Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism.

paper Cited Public

Authors: Doan, Betty Q; Frangakis, Constantine E; Shugart, Yin Y; Bailey-Wilson, Joan E
Year: 2005
Journal: BMC genetics
PMID: 16451643
DOI: 10.1186/1471-2156-6-S1-S33
PMCID: PMC1866752

BACKGROUND: Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated. RESULTS: Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12. CONCLUSION: These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.

#	Section	Preview
0	Background	Alcohol dependence has been shown to cluster in families. Multiple linkage analyses have been…
1	Background	the score is defined as the conditional probability of being assigned to a treatment group given the…
2	Methods — Study population and data collection	The study population consisted of families ascertained by the COGA. The COGA study and the data…
3	Methods — Genome-wide linkage analysis using covariates	Covariate-based affected relative pair linkage analysis using single-point identity-by-descent (IBD)…
4	Methods — Genome-wide linkage analysis using covariates	These PS values were derived from a logistic regression of affection status on the covariate data,…
5	Methods — Genome-wide linkage analysis using covariates	where xj = the jth covariate
6	Methods — Genome-wide linkage analysis using covariates	The affection status was coded as 1 for affected and 0 for unaffected. This logistic regression was…
7	Methods — Determination of significance and comparison of linkage evidence across analysis methods	Significance was determined by permutation testing. Affection status coupled with its covariate…
8	Results and Discussion	Table 1 displays the overall p-value and the number of significant markers across the genome…
9	Results and Discussion	These results suggest that incorporating multiple covariates together may be more productive than…
10	Results and Discussion	variable resulted in one of the lowest ORs (0.108 ± 0.019) of any covariates. For PS2 and PS5,…
11	Results and Discussion	To examine whether the markers yielding significant evidence for linkage were consistent across the…
12	Results and Discussion	D7S509 (15)), and chromosome 12 (#221 D12S1090 (8)). Evidence on chromosome 7 has been widely…
13	Conclusion	The incorporation of covariate information into a linkage analysis can potentially increase the…
14	Conclusion	selection of covariates to include into the analysis method must be done carefully and the…
15	Abbreviations	COGA: Collaborative Study on the Genetics of Alcoholism
16	Abbreviations	GAW14: Genetic Analysis Workshop 14
17	Abbreviations	IBD: Identity by descent
18	Abbreviations	OR: Odds ratio
19	Abbreviations	PS: Propensity score

Citation	PMID	DOI	Status
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, revised, 1987	—	—	—
Bergen, AW et al., BMC Genetics, 2003, Genetic regions linked to alcohol consumption in the Framingham Heart Study	14975169	10.1186/1471-2156-4-S1-S101	Cited
Doan, BQ et al., Genet Epidemiol, 2003, Power to detect linkage using covariates [abstract]	—	—	—
Doan, BQ et al., Genet Epidemiol, 2004, Statistical properties of the propensity score as a single covariate in covariate-based linkage analysis [abstract]	—	—	—
Feighner, JP et al., Arch Gen Psychiatry, 1972, Diagnostic criteria for use in psychiatric research	5009428	10.1001/archpsyc.1972.01750190059011	Cited
Goddard, KA et al., Am J Hum Genet, 2001, Model-free linkage analysis with covariates confirms linkage of prostate cancer to chromosomes 1 and 4	11309685	10.1086/320103	Cited
Hill, SY et al., Am J Med Genet, 2004, A genome wide search for alcoholism susceptibility genes	15211641	10.1002/ajmg.b.30013	Cited
Ma, JZ et al., BMC Genetics, 2003, Mapping susceptibility loci for alcohol consumption using number of grams of alcohol consumed per day as a phenotype measure	14975172	10.1186/1471-2156-4-S1-S104	Cited
Olson, JM, Am J Hum Genet, 1999, A general conditional-logistic model for affected-relative-pair linkage studies	10577930	10.1086/302662	Cited
Reich, T et al., Am J Hum Genet, 1999, Psychiatric genetics '99: genetic studies of alcoholism and substance dependence	10441565	10.1086/302561	Primary
Reich, T et al., Am J Med Genet, 1998, Genome-wide search for genes affecting the risk for alcohol dependence	9603606	10.1002/(SICI)1096-8628(19980508)81:3<207::AID-AJMG1>3.0.CO;2-T	Primary
Rosenbaum, PR et al., Biometrika, 1983, The central role of the propensity score in observational studies for causal effects	—	10.2307/2335942	Cited
StataCorp, STATAv82, 2003	—	—	—
Statistical Solutions Ltd, SAGE Statistical Analysis for Genetic Epidemiology, 2004	—	—	—

Title	Authors	Journal	Year	Link
Familial aggregation and linkage analysis with covariates for metabolic syndrome risk factors.	Naseri P et al.	—	2018	→
Evidence for genes on chromosome 2 contributing to alcohol dependence with conduct disorder and suicide attempts.	Dick DM et al.	—	2010	→
Genetic linkage of region containing the CREB1 gene to depressive disorders in families with recurrent, early-onset, major depression: a re-analysis and confirmation of sex-specific effect.	Maher BS et al.	—	2010	→
A genomic imprinting test for ordinal traits in pedigree data.	Feng R et al.	—	2008	→
A score test for linkage analysis of ordinal traits based on IBD sharing.	Feng R et al.	—	2008	→
Linkage mapping methods applied to the COGA data set: presentation Group 4 of Genetic Analysis Workshop 14.	Daw EW et al.	—	2005	→

Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism.

In this knowledge base

External