Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-18T12:41:30.188Z Has data issue: false hasContentIssue false

Statistical challenges for genome-wide association studies of suicidality using family data

Published online by Cambridge University Press:  16 April 2020

J. Lasky-Su*
Affiliation:
Channing Laboratory, Brigham and Women's Hospital, Boston, 181, Longwood Ave., Boston, MA 02115, MA, USA Department of Medicine, Harvard Medical School, Boston, MA, USA Center for Genomic Medicine, Brigham and Women's Hospital, Boston, MA, USA
C. Lange
Affiliation:
Channing Laboratory, Brigham and Women's Hospital, Boston, 181, Longwood Ave., Boston, MA 02115, MA, USA Center for Genomic Medicine, Brigham and Women's Hospital, Boston, MA, USA Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
*
*Corresponding author. Tel.: +978 465 5398; fax: +617 525 0958. E-mail addresses: [email protected] (J. Lasky-Su).
Get access

Abstract

The etiology of suicide is complex in nature with both environmental and genetic causes that are extremely diverse. This extensive heterogeneity weakens the relationship between genotype and phenotype and as a result, we face many challenges when studying the genetic etiology of suicide. We are now in the midst of a genetics revolution, where genotyping costs are decreasing and genotyping speed is increasing at a fast rate, allowing genetic association studies to genotype thousands to millions of SNPs that cover the entire human genome. As such, genome-wide association studies (GWAS) are now the norm. In this article we address several statistical challenges that occur when studying the genetic etiology of suicidality in the age of the genetics revolution. These challenges include: (1) the large number of statistical tests; (2) complex phenotypes that are difficult to quantify; and (3) modest genetic effect sizes. We address these statistical issues in the context of family-based study designs. Specifically, we discuss several statistical extensions of family-based association tests (FBATs) that work to alleviate these challenges. As our intention is to describe how statistical methodology may work to identify disease variants for suicidality, we avoid the mathematical details of the methodologies presented.

Type
Original article
Copyright
Copyright © Elsevier Masson SAS 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bertram, L, Lange, C, Mullin, K, Parkinson, M, Hsiao, M, Hogan, MFet al.Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE. Am J Hum Genet 2008;83:623632.10.1016/j.ajhg.2008.10.008CrossRefGoogle ScholarPubMed
Chapman, JM, Cooper, JD, Todd, JA, Clayton, DGDetecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. Hum Hered 2003;56:1831.CrossRefGoogle ScholarPubMed
Clayton, D, Chapman, J, Cooper, JUse of unphased multilocus genotype data in indirect association studies. Genet Epidemiol 2004;27:415428.10.1002/gepi.20032CrossRefGoogle ScholarPubMed
Herbert, A, Gerry, NP, McQueen, MB, Heid, IM, Pfeufer, A, Illig, Tet al.A common genetic variant is associated with adult and childhood obesity. Science 2006;312:279283.10.1126/science.1124779CrossRefGoogle ScholarPubMed
Horvath, S, Xu, X, Lake, SL, Silverman, EK, Weiss, ST, Laird, NMFamily-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet Epidemiol 2004;26:6169.10.1002/gepi.10295CrossRefGoogle ScholarPubMed
Ionita-Laza, I, McQueen, MB, Laird, NM, Lange, CGenomewide weighted hypothesis testing in family-based association studies, with an application to a 100K scan. Am J Hum Genet 2007;81:607614.10.1086/519748CrossRefGoogle ScholarPubMed
Lange, C, Laird, NMOn a general class of conditional tests for family-based association studies in genetics: the asymptotic distribution, the conditional power, and optimality considerations. Genet Epidemiol 2002;23:165180.10.1002/gepi.209CrossRefGoogle ScholarPubMed
Lange, C, DeMeo, D, Silverman, EK, Weiss, ST, Laird, NMUsing the noninformative families in family-based association tests: a powerful new testing strategy. Am J Hum Genet 2003;73:801811.10.1086/378591CrossRefGoogle ScholarPubMed
Lange, C, DeMeo, D, Silverman, EK, Weiss, ST, Laird, NMPBAT: tools for family-based association studies. Am J Hum Genet 2004;74:367369.CrossRefGoogle ScholarPubMed
Lange, C, van Steen, K, Andrew, T, Lyon, H, DeMeo, DL, Raby, Bet al.A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects. Stat Appl Genet Mol Biol 2004;3:127.10.2202/1544-6115.1067CrossRefGoogle ScholarPubMed
Lasky-Su, J, Banaschewski, T, Buitelaar, J, Franke, B, Brookes, K, Sonuga-Barke, Eet al.Partial replication of a DRD4 association in ADHD individuals using a statistically derived quantitative trait for ADHD in a family-based association test. Biol Psychiatry 2007;62:985990.10.1016/j.biopsych.2007.03.006CrossRefGoogle Scholar
Lasky-Su, J, Lyon, HN, Emilsson, V, Heid, IM, Molony, C, Raby, BAet al.On the replication of genetic associations: timing can be everything!. Am J Hum Genet 2008;82:849858.10.1016/j.ajhg.2008.01.018CrossRefGoogle ScholarPubMed
Lasky-Su, J, Neale, BM, Franke, B, Anney, RJ, Zhou, K, Maller, JBet al.Genome-wide association scan of quantitative traits for attention deficit hyperactivity disorder identifies novel associations and confirms candidate gene associations. Am J Med Genet B Neuropsychiatr Genet 147B 2008 13451354.CrossRefGoogle ScholarPubMed
Lasky-Su, J, Lange, C, Biederman, J, Tsuang, M, Doyle, AE, Smoller, JWet al.Family-based association analysis of a statistically derived quantitative traits for ADHD reveal an association in DRD4 with inattentive symptoms in ADHD individuals. Am J Med Genet B Neuropsychiatr Genet 147B 2008 100106.CrossRefGoogle ScholarPubMed
Lasky-Su, J, Murphy, A, McQueen, MB, Weiss, ST, Lange, CAn omnibus test for family-based association studies with multiple SNPs and multiple phenotype. Eur J Hum Genet 2010.CrossRefGoogle Scholar
Rabinowitz, D, Laird, NA unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Heredity 2000;50:211223.10.1159/000022918CrossRefGoogle ScholarPubMed
Rakovski, CS, Xu, X, Lazarus, R, Blacker, D, Laird, NMA new multimarker test for family-based association studies. Genet Epidemiol 2007;31:917.CrossRefGoogle ScholarPubMed
Spielman, RS, McGinnis, RE, Ewens, WJTransmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993;52:506516.Google Scholar
Van Steen, K, McQueen, MB, Herbert, A, Raby, B, Lyon, H, Demeo, DLet al.Genomic screening and replication using the same data set in family-based association testing. Nat Genet 2005;37:683691.CrossRefGoogle ScholarPubMed
Wasserman, D, Geijer, T, Sokolowski, M, Rozanov, V, Wasserman, JNature and nurture in suicidal behavior, the role of genetics: some novel findings concerning personality traits and neural conduction. Physiol Behav 2007;92:245249.CrossRefGoogle ScholarPubMed
Submit a response

Comments

No Comments have been published for this article.