• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Gene-based and pathway-based genome-wide association study of alcohol dependence

    2015-12-09 05:19:37LingjunZUOClarenceZHANGFrederickSAYWARDKeiHoiCHEUNGKeshengWANGJohnKRYSTALHongyuZHAOXingguangLUO
    上海精神醫(yī)學(xué) 2015年2期
    關(guān)鍵詞:胞外基質(zhì)基因組關(guān)聯(lián)

    Lingjun ZUO, Clarence K. ZHANG*, Frederick G. SAYWARD, Kei-Hoi CHEUNG, Kesheng WANG,John H. KRYSTAL, Hongyu ZHAO, Xingguang LUO*

    ?Original research article?

    Gene-based and pathway-based genome-wide association study of alcohol dependence

    Lingjun ZUO1, Clarence K. ZHANG2,3*, Frederick G. SAYWARD4,5, Kei-Hoi CHEUNG4, Kesheng WANG6,John H. KRYSTAL1, Hongyu ZHAO2, Xingguang LUO1*

    gene-based GWAS; pathway-based GWAS; cell-extracellular matrix interaction pathway;PXN;paxillin; alcohol dependence

    1. Introduction

    Conventional genome-wide association studies(GWASs) focused on the impact of single nucleotide polymorphisms (SNPs) have identified a large number of significant or suggestive risk genes for alcohol dependence and alcohol consumption.[1-7]However,single-SNP analysis often identifies only a few of the most significant SNPs of the genome and they can only explain a small proportion of the genetic risk for diseases. Accumulating evidence suggests that susceptibility to alcohol dependence emerges from a complex interplay of variants within genes, genomic regions, or gene pathways.[8]Gene variants that individually contribute slightly to alcoholism risk but that may have a more important effect in moderating the impact of other risk genes may be missed by the single-SNP analytic strategy.[9]This problem may be reduced by employing gene-based and pathway-based analytic approaches.

    Gene- and pathway-based methods have many advantages over the single-SNP approach.[9,10]First,the functions of many individual SNPs are not wellcharacterized but the functions of whole genes and particular gene pathways are more clearly characterized;many functional studies (e.g., gene expression studies)have been conducted at the gene or pathway level making it possible to assess the association of biological functions with specific genes and pathways. Second,locus heterogeneity (i.e., alleles at different loci cause diseases in different populations) make it difficult to replicate association findings for a single marker, but replication at the gene or pathway level might still be possible when locus heterogeneity exists because a gene or pathway can harbor multiple alleles of the heterogeneous risk markers. Finally, because the numbers of genes and pathways across the genome are much less than the number of single markers, genebased and pathway-based analyses can substantially reduce the number of comparisons considered and,thus, lead to better statistical power.

    In the present study, we aimed to identify the risk genes for alcohol dependence and the pathways that are enriched in alcohol dependence-related genes. In view of the fact that the effects of an entire gene that integrates many SNPs would be different from those of a single SNP, and the effects of an entire pathway that integrates many genes would be different from those of a single gene, it is anticipated that the results from gene-based analyses might not be completely consistent with those from pathway-based analyses that use the same dataset, and, similarly, the results from geneand pathway-based analyses might not be completely consistent with those from SNP-based analyses in previous GWASs on the same datasets. In other words,gene- and pathway-based analysis may lead to novel findings.

    2. Materials and Methods

    2.1 Subjects

    The identi fication of the GWAS data used in this analysis is shown in Figure 1. Data from 1409 European-American(EA) cases with alcohol dependence (based on DSM-IV criteria),1518 EA healthy controls,681 African-American(AA) cases, and 508 AA healthy controls were included in this analysis. Detailed demographic data on these subjects were presented in previous GWASs.[11,12]These data came from the merged SAGE (Study of Addiction:Genetics and Environment) and COGA (Collaborative Study On The Genetics of Alcoholism) datasets, which are available on the database of Genotypes and Phenotypes(dbGaP)( https://www.ncbi.nlm.nih.gov/gap). The SAGE dataset (dbGaP access number: phs000092.v1.p1) included COGA, COGEND (Collaborative Genetic Study of Nicotine Dependence), and FSCD (Family Study of Cocaine Dependence) subsets. This COGA subset included in the SAGE dataset was a subset of the main dbGaP COGA dataset (access number:phs000125.v1.p1), so when we merged the SAGE and COGA datasets, one copy of 1477 overlapping subjects were excluded.[11]The projects that collected these data were all approved by the respective institutional review boards, all subjects participating in the projects provided written informed consent, and the current analysis was approved by the institutional review board of Yale University.

    Figure 1. Enrollment of subjects in the study

    2.2 Genotyping

    All subjects were genotyped on the Illumina Human 1M beadchip. Phenotype and genotype data were rigorously cleaned before association analysis. Subjects with poor genotypic data, allele discordance, problematic sample identification (relatedness, misidentification,misspecification), duplicated identifiers, gender or chromosomal anomalies, ethnicity issues (including missing information, non-EA or AA, mismatch between self- and genetically-inferred ethnicity), or with a missing genotype call rate ≥2% across all SNPs were excluded. Furthermore, SNPs with allele discordance,chromosomal anomalies or batch effect, SNPs with an overall missing genotype call rate ≥2%, monomorphic SNPs, SNPs with minor allele frequencies <0.01 in either EAs or AAs, and SNPs that deviated from Hardy-Weinberg equilibrium (p<10?4) within EA or AA controls were also excluded. This selection process yielded 805,814 SNPs in EAs and 895,714 SNPs in AAs.[11,12]

    2.3 Statistical methods

    The genotyping data on autosomes were extracted from an Oracle database and stored efficiently in flat files for gene- and pathway-based analysis.

    2.3.1 Gene-based GWAS analysis

    The genotype was con figured into a genotype score of 1, 2, or 3: 1 represented a minor allele homozygote, 2 represented a heterozygote, and 3 represented a major allele homozygote. SNPs were mapped to known genes/exons/introns boundaries obtained from the National Center for Biotechnology Information (NCBI). Principal component analysis was applied to the SNPs within the defined gene boundary and then the components which explained at least 85% of the variation were used as explanatory variables in the regression to explain disease status. The disease status was de fined as 2 for alcohol dependence and 1 for healthy control. The gene level score was defined as the p-value for the genebased association from this multiple regression.

    Gene flanking is defined as increasing the SNPs associated with a gene by extending the gene region by a number of bases in the 5’ and 3’ directions. By doing this, SNPs that may be involved in the transcription process are considered in the analysis. In the discovery analysis in EAs, 50Kb flanking regions were chosen. The 10Kb flanking regions were also explored for top-ranked genes. The top-ranked risk genes identi fied in EAs were also replicated in AAs (with 50Kb flanking regions).

    2.3.2 Pathway-based GWAS analysis

    Pathway annotation was obtained from the collection of pathways curated by the Molecular Signatures database(MSigDB) using seven public databases: BioCarta, Gene arrays, BioSciences Corp, KEGG, REACTOME, Sigma-Aldrich pathways, Signal transduction knowledge environment, and Signaling gateway (http://www.broadinstitute.org/gsea/msigdb/collection_details.jsp#CP). The gene set enrichment method was used to determine the pathway enrichment.[13]The test statistic was calculated as the negative sum of the log p-values for each gene assigned to the pathway. The enrichment was determined by randomly permuting the gene scores (5000 times) and recalculating the test statistic for each pathway. The p-value of each pathway was the percentage of the permuted test statistics larger than the observed p-value. The top-ranked pathways identified in EAs were replicated in AAs (with 50Kb flanking regions).

    2.3.3 Correction for multiple testing in gene- and pathway-based GWAS analyses

    A total of 26 307 genes and 221 pathways were analyzed. The significance levels (α) for gene- and pathway-based GWAS tests were corrected by the Bonferroni correction and, thus, set at 1.9E6 and 2.3E4,respectively. P-values larger than α but less than 0.05 were labelled as ‘nominally signi ficant’.

    3. Results

    A total of 2464 genes were nominally associated with alcohol dependence in EAs (p<0.05). The 20 top-ranked risk genes (based on the level of statistical signi ficance)are listed in Table 1. After correction for multiple testing (α=1.9E-6), the paxillin gene (PXN) (±50kb)was significantly associated with alcohol dependence(p=3.9E-7). If flanking regions were reduced to ±10kb,PXN(±10kb) remained significantly associated with alcohol dependence in EAs (p<E-8), and the other 19 top-ranked risk genes remained nominally significant(p<0.05).

    Table 1. Top-ranked and replicable genes for alcohol dependence

    Among the 2464 nominally associated genes in EA,129 were nominally replicable in AAs (p<0.05) (data not shown). As shown in Table 1, six of these genes (ZNF256,CPLX2, LOC646820, SLC38A1, PGBD3, andAP3S2) were associated with alcohol dependence at thep<0.01 level in both EAs and AAs. Only one of these six genes,SLC38A1, is a component of a nominally significant pathway (the ‘a(chǎn)mino acid transport across the plasma membrane’ pathway, pathway #18 in Table 2). Among the other 123 nominally replicable genes, only two genes are components of top-ranked pathways:BADbelongs to the ‘VEGF signaling’ pathway (pathway #4 in Table 2) andIQSEC3belongs to the ‘endocytosis’pathway (pathway #6 in Table 2).

    Twenty pathways enriched in alcohol dependencerelated genes in EAs are listed in Table 2, including the 17 top-ranked pathways (based on the level of statistical signi ficance) and 3 other important pathways; pathway#18 (the ‘a(chǎn)mino acid transport across the plasma membrane’ pathway) is the only nominally significant pathway that contains one of the six replicable genes with p-values <0.01 shown in Table 1 (SLC38A1),pathway #19 was previously reported to be related to addiction, and pathway #20 was nominally replicable in both EA and AA. Using 50kb flanking regions in the analysis of EAs, the top-ranked (#1) risk pathway was the‘cell-extracellular matrix interactions’ pathway (RSU1,LIMS1, LIMS2, ARHGEF6, FERMT2, ACTN1, BLIM1, FLNC,ITGB1, PXN, FLNA, VASP, ILK, TESK1, PARVB,andPARVA)(p<2.0E-4). Two other pathways of particular interest were the ‘VEGF signaling’ pathway (#4) (PXN, BAD,HRAS, NRAS, et al.) (p=1.4E-3) because it contains the nominally replicableBADgene and the ‘endocytosis’pathway (#6) (IQSEC3, HRAS, et al.) (p=7.4E-3) because it contains the nominally replicableIQSEC3gene.

    After correction for multiple testing, the only pathway that remained significantly associated with alcohol dependence (p<2.3E-4) was pathway #1. If 10kb flanking regions were set, the association of all of the listed pathways with alcohol dependence in EAs remained nominally signi ficant (p<0.05), but none of them were statistically significant after correction for multiple testing. The two pathways most strongly associated with alcohol dependence when using 10kb flanking regions were the ‘Na+/Cl- dependent neurotransmitter transporters’ pathway (#15) (SLC6A1,SLC6A2, SLC6A3, SLC6A5, SLC6A6, SLC6A7, SLC6A9,SLC6A11, SLC6A12, SLC6A13, SLC6A14, SLC6A15,SLC6A18, SLC6A19, SLC6A20, SLC18A1, SLC18A2, andSLC22A2) and the ‘a(chǎn)mino acid transport across the plasma membrane’ pathway (#18) (SLC1A4, SLC1A5,SLC3A1, SLC3A2, SLC6A6, SLC6A12, SLC6A14, SLC6A15,SLC6A18, SLC6A19, SLC6A20, SLC7A1, SLC7A2, SLC7A3,SLC7A5, SLC7A6, SLC7A7, SLC7A8, SLC7A9, SLC7A10,SLC7A11, SLC16A10, SLC36A1, SLC36A2, SLC38A1,SLC38A2, SLC38A3, SLC38A4, SLC38A5, SLC43A1, andSLC43A2) (both p=1.8E-3).

    As shown in Table 2, there were 2 nominally replicable pathways (based on 50kb flanking) enriched in alcohol dependence-related genes in both EAs(0.015≤p≤0.035) and AAs (0.025≤p≤0.050): the‘Na+/Cl- dependent neurotransmitter transporters’pathway (#15) (speci fied above), and the ‘other glycan degradation’ pathway (#20) (AGA, HEXA, HEXB, ENGASE,FUCA2, FUCA1, MANBA, GLB1, MAN2C1, MAN2B2,NEU1, NEU3, MAN2B1, NEU2, GBA, andNEU4).

    4. Discussion

    4.1 Main findings

    In the present study, we found significant genomewide replicable risk genes and risk pathways that were associated with alcohol dependence. Incorporating the biological, bioinformatic, statistical, and association evidence with previous reports of these genes and pathways, the ‘cell-extracellular matrix interactions’pathway (#1) and thePXNgene (which encodes paxillin) were the most promising risk factors for alcohol dependence; their association with alcohol dependence remained statistically significant after adjusting for multiple testing using the Bonferroni correction.

    The ‘cell-extracellular matrix (ECM) interactions’pathway plays a critical role in regulating a variety of cellular processes in multi-cellular organisms including motility, shape change, survival, proliferation, and differentiation. Cell-ECM contact is mediated by transmembrane cell adhesion receptors (integrins)that interact with extracellular matrix proteins and cytoplasmic adaptor proteins. Many of these adaptor proteins physically interact with the actin cytoskeleton or function in signal transduction.[14]Paxillin is an important component of this pathway that binds directly to α-integrins.

    ThePXNgene was significantly associated with alcohol dependence in the present study, suggesting the possible role of paxillin in alcoholism. Paxillin is expressed in multiple tissues (including the brain) where it acts as a multidomain scaffolding protein for bringing together signaling molecules, structural components,and regulatory proteins that control the adhesion and organization of the internal cytoskeleton for processes such as cell migration (reviewed in[15]).

    Paxillin is also a component of the ‘VEGF signaling’pathway (#4). This pathway is enriched in alcohol dependence-related genes in EAs, though the association(p=1.4E-3) does not reach our criteria for statistical significance. This pathway has been implicated in stress reactivity and in the symptoms of mood disorders,[16]potential contributors to the risk for alcohol dependence.[17]It has also been associated with drug addiction (including alcoholism) (p=3.2E-3)in a previous report.[18]Interestingly, theBAD(BCL2-associated agonist of cell death) gene also belongs to this pathway; we found a strong, but not statistically significant, association ofBADto alcohol dependence both in EAs and AAs, supporting the possible role of the‘VEGF signaling’ pathway in alcohol dependence.

    Table 2. Top-ranked and replicable risk pathways for alcohol dependence

    Pathways comprehensively integrate information from multiple genes. The complexity of pathway structure makes the replicability of pathway-wise associations very difficult. Replications between homogeneous samples may be relatively common,but replications between genetically heterogeneous samples, such as that between EAs and AAs, would be relatively uncommon. Therefore, replications of pathway-disease associations between EAs and AAs may indicate a functional relationship between the specific pathways and the disease of interest. We identified two replicable pathways for associations with alcohol dependence across EAs and AAs: the‘Na+/Cl- dependent neurotransmitter transporters’pathway (pathway #15 in Table 2) and the ‘other glycan degradation’ pathway (pathway #20 in Table 2). Among all pathways we studied, pathway #15 had the strongest association with alcohol dependence when 10Kb flanking regions were set (p=1.8E-3). All the genes within this pathway are neurotransmitter transporter genes, encoding proteins that mediate neurotransmitter uptake and, thus, terminate a synaptic signal. These transporters are mainly present in the central and peripheral nervous systems[19]where they mediate transport of GABA (gamma-aminobutyric acid),norepinephrine, dopamine, serotonin, glycine, taurine,L-proline, creatine, and betaine. These genes have been associated with several neuropsychiatric conditions;for example,SLC6A3(the dopamine transporter gene,DAT1) andSLC18A2(the monoamine transporter gene)have been associated with alcohol dependence[20-26]and smoking.[27-31]

    Another pathway of interest is pathway #18 (the‘a(chǎn)mino acid transport across the plasma membrane’pathway) that had a non-significant enrichment of alcohol dependence-related genes in EAs. This pathway was not replicable in AAs, but it contained an important gene,SLC38A1, that was replicable in both the EA population (p=5.7E-3) and the AA population (p=9.9E-3).All genes within this pathway belong to the solute carrier (SLC) family, including amino acid transporter genes which encode proteins that transport amino acid across plasma membranes. These proteins are critical to the uptake of amino acids from the gut, from the renal proximal tubules, and in cells throughout the body where amino acids are required for neurotransmission and for the synthesis of proteins and metabolic intermediates.[32]This pathway is a component of the 18 systems identi fied in physiological studies that mediate amino acid transport, each characterized by its amino acid substrates, its pH sensitivity, and its association(or not) with ion transport.[33]TheSLC38A1(amino acid transporter A1) gene within pathway #18 plays an essential role in the uptake of nutrients, production of energy, chemical metabolism, detoxification, and neurotransmitter cycling. It is an important transporter of glutamine – an intermediate in the detoxification of ammonia and in the production of urea. Glutamine serves as a precursor for the synaptic transmitters glutamate and GABA, both of which have been implicated in the neurobiology of alcohol intoxication and withdrawal.[34]Moreover, glutamate and GABA signaling pathways have been associated with alcohol dependence in a recent pathway-based association study.[35]

    Several other top-ranked pathways identi fied in EAs in our study have also been identi fied as potential risk factors for drug addiction and alcoholism in previous reports.[18]These include the ‘long term depression (LTD)’pathway (#12) (p=2.2E-2 in our study, and p=2.1E-7 in a previous study[18]); the ‘Fc epsilon RI signaling’ pathway(#13) (p=2.3E-2 in our study, and p=6.9E-3 in a previous study[18]); and the ‘a(chǎn)myotrophic lateral sclerosis’pathway (#19) (p=5.8E-3 in our study, and p=3.9E-5 in a previous study[18]). Cerebellar LTD is thought to be a molecular and cellular basis for cerebellar learning which promotes the type of neuroplasticity that underlies development and recovery from addiction; a hypothesis that is supported by the finding that many molecular substrates of addiction are shared with other forms of learning.[36,37]Moreover, the LTD pathway has also been found to be enriched in genes associated with smoking cessation, a close phenotype to alcohol dependence.[38]The ‘Fc epsilon RI signaling’ pathway (#13) in mast cells is initiated by the interaction of an antigen (Ag) with IgE which is bound to the extracellular component of the alpha chain of Fc epsilon RI; the activated pathway is regulated both positively and negatively by the interactions of numerous signaling molecules. Activated mast cells release preformed granules containing biogenic amines, especially histamines—the chemicals that regulate alcohol-related behaviors in the brain.[39,40]The ‘a(chǎn)myotrophic lateral sclerosis (ALS)’ pathway (#19)may be involved in glutamate dysregulation, oxidative stress, and mitochondrial damage which may, in turn, be associated with the development of alcohol dependence[34]and alcohol-related neurotoxicity.[41]

    4.2 Limitations

    With the exception of a significant association of the PXN gene and the ‘cell-extracellular matrix interactions’ pathway in EAs, none of the other topranked risk genes or risk pathways identified in the present study remained significantly associated with alcohol dependence after the results were adjusted for multiple testing using the Bonferroni correction. Further replication studies with even larger samples will be needed to con firm or disprove their relevance to alcohol dependence.

    4.3 Implications

    In summary, a gene- and pathway-based reanalysis of prior GWAS data provides new evidence highlighting several genes and biological signaling processes that may be related to the risk for alcohol dependence. These pathways converge on glutamate neurotransmission, a process previously implicated in both the neurobiology and treatment of alcoholism. These findings may be helpful in linking genes implicated in the heritable risk for alcohol dependence to this underlying neurobiology.

    Acknowledgments

    We thank the NIH GWAS Data Repository, the contributing investigator who provided the phenotype and genotype data from their original studies, and the primary funding organization that supported the contributing study. The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?Db=gap through dbGaP accession numbers phs000092.v1.p1 and phs000125.v1.p1.

    Con flict of interest

    None of the authors report any conflict of interest related to this manuscript.

    Funding

    This work was supported in part by National Institute on Drug Abuse (NIDA) grants K01 DA029643 and R01DA016750, National Institute on Alcohol Abuse and Alcoholism (NIAAA) grants R21 AA021380 and R21 AA020319, the National Alliance for Research on Schizophrenia and Depression (NARSAD) Award 17616 (L.Z.) and ABMRF/The Foundation for Alcohol Research (L.Z.). Funding and other supports for phenotype and genotype data were provided through the National Institutes of Health (NIH) Genes,Environment and Health Initiative (GEI) (U01HG004422,U01HG004436 and U01HG004438); the GENEVA Coordinating Center (U01HG004446); the NIAAA(U10AA008401, R01AA013320, P60AA011998); the NIDA (R01DA013423); the National Cancer Institute(P01 CA089392); the NIH contract ‘High throughput genotyping for studying the genetic contributions to human disease’ (HHSN268200782096C); the Center for Inherited Disease Research (CIDR); and the National Center for Biotechnology Information. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research.

    Ethics approval

    The protocols described in the paper were all approved by the relevant institutional review boards. All subjects were de-identified in this study and the study was approved by the institutional review board at Yale University.

    Informed consent

    All subjects provided written informed consent to participate in the projects at each of the participating institutions.

    1. Treutlein J, Cichon S, Ridinger M, Wodarz N, Soyka M,Zill P, et al. Genome-wide association study of alcohol dependence.Arch Gen Psychiatry. 2009; 66(7): 773-784. doi:http://dx.doi.org/10.1001/archgenpsychiatry.2009.83

    2. Bierut LJ, Agrawal A, Bucholz KK, Doheny KF, Laurie C,Pugh E, et al. A genome-wide association study of alcohol dependence.Proc Natl Acad Sci U S A. 2010; 107(11): 5082-5087. doi: http://dx.doi.org/10.1073/pnas.0911109107

    3. Edenberg HJ, Koller DL, Xuei X, Wetherill L, McClintick JN,Almasy L, et al. Genome-wide association study of alcohol dependence implicates a region on chromosome 11.Alcohol Clin Exp Res. 2010; 34(5): 840-852. doi: http://dx.doi.org/10.1111/j.1530-0277.2010.01156.x

    4. Heath AC, Whitfield JB, Martin NG, Pergadia ML, Goate AM,Lind PA, et al. A quantitative-trait genome-wide association study of alcoholism risk in the community: findings and implications.Biological Psychiatry. 2011; 70(6): 513-518. doi:http://dx.doi.org/10.1016/j.biopsych.2011.02.028

    5. Schumann G, Coin LJ, Lourdusamy A, Charoen P, Berger KH, Stacey D, et al. Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption.Proc Natl Acad Sci U S A. 2011; 108(17): 7119-7124. doi: http://dx.doi.org/10.1073/pnas.1017288108

    6. Zuo L, Lu L, Tan Y, Pan X, Cai Y, Wang X, et al. Genome-wide association discoveries of alcohol dependence.Am J Addict.2014; 23: 526-539. doi: http://dx.doi.org/10.1111/j.1521-0391.2014.12147.x

    7. Zuo L, Wang K, Zhang X, J.H. K, Li CR, Zhang F, et al. NKAIN1-SERINC2 is a functional, replicable and genome-wide significant risk region specific for alcohol dependence in subjects of European descent.Drug Alcohol Depend.2013; 129: 254-264. doi: http://dx.doi.org/10.1016/j.drugalcdep.2013.02.006

    8. Gui H, Li M, Sham PC, Cherny SS. Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn’s Disease dataset.BMC Res Notes. 2011; 4: 386. doi: http://dx.doi.org/10.1186/1756-0500-4-386

    9. Peng G, Luo L, Siu H, Zhu Y, Hu P, Hong S, et al. Gene and pathway-based second-wave analysis of genome-wide association studies.Eur J Hum Genet. 2010; 18(1): 111-117.doi: http://dx.doi.org/10.1038/ejhg.2009.115

    10. Zamar D, Tripp B, Ellis G, Daley D. Path: a tool to facilitate pathway-based genetic association analysis.Bioinformatics.2009; 25(18): 2444-2446. doi: http://dx.doi.org/10.1093/bioinformatics/btp431

    11. Zuo L, Gelernter J, Zhang CK, Zhao H, Lu L, Kranzler HR, et al. Genome-wide association study of alcohol dependence implicates KIAA0040 on chromosome 1q.Neuropsychopharmacology. 2012; 37(2): 557-566. doi:http://dx.doi.org/10.1038/npp.2011.229

    12. Zuo L, Zhang CK, Wang F, Li CS, Zhao H, Lu L, et al. A novel,functional and replicable risk gene region for alcohol dependence identified by genome-wide association study.PLoS One. 2011; 6(11): e26726. doi: http://dx.doi.org/10.1371/journal.pone.0026726

    13. Ballard D, Abraham C, Cho J, Zhao H. Pathway analysis comparison using Crohn’s disease genome wide association studies.BMC Med Genomics. 2010; 3: 25

    14. Sepulveda JL, Gkretsi V, Wu C. Assembly and signaling of adhesion complexes.Curr Top Dev Biol. 2005; 68: 183-225.doi: http://dx.doi.org/10.1016/S0070-2153(05)68007-6

    15. Deakin NO, Turner CE. Paxillin comes of age. J Cell Sci. 2008;121(Pt 15): 2435-2444. doi: http://dx.doi.org/10.1242/jcs.018044

    16. Newton SS, Duman RS. Regulation of neurogenesis and angiogenesis in depression.Curr Neurovasc Res. 2004; 1(3):261-267. doi: http://dx.doi.org/10.2174/1567202043362388

    17. Sinha R, Li CS. Imaging stress- and cue-induced drug and alcohol craving: association with relapse and clinical implications.Drug Alcohol Rev. 2007; 26(1): 25-31 18. Li CY, Mao X, Wei L. Genes and (common) pathways underlying drug addiction.PLoS Comput Biol. 2008; 4(1): e2.doi: http://dx.doi.org/10.1371/journal.pcbi.0040002

    19. Chen NH, Reith ME, Quick MW. Synaptic uptake and beyond:the sodium- and chloride-dependent neurotransmitter transporter family SLC6.Pflugers Arch. 2004; 447(5): 519-531

    20. Lind PA, Eriksson CJ, Wilhelmsen KC. Association between harmful alcohol consumption behavior and dopamine transporter (DAT1) gene polymorphisms in a male Finnish population.Psychiatr Genet. 2009; 19(3): 117-125. doi:http://dx.doi.org/10.1097/YPG.0b013e32832a4f7b

    21. Wernicke C, Smolka M, Gallinat J, Winterer G, Schmidt LG, Rommelspacher H. Evidence for the importance of the human dopamine transporter gene for withdrawal symptomatology of alcoholics in a German population.Neurosci Lett. 2002; 333(1): 45-48

    22. Gorwood P, Limosin F, Batel P, Hamon M, Ades J, Boni C. The A9 allele of the dopamine transporter gene is associated with delirium tremens and alcohol-withdrawal seizure.Biol Psychiatry. 2003; 53(1): 85-92

    23. Ueno S, Nakamura M, Mikami M, Kondoh K, Ishiguro H,Arinami T, et al. Identification of a novel polymorphism of the human dopamine transporter (DAT1) gene and the signi ficant association with alcoholism.Mol Psychiatry. 1999;4(6): 552-557

    24. Vaske J, Beaver KM, Wright JP, Boisvert D, Schnupp R. An interaction between DAT1 and having an alcoholic father predicts serious alcohol problems in a sample of males.Drug Alcohol Depend. 2009; 104(1-2): 17-22. doi: http://dx.doi.org/10.1016/j.drugalcdep.2009.01.020

    25. Kohnke MD, Batra A, Kolb W, Kohnke AM, Lutz U, Schick S,et al. Association of the dopamine transporter gene with alcoholism.Alcohol Alcohol.2005; 40(5): 339-342

    26. Schwab SG, Franke PE, Hoefgen B, Guttenthaler V,Lichtermann D, Trixler M, et al. Association of DNA polymorphisms in the synaptic vesicular amine transporter gene (SLC18A2) with alcohol and nicotine dependence.Neuropsychopharmacology.2005; 30(12): 2263-2268

    27. Ling D, Niu T, Feng Y, Xing H, Xu X. Association between polymorphism of the dopamine transporter gene and early smoking onset: an interaction risk on nicotine dependence.J Hum Genet. 2004; 49(1): 35-39

    28. Sieminska A, Buczkowski K, Jassem E, Niedoszytko M,Tkacz E. Influences of polymorphic variants of DRD2 and SLC6A3 genes, and their combinations on smoking in Polish population.BMC Med Genet. 2009; 10: 92. doi: http://dx.doi.org/10.1186/1471-2350-10-92

    29. Timberlake DS, Haberstick BC, Lessem JM, Smolen A,Ehringer M, Hewitt JK, et al. An association between the DAT1 polymorphism and smoking behavior in young adults from the National Longitudinal Study of Adolescent Health.Health Psychol. 2006; 25(2): 190-197

    30. Stapleton JA, Sutherland G, O’Gara C. Association between dopamine transporter genotypes and smoking cessation: a meta-analysis.Addict Biol. 2007; 12(2): 221-226. doi: http://dx.doi.org/10.1111/j.1369-1600.2007.00058.x

    31. Uhl GR, Drgon T, Johnson C, Walther D, David SP, Aveyard P, et al. Genome-wide association for smoking cessation success: participants in the Patch in Practice trial of nicotine replacement.Pharmacogenomics. 2010; 11(3): 357-367. doi:http://dx.doi.org/10.2217/pgs.09.156

    32. Yu N, Seo J, Rho K, Jang Y, Park J, Kim WK, et al. hiPathDB:a human-integrated pathway database with facile visualization.Nucleic Acids Res. 2012; 40(Database issue):D797-802. doi: http://dx.doi.org/10.1093/nar/gkr1127

    33. Broer S. Amino acid transport across mammalian intestinal and renal epithelia.Physiol Rev. 2008; 88(1): 249-286. doi:http://dx.doi.org/10.1152/physrev.00018.2006

    34. Krystal JH, Petrakis IL, Mason G, Trevisan L, D’Souza DC.N-methyl-D-aspartate glutamate receptors and alcoholism:reward, dependence, treatment, and vulnerability.Pharmacol Ther. 2003; 99(1): 79-94

    35. Reimers MA, Riley BP, Kalsi G, Kertes DA, Kendler KS.Pathway based analysis of genotypes in relation to alcohol dependence.Pharmacogenomics J. 2011; 12(4): 342-348.doi: http://dx.doi.org/10.1038/tpj.2011.1010

    36. Knackstedt LA, Moussawi K, Lalumiere R, Schwendt M,Klugmann M, Kalivas PW. Extinction training after cocaine self-administration induces glutamatergic plasticity to inhibit cocaine seeking.J Neurosci. 2010; 30(23): 7984-7992. doi:http://dx.doi.org/10.1523/JNEUROSCI.1244-10.2010

    37. Nestler EJ. Common molecular and cellular substrates of addiction and memory.Neurobiol Learn Mem. 2002; 78(3):637-647

    38. Wang J, Li MD. Common and unique biological pathways associated with smoking initiation/progression,nicotine dependence, and smoking cessation.Neuropsychopharmacology. 2010; 35(3): 702-719. doi:http://dx.doi.org/10.1038/npp.2009.178

    39. Zimatkin SM, Anichtchik OV. Alcohol-histamine interactions.Alcohol Alcohol. 1999; 34(2): 141-147. doi: http://dx.doi.org/10.1093/alcalc/34.2.141

    40. Panula P, Nuutinen S. Histamine and H3 receptor in alcoholrelated behaviors.J Pharmacol Exp Ther. 2011; 336(1): 9-16.doi: http://dx.doi.org/10.1124/jpet.110.170928

    41. Tsai GE, Ragan P, Chang R, Chen S, Linnoila VM, Coyle JT.Increased glutamatergic neurotransmission and oxidative stress after alcohol withdrawal.Am J Psychiatry. 1998;155(6): 726-732

    , 2015-03-01; accepted, 2015-03-23)

    Dr. Lingjun Zuo has been working in the Department of Psychiatry, Yale University School of Medicine since 2001. She is currently the Director of the Psychiatric Genetics Lab (ZUO) in this department and a faculty member at Yale University. Her research interests are the genetics and epigenetics of psychiatric disorders and related behaviors.

    酒精依賴的基于基因和基于通路的全基因組關(guān)聯(lián)研究

    Zuo L J , Zhang CK, Sayward FG, Cheung KH, Wang KS, Krystal JH, Zhao HY, Luo XG

    基于基因的全基因組關(guān)聯(lián)分析;基于通路的全基因組關(guān)聯(lián)分析;細(xì)胞-細(xì)胞外基質(zhì)相互作用的通路;PXN;樁蛋白;酒精依賴

    Background: The organization of risk genes within signaling pathways may provide clues about the converging neurobiological effects of risk genes for alcohol dependence.Aims: Identify risk genes and risk gene pathways for alcohol dependence.Methods: We conducted a pathway-based genome-wide association study (GWAS) of alcohol dependence using a gene-set-rich analytic approach. Approximately one million genetic markers were tested in the discovery sample which included 1409 European-American (EA) alcohol dependent individuals and 1518 EA healthy comparison subjects. An additional 681 African-American (AA) cases and 508 AA healthy subjects served as the replication sample.Results: We identified several genome-wide replicable risk genes and risk pathways that were significantly associated with alcohol dependence. After applying the Bonferroni correction for multiple testing, the ‘cellextracellular matrix interactions’ pathway (p<2.0E-4 in EAs) and thePXNgene (which encodes paxillin)(p=3.9E-7 in EAs) within this pathway were the most promising risk factors for alcohol dependence. There were also two nominally replicable pathways enriched in alcohol dependence-related genes in both EAs(0.015≤p≤0.035) and AAs (0.025≤p≤0.050): the ‘Na+/Cl- dependent neurotransmitter transporters’ pathway and the ‘other glycan degradation’ pathway.Conclusions: These findings provide new evidence highlighting several genes and biological signaling processes that may be related to the risk for alcohol dependence.

    [Shanghai Arch Psychiatry. 2015; 27(2): 111-118.

    http://dx.doi.org/10.11919/j.issn.1002-0829.215031]

    1Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States

    2Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, United States

    3Biostatistics Resource, Keck Laboratory, Department of Genetics, Yale University School of Medicine, New Haven, CT, United States

    4Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, United States

    5Cooperative Studies Program Coordinating Center, VA Connecticut Healthcare System, West Haven, CT, United States.

    6Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN, United States

    *correspondence: XG Luo, xingguang.luo@yale.edu; CK Zhang, kangyuzhang@hotmail.com

    背景:信號通路中風(fēng)險基因的構(gòu)成可能可以解釋酒精依賴風(fēng)險基因協(xié)同的神經(jīng)生物學(xué)作用。目的:識別酒精依賴的風(fēng)險基因和風(fēng)險基因通路。方法:我們采用基因富集(gene-set-rich)分析方法對酒精依賴進(jìn)行了基于通路的全基因組關(guān)聯(lián)分析(GWAS)。在包括1409名歐裔美國人(European-American,EA)酒精依賴者和1518 名EA健康對照者的探索性樣本人群中檢測了近一百萬個基因標(biāo)志物。此外,將681名非裔美國人(African-American, AA)病例和508名 AA健康受試者作為重測樣本。結(jié)果:我們發(fā)現(xiàn)了幾個與酒精依賴顯著相關(guān)的可重復(fù)的全基因組風(fēng)險基因和風(fēng)險通路。在多重比較Bonferroni校正后,“細(xì)胞 - 細(xì)胞外基質(zhì)相互作用”通路(EA樣本中p<2.0E-4)和該通路中PXN基因(編碼樁蛋白paxillin)(EA 樣本中p=3.9E-7)是最有可能的酒精依賴的危險因素。在EA樣本(0.015≤p≤0.035)和AA樣本(0.025≤p≤0.050)中還有兩條富含酒精依賴相關(guān)基因的可重復(fù)的通路:“Na+/ Cl-依賴性神經(jīng)遞質(zhì)轉(zhuǎn)運(yùn)體”通路和“其他聚糖降解”通路。結(jié)論:一些基因和生物信號傳導(dǎo)過程可能與酒精依賴的風(fēng)險相關(guān),本研究的發(fā)現(xiàn)為此提供了新的證據(jù)。

    本文全文中文版從2015年06月06日起在http://dx.doi.org/10.11919/j.issn.1002-0829.215031可供免費(fèi)閱覽下載

    猜你喜歡
    胞外基質(zhì)基因組關(guān)聯(lián)
    牛參考基因組中發(fā)現(xiàn)被忽視基因
    脫細(xì)胞外基質(zhì)制備與應(yīng)用的研究現(xiàn)狀
    關(guān)于經(jīng)絡(luò)是一種細(xì)胞外基質(zhì)通道的假說
    “一帶一路”遞進(jìn),關(guān)聯(lián)民生更緊
    奇趣搭配
    智趣
    讀者(2017年5期)2017-02-15 18:04:18
    水螅細(xì)胞外基質(zhì)及其在發(fā)生和再生中的作用
    基因組DNA甲基化及組蛋白甲基化
    遺傳(2014年3期)2014-02-28 20:58:49
    有趣的植物基因組
    鐮形棘豆總黃酮對TGF-β1誘導(dǎo)的人腎小管上皮細(xì)胞分泌細(xì)胞外基質(zhì)成分的影響
    一本一本综合久久| 亚洲欧美精品专区久久| 日本猛色少妇xxxxx猛交久久| 97热精品久久久久久| 啦啦啦啦在线视频资源| 日韩欧美一区视频在线观看 | 校园人妻丝袜中文字幕| 五月玫瑰六月丁香| 三级经典国产精品| 国产亚洲精品av在线| 熟女人妻精品中文字幕| 久久人人爽人人片av| 搡老乐熟女国产| 最近视频中文字幕2019在线8| 一二三四中文在线观看免费高清| 欧美日韩亚洲高清精品| 亚洲无线观看免费| 中文字幕av成人在线电影| 中文精品一卡2卡3卡4更新| 最后的刺客免费高清国语| 精品99又大又爽又粗少妇毛片| 欧美+日韩+精品| 免费看a级黄色片| 男女国产视频网站| 国产成人aa在线观看| 久久久久久久亚洲中文字幕| 欧美日韩一区二区视频在线观看视频在线 | www.av在线官网国产| 亚洲欧美精品专区久久| 美女xxoo啪啪120秒动态图| 狂野欧美白嫩少妇大欣赏| 精品国内亚洲2022精品成人| 国内精品美女久久久久久| 美女黄网站色视频| 亚洲综合精品二区| 真实男女啪啪啪动态图| 国产有黄有色有爽视频| 色吧在线观看| 亚洲综合精品二区| 亚洲在线自拍视频| 亚洲av日韩在线播放| 亚洲精品乱久久久久久| 一本久久精品| 男女那种视频在线观看| 人体艺术视频欧美日本| 久久久久久久久大av| 丰满少妇做爰视频| 国产综合懂色| av福利片在线观看| 久久精品国产自在天天线| 国产精品国产三级国产av玫瑰| 色哟哟·www| 国产成人a∨麻豆精品| 中文字幕制服av| 成人高潮视频无遮挡免费网站| 哪个播放器可以免费观看大片| 少妇裸体淫交视频免费看高清| 欧美丝袜亚洲另类| a级一级毛片免费在线观看| 美女被艹到高潮喷水动态| 亚洲aⅴ乱码一区二区在线播放| 老司机影院成人| 免费黄网站久久成人精品| 一级毛片aaaaaa免费看小| 看十八女毛片水多多多| 精品熟女少妇av免费看| 五月伊人婷婷丁香| 国产一区亚洲一区在线观看| 久久精品久久精品一区二区三区| 国产精品熟女久久久久浪| 久久久久精品性色| 久久精品久久久久久久性| 成人亚洲精品av一区二区| 内地一区二区视频在线| 久久久久久国产a免费观看| 九九久久精品国产亚洲av麻豆| 国产不卡一卡二| 精华霜和精华液先用哪个| 国产黄色免费在线视频| 精品人妻视频免费看| 久久久久久伊人网av| 亚洲精品国产成人久久av| 大片免费播放器 马上看| 久久久久性生活片| 欧美一级a爱片免费观看看| 伦精品一区二区三区| 哪个播放器可以免费观看大片| 国精品久久久久久国模美| 免费黄网站久久成人精品| 欧美最新免费一区二区三区| 97超视频在线观看视频| 好男人在线观看高清免费视频| 欧美潮喷喷水| 久久精品人妻少妇| 国产综合精华液| 欧美 日韩 精品 国产| www.av在线官网国产| 日本wwww免费看| 精品久久久精品久久久| 亚洲在线自拍视频| 亚洲激情五月婷婷啪啪| 少妇裸体淫交视频免费看高清| 欧美日韩亚洲高清精品| 精品国产一区二区三区久久久樱花 | 韩国av在线不卡| 日日摸夜夜添夜夜添av毛片| 26uuu在线亚洲综合色| 少妇猛男粗大的猛烈进出视频 | 人人妻人人澡人人爽人人夜夜 | 午夜精品国产一区二区电影 | 1000部很黄的大片| 狂野欧美激情性xxxx在线观看| 国产精品一二三区在线看| 日韩国内少妇激情av| 亚洲欧洲国产日韩| 亚洲四区av| 久久精品熟女亚洲av麻豆精品 | 成年人午夜在线观看视频 | 亚洲熟妇中文字幕五十中出| 三级毛片av免费| 久久久久免费精品人妻一区二区| 国产不卡一卡二| 性色avwww在线观看| 国产探花在线观看一区二区| 亚洲乱码一区二区免费版| 亚洲欧美日韩卡通动漫| 在线观看人妻少妇| 国产乱人视频| 啦啦啦中文免费视频观看日本| 别揉我奶头 嗯啊视频| 欧美日韩一区二区视频在线观看视频在线 | 日韩不卡一区二区三区视频在线| 亚洲精品国产av成人精品| 一级毛片aaaaaa免费看小| 人人妻人人澡欧美一区二区| 天天躁日日操中文字幕| 国产探花在线观看一区二区| 街头女战士在线观看网站| 91久久精品国产一区二区成人| 最近最新中文字幕大全电影3| 欧美精品一区二区大全| 午夜亚洲福利在线播放| 观看免费一级毛片| 男女边摸边吃奶| 国产黄色小视频在线观看| 九九爱精品视频在线观看| 亚洲欧美一区二区三区黑人 | 亚洲欧美一区二区三区黑人 | 亚洲欧美日韩东京热| 免费播放大片免费观看视频在线观看| 免费观看的影片在线观看| 国产激情偷乱视频一区二区| 美女主播在线视频| 国产亚洲av嫩草精品影院| 成人午夜精彩视频在线观看| 少妇被粗大猛烈的视频| 国产亚洲精品久久久com| 国产成人午夜福利电影在线观看| 美女主播在线视频| 老司机影院成人| 国产午夜精品论理片| 亚洲美女视频黄频| 成人av在线播放网站| 中文资源天堂在线| www.色视频.com| 精品人妻熟女av久视频| 免费大片黄手机在线观看| 国产精品国产三级国产av玫瑰| 少妇人妻精品综合一区二区| 亚洲最大成人中文| 亚洲国产欧美在线一区| 一级黄片播放器| www.av在线官网国产| 亚洲18禁久久av| 韩国av在线不卡| 免费av不卡在线播放| 日本猛色少妇xxxxx猛交久久| 成人二区视频| 在线天堂最新版资源| 亚洲精品国产成人久久av| 亚洲久久久久久中文字幕| av在线蜜桃| 综合色丁香网| 精品人妻一区二区三区麻豆| 蜜臀久久99精品久久宅男| 久久午夜福利片| 80岁老熟妇乱子伦牲交| 久久精品夜夜夜夜夜久久蜜豆| 老司机影院成人| 搞女人的毛片| 国产av不卡久久| 啦啦啦啦在线视频资源| 亚洲欧美一区二区三区黑人 | 欧美三级亚洲精品| 偷拍熟女少妇极品色| 老师上课跳d突然被开到最大视频| 国产乱人视频| av国产久精品久网站免费入址| 日韩欧美一区视频在线观看 | 亚洲丝袜综合中文字幕| 亚洲av成人精品一二三区| 麻豆成人午夜福利视频| 国产毛片a区久久久久| 久久人人爽人人爽人人片va| 女的被弄到高潮叫床怎么办| 日韩欧美 国产精品| 成人午夜高清在线视频| av专区在线播放| 国内揄拍国产精品人妻在线| 最近的中文字幕免费完整| 精品酒店卫生间| 黑人高潮一二区| 久久久久九九精品影院| 欧美丝袜亚洲另类| 午夜久久久久精精品| 亚洲在久久综合| 日韩中字成人| 国产黄色小视频在线观看| 国产精品伦人一区二区| 国产伦理片在线播放av一区| 精品人妻视频免费看| 精品久久久精品久久久| 晚上一个人看的免费电影| 中文字幕久久专区| 精品国产露脸久久av麻豆 | 少妇猛男粗大的猛烈进出视频 | av在线老鸭窝| 亚洲电影在线观看av| 亚洲怡红院男人天堂| 亚洲婷婷狠狠爱综合网| 精品人妻一区二区三区麻豆| 九九在线视频观看精品| 最近最新中文字幕免费大全7| 精品久久久精品久久久| av在线老鸭窝| 大话2 男鬼变身卡| 国产成人精品婷婷| 亚洲欧美一区二区三区国产| 亚洲人成网站在线观看播放| 国产亚洲5aaaaa淫片| 欧美一区二区亚洲| 国产精品蜜桃在线观看| 国产黄色免费在线视频| 亚洲最大成人手机在线| 国产白丝娇喘喷水9色精品| 久久久久久久午夜电影| 直男gayav资源| av在线蜜桃| 免费观看在线日韩| 99久久九九国产精品国产免费| 国产真实伦视频高清在线观看| 日日干狠狠操夜夜爽| 99热这里只有精品一区| 国产白丝娇喘喷水9色精品| 少妇的逼好多水| 少妇熟女欧美另类| 99re6热这里在线精品视频| 国产伦精品一区二区三区视频9| 一二三四中文在线观看免费高清| 晚上一个人看的免费电影| 国产成人精品福利久久| 久久这里只有精品中国| 禁无遮挡网站| 国产又色又爽无遮挡免| 黄片无遮挡物在线观看| 午夜福利视频精品| 久久久a久久爽久久v久久| 免费观看在线日韩| 黑人高潮一二区| 亚洲精品成人av观看孕妇| 在线播放无遮挡| 亚洲成人精品中文字幕电影| 女人十人毛片免费观看3o分钟| 精品亚洲乱码少妇综合久久| 日本黄大片高清| 青春草亚洲视频在线观看| 亚洲不卡免费看| 国产有黄有色有爽视频| 色哟哟·www| 国产成人福利小说| 人妻系列 视频| 日韩国内少妇激情av| 免费不卡的大黄色大毛片视频在线观看 | 亚洲精品日本国产第一区| 日韩视频在线欧美| 美女大奶头视频| 亚洲国产最新在线播放| 久久久久久久亚洲中文字幕| 亚洲av国产av综合av卡| 久久99热这里只频精品6学生| 婷婷色av中文字幕| 波野结衣二区三区在线| 丰满人妻一区二区三区视频av| 在现免费观看毛片| 成人一区二区视频在线观看| 亚洲成人精品中文字幕电影| 少妇裸体淫交视频免费看高清| 精品久久久久久成人av| 亚洲成色77777| 久久久久久久久久黄片| 97热精品久久久久久| 直男gayav资源| 亚洲av日韩在线播放| 久久久久久久久久黄片| 精品欧美国产一区二区三| 青春草视频在线免费观看| 纵有疾风起免费观看全集完整版 | 性插视频无遮挡在线免费观看| 日日啪夜夜爽| 成人亚洲欧美一区二区av| 日韩三级伦理在线观看| 欧美xxxx性猛交bbbb| 日本熟妇午夜| 美女国产视频在线观看| 国产片特级美女逼逼视频| 在线播放无遮挡| 在线观看一区二区三区| 午夜免费激情av| 18+在线观看网站| 日韩不卡一区二区三区视频在线| 精品一区二区三区视频在线| 国产成人一区二区在线| 菩萨蛮人人尽说江南好唐韦庄| 高清欧美精品videossex| 最近最新中文字幕大全电影3| 精品熟女少妇av免费看| 777米奇影视久久| 少妇人妻精品综合一区二区| 中文资源天堂在线| 91av网一区二区| 91精品伊人久久大香线蕉| 尾随美女入室| 我要看日韩黄色一级片| 国产精品人妻久久久久久| 九色成人免费人妻av| 精品久久久噜噜| 七月丁香在线播放| 午夜久久久久精精品| 久久99热这里只有精品18| 亚洲av不卡在线观看| 国产一区二区三区av在线| 国产精品一二三区在线看| 午夜福利在线在线| 色视频www国产| 只有这里有精品99| 天堂俺去俺来也www色官网 | 日韩欧美精品免费久久| 国产高清不卡午夜福利| 日韩欧美精品免费久久| 高清午夜精品一区二区三区| 禁无遮挡网站| 麻豆久久精品国产亚洲av| 国产亚洲av嫩草精品影院| 日本免费在线观看一区| 亚洲最大成人手机在线| 午夜福利网站1000一区二区三区| 亚洲精品日本国产第一区| 大话2 男鬼变身卡| 久99久视频精品免费| 国内少妇人妻偷人精品xxx网站| 中文乱码字字幕精品一区二区三区 | 日本av手机在线免费观看| 国产成人午夜福利电影在线观看| 国产精品精品国产色婷婷| 91精品一卡2卡3卡4卡| 成人特级av手机在线观看| 老女人水多毛片| 亚洲自拍偷在线| 白带黄色成豆腐渣| 亚洲乱码一区二区免费版| 欧美日韩视频高清一区二区三区二| 蜜桃久久精品国产亚洲av| 国内少妇人妻偷人精品xxx网站| 91在线精品国自产拍蜜月| 成人美女网站在线观看视频| 精品久久久久久久久亚洲| 亚洲人与动物交配视频| 久久亚洲国产成人精品v| 日韩欧美三级三区| 亚洲精品国产av蜜桃| 国产精品一区二区三区四区免费观看| 精品一区二区免费观看| 99热全是精品| 黄片无遮挡物在线观看| 午夜精品国产一区二区电影 | 日本午夜av视频| 如何舔出高潮| www.色视频.com| 老女人水多毛片| 黑人高潮一二区| 中文字幕久久专区| 狂野欧美激情性xxxx在线观看| 91精品一卡2卡3卡4卡| 18禁在线播放成人免费| www.av在线官网国产| 毛片一级片免费看久久久久| 亚洲电影在线观看av| 久久久色成人| 婷婷色麻豆天堂久久| 国产亚洲一区二区精品| 少妇被粗大猛烈的视频| 亚洲天堂国产精品一区在线| 国产成年人精品一区二区| 国产探花在线观看一区二区| or卡值多少钱| 久久久久国产网址| 能在线免费看毛片的网站| 天堂中文最新版在线下载 | 99久国产av精品| av国产免费在线观看| 熟妇人妻不卡中文字幕| 日韩欧美三级三区| 80岁老熟妇乱子伦牲交| 少妇裸体淫交视频免费看高清| 久久久久国产网址| 插阴视频在线观看视频| 午夜福利高清视频| 美女高潮的动态| 黄片wwwwww| 伊人久久国产一区二区| 日韩av免费高清视频| 亚洲人成网站在线观看播放| 69人妻影院| .国产精品久久| 午夜福利视频1000在线观看| 男的添女的下面高潮视频| 欧美xxxx性猛交bbbb| 中文欧美无线码| 中文字幕免费在线视频6| 亚洲综合色惰| 一级毛片aaaaaa免费看小| or卡值多少钱| 欧美不卡视频在线免费观看| 国语对白做爰xxxⅹ性视频网站| 永久网站在线| 国产乱来视频区| 哪个播放器可以免费观看大片| 国产久久久一区二区三区| 亚州av有码| a级一级毛片免费在线观看| 国产精品精品国产色婷婷| 人妻制服诱惑在线中文字幕| 国产在视频线精品| 在线观看av片永久免费下载| 国产精品久久久久久久久免| 高清日韩中文字幕在线| 亚洲av中文字字幕乱码综合| 内地一区二区视频在线| 日韩制服骚丝袜av| 成人综合一区亚洲| 超碰av人人做人人爽久久| 国产精品.久久久| 国语对白做爰xxxⅹ性视频网站| 永久免费av网站大全| 国产中年淑女户外野战色| 少妇人妻一区二区三区视频| 国产乱人偷精品视频| 亚洲18禁久久av| 欧美高清成人免费视频www| 一区二区三区四区激情视频| 成年女人看的毛片在线观看| 麻豆精品久久久久久蜜桃| 男女下面进入的视频免费午夜| 久久久a久久爽久久v久久| 欧美另类一区| 伦理电影大哥的女人| 亚洲欧美清纯卡通| 男人舔奶头视频| 中文字幕制服av| 黑人高潮一二区| 亚洲欧美成人精品一区二区| 日韩av在线大香蕉| 国产精品一区二区三区四区免费观看| 嫩草影院新地址| 国产色爽女视频免费观看| 亚洲人成网站在线观看播放| 久久久久久久久久黄片| 亚洲国产精品国产精品| 久久久亚洲精品成人影院| 国产精品熟女久久久久浪| 人体艺术视频欧美日本| 美女内射精品一级片tv| 国产精品一区二区三区四区免费观看| 欧美日韩视频高清一区二区三区二| 人人妻人人看人人澡| 国产精品日韩av在线免费观看| 成人综合一区亚洲| 色吧在线观看| 免费av观看视频| 91精品伊人久久大香线蕉| 我的老师免费观看完整版| 韩国高清视频一区二区三区| 色综合亚洲欧美另类图片| 日韩一区二区视频免费看| 观看美女的网站| 欧美区成人在线视频| 在线免费观看不下载黄p国产| 国产一区二区在线观看日韩| 亚洲三级黄色毛片| 国产黄片视频在线免费观看| 亚洲国产高清在线一区二区三| 日韩一区二区视频免费看| 在线观看美女被高潮喷水网站| 一个人看的www免费观看视频| 久久久久网色| 3wmmmm亚洲av在线观看| 免费观看精品视频网站| av在线播放精品| 大话2 男鬼变身卡| 少妇熟女欧美另类| 国产精品无大码| 日本黄色片子视频| 极品少妇高潮喷水抽搐| 成年版毛片免费区| 免费观看无遮挡的男女| 大香蕉久久网| 搡老妇女老女人老熟妇| 少妇高潮的动态图| 午夜免费激情av| 成人毛片a级毛片在线播放| 插阴视频在线观看视频| 国产黄片美女视频| 在线观看免费高清a一片| 久久久久久久久久人人人人人人| 毛片女人毛片| 亚洲精品aⅴ在线观看| 午夜激情福利司机影院| 国产 一区精品| 午夜福利成人在线免费观看| 久久国产乱子免费精品| 男人狂女人下面高潮的视频| 亚洲欧美日韩无卡精品| 超碰97精品在线观看| 亚洲自拍偷在线| a级毛色黄片| 日产精品乱码卡一卡2卡三| 97精品久久久久久久久久精品| 纵有疾风起免费观看全集完整版 | 久久99热6这里只有精品| 国产精品无大码| 亚洲av不卡在线观看| 亚洲欧美成人综合另类久久久| 亚洲精品一区蜜桃| 亚洲精品乱久久久久久| 成人一区二区视频在线观看| 精品一区在线观看国产| 国产高清不卡午夜福利| a级毛色黄片| 亚洲精品乱久久久久久| 免费看光身美女| av女优亚洲男人天堂| 日韩精品有码人妻一区| 欧美变态另类bdsm刘玥| 日韩欧美精品免费久久| 中文欧美无线码| 黄色欧美视频在线观看| 91久久精品国产一区二区成人| 天天躁夜夜躁狠狠久久av| av线在线观看网站| 成人漫画全彩无遮挡| 日本-黄色视频高清免费观看| 91精品国产九色| 麻豆国产97在线/欧美| 黄色日韩在线| a级毛色黄片| 国产一区亚洲一区在线观看| 人妻制服诱惑在线中文字幕| 国产黄色视频一区二区在线观看| 男人舔女人下体高潮全视频| 天堂影院成人在线观看| 久久人人爽人人爽人人片va| 美女被艹到高潮喷水动态| 成人特级av手机在线观看| 欧美日本视频| 国产黄片视频在线免费观看| 老司机影院毛片| 在线 av 中文字幕| 亚洲精品久久久久久婷婷小说| 一级黄片播放器| 日韩av在线免费看完整版不卡| 少妇人妻精品综合一区二区| 亚洲欧美精品专区久久| 成年av动漫网址| 亚州av有码| 午夜免费男女啪啪视频观看| 天美传媒精品一区二区| 国产黄片美女视频| 久久久久网色| 国产三级在线视频| 丝瓜视频免费看黄片| 亚洲av男天堂| 毛片女人毛片| 91精品一卡2卡3卡4卡| 欧美日韩视频高清一区二区三区二| 伊人久久精品亚洲午夜| 国产成人a∨麻豆精品| 成年女人看的毛片在线观看| 在线免费十八禁| 久久久久久九九精品二区国产| 国产成人aa在线观看| 日产精品乱码卡一卡2卡三| 久久久久久久国产电影| 免费不卡的大黄色大毛片视频在线观看 | 欧美人与善性xxx| 国产成人a∨麻豆精品| 纵有疾风起免费观看全集完整版 | 97超碰精品成人国产| 性插视频无遮挡在线免费观看| 国产免费一级a男人的天堂| 欧美3d第一页| 最近最新中文字幕免费大全7| 免费av观看视频| 久久午夜福利片| 91午夜精品亚洲一区二区三区| 午夜福利成人在线免费观看|