Regression methods for assessing familial aggregation of disease

Familial aggregation of traffic risky behaviours among pedestrians: a cross-sectional study in northwestern Iran

OBJECTIVE

This study aims to assess the familial aggregation of traffic risky behaviours among pedestrians and describe the sociodemographic profile of pedestrians in northwestern Iran.

METHODS

A cross-sectional study was conducted among 933 pedestrians in 2023. Participants were selected using stratified random sampling. Traffic risky behaviour was measured using a validated instrument among heads of households and their first relatives. The generalised estimating equations were computed to estimate the adjusted OR and 95% CI for familial aggregation of traffic risky behaviours.

RESULTS

Of the total sample, 52.2% and 27.7% of the participants were male and aged 41-50, respectively. The majority of respondents were categorised in middle socioeconomic class (36.9%). The OR for familial aggregation of traffic risky behaviours was 1.42 (95% CI 1.07 to 1.89), indicating that the presence of traffic risky behaviours in at least one family member increased the likelihood of similar behaviour in other members. Fathers showing violation behaviours were associated by 1.98-fold increase in violation behaviours among their offspring. Similarly, the existence of violation behaviour in one sibling increased the odds of violation behaviour among other siblings (OR 1.99, 95% CI 1.18 to 3.73).

CONCLUSIONS

This study revealed the familial aggregation of traffic risky behaviours of pedestrians, with father-offspring and sibling aggregations emerging as prominent components of familial aggregation. The findings suggested that family-based prevention programmes may yield greater effectiveness than individual-based approaches. As such, implementing targeted interventions focusing on family might have a substantial impact on reducing pedestrian traffic risky behaviours.

Fitting ACE structural equation models to case-control family data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for members of families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). We describe an ACE model for binary family data; this structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. We then introduce our contribution, a likelihood-based approach to fitting the model to singly ascertained case-control family data. The approach, which involves conditioning on the proband's disease status and also setting prevalence equal to a prespecified value that can be estimated from the data, makes it possible to obtain valid estimates of the A, C, and E variance components from case-control (rather than only from population-based) family data. In fact, simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly made assumptions hold. Further, when our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Choline transporter gene variation is associated with attention-deficit hyperactivity disorder

The neurotransmitter acetylcholine (ACh) plays a critical role in brain circuits mediating motor control, attention, learning and memory. Cholinergic dysfunction is associated with multiple brain disorders including Alzheimer’s Disease, addiction, schizophrenia and Attention-Deficit Hyperactivity Disorder (ADHD). The presynaptic choline transporter (CHT, SLC5A7) is the major, rate-limiting determinant of ACh production in the brain and periphery and is consequently upregulated during tasks that require sustained attention. Given the contribution of central cholinergic circuits to the control of movement and attention, we hypothesized that functional CHT gene variants might impact risk for ADHD. We performed a case-control study, followed by family-based association tests on a separate cohort, of two purportedly functional CHT polymorphisms (coding variant Ile89Val (rs1013940) and a genomic SNP 3’ of the CHT gene (rs333229), affording both a replication sample and opportunities to reduce potential population stratification biases. Initial genotyping of pediatric ADHD subjects for two purportedly functional CHT alleles revealed a 2–3 fold elevation of the Val89 allele (n = 100; P = 0.02) relative to healthy controls, as well as a significant decrease of the 3’SNP minor allele in Caucasian male subjects (n = 60; P = 0.004). In family based association tests, we found significant overtransmission of the Val89 variant to children with a Combined subtype diagnosis (OR = 3.16; P = 0.01), with an increased Odds Ratio for a haplotype comprising both minor alleles. These studies show evidence of cholinergic deficits in ADHD, particularly for subjects with the Combined subtype, and, if replicated, may encourage further consideration of cholinergic agonist therapy in the disorder.

Analysis of familial aggregation studies with complex ascertainment schemes

Familial aggregation studies are a common first step in the identification of genetic determinants of disease. If aggregation is found, more refined genetic studies may be undertaken. Complex ascertainment schemes are frequently employed to ensure that the sample contains a sufficient number of families with multiple affected members, as required to detect aggregation. For example, an eligibility criterion for a family might be that both the mother and daughter have disease. Adjustments must be made for ascertainment to avoid bias. We propose adjusting for complex ascertainment schemes through a joint model for the outcomes of disease and ascertainment. This approach improves upon previous simplifying assumptions regarding the ascertainment process.

A structural approach to the familial coaggregation of disorders

The demonstration that 2 disorders coaggregate in families is often the first step in the exploration of genetic factors common to the 2 disorders. Previous methods of analyzing familial coaggregation have used either (1) a typical measure of association (eg, the odds ratio) between a disorder in an individual and another disorder in family members, or (2) a linear structural equation model (SEM). The association method accommodates case-control sampling of families, but may not assess the causal effect of interest because it is not based on an underlying causal model. The SEM method is based on a causal model, but cannot easily accommodate case-control sampling or direct effects of 1 disorder on the other within individuals. We develop a new method of analyzing coaggregation based on directed acyclic graphs. Because this method is a generalization of structural equation models and uses measures of association that accommodate case-control sampling and direct effects, it combines the strengths of both previous methods. In the absence of direct effects between disorders, our approach provides a valid estimate of the causal coaggregation effect. In the presence of direct effects, our approach provides an upper-bound estimate and (assuming additive linear effects of latent familial and nonfamilial factors) a lower-bound estimate of the causal coaggregation effect. For illustration, we applied our method to a family study of binge eating disorder and bipolar disorder.

A statistical comparison of different family history scores

Family history (FH) scores are used for estimating the familial risk (FR), i.e. the level of risk for a particular disease among members of that family. An FH score is created from reports about the disease status of the relatives in each family. The most commonly used score is the dichotomous score (positive when at least one relative is affected), which does not consider the family size, number of affected relatives nor each relative's risk factor profile. Authors have proposed many other FH scores that overcome these deficiencies by using external expected risks adjusted for important risk factors. We consider the use of FH scores in studies, which investigate risk factors for a disease and where family risk is considered as a confounder, and examine through simulations the performance of a variety of FH scores in controlling the FR status. We also examine performance in predicting true FR status. For both criteria, only small differences were found between most of the FH scores, although the dichotomous score performed the poorest. Since the proportion score (the proportion of first-degree relatives of the index who have the disease) is the simplest to calculate, use of this score seems to be justified.

Multivariate logistic regression for familial aggregation in age at disease onset

Familial aggregation studies seek to identify diseases that cluster in families. These studies are often carried out as a first step in the search for hereditary factors affecting the risk of disease. It is necessary to account for age at disease onset to avoid potential misclassification of family members who are disease-free at the time of study participation or who die before developing disease. This is especially true for late-onset diseases, such as prostate cancer or Alzheimer's disease. We propose a discrete time model that accounts for the age at disease onset and allows the familial association to vary with age and to be modified by covariates, such as pedigree relationship. The parameters of the model have interpretations as conditional log-odds and log-odds ratios, which can be viewed as discrete time conditional cross hazard ratios. These interpretations are appealing for cancer risk assessment. Properties of this model are explored in simulation studies, and the method is applied to a large family study of cancer conducted by the National Cancer Institute-sponsored Cancer Genetics Network (CGN).

Familial aggregation in the night eating syndrome

OBJECTIVE

This study examined the extent to which the night eating syndrome (NES) affects first-degree relatives of NES and control probands.

METHOD

NES participants and controls were assessed with the Night Eating Questionnaire (NEQ), the Night Eating Syndrome History and Inventory (NESHI), 10 day sleep and food records, the Eating Disorder Examination (EDE), the Structured Clinical Interview for DSM IV Axis I Disorders (SCID I), and a Family History Questionnaire (FHQ) to assess the presence of NES among first-degree relatives. A proband predictive model, using logistic regression analyses and the generalized estimating equation to control for correlation among observations within families was used to assess familial aggregation.

RESULTS

The odds of an NES proband having an affected first-degree relative were significantly greater than that of a control proband (odds ratio=4.9, p<.001). A number of covariates were included in the model: proband body mass index (BMI) (kg/m2), proband gender, proband age, proband ethnicity, first-degree relative gender, relationship to proband (i.e., mother, father, or sibling), and the interaction between relationship to proband and proband status (night eater or control); none was statistically significant (p>.05).

CONCLUSION

The study showed a strong aggregation of NES in families.

Familial clustering of autoimmune diseases in patients with type 1 diabetes mellitus

We investigated the familial aggregation of autoimmune diseases (AIDs) among first-degree relatives (FDR) of patients with type 1 diabetes mellitus (T1D). Relatives of 98 T1D patients defined according to the guidelines diagnosis of the American Diabetes Association and 113 matched controls without any AID, were interviewed using a questionnaire that sought information about demographic and medical characteristics including a list of 18 AIDs. Genetic analysis was performed using the program ASSOC and by calculating recurrent risk ratios. In cases, 25.5% of the families had at least one member having an AID, while in controls there were 9% (odds ratio [OR]: 3.96, 95% confidence interval [CI]=1.74-9.0, p=0.0006). An AID was registered in 8.3% of 312 FDR of patients as compared with 2.4% of 362 FDR in controls (OR: 3.56, 95% CI=1.64-7.73, p=0.0008). The most frequent AIDs registered in FDR of cases were autoimmune thyroid disease (AITD) and T1D, which disclosed coefficients of aggregation. These results indicate that AIDs cluster within families of T1D patients adding further evidence to consider that clinically different autoimmune phenotypes may share common susceptibility gene variants, which may act pleiotropically as risk factors for autoimmunity.