Statistical Methods and Software for Substance Use and Dependence Genetic Research

Author(s): Tongtong Lan, Bo Yang, Xuefen Zhang, Tong Wang*, Qing Lu*

Journal Name: Current Genomics

Volume 20 , Issue 3 , 2019

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Background: Substantial substance use disorders and related health conditions emerged during the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of progress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

Methods: Various statistical methods and software have been employed in different types of SUD genetic studies, facilitating the identification of new SUD-related variants.

Conclusion: In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Keywords: Substance dependence, linkage analysis, association analysis, interaction analysis, meta-analysis, GCTA.

Peiper, N.C.; Ridenour, T.A.; Hochwalt, B.; Coyne-Beasley, T. Overview on prevalence and recent trends in adolescent substance use and abuse. Child Adolesc. Psychiatr. Clin. N. Am., 2016, 25(3), 349-365.
Bevilacqua, L.; Goldman, D. Genes and addictions. Clin. Pharmacol. Ther., 2009, 85(4), 359-361.
Prom-Wormley, E.C.; Ebejer, J.; Dick, D.M.; Bowers, M.S. The genetic epidemiology of substance use disorder: a review. Drug Alcohol Depend., 2017, 180, 241-259.
Laura Jean, B. Genetic vulnerability and susceptibility to substance dependence. Neuron, 2011, 69(4), 618-627.
Wang, J.C.; Kapoor, M.; Goate, A.M. The Genetics of Substance Dependence. Annual Review of Genomics & Human Genetics., 2012, 13(1), 241.
Vink, J.M.; Willemsen, G.; Boomsma, D.I. Heritability of smoking initiation and nicotine dependence. Behav. Genet., 2005, 35(4), 397-406.
Neil, C.; Fangyi, G.; Nilanjan, C.; Jin, S.C.; Kai, Y.; Meredith, Y.; Constance, C.; Kevin, J.; William, W.; Maria, T.L. Genome-wide and candidate gene association study of cigarette smoking behaviors. PLoS One, 2009, 4(2), e4653.
Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; Sham, P.C. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 2007, 81(3), 559-575.
Mcpeek, M.S.; Sun, L. Statistical tests for detection of misspecified relationships by use of genome-screen data. Am. J. Hum. Genet., 2000, 66(3), 1076-1094.
Gizer, I.R.; Ehlers, C.L.; Vieten, C.; Seaton-Smith, K.L.; Feiler, H.S.; Lee, J.V.; Segall, S.K.; Gilder, D.A.; Wilhelmsen, K.C. Linkage scan of nicotine dependence in the University of California, San Francisco (UCSF) Family Alcoholism Study. Psychol. Med., 2011, 41(4), 799-808.
Shizhong, H.; Bao-Zhu, Y.; Kranzler, H.R.; David, O.; Raymond, A.; Farrer, L.A.; Joel, G. Linkage analysis followed by association show NRG1 associated with cannabis dependence in African Americans. Biol. Psychiatry, 2012, 72(8), 637-644.
Bao-Zhu, Y.; Shizhong, H.; Kranzler, H.R.; Farrer, L.A.; Joel, G. A genomewide linkage scan of cocaine dependence and major depressive episode in two populations. Neuropsychopharmacology, 2011, 36(12), 2422-2430.
Gizer, I.R.; Ehlers, C.L.; Vieten, C.; Seaton-Smith, K.L.; Feiler, H.S.; Lee, J.V.; Segall, S.K.; Gilder, D.A.; Wilhelmsen, K.C. Linkage scan of alcohol dependence in the UCSF Family Alcoholism Study. Drug Alcohol Depend., 2011, 113(2), 125-132.
O’Connell, J.R.; Weeks, D.E. PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am. J. Hum. Genet., 1998, 63(1), 259-266.
Abecasis, G.A.R.; Cherny, S.S.; Cookson, W.O.; Cardon, L.R. Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet., 2002, 30(1), 97-101.
Schnell, A.H.; Sun, X. Model-based linkage analysis of a quantitative trait.In: Statistical Human Genetics. Methods in Moecular Biololgy; Elston, R.C.; Satagopan, J.M.; Sun, S., Eds.; Humana Press, 2012, Vol. 850, pp. 263-283.
Xu, W.; Bull, S.B.; Mirea, L.; Greenwood, C.M.T. Model-free linkage analysis of a binary trait.In: Statistical Human Genetics. Methods in Moecular Biololgy; Elston, R.C.; Satagopan, J.M.; Sun, S., Eds.; Humana Press, 2012, Vol. 850, p. 317.
Lu, Q.; Song, Y.; Gray-Mcguire, C. Software for Genetics/Genomics; John Wiley & Sons: New York, 2013.
Haseman, J.K.; Elston, R.C. The investigation of linkage between a quantitative trait and a marker locus. Behav. Genet., 1972, 2(1), 3-19.
Sham, P.C.; Purcell, S.; Cherny, S.S.; Abecasis, G.R. Powerful regression-based quantitative-trait linkage analysis of general pedigrees. Am. J. Hum. Genet., 2002, 71(2), 238-253.
Joel, G.; Carolien, P.; Roger, W.; Kathleen, B.; James, P.; Michael, K.; Lindsay, F.; Kranzler, H.R. Genomewide linkage scan for nicotine dependence: identification of a chromosome 5 risk locus. Biol. Psychiatry, 2007, 61(1), 119-126.
Kruglyak, L.; Lander, E.S. Faster multipoint linkage analysis using Fourier transforms. J. Comput. Biol., 1998, 5(1), 1-7.
Gelernter, J.; Panhuysen, C.; Weiss, R.; Brady, K.; Hesselbrock, V.; Rounsaville, B.; Poling, J.; Wilcox, M.; Farrer, L.; Kranzler, H.R. Genomewide linkage scan for cocaine dependence and related traits: significant linkages for a cocaine-related trait and cocaine-induced paranoia. Am. J. Med. Genet. B. Neuropsychiatr. Genet., 2010, 136B(1), 45-52.
Amy, W.; Lind, P.A.; Jelger, K.; Feiler, H.S.; Smith, T.L.; Schuckit, M.A.; Kirk, W. The investigation into CYP2E1 in relation to the level of response to alcohol through a combination of linkage and association analysis. Alcohol. Clin. Exp. Res., 2011, 35(1), 10-18.
Nielsen, D.A.; Kreek, M.J. Common and specific liability to addiction: approaches to association studies of opioid addiction. Drug Alcohol Depend., 2012, 123(1), S33-S41.
Balding, D.J. A tutorial on statistical methods for population association studies. Nat. Rev. Genet., 2006, 7(10), 781-791.
Mccarthy, M.; Abecasis, G.; Cardon, L.; Goldstein, D.; Little, J.; Ioannidis, J.; Hirschhorn, J. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet., 2008, 9(5), 356-369.
Wang, M.H.; Cordell, H.J.; Steen, K.V. Statistical methods for genome-wide association studies. Semin. Cancer Biol., 2018, 55, 53-60.
Armitage, P. Tests for linear trends in proportions and frequencies. Biometrics, 1955, 11(3), 375-386.
Agresti, A. Categorical Data Analysis, 2nd ed; Wiley & Sons: New York, 2003.
Balding, D.J. A tutorial on statistical methods for population association studies. Nat. Rev. Genet., 2006, 7, 781-791.
Zeng, P.; Zhao, Y.; Qian, C.; Zhang, L.; Zhang, R.; Gou, J.; Liu, J.; Liu, L.; Chen, F. Statistical analysis for genome-wide association study. J. Biomed. Res., 2015, 29(4), 285-297.
Langefeld, C.D.; Fingerlin, T.E. Association Methods in Human Genetics.In: Topics in Biostatistics; Ambrosius, W.T., Ed.; Humana Press: Totowa, NJ, 2007, pp. 431-460.
Camastra, F.; Di, T.M.; Staiano, A. Statistical and computational methods for genetic diseases: an overview. Comput. Math. Methods Med., 2015, 2015, 1-8.
Price, A.L.; Patterson, N.J.; Plenge, R.M.; Weinblatt, M.E.; Shadick, N.A.; David, R. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet., 2006, 38(8), 904-909.
Schaid, D.J.; Rowland, C.M.; Tines, D.E.; Jacobson, R.M.; Poland, G.A. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am. J. Hum. Genet., 2002, 70(2), 425-434.
Levran, O.; Londono, D.; O’Hara, K.; Nielsen, D.A.; Peles, E.; Rotrosen, J.; Casadonte, P.; Linzy, S.; Randesi, M.; Ott, J. Genetic susceptibility to heroin addiction: a candidate gene association study. Genes Brain Behav., 2010, 7(7), 720-729.
Stephens, M.; Donnelly, P. A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am. J. Hum. Genet., 2003, 73(5), 1162-1169.
Stephens, M.; Smith, N.J.; Donnelly, P. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet., 2001, 68(4), 978-989.
Niu, T.; Qin, Z.S.; Xu, X.; Liu, J.S. Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am. J. Hum. Genet., 2002, 70(1), 157-169.
Wang, S. A, D.v.d.V.; Xu, Q.; Seneviratne, C.; Pomerleau, O.F.; Pomerleau, C.S.; Payne, T.J.; Ma, J.Z.; Li, M.D. Significant associations of CHRNA2 and CHRNA6 with nicotine dependence in European American and African American populations. Hum. Genet., 2014, 133(5), 575-586.
Itsik, P.E.; Roman, Y.; David, A.; Daly, M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol., 2010, 32(4), 381-385.
Mckay, J.D.; Hung, R.J.; Han, Y.; Zong, X.; Carreras-Torres, R.; Christiani, D.C.; Caporaso, N.E.; Johansson, M.; Xiao, X.; Li, Y. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat. Genet., 2017, 49(7), 1126.
Risch, N.; Merikangas, K. The future of genetic studies of complex human diseases. Science, 1996, 273(5281), 1516-1517.
Treutlein, J.; Rietschel, M. Genome-wide association studies of alcohol dependence and substance use disorders. Curr. Psychiatry Rep., 2011, 13(2), 147-155.
Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol., 1995, 57(1), 289-300.
Bierut, L.J.; Arpana, A.; Bucholz, K.K.; Doheny, K.F.; Cathy, L.; Elizabeth, P.; Sherri, F.; Louis, F.; William, H.; Sarah, B. A genome-wide association study of alcohol dependence. Proc. Natl. Acad. Sci. USA, 2010, 107(11), 5082-5087.
Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics, 2000, 155(2), 945-959.
Rivas, M.A.; Beaudoin, M.; Gardet, A.; Stevens, C.; Sharma, Y.; Zhang, C.K.; Boucher, G.; Ripke, S.; Ellinghaus, D.; Burtt, N.; Fennell, T.; Kirby, A.; Latiano, A.; Goyette, P.; Green, T.; Halfvarson, J.; Haritunians, T.; Korn, J.M.; Kuruvilla, F.; Lagace, C.; Neale, B.; Lo, K.S.; Schumm, P.; Torkvist, L.; Dubinsky, M.C.; Brant, S.R.; Silverberg, M.S.; Duerr, R.H.; Altshuler, D.; Gabriel, S.; Lettre, G.; Franke, A.; D’Amato, M.; McGovern, D.P.; Cho, J.H.; Rioux, J.D.; Xavier, R.J.; Daly, M.J. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet., 2011, 43(11), 1066-1073.
Gudmundsson, J.; Sulem, P.; Gudbjartsson, D.F.; Masson, G.; Agnarsson, B.A.; Benediktsdottir, K.R.; Sigurdsson, A.; Magnusson, O.T.; Gudjonsson, S.A.; Magnusdottir, D.N.; Johannsdottir, H.; Helgadottir, H.T.; Stacey, S.N.; Jonasdottir, A.; Olafsdottir, S.B.; Thorleifsson, G.; Jonasson, J.G.; Tryggvadottir, L.; Navarrete, S.; Fuertes, F.; Helfand, B.T.; Hu, Q.; Csiki, I.E.; Mates, I.N.; Jinga, V.; Aben, K.K.; van Oort, I.M.; Vermeulen, S.H.; Donovan, J.L.; Hamdy, F.C.; Ng, C.F.; Chiu, P.K.; Lau, K.M.; Ng, M.C.; Gulcher, J.R.; Kong, A.; Catalona, W.J.; Mayordomo, J.I.; Einarsson, G.V.; Barkardottir, R.B.; Jonsson, E.; Mates, D.; Neal, D.E.; Kiemeney, L.A.; Thorsteinsdottir, U.; Rafnar, T.; Stefansson, K. A study based on whole-genome sequencing yields a rare variant at 8q24 associated with prostate cancer. Nat. Genet., 2012, 44(12), 1326-1329.
Jonsson, T.; Atwal, J.K.; Steinberg, S.; Snaedal, J.; Jonsson, P.V.; Bjornsson, S.; Stefansson, H.; Sulem, P.; Gudbjartsson, D.; Maloney, J.; Hoyte, K.; Gustafson, A.; Liu, Y.; Lu, Y.; Bhangale, T.; Graham, R.R.; Huttenlocher, J.; Bjornsdottir, G.; Andreassen, O.A.; Jonsson, E.G.; Palotie, A.; Behrens, T.W.; Magnusson, O.T.; Kong, A.; Thorsteinsdottir, U.; Watts, R.J.; Stefansson, K. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature, 2012, 488(7409), 96-99.
Lee, S.; Abecasis, G.R.; Boehnke, M.; Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet., 2014, 95(1), 5-23.
Tong, W.T.; Fang, C.Y.; Trevor, H.; Eric, S.; Kenneth, L. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics, 2009, 25(6), 714-721.
Jiahan, L.; Kiranmoy, D.; Guifang, F.; Runze, L.; Rongling, W. The Bayesian lasso for genome-wide association studies. Bioinformatics, 2011, 27(4), 516-523.
Asimit, J.; Zeggini, E. Rare variant association analysis methods for complex traits. Annu. Rev. Genet., 2010, 44, 293-308.
Spielman, R.S.; Mcginnis, R.E.; Ewens, W.J. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am. J. Hum. Genet., 1993, 52(3), 506-516.
Wen, Y.; Lu, Q. Analysis of Gene-Gene Interactions Underlying Human Disease; John Wiley & Sons: New York, 2014.
Teng, J.; Risch, N. The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. Genome Res., 1999, 9(3), 234-241.
Nan, M.L.; Lange, C. Family- based methods for linkage and association analysis. Adv. Genet., 2008, 60(4), 219-252.
Rabinowitz, D.; Laird, N. A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum. Hered., 2000, 50(4), 211-223.
Lange, C.; Van, S.K.; Andrew, T.; Lyon, H.; Demeo, D.L.; Raby, B.; Murphy, A.; Silverman, E.K.; Macgregor, A.; Weiss, S.T. A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects. Stat. Appl. Genet. Mol. Biol., 2004, 3(1), 1-17.
Sungho, W.; Wilk, J.B.; Mathias, R.A.; O’Donnell, C.J.; Silverman, E.K.; Kathleen, B.; O’Connor, G.T.; Weiss, S.T.; Christoph, L. On the analysis of genome-wide association studies in family-based designs: a universal, robust analysis approach and an application to four genome-wide association studies. PLoS Genet., 2009, 5(11), e1000741.
Steve, H.; Xin, X.; Lake, S.L.; Silverman, E.K.; Weiss, S.T.; Laird, N.M. Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet. Epidemiol., 2004, 26(1), 61-69.
Hill, S.Y.; Jones, B.L.; Zezza, N.; Stiffler, S. ACN9 and alcohol dependence: family-based association analysis in multiplex alcohol dependence families. Am. J. Med. Genet. B. Neuropsychiatr. Genet., 2015, 168b(3), 179-187.
Martin, E.R.; Monks, S.A.; Warren, L.L.; Kaplan, N.L. A test for linkage and association in general pedigrees: the pedigree disequilibrium test. Am. J. Hum. Genet., 2000, 67(1), 146-154.
Laird, N.M.; Lange, C. Family-based designs in the age of large-scale gene-association studies. Nat. Rev. Genet., 2006, 7(5), 385-394.
James, G.W. Sample size requirements for association studies of gene-gene interaction. Am. J. Epidemiol., 2002, 155(5), 478-484.
Wang, S.; Zhao, H. Sample size needed to detect gene-gene interactions using association designs. Am. J. Epidemiol., 2003, 158(9), 899-914.
Cordell, H.J.; Clayton, D.G. A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am. J. Hum. Genet., 2002, 70(1), 124-141.
Josephine, H.; Jurg, O. Mathematical multi-locus approaches to localizing complex human trait genes. Nat. Rev. Genet., 2003, 4(9), 701-709.
Marchini, J.; Donnelly, P.; Cardon, L.R. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat. Genet., 2005, 37, 413-417.
Dahinden, C.; Parmigiani, G.; Emerick, M.C.; Bühlmann, P. Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries. BMC Bioinformatics, 2007, 8(1), 476.
Li, M.; Romero, R.; Fu, W.J.; Cui, Y. Mapping haplotype-haplotype interactions with adaptive LASSO. BMC Genet., 2010, 11(1), 79.
Li, M.; Lou, X.Y.; Lu, Q. On epistasis: a methodological review for detecting gene-gene interactions underlying various types of phenotypic traits. Recent Pat. Biotechnol., 2012, 6(3), 230-236.
Ritchie, M.D.; Hahn, L.W.; Roodi, N.; Bailey, L.R.; Dupont, W.D.; Parl, F.F.; Moore, J.H. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet., 2001, 69(1), 138-147.
Lou, X.Y.; Chen, G.B.; Yan, L.; Ma, J.Z.; Zhu, J.; Elston, R.C.; Li, M.D. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am. J. Hum. Genet., 2007, 80(6), 1125-1137.
Zhu, Z.; Tong, X.; Zhu, Z.; Liang, M.; Cui, W.; Su, K.; Li, M.D.; Zhu, J. Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes. PLoS One, 2013, 8(4), e61943.
Jiekun, Y.; Ming, D.L. Association and interaction analyses of 5-HT3 receptor and serotonin transporter genes with alcohol, cocaine, and nicotine dependence using the SAGE data. Hum. Genet., 2014, 133(7), 905-918.
Li, M.D. Detection of gene-gene interaction among CHRNA4, CHRNB2, BDNF and NTRK2 in nicotine dependence. Biol. Psychiatry, 2008, 64(11), 951-957.
Martin, E.R.; Ritchie, M.D.; Hahn, L.; Kang, S.; Moore, J.H. A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. Genet. Epidemiol., 2006, 30(2), 111-123.
Lou, X.Y.; Chen, G.B.; Yan, L.; Ma, J.Z.; Mangold, J.E.; Zhu, J.; Elston, R.C.; Li, M.D. A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies. Am. J. Hum. Genet., 2008, 83(4), 457-467.
Evangelou, E.; Ioannidis, J.P. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet., 2013, 14(6), 379-389.
Sagoo, G.S.; Little, J.; Higgins, J.P. Systematic reviews of genetic association studies. Hum. Genome Epidemiol. Network. PLoS Med., 2009, 6(3), e28.
Higgins, J.P.; Thompson, S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med., 2002, 21(11), 1539-1558.
Willer, C.J.; Abecasis, G.R.; Li, Y. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics, 2010, 26(17), 2190-2191.
Magi, R.; Morris, A.P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics, 2010, 11, 288.
Eleftheria, Z.; Ioannidis, J.P.A. Meta-analysis in genome-wide association studies. Pharmacogenomics, 2009, 10(2), 191-201.
Dersimonian, R.; Nan, L. Meta-analysis in clinical trials. Control. Clin. Trials, 1986, 7(3), 177-188.
Pereira, T.V.; Patsopoulos, N.A.; Salanti, G.; Ioannidis, J.P. Discovery properties of genome-wide association signals from cumulatively combined data sets. Am. J. Epidemiol., 2009, 170(10), 1197-1206.
Kavvoura, F.K.; Ioannidis, J.P. Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls. Hum. Genet., 2008, 123(1), 1-14.
Mantel, N. Chi-square tests with one degree of freedom; extensions of the mantel- haenszel procedure. J. Am. Stat. Assoc., 1963, 58(303), 690-700.
Goldstein, D.B. Common genetic variation and human traits. N. Engl. J. Med., 2009, 360(17), 1696-1698.
Yang, J.; Lee, S.H.; Goddard, M.E.; Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet., 2011, 88(1), 76-82.
Jian, Y.; Beben, B.; Mcevoy, B.P.; Scott, G.; Henders, A.K.; Nyholt, D.R.; Madden, P.A.; Heath, A.C.; Martin, N.G.; Montgomery, G.W. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet., 2010, 42(7), 565-569.
Vrieze, S.I.; McGue, M.; Miller, M.B.; Hicks, B.M.; Iacono, W.G. Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: twin biometry, GCTA, and genome-wide scoring. Behav. Genet., 2013, 43(2), 97-107.
Palmer, R.H.; McGeary, J.E.; Heath, A.C.; Keller, M.C.; Brick, L.A.; Knopik, V.S. Shared additive genetic influences on DSM-IV criteria for alcohol dependence in subjects of European ancestry. Addiction, 2015, 110(12), 1922-1931.
Yang, J.; Ferreira, T.; Morris, A.P.; Medland, S.E.; Madden, P.A.; Heath, A.C.; Martin, N.G.; Montgomery, G.W.; Weedon, M.N.; Loos, R.J.; Frayling, T.M.; McCarthy, M.I.; Hirschhorn, J.N.; Goddard, M.E.; Visscher, P.M. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet., 2012, 44(4), 369-375.
Clarke, T.K.; Adams, M.J.; Davies, G.; Howard, D.M.; Hall, L.S.; Padmanabhan, S.; Murray, A.D.; Smith, B.H.; Campbell, A.; Hayward, C. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Mol. Psychiatry, 2017, 22(10), 1376-1384.
Otto, J.M.; Gizer, I.R.; Ellingson, J.M.; Wilhelmsen, K.C. Genetic variation in the exome: Associations with alcohol and tobacco co-use. Psychol. Addict. Behav., 2017, 31(3), 354-366.
Brazel, D.M.; Jiang, Y.; Hughey, J.M.; Turcot, V.; Zhan, X.; Gong, J.; Batini, C.; Weissenkampen, J.D.; Liu, M.; Barnes, D.R.; Bertelsen, S.; Chou, Y.L.; Erzurumluoglu, A.M.; Faul, J.D.; Haessler, J.; Hammerschlag, A.R.; Hsu, C.; Kapoor, M.; Lai, D.; Le, N.; de Leeuw, C.A.; Loukola, A.; Mangino, M.; Melbourne, C.A.; Pistis, G.; Qaiser, B.; Rohde, R.; Shao, Y.; Stringham, H.; Wetherill, L.; Zhao, W.; Agrawal, A.; Bierut, L.; Chen, C.; Eaton, C.B.; Goate, A.; Haiman, C.; Heath, A.; Iacono, W.G.; Martin, N.G.; Polderman, T.J.; Reiner, A.; Rice, J.; Schlessinger, D.; Scholte, H.S.; Smith, J.A.; Tardif, J.C.; Tindle, H.A.; van der Leij, A.R.; Boehnke, M.; Chang-Claude, J.; Cucca, F.; David, S.P.; Foroud, T.; Howson, J.M.M.; Kardia, S.L.R.; Kooperberg, C.; Laakso, M.; Lettre, G.; Madden, P.; McGue, M.; North, K.; Posthuma, D.; Spector, T.; Stram, D.; Tobin, M.D.; Weir, D.R.; Kaprio, J.; Abecasis, G.R.; Liu, D.J.; Vrieze, S. Exome chip meta-analysis fine maps causal variants and elucidates the genetic architecture of rare coding variants in smoking and alcohol use. Biol. Psychiatry, 2018, 85(11), 946-955.
Liu, C. Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases. Neurosci. Bull., 2011, 27(2), 123-133.
Gaffney, D.J.; Veyrieras, J.B.; Degner, J.F.; Pique-Regi, R.; Pai, A.A.; Crawford, G.E.; Stephens, M.; Gilad, Y.; Pritchard, J.K. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol., 2012, 13(1), R7.
Cookson, W.; Liang, L.; Abecasis, G.; Moffatt, M.; Lathrop, M. Mapping complex disease traits with global gene expression. Nat. Rev. Genet., 2009, 10(3), 184-194.
Kendziorski, C.; Wang, P. A review of statistical methods for expression quantitative trait loci mapping. Mamm. Genome, 2006, 17(6), 509-517.
Storey, J.D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA, 2003, 100(16), 9440-9445.
Kendziorski, C.M.; Chen, M.; Yuan, M.; Lan, H.; Attie, A.D. Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics, 2006, 62(1), 19-27.
Sun, Y.V.; Hu, Y.J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv. Genet., 2016, 93, 147-190.
Sun, L.; Dimitromanolakis, A. PREST-plus identifies pedigree errors and cryptic relatedness in the GAW18 sample using genome-wide SNP data. BMC Proc., 2014, 8(Suppl. 1), S23.
Kruglyak, L.; Daly, M.J.; Reeve-Daly, M.P.; Lander, E.S. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am. J. Hum. Genet., 1996, 58(6), 1347-1363.
Li, S.S.; Cheng, J.J.; Zhao, L.P. Empirical vs. Bayesian approach for estimating haplotypes from genotypes of unrelated individuals. BMC Genet., 2007, 8, 2.
Price, A.L.; Patterson, N.J.; Plenge, R.M.; Weinblatt, M.E.; Shadick, N.A.; Reich, D. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet., 2006, 38(8), 904-909.
Horvath, S.; Xu, X.; Lake, S.L.; Silverman, E.K.; Weiss, S.T.; Laird, N.M. Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet. Epidemiol., 2004, 26(1), 61-69.
Mayhew, A.J.; Meyre, D. Assessing the heritability of complex traits in humans: methodological challenges and opportunities. Current genomics, 2017, 18(4), 332-340.

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Article Details

Year: 2019
Published on: 16 June, 2019
Page: [172 - 183]
Pages: 12
DOI: 10.2174/1389202920666190617094930
Price: $65

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