Statistical Inference for Familial Disease Clusters

Abstract

In many epidemiologic studies, the first indication of an environmental or genetic contribution to the disease is the way in which the diseased cases cluster within the same family units. The concept of clustering is contrasted with incidence. We assume that all individuals are exchangeable except for their disease status. This assumption is used to provide an exact test of the initial hypothesis of no familial link with the disease, conditional on the number of diseased cases and the distribution of the sizes of the various family units. New parametric generalizations of binomial sampling models are described to provide measures of the effect size of the disease clustering. We consider models and an example that takes covariates into account. Ascertainment bias is described and the appropriate sampling distribution is demonstrated. Four numerical examples with real data illustrate these methods.

The genetic study of three population microisolates in South Tyrol (MICROS): study design and epidemiological perspectives

“…For each trait, the probability of familial clustering (PFC) was assessed through the estimation of a chi-square statistic, which compares the observed with the expected distribution of affected siblings [38,39]. To this aim, we used the GAP software package under R 2.3.1 [40]: due to the high dimensionality of the tables, the enumeration of all possible scenarios was not possible, thus simulated p-values were estimated via Monte Carlo permutations (10 million runs).…”

Peutz–Jeghers syndrome and screening for pancreatic cancer

“…First, the authors make no calculation of risk over the age 33 . As explained above, there is marked interfamily variation in disease expression and, owing to publication bias, those families with a higher risk of cancer may have been over-represented.…”

Integrated analysis of genetic data with R

“…The functions included are: hwe, hwe.hardy for Hardy - Weinberg equilibria involving SNPs and highly polymorphic microsatellite markers; s2k, gcontrol for single-locus association analysis of polymorphic markers and genomic control; [22,23] genecounting; gcp for haplotype analysis of all chromosomes and missing data [24] and permutation tests; tbyt, kbyl for linkage disequilibrium statistics for SNPs and multiallelic markers; htr, hap.score for extracting haplotype information for haplotype trend regression analysis and regression incorporating covariates based on conditional regression, as implemented in the haplo.score package [15]. For family data, it includes family plotting through graphviz ( pedtodot ), exact probability of familial clustering disease ( pfc and pfc.sim ) [25], kinship calculation, involves genetic index of familiality ( gif ) and a simple kinship calculation ( kin.morgan ). Currently, it is bundled with an experimental version of POINTER and PATHMIX [8].…”