A robust and unified framework for estimating heritability in twin studies using generalized estimating equations

Energy and macronutrient intake heritability: A systematic review and meta-analysis of twin and family-based studies

BACKGROUND/AIMS

The current meta-analysis aimed to examine the heritability and familial resemblance of dietary intakes, including energy and macronutrients in both twin and family-based studies.

METHODS

The online literature databases, including PubMed, Scopus, and Web of Science were searched comprehensively until 2023 to identify the relevant studies. The heritability index in family studies was h2 and the heritability indices for twin studies were h2, A2, and E2. Three weighted methods were used to calculate the mean and SE of heritability dietary intakes.

RESULTS

Eighteen papers including 8 studies on familial population and 12 for twin population studies were included in the present meta-analysis. The heritability of dietary intakes in twin studies (range of pooled estimated h2, A2, and E2 was 30-55%, 14-42%, and 52-79%, respectively) was higher than family studies (range of pooled estimated h2 = 16-39%). In family studies, the highest and lowest heritability for various nutrients was observed for the fat (%Kcal) (h2 range:36-38%) and carbohydrate in g (h2 range:16-18%), respectively. In twin studies, based on mean h2, the highest and lowest heritability for various nutrients was reported for the fat (%Kcal) (h2 range:49-55%) and protein intake in g (h2 range:30-35%), respectively. Also, based on the mean of A2, the highest and lowest heritability was observed for carbohydrates (% Kcal) (A2 range:42-42%), and protein (% Kcal) (A2 range:14-16%), respectively. Furthermore, in twin studies, the highest and lowest mean of E2 was shown for saturated fats (E2 range:74-79%) and energy intake (E2 range:52-57%), respectively.

CONCLUSION

Our analysis indicated that both environmental factors and genetics have noticeable contributions in determining the heritability of dietary intakes. Also, we observed higher heritability in twins compared to family studies.

Connecting loneliness with pathological personality traits: Evidence for genetic and environmental mediation from a study of older twins

Loneliness has broad public health importance, especially in older adulthood, and there is some evidence suggesting it is associated with several personality disorders (PDs). The etiology of these PD-loneliness associations, however, has rarely been studied, especially in the context of the maladaptive traits of the DSM-5 alternative model of personality disorder (AMPD). To address these limitations, we estimated phenotypic, genetic, and unique environmental associations between loneliness and maladaptive personality traits in a sample of older adults from the Minnesota Twin Registry (n = 1,356, Mage = 70.4). Loneliness was moderately to strongly associated with each of the AMPD domains of negative affect, detachment, antagonism, disinhibition, and psychoticism (r = .22-.58), with evidence of both genetic (rg = .45-.75) and unique environmental (re = .10-.48) influences explaining the associations to varying degrees. We argue that loneliness may be an underappreciated concomitant of personality pathology, with PD traits perhaps underlying its development. Indeed, these findings suggest that loneliness may be a manifestation of the genetic and environmental forces that also lead to pathological personality variation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study

We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].

Heritability Estimation of Cognitive Phenotypes in the ABCD Study® Using Mixed Models

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

Environmental effects on brain functional networks in a juvenile twin population

The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.

Comparing Phenotypic, Genetic, and Environmental Associations between Personality and Loneliness

As a strong risk factor for mortality, individual differences in loneliness are of clear public health significance. Four of the Big Five traits have emerged as cross-sectional correlates, but the etiology of these links is unclear, as are relations with more specific personality facets. Thus, we estimated phenotypic, genetic, and environmental associations between loneliness and both broader and narrower personality dimensions. Traits that indexed Negative Emotionality (e.g., Neuroticism, Stress Reactivity, Alienation) and low Positive Emotionality (e.g., low Extraversion, low Well-Being) had the strongest associations with loneliness, though low Conscientiousness, low Agreeableness, and high Aggression were also implicated. These associations were explained by both genetic (0.30<|rg|<0.80) and unique environmental (0.10<|re|<0.35) influences, consistent with an etiology of loneliness involving several personality domains.

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.

Estimating heritability in heights without zygosity information for under-five children in low- and middle-income countries: An application of normal finite mixture distribution model

Twin studies are widely used to estimate heritability of traits and typically rely on knowing the zygosity of twin pairs in order to determine variation attributable to genetics. Most twin studies are conducted in high resource settings. Large scale household survey data, such as the Demographic and Health Surveys, collect various biomarkers for children under five years old in low- and middle-income countries. These data include twins but no information on zygosity. We applied mixture models to obtain heritability estimates without knowing zygosity of twins, using 249 Demographic and Health Surveys from 79 low- and middle-income countries (14,524 twin pairs). We focused on height of children, adjusted for age and sex, but also provided estimates for other biomarkers available in the data. We estimated that the heritability of height in our sample was 46%.

•

Mixture model was used to obtain heritability estimates for biomarkers for children under five without zygosity information.

•

46% of height was determined by heritability.

•

Heritability estimate was 0.54 for weight-for-age z-score and 0.51 for residualized weight.

•

An implausible heritability estimate of 0.93 was found for weight-for-height z-score.

•

Birthweight had a heritability estimate of 0.71 and hemoglobin level had a heritability estimate of 0.61.