The estimation of sibling genetic risk parameters revisited

Analysis of Familial Aggregation Using Recurrence Risk for Complex Survey Data

Familial or family aggregation of a disease is important for studying possible genetic etiology of a disease. A popular and useful measure of family aggregation is recurrence risk. Household health surveys with (family) network sampling, which surveyed individuals report about disease status of themselves and specified relatives, has been shown to be useful for estimating prevalence of diseases and more recently for estimating recurrence risk of disease using nonparametric classical survey methods. Because these surveys have complex sample designs with sample weighting for differential sample selection rates, this paper extends the composite-likelihood estimation and hypothesis of parameters of the quadratic exponential model (QEM) for simple random samples to data from these complex sample designs. In addition, the QEM is extended to simultaneously estimate and test parameters and recurrence risk for multiple family relationships, for comparing recurrence risk across family-level covariates (e.g., race) and utilizing propensity score weighting to adjust for confounding by individual-level covariates (e.g., age). Simulations are used to study the finite sample properties of the parameter estimation, variance estimation and level and power of hypothesis testing based on derived Wald and Quasi-Score tests for these extended QEMs. Finally, our methods are illustrated using the 1976 National Health Interview Survey diabetes data set.

Estimating sibling recurrence risk in population sample surveys

BACKGROUND/AIMS

Sibling recurrence risk (SRR) is a measure of familial aggregation of a disease and is often used in family-based studies in genetic epidemiology to indicate the existence of possible genes conferring susceptibility of disease. Estimating SRR requires information about the disease status of sibships of families with two or more siblings where at least one is affected. Since family-based studies are not usually random samples, estimates of SRR derived from these studies may be biased. Network sampling used in survey research offers a way to ascertain the disease status of sibships from interviewed individuals in household survey samples, in order to obtain (approximately) unbiased estimators of SRR and its related SRR ratio (SRR divided by the prevalence of disease).

METHODS

Two methods of ascertaining sibships of affected families are considered: in one method the siblings' affected status is reported by an individual, regardless of the individual's affected status, and in the other method only affected individuals can report their siblings' affected status. Network estimators of SRR and SRR ratio and estimators of their standard errors are provided.

RESULTS

Reported diabetes for siblings from the 1976 National Health Interview is used to illustrate the estimation methods. The SRR ratio for diabetes among living siblings was 5.79% [relative standard error (RSE) = 5.12%], and for living or deceased siblings, it was 7.66% (RSE = 3.76%).

CONCLUSIONS

Network sampling estimators can provide population estimates of SRR and SRR ratio for diseases such as diabetes.

Genetic variance components estimation for binary traits using multiple related individuals

Understanding and modeling genetic or nongenetic factors that influence susceptibility to complex traits has been the focus of many genetic studies. Large pedigrees with known complex structure may be advantageous in epidemiological studies since they can significantly increase the number of factors whose influence on the trait can be estimated. We propose a likelihood approach, developed in the context of generalized linear mixed models, for modeling dichotomous traits based on data from hundreds of individuals all of whom are potentially correlated through either a known pedigree or an estimated covariance matrix. Our approach is based on a hierarchical model where we first assess the probability of each individual having the trait and then formulate a likelihood assuming conditional independence of individuals. The advantage of our formulation is that it easily incorporates information from pertinent covariates as fixed effects and at the same time takes into account the correlation between individuals that share genetic background or other random effects. The high dimensionality of the integration involved in the likelihood prohibits exact computations. Instead, an automated Monte Carlo expectation maximization algorithm is employed for obtaining the maximum likelihood estimates of the model parameters. Through a simulation study we demonstrate that our method can provide reliable estimates of the model parameters when the sample size is close to 500. Implementation of our method to data from a pedigree of 491 Hutterites evaluated for Type 2 diabetes (T2D) reveal evidence of a strong genetic component to T2D risk, particularly for younger and leaner cases.

Efficient intermediate fine mapping: confidence set inference with likelihood ratio test statistic

In positional cloning of disease causing genes, identification of a linked chromosomal region via linkage studies is often followed by fine mapping via association studies. Efficiency can be gained with an intermediate step where confidence regions for the locations of disease genes are constructed. The confidence set inference [CSI; Papachristou and Lin, 2006b] achieves this goal by replacing the traditional null hypothesis of no linkage with a new set of null hypotheses where the chromosomal position under consideration is in tight linkage with a trait locus. This approach was shown to perform favorably compared with several competing methods. Using the duality of confidence sets and hypothesis testing, CSI was proposed for the Mean test statistics with affected sibling pair data (CSI-Mean). We postulate that more efficient confidence sets will result if more efficient test statistics are used in the CSI framework. One promising candidate, the maximum LOD score (MLS) statistic, makes maximum use of available identity by descent information, in addition to handling markers with incomplete polymorphism naturally. We propose a procedure that tests the CSI null hypotheses using the MLS statistic (CSI-MLS). Compared with CSI-Mean, CSI-MLS provides tighter confidence regions over a range of single and two-locus disease models. The MLS test is also shown to be more powerful than the Mean test in testing the CSI null over a wide range of disease models, the advantage being most pronounced for recessive models. In addition, CSI-MLS is computationally much more efficient than CSI-Mean.

Genetic, household and spatial clustering of leprosy on an island in Indonesia: a population-based study

Background

It is generally accepted that genetic factors play a role in susceptibility to both leprosy per se and leprosy type, but only few studies have tempted to quantify this. Estimating the contribution of genetic factors to clustering of leprosy within families is difficult since these persons often share the same environment. The first aim of this study was to test which correlation structure (genetic, household or spatial) gives the best explanation for the distribution of leprosy patients and seropositive persons and second to quantify the role of genetic factors in the occurrence of leprosy and seropositivity.

Methods

The three correlation structures were proposed for population data (n = 560), collected on a geographically isolated island highly endemic for leprosy, to explain the distribution of leprosy per se, leprosy type and persons harbouring Mycobacterium leprae-specific antibodies. Heritability estimates and risk ratios for siblings were calculated to quantify the genetic effect. Leprosy was clinically diagnosed and specific anti-M. leprae antibodies were measured using ELISA.

Results

For leprosy per se in the total population the genetic correlation structure fitted best. In the population with relative stable household status (persons under 21 years and above 39 years) all structures were significant. For multibacillary leprosy (MB) genetic factors seemed more important than for paucibacillary leprosy. Seropositivity could be explained best by the spatial model, but the genetic model was also significant. Heritability was 57% for leprosy per se and 31% for seropositivity.

Conclusion

Genetic factors seem to play an important role in the clustering of patients with a more advanced form of leprosy, and they could explain more than half of the total phenotypic variance.

Microsatellites versus single-nucleotide polymorphisms in confidence interval estimation of disease loci

With cost-effective high-throughput Single Nucleotide Polymorphism (SNP) arrays now becoming widely available, it is highly anticipated that SNPs will soon become the choice of markers in whole genome screens. This optimism raises a great deal of interest in assessing whether dense SNP maps offer at least as much information as their microsatellite (MS) counterparts. Factors considered to date include information content, strength of linkage signals, and effect of linkage disequilibrium. In the current report, we focus on investigating the relative merits of SNPs vs. MS markers for disease gene localization. For our comparisons, we consider three novel confidence interval estimation procedures based on confidence set inference (CSI) using affected sib-pair data. Two of these procedures are multipoint in nature, enabling them to capitalize on dense SNPs with limited heterozygosity. The other procedure makes use of markers one at a time (two-point), but is much more computationally efficient. In addition to marker type, we also assess the effects of a number of other factors, including map density and marker heterozygosity, on disease gene localization through an extensive simulation study. Our results clearly show that confidence intervals derived based on the CSI multipoint procedures can place the trait locus in much shorter chromosomal segments using densely saturated SNP maps as opposed to using sparse MS maps. Finally, it is interesting (although not surprising) to note that, should one wish to perform a quick preliminary genome screening, then the two-point CSI procedure would be a preferred, computationally cost-effective choice.