Estimating Heritability from Nuclear Family and Pedigree Data

Gene-environment interactions explain a substantial portion of variability of common neuropsychiatric disorders

In complex diseases, the phenotypic variability can be explained by genetic variation (G), environmental stimuli (E), and interaction of genetic and environmental factors (G-by-E effects), among which the contribution G-by-E remains largely unknown. In this study, we focus on ten major neuropsychiatric disorders using data for 138,383 United States families with 404,475 unique individuals. We show that, while gene-environment interactions account for only a small portion of the total phenotypic variance for a subset of disorders (depression, adjustment disorder, substance abuse), they explain a rather large portion of the phenotypic variation of the remaining disorders: over 20% for migraine and close to or over 30% for anxiety/phobic disorder, attention-deficit/hyperactivity disorder, recurrent headaches, sleep disorders, and post-traumatic stress disorder. In this study, we have incorporated-in the same analysis-clinical data, family pedigrees, the spatial distribution of individuals, their socioeconomic and demographic confounders, and a collection of environmental measurements.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

Inheritance pattern of molar-incisor hypomineralization

The aim of this study was to investigate the segregation patterns of molar incisor hypomineralization (MIH) in families, given the evidence that its etiology is influenced by genetics. Clinically, MIH may be detected in parents and/or siblings of MIH-affected children. Our study included children with at least one first permanent molar affected by MIH (proband) and their first-degree relatives (parents and siblings). The participants were examined clinically to detect MIH, according to the European Academy of Paediatric Dentistry criteria (2003). A total of 101 nuclear families (391 individuals) were studied. Proband diagnosis was followed by MIH classification of the subject, his parents and siblings, as affected, unaffected, or unknown. Segregation analysis was performed using the multivariate logistic regression model of the Statistical Analysis for Genetic Epidemiology package, and segregation models (general transmission, environmental, major gene, dominant, codominant and recessive models). The Akaike information criterion (AIC) was used to evaluate the most parsimonious model. In all, 130 affected individuals, 165 unaffected individuals, and 96 unknown individuals were studied. Severe MIH was found in 50.7% of the cases. A segregation analysis performed for MIH revealed the following different models: environmental and dominance (p = 0.05), major gene (p = 0.04), codominant (p = 0.15) and recessive models (p = 0.03). According to the AIC values, the codominant model was the most parsimonious (AIC = 308.36). Our results suggest that the codominant model could be the most likely for inheriting MIH. This result strengthens the evidence that genetic factors, such as multifactorial complex defect, influence MIH.

Estimation of heritability and familial correlation in myopia is not affected by past sun exposure

Purpose: To consider the effect of including past sun exposure in estimating heritability and familial correlation of myopia-related traits.Methods: We calculate familial correlation and heritability of anterior chamber depth (ACD), axial length (AL), corneal curvature (CC), and spherical equivalent (SphE), with or without past sun exposure as a covariate, in a large number of unrelated nuclear families from the Raine Study (parents: Gen1, offspring: Gen2) residing in Perth, Australia, a city with a high amount of daily sunlight. Past sun exposure was objectively measured using conjunctival ultraviolet autofluorescence (CUVAF) photography.Results: When sun exposure was not included in the analysis, both familial correlation (correlation±SE; ACD: 0.308 ± 0.065, AL: 0.374 ± 0.061, CC: 0.436 ± 0.063, SphE: 0.281 ± 0.070) and heritability (ACD: 0.606 ± 0.104, AL: 0.623 ± 0.098, CC: 0.793 ± 0.079, SphE: 0.591 ± 0.106) were significant for all traits (all P < .001). However, there was no significant change in both familial correlation and heritability estimates when sun exposure was included as an additional covariate.Conclusions: Past sun exposure does not affect the estimation of the additive genetic component in myopia-related traits.

How Consistent are Genetic Factors in Explaining Leisure-Time Physical Activity and Sport Participation? The Portuguese Healthy Families Study

This study investigates how consistent genetic factors are, as measured by heritability estimates (h2), in the leisure-time physical activity index (LTPAI) and sport participation index (SPI) from early (10-14 yrs) to late adolescence (15-19 yrs). The sample comprises 12,385 subjects from 3,378 Portuguese nuclear families. Height and weight were measured and body mass index (BMI) was calculated, and the LTPAI and SPI were estimated by questionnaire. Socioeconomic status (SES) was assessed by parental occupation. Analyses were done using S.A.G.E. software. Our results showed that h2 estimates for the LTPAI and SPI in the two age groups (10-14 yrs and 15-19 yrs) were stable: for the LTPAI, h2 = 0.297 and 0.322, respectively; and for the SPI, h2 = 0.413 and 0.428, respectively. Sibling correlations and environmental correlations are higher in the younger age group for both the LTPAI and the SPI. Spousal correlations are higher in the younger age group for the LTPAI and lower for the SPI than the older group. Parent-offspring correlations are similar in both age groups for the LTPAI and SPI. In conclusion, the influence of genetic factors on physical activity and sport participation remains stable across age in adolescence. However, variation in sibling correlations - in particular, environmental correlations - was observed. These findings suggest that shared/non-shared environmental factors express different degrees of importance across age. Future intervention programs aiming to promote change in behaviors need to consider these results to bring about positive changes in physical activity and sport participation behaviors within the family setting.