Design considerations for association studies of candidate genes in families

Bias of Two-Level Scalability Coefficients and Their Standard Errors

Two-level Mokken scale analysis is a generalization of Mokken scale analysis for multi-rater data. The bias of estimated scalability coefficients for two-level Mokken scale analysis, the bias of their estimated standard errors, and the coverage of the confidence intervals has been investigated, under various testing conditions. It was found that the estimated scalability coefficients were unbiased in all tested conditions. For estimating standard errors, the delta method and the cluster bootstrap were compared. The cluster bootstrap structurally underestimated the standard errors of the scalability coefficients, with low coverage values. Except for unequal numbers of raters across subjects and small sets of items, the delta method standard error estimates had negligible bias and good coverage. Post hoc simulations showed that the cluster bootstrap does not correctly reproduce the sampling distribution of the scalability coefficients, and an adapted procedure was suggested. In addition, the delta method standard errors can be slightly improved if the harmonic mean is used for unequal numbers of raters per subject rather than the arithmetic mean.

Genetic association analysis using sibship data: a multilevel model approach

Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.

Methods for interaction analyses using family-based case-control data: conditional logistic regression versus generalized estimating equations

A complex web of gene-gene and gene-environment interactions likely underlies late-onset disease development. We compared conditional logistic regression (CLR) and generalized estimating equations (GEE) in modeling such interactions in pedigrees with missing parents. Using the simulation of linkage and association (SIMLA) program, disease genes, an environmental risk factor, gene-gene interaction, and gene-environment interaction were generated in family-based data sets. Four scenarios for the relationship between the marker and disease loci were examined: linkage and association, linkage without association, association without linkage, and absence of both linkage and association. Models for CLR and GEE (with exchangeable and independence correlation matrices) were built, and type I error, power, average odds ratio (OR), standard deviation, and 95% confidence intervals were estimated. CLR and GEE were valid tests of association in the presence of linkage, but type I error was inflated for association without linkage, particularly with GEE. CLR generated estimates of the OR with lower bias but often more variability than the OR estimates observed for GEE. Further, GEE was more powerful than CLR in detecting main and interactive effects. Although GEE with both matrices had similar power, use of the independence matrix resulted in lower type I error and less biased OR estimation as compared to the exchangeable matrix. Our findings support the use of GEE in maximizing power to detect gene-gene and gene-environment interactions but caution its use under potential association without linkage (e.g., population stratification) and the interpretation of its OR estimates.

Characterization of LD structures and the utility of HapMap in genetic association studies

Observed distribution of and variation in linkage disequilibrium (LD) with respect to the evolution history and disease transmission in a population is the driving force behind the current wave of genome-wide association (GWA) studies of complex human diseases. An extensive literature covers topics from haplotype analysis that utilizes local LD structures in candidate genes and regions to genome-wide organization of LD blocks (neighborhood) that led to the development of International HapMap Project and panels of "tagSNPs" used by current GWA studies. In this chapter, we examine the scenarios where each of the major types of analysis methods may be applicable and where the current popular genotyping platforms for GWA might come short. We discuss current association analysis methods by emphasizing their reliance on the local LD structures or the global organization of the LD structures, and highlight the need to consider individual marker information content in large-scale association mapping.

Confidence intervals for candidate gene effects and environmental factors in population-based association studies of families

Complex diseases are influenced by both genetic and environmental factors. Studies of individuals or of families can be used to examine the association of genetic factors, such as candidate genes, and other risk factors with the presence or absence of complex disorders. If families are investigated, whether or not they are randomly ascertained, possible familial correlation among observations must be considered. We have compared two statistical approaches for analyzing correlated binary data from randomly ascertained nuclear families. The generalized estimating equations approach (GEE) can be used to adjust for familial correlation. The relationship between covariates and the response is modelled, and the correlations among family members are treated as nuisance parameters. For comparison, we have proposed two strategies from a hierarchical nonparametric bootstrap approach. One strategy (S1) samples family units, preserving the structure and correlation within each family. A second and novel strategy (S2) also samples family units but then randomly samples offspring with replacement in each family. We applied the methods to data from a study of cardiovascular disease, and followed up with a simulation study in which family data were generated from an underlying multifactorial genetic model. Although the bootstrap approach was more computationally demanding, it outperformed the GEE in terms of confidence interval coverage probabilities for all sample sizes considered.

A population- and family-based study of Canadian families reveals association of HLA DRB1*0103 with colonic involvement in inflammatory bowel disease

The aim of this study was to identify major histocompatibility complex alleles associated with the development and clinical features of inflammatory bowel disease (IBD). Genotyping at the human leukocyte antigen (HLA) DRB1 and DQB1 loci was performed on individuals from 118 Caucasian IBD sibling pair families and on 216 healthy controls. Both population- and family-based association tests were used to analyze data obtained on the entire study population and on clinical subgroups stratified by diagnosis, ethnicity, and disease distribution. HLA DRB1*0103 was significantly associated with IBD (OR = 6.0, p = 0.0001) in a case-control analysis of non-Jewish IBD-affected individuals. This association was apparent among both Crohn's disease (OR = 5.23, p = 0.0007) and ulcerative colitis (OR = 7.9, p = 0.0001) patients and was confirmed in the non-Jewish IBD population by results of family-based association analysis using the transmission disequilibrium test. HLA DQB1*0501 was also associated with IBD (OR = 1.64, p = 0.02) in the non-Jewish population. but statistically significant association of this allele with disease was not detected for Crohn's disease and ulcerative colitis separately. No significant associations were identified among the Jewish patients. In the non-Jewish IBD families, IBD was as strongly associated with the DRB1*0103 DQB1*0501 haplotype as with the DRB1*0103 allele alone. The carrier frequency of the DRB1*0103 allele was found to be 10-fold higher in Crohn's disease patients with pure colonic involvement than in healthy controls (38.5% vs. 3.2%; p = 0.0002). These data demonstrate the association of the HLA DRB1*0103 allele with both Crohn's disease and ulcerative colitis and with large intestine-restricted disease in non-Jewish IBD patients and therefore identify HLA DRB1*0103 as a potentially important contributor to disease susceptibility and to expression of colonic involvement in IBD.

Elevated dehydroepiandrosterone sulfate levels as the reproductive phenotype in the brothers of women with polycystic ovary syndrome

There is an inherited susceptibility to polycystic ovary syndrome (PCOS). Some investigators have suggested that premature male-pattern balding is a male phenotype in PCOS families, but this remains controversial. We recently reported evidence for an autosomal monogenic abnormality in ovarian and adrenal steroidogenesis in the sisters of women with PCOS. We performed this study to determine whether we could identify a clinical or biochemical phenotype in the brothers of women with PCOS. One hundred nineteen brothers of 87 unrelated women with PCOS and 68 weight- and ethnicity-comparable unrelated control men were examined and had fasting blood samples obtained. The odds of balding (Hamilton score > or = V) did not differ in the brothers of PCOS women compared with control men. Brothers of women with PCOS had significantly elevated dehydroepiandrosterone sulfate (DHEAS) levels [brothers 3035 +/- 1132 ng/ml (mean +/- SD) vs. control men 2494 +/- 1172 ng/ml; P < 0.05]. There was a significant positive linear relationship between DHEAS levels in PCOS probands and their brothers (r = 0.35; P = 0.001). There was no significant bimodal distribution in DHEAS levels, and there were no significant differences in other parameters in brothers of PCOS women with high DHEAS levels compared with those with low DHEAS levels. There is familial clustering of elevated DHEAS levels in the brothers of women with PCOS, suggesting that this is a genetic trait. This might reflect the same underlying defect in steroidogenesis that we found in the sisters of women with PCOS. Balding was not increased in the brothers of women with PCOS. We conclude that there is a biochemical reproductive endocrine phenotype in men in PCOS families.