA1 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 [2632]. 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 [33]. 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) [3438] described by reduced HDL-c plasma level (<5%), impaired cholesterol efflux, and a trend to accumulate intracellular cholesterol [3443]. 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 [3438, 44]. Compared with unaffected family members, heterozygotes and homozygotes have a more prevalent, premature, and severe atherosclerosis [42].

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 α-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 (, 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) [45] and PolyPhen2 (polymorphism phenotyping 2) algorithms [46], 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.

2. Methods

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 [47] 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 ( algorithm uses a naive Bayesian classifier to predict allele function based on a combination of sequence and structure-based attributes (if available) [46]. It calculates the probability for a given mutation to be benign, possibly damaging, or probably damaging. Then, we used MutPred ( [45] based upon SIFT algorithm [48] 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 ( 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 [49]. Similarly, about 25% of nsSNPs affecting protein activity was predicted by PolyPhen2 [49]. 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 [45].


Sequence based properties
 Bond annotationX
 Functional site annotationXX
 Region annotationXX
 PHAT scoreX
 PSIC scoreX
 SIFT scoreX
 Evolutionary attributesXX
Structural properties
 Secondary structureXX
 Solvent-accessible surface areaXX
 Phi-psi dihedral anglesX
 Normalized accessible surface areaX
 Change in accessible surface propensityX
 Change in residue side chain volumeX
 Region of the phi-psi map (Ramachandran map)X
 Normalized B-factorXX
 Ligand contactsX
 Interchain contactsX
 Functional site contactsX
 Molecular Recognition Fragments (MoRFs)X

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 [3438]. 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 3′-UTR 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 [3].

Mutationpph2_probPolyphen classRFscoreMutpred hypothesisMAFDeviation

A1010V0,104neutral0,816Loss of ubiquitination at K1009NA0,50346003
A1046D0,999deleterious0,938Loss of MoRF bindingNA0,04313351
A1182T0,001neutral0,425Loss of sheet0,000459140,29981328
A1407T0,119neutral0,277Gain of helix0,000459140,11172287
A1631V0,401neutral0,321Loss of sheetNA0,05656854
A1670T0,997deleterious0,566Gain of glycosylation at A1670NA0,30476302
A1756T0,81deleterious0,418Gain of glycosylation at A1756NA0,27718586
A2028V0,146neutral0,564Gain of methylation at K202300,29557064
A255T0neutral0,755Loss of helixNA0,53386562
A343V0,001neutral0,227Loss of disorderNA0,15980613
A391S0,318neutral0,34Gain of disorderNA0,01555635
A594T0,02neutral0,406Loss of catalytic residue at A594NA0,27294322
A697T0,416neutral0,508Gain of sheet0,000459140,06505382
A746G1deleterious0,608Loss of stability0,000459140,27718586
A779T0,411neutral0,468Loss of helixNA0,04030509
A795S0,07neutral0,494Loss of helixNA0,29981328
A937V0,991deleterious0,921Loss of disorderNA0,04949748
C1477F1deleterious0,496Loss of catalytic residue at C1477NA0,35638182
C1477R1deleterious0,852Gain of methylation at C1477NA0,10465181
C1660R0,954deleterious0,917Gain of methylation at C1660NA0,02616295
C3S0,005neutral0,48Gain of disorderNA0,33587572
C887F0,002neutral0,604Gain of phosphorylation at S8840,001377410,42567828
D1018G0,992deleterious0,535Loss of catalytic residue at D1018NA0,3231478
D1099N1deleterious0,906Gain of sheetNA0,06646804
D1099Y1deleterious0,946Loss of disorderNA0,03818377
D1263E0,003neutral0,214Gain of helixNA0,14919953
D1289N0,339neutral0,573Gain of sheetNA0,16546299
D1553E0,001neutral0,273Loss of helixNA0,19233304
D1706N1deleterious0,611Loss of helixNA0,27506454
D2243E0,078neutral0,491Gain of relative solvent accessibility0,004591370,2920351
D434E0,005neutral0,413Gain of disorderNA0,28849957
D441N0neutral0,348Loss of helixNA0,24607316
D444E0neutral0,338Loss of helix NA0,23900209
D446E0neutral0,301Loss of helixNA0,21283914
D457E0,001neutral0,566Loss of helixNA0,39951533
D462G0,071neutral0,555Loss of catalytic residue at D462NA0,34223968
D581E0,38neutral0,346Loss of helixNA0,02404163
D677E0,158neutral0,846Loss of loopNA0,48648947
D831N0,999deleterious0,589Gain of helixNA0,28991378
D917N0,1neutral0,577Gain of MoRF bindingNA0,33728994
E1005K0,026neutral0,542Gain of MoRF bindingNA0,3648671
E1172D0,003neutral0,253Loss of sheet0,05096420,1767767
E1172G0,003neutral0,281Loss of sheet0,000918270,19657569
E1253K0,048neutral0,453Gain of ubiquitination at E1253NA0,28637825
E1916A0,027neutral0,34Gain of MoRF binding0,001836550,22132442
E226G0,019neutral0,47Loss of stabilityNA0,31890516
E284K0,206neutral0,842Gain of methylation at E284NA0,44971991
E815G0,072neutral0,361Loss of disorderNA0,20435386
E868K0,051neutral0,69Gain of methylation at E8680,002754820,45184123
F1285L0,001neutral0,235Loss of loopNA0,16546299
F1573S0,065neutral0,532Gain of disorder NA0,33021887
F2009S0,811deleterious0,932Gain of disorderNA0,08555992
F2082V0,266neutral0,442Gain of MoRF bindingNA0,12445079
F2163S0,939deleterious0,727Loss of sheetNA0,14990664
F346L0neutral0,239Gain of disorderNA0,16899852
F426L0,94deleterious0,549Gain of helix0,000459140,27647875
F632C0,998deleterious0,694Loss of stability0,000459140,21496046
F950S0,995deleterious0,664Gain of disorderNA0,23405235
G1049R1deleterious0,638Gain of MoRF bindingNA0,25597266
G1216V0,951deleterious0,844Loss of ubiquitination at K1214NA0,07566043
G1321A0,936deleterious0,715Gain of catalytic residue at G1321NA0,15627061
G156V0,017neutral0,389Loss of helixNA0,26304372
G2147E0,49neutral0,397Loss of catalytic residue at S2148NA0,06576093
G315W0,997deleterious0,526Loss of disorderNA0,33304729
G616V0,998deleterious0,444Loss of disorderNA0,39173716
G788W1deleterious0,664Loss of catalytic residue at P784NA0,23758788
G851R0,998deleterious0,71Gain of catalytic residue at G851NA0,20364675
H263D0neutral0,399Loss of helixNA0,28213561
H551D0,613deleterious0,468Gain of sheetNA0,10253048
I1084V0,241neutral0,511Gain of MoRF bindingNA0,19091883
I1239V0,078neutral0,376Loss of catalytic residue at L1244NA0,21071782
I1517R0,962deleterious0,917Gain of catalytic residue at I1517NA0,03181981
I1555T0,439neutral0,484Loss of stabilityNA0,03181981
I1911T0,006neutral0,437Loss of stability0,000459140,30476302
I35V0,066neutral0,31Loss of helix0,000459140,17253406
I546V0neutral0,31Gain of MoRF bindingNA0,2192031
I560T0,829deleterious0,496Loss of stabilityNA0,23546656
I649F0,97deleterious0,669Loss of catalytic residue at I649NA0,21283914
I659V0,034neutral0,405Loss of methylation at K663NA0,26233662
I883M0,002neutral0,296Gain of disorder0,3654730,20788939
K1250R0,118neutral0,377Loss of methylation at K12500,000459140,18314066
K1587R0,034neutral0,377Gain of helix0,4109270,24253763
K166R0neutral0,251Loss of ubiquitination at K166NA0,1774838
K1761T0,038neutral0,545Loss of methylation at K17610,000459140,35850314
K1974R0,158neutral0,679Loss of ubiquitination at K1974NA0,36840263
K2040E0,243neutral0,459Loss of MoRF binding0,000459140,15273507
K401Q0,007neutral0,322Loss of ubiquitination at K401NA0,22273864
K613E0,002neutral0,428Loss of ubiquitination at K6130,000459140,30122749
K776N0,985deleterious0,568Loss of methylation at K7760,001836550,29486353
L1041V0,999deleterious0,625Loss of ubiquitination at K10400,001377410,26445794
L1379F0,987deleterious0,866Loss of catalytic residue at L1379NA0,08555992
L1408F0,443neutral0,418Loss of stability0,000459140,01767767
L1648P0,961deleterious0,749Loss of stabilityNA0,14990664
L184S0,007neutral0,576Loss of sheet0,000459140,40234376
L184W0,928deleterious0,54Gain of helix0,000459140,27435743
L2032M0,897deleterious0,688Gain of methylation at K2036NA0,14778532
L2168P0,984deleterious0,693Gain of disorderNA0,20576807
L2187Q0,995deleterious0,698Gain of disorder0,000459140,21001071
M1012I0,008neutral0,595Loss of catalytic residue at V1008NA0,41507168
M1091T0,98deleterious0,955Gain of disorderNA0,01767767
M233V0neutral0,417Loss of stabilityNA0,29486353
M415L0,002neutral0,423Gain of relative solvent accessibilityNA0,29769196
M674L0,534deleterious0,599Loss of catalytic residue at M6740,000459140,04596194
N1185K0,01neutral0,583Gain of MoRF bindingNA0,40517219
N1185S0,002neutral0,358Gain of helixNA0,25173001
N1406K0,006neutral0,408Gain of ubiquitination at N1406NA0,28425693
N1611D0,968deleterious0,921Loss of MoRF bindingNA0,03323402
N1800H0,758deleterious0,826Loss of stabilityNA0,04808326
N1800S0,241neutral0,84Loss of stabilityNA0,42355696
N2119Y0,96deleterious0,727Loss of MoRF bindingNA0,16475588
N820S0,001neutral0,348Loss of catalytic residue at N820NA0,24536605
N935H0,996deleterious0,966Gain of disorderNA0,02121321
N935S0,831deleterious0,969Gain of disorderNA0,09758074
P1065S0,998deleterious0,906Gain of MoRF bindingNA0,06505382
P1475S1deleterious0,235Gain of phosphorylation at P1475NA0,54093669
P1878T0,019neutral0,482Gain of helixNA0,32739044
P2150L0,068neutral0,826Loss of disorderNA0,53598694
P248A0neutral0,207Loss of helixNA0,1463711
P250L0neutral0,244Loss of glycosylation at P250NA0,17253406
P641L1deleterious0,603Gain of MoRF bindingNA0,28072139
P855S0,97deleterious0,59Loss of catalytic residue at W856NA0,26870058
P85L0,394neutral0,618Loss of disorder0,000459140,15839192
Q1279K0,032neutral0,715Gain of MoRF bindingNA0,48295393
Q188K0,001neutral0,543Gain of ubiquitination at Q18800,38325188
Q205E0,01neutral0,255Loss of loopNA0,17324116
Q205R0neutral0,279Gain of helixNA0,19728279
Q2196H0,011neutral0,798Loss of MoRF bindingNA0,55649304
Q2210H0,984deleterious0,388Gain of catalytic residue at D2214NA0,42143564
Q597R1deleterious0,71Gain of MoRF bindingNA0,20506097
Q849R0,689deleterious0,662Gain of methylation at Q849NA0,01909188
R104C0,985deleterious0,589Loss of MoRF bindingNA0,28001429
R1068C1deleterious0,962Loss of MoRF bindingNA0,02687006
R1068H1deleterious0,972Loss of MoRF bindingNA0,01979899
R1082C1deleterious0,694Loss of MoRF bindingNA0,21637468
R1195Q0,044neutral0,364Loss of loopNA0,22627417
R1195W0,952deleterious0,589Gain of catalytic residue at A1194NA0,25667976
R126H0neutral0,467Loss of MoRF bindingNA0,33021887
R1273L0,007neutral0,35Loss of loopNA0,24253763
R1283C0,001neutral0,449Gain of ubiquitination at K1278NA0,31678384
R1341T0,016neutral0,604Loss of methylation at R13410,0010,41577879
R1344W0,993deleterious0,64Loss of methylation at K13450,000459140,24960869
R1417H0,837deleterious0,457Loss of phosphorylation at T14160,001836550,26870058
R1615P0,947deleterious0,895Loss of methylation at R16150,0010,03676955
R1615Q0,187neutral0,679Gain of helixNA0,34789654
R1680Q0,997deleterious0,807Gain of ubiquitination at K1683NA0,13435029
R1680W1deleterious0,911Loss of disorderNA0,06293251
R1839H0,006neutral0,48Loss of phosphorylation at S18420,000459140,33516861
R1851Q0,015neutral0,897Gain of catalytic residue at R1851NA0,62366818
R1897W0,009neutral0,408Probably damagingNA0,28213561
R1901S0,091neutral0,81Gain of phosphorylation at R1901NA0,50840978
R1925Q0,006neutral0,69Loss of MoRF binding0,002295680,48366104
R2004K0,911deleterious0,699Gain of ubiquitination at R2004NA0,14990664
R2030Q0,032neutral0,442Loss of MoRF bindingNA0,28991378
R2081W0,998deleterious0,936Loss of MoRF bindingNA0,04384062
R2173Q0,008neutral0,382Gain of sheetNA0,26445794
R2189G0,142neutral0,443Loss of MoRF bindingNA0,21283914
R219K0neutral0,41Gain of ubiquitination at R2190,4191920,28991378
R230C0neutral0,68Loss of MoRF binding0,007346190,48083261
R306G0,04neutral0,463Loss of helix0,0010,29910617
R306H0,899deleterious0,431Loss of helixNA0,33092597
R369H0,937deleterious0,433Loss of MoRF bindingNA0,35638182
R437Q0,088neutral0,48Loss of helixNA0,27718586
R437W0,991deleterious0,562Gain of catalytic residue at L435NA0,30334881
R443K0neutral0,44Loss of stability0,000459140,31112698
R496W0,209neutral0,576Loss of loopNA0,25950819
R500H0,013neutral0,519Loss of helixNA0,35779603
R587W1deleterious0,688Loss of disorderNA0,22061732
R638Q0,957deleterious0,664Loss of methylation at R638NA0,20718229
R638W0,999deleterious0,671Loss of methylation at R6380,000459140,23193102
R666Q1deleterious0,437Gain of ubiquitination at K668NA0,39810112
R666W1deleterious0,56Gain of catalytic residue at R666NA0,31112698
R672Q0,832deleterious0,501Loss of MoRF bindingNA0,23405235
R965C0,302neutral0,608Loss of disorder0,0010,21637468
R999C0,127neutral0,766Loss of solvent accessibilityNA0,45184123
R999L0,208neutral0,748Loss of solvent accessibilityNA0,38183766
S107A0,002neutral0,342Gain of helixNA0,24041631
S1141Y0,805deleterious0,306Loss of phosphorylation at S1141NA0,35284628
S1157N0,022neutral0,355Gain of sheet0,000459140,23546656
S116N0,196neutral0,241Loss of phosphorylation at S116NA0,03181981
S1181F0,042neutral0,542Loss of disorder0,000459140,35355339
S1255R0,027neutral0,326Loss of glicosilation at S12550,001836550,21142493
S1280R0,009neutral0,351Gain of sheet0,000459140,24183052
S1376G0,007neutral0,407Gain of methylation at K13730,0010,28284271
S139G0neutral0,284Gain of helixNA0,20081833
S1506L0,996deleterious0,733Loss of helixNA0,18596908
S1536F0,998deleterious0,594Loss of disorderNA0,28567114
S1731C0,893deleterious0,526Loss of helix0,0010,25950819
S212T0,028neutral0,375Gain of helix0,004591370,24536605
S2182F0,578deleterious0,498Loss of disorderNA0,05656854
S2186F0,862deleterious0,458Loss of disorderNA0,28567114
S442R0,001neutral0,449Loss of helixNA0,31678384
S713G1deleterious0,576Loss of stabilityNA0,29981328
S780N0,996deleterious0,559Loss of stabilityNA0,30900566
T1175M0,432neutral0,331Loss of sheet0,000918270,07141779
T1242M0,993deleterious0,62Loss of catalytic residue at T1242NA0,26375083
T1399M0,003neutral0,279Loss of glycosylation at T1399NA0,19516147
T1401I0,003neutral0,368Loss of glycosylation at T1401NA0,25809398
T1427M0,005neutral0,352Gain of catalytic residue at T14270,000918270,24536605
T2073A0,852deleterious0,657Loss of phosphorylation at T20730,0010,13788582
T459P0,03neutral0,534Gain of helix0,0010,35638182
T515A0,04neutral0,449Gain of helixNA0,28920667
T774P0,003neutral0,61Gain of methylation at K7760,001836550,42921382
T929I0,946deleterious0,889Loss of disorderNA0,04030509
T940M1deleterious0,844Loss of methylation at K939NA0,11030866
V1054I0,951deleterious0,51Loss of ubiquitination at K1052NA0,31183409
V1096I0neutral0,467Gain of helixNA0,33021887
V1158I0,065neutral0,226Loss of sheetNA0,11384419
V1674I0,053neutral0,376Loss of catalytic residue at V16740,001377410,22839549
V1704D0,752deleterious0,876Loss of helixNA0,08768124
V1806M0,98deleterious0,417Gain of ubiquitination at K1804NA0,39810112
V1858M0,305neutral0,472Loss of catalytic residue at V1858NA0,11808683
V2035M0,042neutral0,38Loss of methylation at K2036NA0,23900209
V2244I0,002neutral0,386Gain of sheetNA0,271529
V304M0,823deleterious0,387Gain of disorderNA0,30829856
V380I0,001neutral0,353Gain of methylation at K376NA0,24890159
V399A0,071neutral0,563Gain of disorder0,001836550,34789654
V408G0,101neutral0,431Loss of stabilityNA0,23334524
V481L0,001neutral0,374Gain of sheet0,000459140,26375083
V589I0,004neutral0,334Gain of catalytic residue at V5890,000918270,23334524
V654G0,433neutral0,438Loss of stabilityNA0,00353553
V702A0,002neutral0,408Loss of stabilityNA0,28708535
V724M0,833deleterious0,462Loss of catalytic residue at V7240,000459140,26233662
V771M0,034neutral0,421Loss of ubiquitination at K7760,06060610,27365032
V825I0,001neutral0,401Loss of catalytic residue at V8250,1280990,28284271
W1699C1deleterious0,915Gain of catalytic residue at L1700NA0,06010408
W590L0,841deleterious0,888Loss of helix0,0010,03323402
W590S0,889deleterious0,857Gain of disorder0,0010,02262742
W840R0,998deleterious0,923Gain of methylation at W840NA0,05303301
Y1921H0,986deleterious0,752Gain of disorderNA0,16546299
Y2178H0,982deleterious0,826Gain of disorderNA0,11030866
Y2206D0,992deleterious0,878Gain of sheetNA0,08061017
Y482C0,03neutral0,826Gain of loopNA0,56285691
Y835H0,967deleterious0,653Loss of stabilityNA0,22203153

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 β-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 deleterious 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 [50]. 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 [51] 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 ≤ 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) [52]. 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 ≥ 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 [54]. Even similar physicochemical properties (polar, medium size) of exchanged residues the N1800H nsSNP, located between transmembrane domains [54], 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 [54]. Distribution of W590S is identical to WT [55] as well as apoA1 binding activity [5457]; 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 [27] but not in subjects with normal [60] 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 [60] able to produce the most severe effects on HDL-c levels and on cholesterol efflux [62]. These data along with our in silico predictions indicate that conserved S1731C is highly likely to affect protein function.

S1506L, Q597R, and C1477R variants are linked to TD and FHA and found in tumor cancer [54]. Normal function of ABCA1 inhibits tumor growth in human cancer cells [54]. However, although expressed to similar levels as WT, these alleles show deficient cellular cholesterol efflux and HDL production and do not decrease tumor growth [17]. The three are located intracellularly but C1477R is also found in membrane [54], 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 [64]. 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% [56]. Others studies indicate that this allele is mainly retained intracellularly decreasing cholesterol efflux and apoA1 binding ~75% [54]. Severe HDL deficiency [34] and premature CVD is caused by R587W [65]. 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 Aβ-peptide synthesis ~45% [66], but increase by ~2-fold (R587W) and by 25% (W590S) amyloid precursor protein intracellular domain, a major cytotoxic of AD [66].

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 [54]. In heterozygous, M1091T is lowered by 50% HDL and inhibits apoA1 binding and cholesterol efflux [54]. 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 [69]. 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, and apoA1 levels [70]. 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 [54] 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 [73]. Since the R1068H mutation is likely to produce a dysfunctional protein, one would expect it to be associated with FHA in the heterozygous state [73]. Residue R1068 is located in an α-helix of the Walker B motif in the NBD, vulnerable to interaction with D1092 and E1093 [74]. 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 [74]. 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 [31].

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 [76]. 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 [76]. 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 [54]. Besides, TD patients with D1289N variant were homozygous for a second mutation R2081W that could cause the shown pathological phenotype [79]. R2081W is missed at the plasma membrane and instead accumulated intracellularly [54]. 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, 8183]. While some reports suggest an association of R219K is with risk of CVD [84], 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 [87]. Unexpectedly, the K219 allele was associated with a decreased risk of myocardial infarction [18, 60, 84]. Also, this variant has effect on triglycerides [60] but not with HDL-c [84] or with apoA1 levels [85]. Otherwise, a study indicates that blood lipid levels do not seem to be R219K dependent [88]. 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 [1820, 89, 90]. Although conflicting results were noted, a study observed a protective dependence in delaying the risk of late-onset AD [18]. 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, 9193] but not all [39, 94] indicate that I883M variant severely increases the risk of atherosclerosis and AD [20]. The I883M has been reported as a milder phenotype with a significant reduction of HDL-c and cholesterol efflux (~70% of WT) [51]. 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 [97] 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 [101]. These results are confirmed after adjusting for gender, BMI, and waist circumference [102]. Besides, the C230 allele is associated with obesity, metabolic syndrome, and T2D in Mexican population [101]. Still, R230C may have conferred resistance against certain infectious diseases [101]. R230C has been reported as a rare variant causing FHA in an Oji-Cree individual [67]. 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 [76]. The A594T, I659V, T1512M, and R2004K polymorphisms display different degrees of mislocalization to the plasma membrane and slight impacts on cholesterol efflux [103]. These nsSNPs were identified in low-HDL subjects [29]. The A594T, I659V, and T1512M were predicted to be functionally neutral and the R2004K mutation possibly damaging [29]. 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 [104]. 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 noncoding 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.


  1. E. Pennisi, “ENCODE project writes eulogy for junk DNA,” Science, vol. 337, no. 6099, pp. 1159–1161, 2012. View at: Publisher Site | Google Scholar
  2. C. M. Yates and M. J. E. Sternberg, “The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions,” Journal of Molecular Biology, vol. 425, no. 21, pp. 3949–3969, 2013. View at: Publisher Site | Google Scholar
  3. 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. View at: Publisher Site | Google Scholar
  4. M. González-Castejón, F. Marín, C. Soler-Rivas, G. Reglero, F. Visioli, and A. Rodríguez-Casado, “Functional non-synonymous polymorphisms prediction methods: current approaches and future developments,” Current Medicinal Chemistry, vol. 18, no. 33, pp. 5095–5103, 2011. View at: Publisher Site | Google Scholar
  5. 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. View at: Google Scholar
  6. 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. View at: Publisher Site | Google Scholar
  7. D. R. Blair, C. S. Lyttle, and J. M. Mortensen, “A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk,” Cell, vol. 155, no. 1, pp. 70–80, 2013. View at: Publisher Site | Google Scholar
  8. 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. View at: Publisher Site | Google Scholar
  9. 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. View at: Google Scholar
  10. 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. View at: Publisher Site | Google Scholar
  11. 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. View at: Publisher Site | Google Scholar
  12. 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. View at: Google Scholar
  13. L. R. Brunham, J. K. Kruit, T. D. Pape et al., “β-cell ABCA1 influences insulin secretion, glucose homeostasis and response to thiazolidinedione treatment,” Nature Medicine, vol. 13, no. 3, pp. 340–347, 2007. View at: Publisher Site | Google Scholar
  14. 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. View at: Publisher Site | Google Scholar
  15. 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. View at: Publisher Site | Google Scholar
  16. 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. View at: Google Scholar
  17. B. Smith and H. Land, “Anticancer activity of the cholesterol exporter ABCA1 gene,” Cell Reports, vol. 2, no. 3, pp. 580–590, 2012. View at: Publisher Site | Google Scholar
  18. M. A. Wollmer, J. R. Streffer, D. Lütjohann et al., “ABCA1 modulates CSF cholesterol levels and influences the age at onset of Alzheimerߣs disease,” Neurobiology of Aging, vol. 24, no. 3, pp. 421–426, 2003. View at: Publisher Site | Google Scholar
  19. 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. View at: Publisher Site | Google Scholar
  20. 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. View at: Publisher Site | Google Scholar
  21. 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. View at: Publisher Site | Google Scholar
  22. 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. View at: Google Scholar
  23. 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. View at: Google Scholar
  24. F. Scheffel, U. Demmer, E. Warkentin, A. Hülsmann, 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. View at: Publisher Site | Google Scholar
  25. 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. View at: Publisher Site | Google Scholar
  26. 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. View at: Google Scholar
  27. 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. View at: Publisher Site | Google Scholar
  28. 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. View at: Publisher Site | Google Scholar
  29. 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. View at: Publisher Site | Google Scholar
  30. 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. View at: Google Scholar
  31. 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. View at: Publisher Site | Google Scholar
  32. 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. View at: Publisher Site | Google Scholar
  33. 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. View at: Publisher Site | Google Scholar
  34. 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. View at: Publisher Site | Google Scholar
  35. 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. View at: Publisher Site | Google Scholar
  36. 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. View at: Publisher Site | Google Scholar
  37. 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. View at: Publisher Site | Google Scholar
  38. 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. View at: Publisher Site | Google Scholar
  39. R. Frikke-Schmidt, B. G. Nordestgaard, G. B. Jensen, and A. Tybjærg-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. View at: Publisher Site | Google Scholar
  40. 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. View at: Publisher Site | Google Scholar
  41. 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. View at: Google Scholar
  42. 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. View at: Publisher Site | Google Scholar
  43. 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. View at: Publisher Site | Google Scholar
  44. 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. View at: Publisher Site | Google Scholar
  45. 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. View at: Publisher Site | Google Scholar
  46. 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. View at: Publisher Site | Google Scholar
  47. 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 1118; iso-2; iso-3,” Fly, vol. 6, no. 2, pp. 80–92, 2012. View at: Publisher Site | Google Scholar
  48. 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. View at: Publisher Site | Google Scholar
  49. 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. View at: Publisher Site | Google Scholar
  50. S. Chun and J. C. Fay, “Identification of deleterious mutations within three human genomes,” Genome Research, vol. 19, no. 9, pp. 1553–1561, 2009. View at: Publisher Site | Google Scholar
  51. 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. View at: Publisher Site | Google Scholar
  52. 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. View at: Google Scholar
  53. 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. View at: Publisher Site | Google Scholar
  54. 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. View at: Publisher Site | Google Scholar
  55. 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. View at: Publisher Site | Google Scholar
  56. 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. View at: Publisher Site | Google Scholar
  57. 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. View at: Publisher Site | Google Scholar
  58. R. Frikke-Schmidt, C. F. Sing, B. G. Nordestgaard, and A. Tybjærg-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. View at: Publisher Site | Google Scholar
  59. R. Frikke-Schmidt, B. G. Nordestgaard, P. Schnohr, and A. Tybjærg-Hansen, “Single nucleotide polymorphism in the low-density lipoprotein receptor is associated with a threefold risk of stroke: a case-control and prospective study,” European Heart Journal, vol. 25, no. 11, pp. 943–951, 2004. View at: Publisher Site | Google Scholar
  60. 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. View at: Publisher Site | Google Scholar
  61. 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. View at: Google Scholar
  62. 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. View at: Publisher Site | Google Scholar
  63. 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. View at: Publisher Site | Google Scholar
  64. 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. View at: Publisher Site | Google Scholar
  65. 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. View at: Publisher Site | Google Scholar
  66. 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-β production independent of cholesterol efflux activity,” Journal of Alzheimer's Disease, vol. 27, no. 2, pp. 441–452, 2011. View at: Publisher Site | Google Scholar
  67. 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. View at: Google Scholar
  68. 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. View at: Publisher Site | Google Scholar
  69. 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. View at: Publisher Site | Google Scholar
  70. 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. View at: Publisher Site | Google Scholar
  71. 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. View at: Publisher Site | Google Scholar
  72. R. P. Koldamova, I. M. Lefterov, M. Staufenbiel et al., “The liver X receptor ligaun T0901317 decreases amyloid β production in vitro and in a mouse model of Alzheimer's disease,” The Journal of Biological Chemistry, vol. 280, no. 6, pp. 4079–4088, 2005. View at: Publisher Site | Google Scholar
  73. T. L. Slatter, M. J. A. Williams, R. Frikke-Schmidt, A. Tybjærg-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. View at: Publisher Site | Google Scholar
  74. 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. View at: Publisher Site | Google Scholar
  75. 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. View at: Google Scholar
  76. 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. View at: Publisher Site | Google Scholar
  77. 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. View at: Publisher Site | Google Scholar
  78. 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. View at: Google Scholar
  79. 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. View at: Publisher Site | Google Scholar
  80. 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. View at: Publisher Site | Google Scholar
  81. 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. View at: Publisher Site | Google Scholar
  82. R. Frikke-Schmidt, B. G. Nordestgaard, G. B. Jensen, R. Steffensen, and A. Tybjærg-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. View at: Publisher Site | Google Scholar
  83. 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. View at: Publisher Site | Google Scholar
  84. 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. View at: Publisher Site | Google Scholar
  85. 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. View at: Publisher Site | Google Scholar
  86. 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. View at: Publisher Site | Google Scholar
  87. 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. View at: Publisher Site | Google Scholar
  88. 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. View at: Publisher Site | Google Scholar
  89. 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. View at: Publisher Site | Google Scholar
  90. F. Wavrant-De Vrièze, D. Compton, M. Womick et al., “ABCA1 polymorphisms and Alzheimer's disease,” Neuroscience Letters, vol. 416, no. 2, pp. 180–183, 2007. View at: Publisher Site | Google Scholar
  91. 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. View at: Publisher Site | Google Scholar
  92. 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. View at: Publisher Site | Google Scholar
  93. 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. View at: Google Scholar
  94. A. Sandhofer, B. Iglseder, S. Kaser, E. Morè, 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. View at: Publisher Site | Google Scholar
  95. 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. View at: Publisher Site | Google Scholar
  96. 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. View at: Publisher Site | Google Scholar
  97. I. Porchay-Baldérelli, F. Péan, 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. View at: Publisher Site | Google Scholar
  98. 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. View at: Publisher Site | Google Scholar
  99. 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. View at: Publisher Site | Google Scholar
  100. 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. View at: Google Scholar
  101. M. T. Villarreal-Molina, C. A. Aguilar-Salinas, M. Rodríguez-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. View at: Publisher Site | Google Scholar
  102. C. A. Aguilar-Salinas, S. Canizales-Quinteros, R. Rojas-Martínez 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. View at: Publisher Site | Google Scholar
  103. 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. View at: Publisher Site | Google Scholar
  104. S. H. Hong, J. Rhyne, K. Zeller, and M. Miller, “ABCA1Alabama: a novel variant associated with HDL deficiency and premature coronary artery disease,” Atherosclerosis, vol. 164, no. 2, pp. 245–250, 2002. View at: Publisher Site | Google Scholar
  105. 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. View at: Publisher Site | Google Scholar

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