Combined Association Analysis Research Articles

The quantitative trait linkage disequilibrium test: a more powerful alternative to the quantitative transmission disequilibrium test for use in the absence of population stratification

Linkage analysis based on identity-by-descent allele-sharing can be used to identify a chromosomal region harboring a quantitative trait locus (QTL), but lacks the resolution required for gene identification. Consequently, linkage disequilibrium (association) analysis is often employed for fine-mapping. Variance-components based combined linkage and association analysis for quantitative traits in sib pairs, in which association is modeled as a mean effect and linkage is modeled in the covariance structure has been extended to general pedigrees (quantitative transmission disequilibrium test, QTDT). The QTDT approach accommodates data not only from parents and siblings, but also from all available relatives. QTDT is also robust to population stratification. However, when population stratification is absent, it is possible to utilize even more information, namely the additional information contained in the founder genotypes. In this paper, we introduce a simple modification of the allelic transmission scoring method used in the QTDT that results in a more powerful test of linkage disequilibrium, but is only applicable in the absence of population stratification. This test, the quantitative trait linkage disequilibrium (QTLD) test, has been incorporated into a new procedure in the statistical genetics computer package SOLAR. We apply this procedure in a linkage/association analysis of an electrophysiological measurement previously shown to be related to alcoholism. We also demonstrate by simulation the increase in power obtained with the QTLD test, relative to the QTDT, when a true association exists between a marker and a QTL.

Genetic Association Analysis Using Data from Triads and Unrelated Subjects

The selection of an appropriate control sample for use in association mapping requires serious deliberation. Unrelated controls are generally easy to collect, but the resulting analyses are susceptible to spurious association arising from population stratification. Parental controls are popular, since triads comprising a case and two parents can be used in analyses that are robust to this stratification. However, parental controls are often expensive and difficult to collect. In some situations, studies may have both parental and unrelated controls available for analysis. For example, a candidate-gene study may analyze triads but may have an additional sample of unrelated controls for examination of background linkage disequilibrium in genomic regions. Also, studies may collect a sample of triads to confirm results initially found using a traditional case-control study. Initial association studies also may collect each type of control, to provide insurance against the weaknesses of the other type. In these situations, resulting samples will consist of some triads, some unrelated controls, and, possibly, some unrelated cases. Rather than analyze the triads and unrelated subjects separately, we present a likelihood-based approach for combining their information in a single combined association analysis. Our approach allows for joint analysis of data from both triad and case-control study designs. Simulations indicate that our proposed approach is more powerful than association tests that are based on each separate sample. Our approach also allows for flexible modeling and estimation of allele effects, as well as for missing parental data. We illustrate the usefulness of our approach using SNP data from a candidate-gene study of psoriasis.

Genomic screen for loci associated with alcohol dependence in Mission Indians.

Alcohol dependence is a leading cause of morbidity and mortality in Native Americans, yet biological factors underlying the disorder in this ethnic group remain elusive. This study's aims were to map susceptibility loci for DSM-III-R alcohol dependence and two narrower alcohol-related phenotypes in Mission Indian families. Each participant gave a blood sample and completed an interview using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) that was used to make alcohol dependence diagnoses and the narrower phenotypes of withdrawal, and drinking severity. Genotypes were determined for a panel 791 microsatellite polymorphisms. Analyses of multipoint variance component LOD scores for the dichotomous DSM-III-R phenotype revealed no peak LOD scores that exceeded 2.0 at any chromosome location. Two chromosomes, 4 and 12, had peak LOD scores that exceeded 2 for the alcohol use severity phenotype and three chromosomes 6, 15, 16 were found to have peaks with LOD scores that exceeded 2 for the withdrawal phenotype. Evidence for linkage to chromosomes 4 and 15, and 16 have been reported previously for alcohol related phenotypes whereas no evidence has as yet been reported for chromosomes 6 and 12. Combined linkage and association analysis suggest that alcohol dehydrogenase 1B gene polymorphisms are partially responsible for the linkage result on chromosome 4 in this population. These results corroborate the importance of several chromosomal regions highlighted in prior segregation studies in alcoholism and further identify new regions of the genome that may be unique to either the restricted phenotypes evaluated or this population of Mission Indians.

Combined association and linkage analysis applied to the APOE locus.

Combined association and linkage analysis is a powerful tool for pinpointing functional quantitative traits (QTLs) responsible for regions of significant linkage identified in genome-wide scans. We applied this technique to apoE plasma levels and the APOEepsilon2/epsilon3/epsilon4 polymorphism in two Dutch twin cohorts of different age ranges. Across chromosome 19, short tandem repeats and the APOEepsilon2/epsilon3/epsilon4 polymorphism were genotyped in adolescent (aged 13-22 years) and adult (aged 34-62 years) Dutch twins. In both samples, evidence for indicative linkage with plasma apoE levels was found (maximum LOD score (MLS)=0.8, MLS=2.5, respectively) at 19q13.32. These linkage regions included the APOE locus. As expected, the APOEepsilon2/epsilon3/epsilon4 polymorphism was strongly associated with apoE plasma levels in both samples. An extension of the between/within families association test developed by Fulker et al. ([1999] Am. J. Hum. Genet. 64:259-267) showed that these associations were not due to population stratification. The combined association and linkage analyses revealed that the association of the APOEepsilon2/epsilon3/epsilon4 polymorphism with apoE plasma levels completely explained the linkage in the adolescent twins and partly in the adult twins.

Genetics of familial combined hyperlipidemia and risk of coronary heart disease.

Coronary heart disease is the leading cause of death in developed countries. This alarming statistic is partly attributable to lifestyle, and partly due to the genetic factors that make humans highly susceptible to atherosclerotic vascular disease. The principal metabolic causes of atherosclerosis include hyperlipidemia, hypertension, obesity, insulin resistance and diabetes mellitus. Here we discuss the aetiology of familial combined hyperlipidemia (FCHL), a highly atherogenic disorder affecting 1-2% of the Western world. Genome-wide linkage studies indicate that more than three genes contribute to the pernicious lipid profile of FCHL, and that these genes reside within the 1q21-23, 11p14.1-q12.1 and 16q22-24.1 chromosomal regions. Other loci include 1p31, 6q16.1-16.3 and 8p23.3-22, but the linkage data for these are not yet persuasive. Combined linkage and association analyses provide compelling evidence for the involvement of two distinct alleles at the APOA1/C3/A4/A5 gene cluster in the transmission of FCHL. An important lesson arising from the study of a complex genetic disorder, such as FCHL, that lacks a consensus on diagnostic criteria, is that an understanding of complex genetic disorders can derive from comparative analyses of genome-wide linkage data generated from conditions that share phenotypic overlap. The identification of potential genetic overlap between FCHL and the Metabolic Syndrome, which is estimated to affect 47 million Americans, promises to deliver new targets for reducing the risk of important conditions such as cardiovascular disease and stroke.

Frequencies of Apolipoprotein A4 Gene Polymorphisms and Association with Serum Lipid Concentrations in Two Healthy Spanish Populations

Apolipoprotein A4 (apoA4) plays a role in intestinal lipid absorption. Several experimental interventions have shown that common variations at residues 347 (Thr --> Ser) and 360 (Gln --> His) on apoA4 are associated with differences in plasma lipid response to dietary fat; however, association studies between these variants and plasma lipid concentrations in populations reveal mixed results. We examined the effects of these polymorphisms in 758 randomly selected subjects (mean age 36.7+/-9.5 years) from 2 Spanish regions differing in latitude and fat intake: Aragón and Comunidad Valenciana. Subjects were matched one to one by sex and age. Frequencies for the less common alleles were similar in both regions: 0.096 (95% CI: 0.111-0.081) for codon 360 and 0.196 (95% CI: 0.216-0.176) for codon 347. In men and women there was no association between the codon 360 polymorphism and total cholesterol or triglyceride levels. However, subjects carrying the 360His allele had low-density lipoprotein (LDL) cholesterol concentrations statistically lower than homozygotes for the 360Gln allele, even after further adjustment for sex, age, region, body mass index, and APOE polymorphism (p = 0.043). The less common allele at codon 347 (the 347Ser allele) was associated with increased LDL-cholesterol concentrations with a clear gene-dosage effect after multivariate adjustment (p = 0.029). Although these polymorphisms showed no heterogeneity by geographic region, the magnitude of the effect was higher in subjects from Aragón compared with the Comunidad Valenciana, suggesting a possible influence of the higher fat intake in Aragón. In the combined association analysis subjects with the 360Gln/347Ser pseudohaplotype had the highest LDL-cholesterol concentrations, supporting the antagonistic effect between the 360His and the 347Ser alleles on this trait.

Linkage and association between distinct variants of the APOA1/C3/A4/A5 gene cluster and familial combined hyperlipidemia.

Combined hyperlipidemia is a common disorder, characterized by a highly atherogenic lipoprotein profile and a substantially increased risk of coronary heart disease. The purpose of this study was to establish whether variations of apolipoprotein A5 (APOA5), a newly discovered gene of lipid metabolism located 30 kbp downstream of the APOA1/C3/A4 gene cluster, contributes to the transmission of familial combined hyperlipidemia (FCHL). We performed linkage and association tests on 128 families. Two independent alleles, APOA5c.56G and APOC3c.386G, of the APOA1/C3/A4/A5 gene cluster were overtransmitted in FCHL (P=0.004 and 0.007, respectively). This was paired with reduced transmission of the common APOA1/C3/A4/A5 haplotype (frequency 0.4461) to affected subjects (P=0.012). The APOA5c.56G genotype accounted for 7.3% to 13.8% of the variance in plasma triglyceride levels in probands (P<0.004). The APOC3c.386G genotypes accounted for 4.4% to 5.1% of the variance in triglyceride levels in FCHL spouses (P<0.007), suggesting that this allele marks a FCHL quantitative trait as well as representing a susceptibility locus for the condition. A combined linkage and association analysis establishes that variation at the APOA1/C3/A4/A5 gene cluster contributes to FCHL transmission in a substantial proportion of northern European families.

Mapping quantitative effects of oligogenes by allelic association.

Regression analysis of a quantitative trait as a function of a single diallelic polymorphism has been extended to allelic association by composite likelihood under the Malecot model for multiple markers. We applied the method to 10 single nucleotide polymorphisms (SNPs) spanning 27 kb of the angiotensin-I converting enzyme (ACE) gene in British families, localising a causal SNP between G2530A and 4656(CT)3/2 in the 3' region, at a distance of 21.6+/-0.9 kb from the most proximal SNP T-5491C. Neither they nor the I/D polymorphism is causal. To clarify genetic parameters we applied combined segregation, linkage and association analysis. Stronger evidence for the 3' region was obtained, with significant evidence of a lesser 5' effect as reported in French and Nigerian families. However, rigorous confirmation requires that the causal SNPs be identified. Both Malecot and parametric analysis appear to have high power by comparison with alternative methods for localizing oligogenes and their causal polymorphisms.

Combined association, segregation and aggregation analysis on case-control family data.

Recently genetic epidemiologists have begun using case-control family study designs to investigate the role of genetic and environmental risk factors in disease etiology. The objective of these studies is to assess the association of environmental factors with the disease trait; to characterize the disease genes using segregation analysis; and to quantify the residual familial aggregation after controlling for environmental and genetic factors. Typically these objectives are achieved by conducting separate studies and analysis. This paper describes an estimating equation based approach for a combined association, segregation and aggregation analysis on data from case-control family studies. Simulations indicate that the method performs well in a variety of settings. The method is illustrated using simulated family history data made available to participants in a recent Genetic Analysis Workshop.

High-resolution genetic mapping of the ACE-linked QTL influencing circulating ACE activity.

Fine-mapping of trait loci through combined linkage and association analysis is an important component of strategies designed to identify causative gene variants, particularly in situations where the trait may be influenced by one or more of many polymorphisms within the same gene. Angiotensin-1 converting enzyme (ACE) provides one of the best models for developing and testing such methodologies, as a major fraction of the heritable variation in the activity of the angiotensin-1 converting enzyme (ACE) is tightly linked to the ACE gene. Moreover, ACE contains many frequent polymorphisms that are in strong linkage disequilibrium with each other. Although none of these variants induces a significant amino-acid change, one or more, either singly or in combination, are likely to have a strong effect on the quantitative phenotype. Here, we show that measured-haplotype analysis of SNP data from a large European family cohort can be used to localise the major ACE-linked genetic factors influencing the trait to a 16 kb interval within the gene, thus limiting the number of ACE variants that need to be considered in future studies designed to elucidate their biological effects. The approaches developed will be applicable to the fine-mapping of other quantitative trait loci in humans.

Mapping quantitative effects of oligogenes by allelic association

Regression analysis of a quantitative trait as a function of a single diallelic polymorphism has been extended to allelic association by composite likelihood under the Malecot model for multiple markers. We applied the method to 10 single nucleotide polymorphisms (SNPs) spanning 27 kb of the angiotensin‐I converting enzyme (ACE) gene in British families, localising a causal SNP between G2530A and 4656(CT)3/2 in the 3′ region, at a distance of 21.6±0.9 kb from the most proximal SNP T‐5491C. Neither they nor the I/D polymorphism is causal. To clarify genetic parameters we applied combined segregation, linkage and association analysis. Stronger evidence for the 3′ region was obtained, with significant evidence of a lesser 5′ effect as reported in French and Nigerian families. However, rigorous confirmation requires that the causal SNPs be identified. Both Malecot and parametric analysis appear to have high power by comparison with alternative methods for localizing oligogenes and their causal polymorphisms.

A candidate gene study of F cell levels in sibling pairs using a joint linkage and association analysis

AbstractIntroduction Fetal haemoglobin (Hb F) and fetal cell (FC) levels in adults show considerable variation and the heritability of FC levels has been estimated to be 89% in the healthy European population; measured genotype analyses have shown the trait to be influenced by several genetic factors. In addition to the β‐globin gene complex on chromosome 11p, linkage analyses have reported loci affecting FC levels on chromosomes Xp22.2–p22.3 and 6q23.Methods We have genotyped a sample of approximately 300 unselected, same sex, dizygotic twin pairs from the St. Thomas’ UK Adult Twin Register for markers in these three regions and carried out a linkage analysis of FC levels. We also used a new method to simultaneously test for linkage and allelic association, and association caused by population stratification or admixture.Results There was no evidence for linkage of FC levels to chromosomes Xp22.2–p22.3 and 6q23. However, the β‐globin cluster was shown to be significantly linked and to be responsible for approximately one‐third of the additive genetic variance in FC levels in the sample.Conclusions The report represents the first application of this method to a sample of nonsimulated data and demonstrates the effectiveness of the approach. Combined linkage and association analysis of the β‐globin complex showed a strong association between the XmnI‐Gγ site and FC levels and that the observed association is not an artifact of recent population admixture or stratification.

Population-based family study designs: an interdisciplinary research framework for genetic epidemiology.

Most complex traits such as cancer and coronary heart diseases are attributed either to heritable factors or to environmental factors or to both. Dissecting the genetic and environmental etiology of complex traits thus requires an interdisciplinary research strategy. Genetic studies generally involve families and investigate familial aggregations of traits, segregation of major disease genes, and locations of disease genes on the human genome, the latter of which can be identified via linkage analysis. Epidemiologic studies often use population-based case-control studies to establish the role of specific environmental factors. Integrating both objectives, genetic epidemiology is to assess the associations of environmental factors with disease status, to quantify the aggregation of cases within families, to characterize putative disease genes via segregation analysis, and to localize disease genes via linkage analysis with genetic markers. To accomplish these objectives through designed studies, we propose a class of population-based family study designs, which are formed by choosing among sampling designs at three stages. The objectives of sampling at these three stages are 1) combined aggregation and association analysis, 2) combined segregation, aggregation, and association analysis, and 3) combined linkage, segregation, aggregation, and association analysis. These designs form an interdisciplinary research framework for genetic epidemiology. Our preliminary exploration of this framework and related analytic methods indicates that population-based family study designs retain the efficiency of linkage analysis for localizing disease genes without losing the property of being population-based, and they will therefore allow an assessment of a joint contribution of genetic and environmental factors to complex traits.

Population–based family study designs: An interdisciplinary research framework for genetic epidemiology

Most complex traits such as cancer and coronary heart diseases are attributed either to heritable factors or to environmental factors or to both. Dissecting the genetic and environmental etiology of complex traits thus requires an interdisciplinary research strategy. Genetic studies generally involve families and investigate familial aggregations of traits, segregation of major disease genes, and locations of disease genes on the human genome, the latter of which can be identified via linkage analysis. Epidemiologic studies often use population-based case-control studies to establish the role of specific environmental factors. Integrating both objectives, genetic epidemiology is to assess the associations of environmental factors with disease status, to quantify the aggregation of cases within families, to characterize putative disease genes via segregation analysis, and to localize disease genes via linkage analysis with genetic markers. To accomplish these objectives through designed studies, we propose a class of population-based family study designs, which are formed by choosing among sampling designs at three stages. The objectives of sampling at these three stages are 1) combined aggregation and association analysis, 2) combined segregation, aggregation, and association analysis, and 3) combined linkage, segregation, aggregation, and association analysis. These designs form an interdisciplinary research framework for genetic epidemiology. Our preliminary exploration of this framework and related analytic methods indicates that population-based family study designs retain the efficiency of linkage analysis for localizing disease genes without losing the property of being population-based, and they will therefore allow an assessment of a joint contribution of genetic and environmental factors to complex traits. Genet. Epidemiol. 14:365–388,1997. © 1997 Wiley-Liss, Inc.

Genetic Analysis Workshop II: combined segregation, linkage, and association analysis.

A combined segregation, linkage, and association analysis using the program COMBIN was performed on the simulated pedigree data prepared for the Second Genetic Analysis Workshop. The model used in COMBIN is described and the presented results illustrate its effectiveness in the analysis of such data. Linkage analysis was performed and maps for each linkage group are presented.

Genetic Analysis Workshop II: multiple-locus segregation analysis incorporating linkage markers.

The workshop data were examined using a newly developed methodology (MILINK, Risch, 1984) for combined segregation, linkage, and association analysis of a complex disease trait in pedigree data. Results from problems two and three suggest that the method is powerful both for determining mode of disease inheritance and for resolution of linkage disequilibrium versus pleiotrophy (with epistasis) of marker alleles.