A note on a conditional-likelihood approach for family-based association studies of candidate genes

Estimating the penetrance of pathogenic gene variants in families with missing pedigree information

Accurate assessment of the age-dependent disease risk conferred by germline variants in disease susceptibility genes is often hampered by the way the data are collected. Cohort-based data sets frequently contain an overrepresentation of patients (i.e. carriers of the gene variant of interest affected with the associated disease), and an underrepresentation of disease-free carriers. In order to overcome this problem, penetrance estimates can be based on family-based study designs, through the evaluation of index patients and their family members. This approach facilitates the identification of asymptomatic germline variant carriers. By adjusting for the way these family data are ascertained, an estimate for the penetrance of the pathogenic gene variant can be obtained. However, the family structure is often incomplete or missing. This complicates the estimation of the penetrance, because full adjustment of the likelihood is not possible. We present a conditional likelihood for the estimation of the penetrance of pathogenic gene variants, based on a cohort of multiple families comprising index patients, disease-free and affected non-index carriers, but with missing information on pedigree structure. The proposed estimator corrects for the ascertainment in a robust way and is shown to be more accurate than the frequently used Kaplan–Meier estimator of the penetrance function.

Estimating haplotype relative risks in complex disease from unphased SNPs data in families using a likelihood adjusted for ascertainment

The understanding of complex diseases and insights to improve their medical management may be achieved through the deduction of how specific haplotypes may play a joint effect to change relative risk information. In this paper we describe an ascertainment adjusted likelihood-based method to estimate haplotype relative risks using pooled family data coming from association and/or linkage studies that were used to identify specific haplotypes. Haplotype-based analysis tends to require a large amount of parameters to capture all the information that leads to efficiency problems. An adaptation of the Stochastic Expectation Maximization algorithm is used for haplotypes inference from genotypic data and to reduce the number of nuisance parameters for risk estimation. Using different simulations, we show that this method provides unbiased relative risk estimates even in case of departure from Hardy-Weinberg equilibrium.

Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects

Family-based case-control studies are popularly used to study the effect of genes and gene-environment interactions in the etiology of rare complex diseases. We consider methods for the analysis of such studies under the assumption that genetic susceptibility (G) and environmental exposures (E) are independently distributed of each other within families in the source population. Conditional logistic regression, the traditional method of analysis of the data, fails to exploit the independence assumption and hence can be inefficient. Alternatively, one can estimate the multiplicative interaction between G and E more efficiently using cases only, but the required population-based G-E independence assumption is very stringent. In this article, we propose a novel conditional likelihood framework for exploiting the within-family G-E independence assumption. This approach leads to a simple and yet highly efficient method of estimating interaction and various other risk parameters of scientific interest. Moreover, we show that the same paradigm also leads to a number of alternative and even more efficient methods for analysis of family-based case-control studies when parental genotype information is available on the case-control study participants. Based on these methods, we evaluate different family-based study designs by examining their relative efficiencies to each other and their efficiencies compared to a population-based case-control design of unrelated subjects. These comparisons reveal important design implications. Extensions of the methodologies for dealing with complex family studies are also discussed.

Estimating penetrance from family data using a retrospective likelihood when ascertainment depends on genotype and age of onset

In diseases caused by deleterious gene mutations, knowledge of age-specific cumulative risks is necessary for medical management of mutation carriers. When pedigrees are ascertained through several affected persons, ascertainment bias can be corrected by using a retrospective likelihood. This likelihood is a function of the genotypes of pedigree members given their phenotypes and provides unbiased estimates of penetrance without modeling the selection process, provided that selection is independent of genotypes. However, since mutation testing is offered only to relatives of mutation carriers, the genotypes of family members are available only in mutated families and selection does depend on genotype. In the present study, we quantified the bias due to selection on genotype using simulations. We found that this bias depended on the true penetrance value: the lower the penetrance, the higher the bias (risk by age 80 estimated to be 46% for a true penetrance value of 20%). When age of onset is added to the selection criteria, as usually done, we showed that the bias was even higher. We modified the conditioning in the retrospective likelihood, what we call "genotype restricted likelihood" (GRL). Using simulations, we show that this method provided unbiased parameter estimates under all the selection designs considered.

Spouses and unrelated friends of probands as controls for stroke genetics studies

To plan a multisite, ischemic stroke genetic study, stroke patients were surveyed about the availability and characteristics of a convenience sample of spouse/friend controls. 65% of all stroke-affected probands reported a living spouse. A more detailed survey was conducted at the University of Virginia, Charlottesville, Va., USA: 51% of stroke patients reported a living, stroke-free spouse who would be willing to serve as a control, and 49% reported having a stroke-free friend who would be willing to serve as a control. Overall, 75% of stroke patients reported at least 1 individual willing to participate as a control. Cases without an identified control were more likely to be non-white (48%) than were cases with a control (13%; p = 0.00004). Cases were older than controls (67.3 vs. 59.2 years; p = 0.000002), and a greater proportion of cases than controls were male (57 vs. 33%; p = 0.0002). Without proper attention to matching, the use of a spouse/friend convenience sample would result in imbalances in basic demographic characteristics.

Polymorphic repeats in the androgen receptor gene in high-risk sibships

BACKGROUND

Genetic susceptibility may explain some familial clusters of prostate cancer. The polymorphic androgen receptor (AR) gene, which mediates androgen activity in the prostate, is a candidate gene that may influence predisposition to the disease.

METHODS

We analyzed the polymorphic (CAG)n and (GGN)n repeats within the AR gene in men from 51 high-risk prostate cancer sibships, which included at least one affected and one unaffected man (n = 210). We compared repeat lengths of men with prostate cancer (n = 140) to their brothers (n = 70) without disease, stratified by median age at diagnosis of affected men within each sibship. Conditional logistic regression was used to compute odds ratios (OR) and 95% confidence intervals to evaluate associations between prostate cancer and repeat length.

RESULTS

The OR for prostate cancer associated with short (CAG)n repeats (< 22) compared to longer repeats (> or =22) was 1.13 (95% CI 0.5-2.4) overall, but was higher in sibships with a median age of <66 years at diagnosis (OR = 1.72, 95% CI 0.5-6.0). The (GGN)n array also was not associated with prostate cancer in general. However, in older men (> or = 66 years), there was a modest elevation in risk (OR = 1.56, 95% CI 0.6-4.1) among those with short repeats (GGN of < or =16). Men with both a short (CAG)n (< 22) and a short (GGN)n (< or =16) array were not at higher risk (OR = 1.06) compared to men with two long repeats [(CAG)n > or =22 and (GGN)n >16)].

CONCLUSIONS

These results suggest that the (CAG)n and (GGN)n repeats in the AR gene do not play a major role in familial prostate cancer.