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Family aggregation

Family aggregation, also known as familial aggregation, refers to the clustering of certain diseases, traits, behaviors, or disorders within families at rates higher than would be expected by chance alone. This pattern arises from shared genetic factors, environmental exposures, or interactions between the two, serving as an initial indicator of potential heritability or familial risk.^[1]^[2] In epidemiology and genetics, family aggregation studies provide a foundational tool for assessing disease susceptibility by quantifying recurrence risks among relatives, such as siblings or offspring, relative to the general population prevalence. These risks are often measured using metrics like the sibling relative recurrence risk (λ_S) or familial risk ratio (FRR), which compare the probability of disease in relatives of affected individuals to baseline rates—for instance, λ_S for hypertension is approximately 3.5, indicating a substantially elevated risk.^[2]^[3] Such studies help disentangle genetic contributions from environmental confounders, with twin and family designs revealing heritabilities ranging from 50-60% for dyslexia to 80-90% for attention-deficit/hyperactivity disorder (ADHD).^[2] Historically, family aggregation analyses predated modern genomics and were crucial for early genetic counseling, offering recurrence risk estimates for conditions like congenital heart defects, where siblings of affected probands face risks up to several times higher than the population average, varying by factors such as proband sex.^[4] Today, these studies inform precision medicine by identifying high-risk families for targeted screening and intervention, while also highlighting etiologic heterogeneity across diseases like thyroid autoimmunity and cardiovascular disorders. Methods include case-control designs, which compare disease rates in relatives of cases versus controls, and reconstructed cohort approaches that leverage family history data for risk prediction, with first-degree relative histories proving particularly sensitive for detecting aggregation in genetically influenced conditions.^[1]^[4]

Definition and Concepts

Core Definition

Family aggregation refers to the clustering of diseases, behaviors, or traits within families at rates higher than expected by chance, indicating potential influences from shared genetic, environmental, or gene-environment factors among biological relatives. This phenomenon serves as an initial indicator in genetic epidemiology for the presence of familial factors contributing to disease risk, distinct from sporadic occurrences in the general population.^[5]^[4] A key distinction exists between family aggregation and segregation: aggregation describes the observed non-random clustering without implying a specific inheritance mechanism, whereas segregation analysis statistically evaluates patterns of transmission within families to test for underlying genetic models, such as Mendelian inheritance.^[5] One widely used metric for quantifying aggregation is the sibling relative risk, denoted as \lambda_s, which measures the elevated risk to siblings of affected individuals relative to the population baseline and is calculated as \lambda_s = \frac{K_s}{K}, where K_s is the recurrence risk among siblings and K is the population prevalence.^[6] Examples of aggregation measurement include relative risk ratios for first-degree relatives, which compare their disease incidence to that in unrelated individuals, and recurrence risk percentages that express the probability of trait occurrence in relatives of probands.^[4] These metrics provide a descriptive assessment of clustering scale, often guiding further genetic investigations. Family aggregation represents purely observational clustering, in contrast to familiality, which encompasses the causal family-related components—such as shared genetics or environment—explaining the variance in the trait.^[7] Twin studies briefly illustrate aggregation by estimating concordance differences between monozygotic and dizygotic pairs to parse genetic versus environmental contributions.^[5]

Historical Development

The concept of family aggregation in psychiatric and neurological disorders traces its roots to 19th-century observations of hereditary patterns in mental illness. French psychiatrist Bénédict Augustin Morel introduced the idea of hereditary degeneration in his 1857 treatise, positing that physical, intellectual, and moral decline could propagate across generations within families, manifesting in conditions like idiocy and insanity.^[8] This framework influenced early understandings of familial clustering, though it was later critiqued for its deterministic and eugenic implications. In the early 20th century, Emil Kraepelin advanced these ideas through his delineation of "dementia praecox"—a precursor to modern schizophrenia—in the 1899 edition of his textbook Psychiatrie, where he noted elevated rates of the disorder among relatives, suggesting a hereditary basis while distinguishing it from manic-depressive illness.^[9] Schizophrenia served as an early exemplar of family aggregation research, highlighting patterns of increased risk in biological kin.^[8] Mid-20th-century developments shifted toward quantitative frameworks, integrating Mendelian genetics with statistical methods to quantify heritability from familial patterns. Ronald A. Fisher's seminal 1918 paper reconciled biometrical observations of continuous traits with Mendelian inheritance, demonstrating how correlations among relatives could estimate genetic contributions to phenotypic variation, laying the groundwork for analyzing disease aggregation.^[10] By the 1960s, empirical studies like those by Seymour S. Kety and colleagues in Denmark used adoption designs to disentangle genetic from environmental influences, revealing significantly higher schizophrenia rates in biological relatives of adoptees compared to adoptive families.^[11] Key theoretical contributions included Irving I. Gottesman's 1967 polygenic threshold model, co-developed with James Shields, which explained schizophrenia's familial aggregation as the result of multiple genetic liabilities surpassing a liability threshold, influenced by environmental factors. The late 20th century saw advancements in linkage analysis, enabling the mapping of genomic regions associated with familial disease clustering; for instance, Neil Risch's work in the 1980s refined statistical models for segregation and linkage in complex traits like schizophrenia, improving detection of genetic contributions amid heterogeneity. The completion of the Human Genome Project in 2003 marked a pivotal integration of family aggregation studies with molecular genetics, providing a comprehensive reference for identifying variants underlying inherited risks. This ushered in the modern era of genome-wide association studies (GWAS) in the 2000s, which systematically scanned common variants across populations to dissect aggregation patterns, revealing polygenic architectures for disorders like schizophrenia and shifting focus from rare mutations to cumulative small-effect loci.^[12]

Methods of Investigation

Family History and Pedigree Analysis

Family history collection involves structured interviews and questionnaires designed to systematically assess the occurrence of diseases among biological relatives, enabling researchers to identify patterns of familial aggregation without direct examination of family members.^[13] This approach relies on probands—individuals affected by the condition of interest—reporting details about their relatives' health histories, often focusing on first-degree relatives such as parents, siblings, and children to minimize recall bias.^[1] A seminal tool in this domain is the Family History Research Diagnostic Criteria (FH-RDC), developed by Andreasen et al. in 1977, which provides standardized operational criteria for diagnosing psychiatric disorders in relatives based on informant reports, enhancing reliability and validity in retrospective studies. Pedigree construction builds on collected family history data by creating visual representations of family trees, or pedigrees, that map relationships, affected individuals, and disease transmission across generations to reveal inheritance patterns such as autosomal dominant vertical transmission or multifactorial clustering.^[13] These diagrams facilitate the identification of high-density families where multiple members are affected, aiding in hypothesis generation for genetic underpinnings. Specialized software supports this process; for instance, Progeny enables automated pedigree drawing from imported data files, including compatibility with formats from other tools, while Cyrillic offers comprehensive features for managing complex pedigrees, haplotyping, and exporting data for further genetic analysis.^[14]^[15] Both retrospective (using existing records) and prospective (ongoing family monitoring) designs can incorporate pedigrees to track aggregation over time.^[1] Quantitative analysis of family history and pedigree data quantifies aggregation through statistics like odds ratios derived from case-control family studies, where the risk of disease in relatives of affected probands is compared to relatives of unaffected controls to estimate familial clustering.^[16] For example, odds ratios greater than 1 indicate increased risk due to shared factors, with regression models adjusting for covariates such as age and sex to refine estimates.^[16] Lifetime morbid risk, which estimates the probability of an individual developing the condition given their age and family status, is often calculated using Weinberg's proband method (introduced in 1928), which accounts for incomplete penetrance and censoring by weighting affected relatives based on their ascertainment through probands.^[17] This method applies age-of-onset corrections to truncate risk periods, providing a more accurate projection than simple prevalence measures.^[18] These methods offer key advantages in genetic epidemiology, including cost-effectiveness for studying large populations where direct genotyping is impractical, and the ability to pinpoint high-risk families for targeted follow-up interventions or deeper genomic investigations.^[19] By leveraging readily available informant data, family history and pedigree analysis serve as an initial, accessible step in detecting aggregation, often informing subsequent estimates of heritability without requiring controlled experimental designs.^[20]

Twin, Adoption, and Segregation Studies

Twin studies represent a cornerstone method for partitioning the variance in traits contributing to family aggregation into genetic and environmental components by comparing monozygotic (MZ) twins, who share nearly 100% of their genetic material, with dizygotic (DZ) twins, who share approximately 50% on average.^[21] Concordance rates, or the probability that both twins exhibit the trait if one does, are typically higher in MZ pairs than in DZ pairs, indicating a genetic influence when environmental exposures are assumed similar within twin pairs.^[22] A key quantitative approach in these studies is Falconer's formula, which estimates broad-sense heritability as the proportion of phenotypic variance attributable to genetic factors:

h^2 = 2(r_{MZ} - r_{DZ})

where r_{MZ} and r_{DZ} are the phenotypic correlations for MZ and DZ twins, respectively; this formula assumes additive genetic effects and equal environmental influences across twin types.^[21] Adoption studies employ cross-fostering designs, where children are reared by non-biological parents, to isolate the effects of genetic inheritance from shared rearing environments on familial aggregation of traits.^[23] By comparing outcomes in adopted individuals with their biological versus adoptive relatives, these studies reveal the relative contributions of prenatal and postnatal environmental factors versus heritable influences, often showing stronger associations with biological kin for genetically influenced traits.^[24] For instance, large-scale registries facilitate such analyses by providing comprehensive data on adoptee outcomes independent of family rearing history.^[25] Segregation analysis uses statistical modeling of pedigree data to test hypotheses about inheritance patterns underlying family aggregation, such as single-gene Mendelian transmission, polygenic effects, or environmental residuals.^[26] Likelihood-based methods, including regressive models that account for parent-offspring and sibling dependencies, evaluate whether observed transmission deviates from random expectations, often fitting mixtures of major gene and multifactorial components.^[27] Software like the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) implements these regressive models to estimate parameters such as allele frequencies and penetrances.^[28] For binary traits common in family aggregation studies, tetrachoric correlations estimate the underlying liability scale correlation between relatives, assuming a latent continuous distribution thresholded to produce the observed dichotomy, which informs heritability calculations under liability-threshold models.^[29] Shared environmental effects, representing variance due to family-wide exposures, are quantified using eta-squared (η²) in some analytical frameworks, particularly when decomposing twin or adoption data into additive components beyond genetics.^[30] These metrics complement pedigree-based approaches by providing robust estimates in controlled designs.

Psychiatric Disorders

Schizophrenia

Schizophrenia exhibits clear patterns of family aggregation, with the lifetime risk in the general population estimated at approximately 1%. However, this risk increases substantially among relatives; first-degree relatives of affected individuals face a lifetime risk of about 10-11%, while second-degree relatives experience a risk of around 3%. The risk escalates further in cases of dual parental affection, reaching approximately 46% for offspring of two parents with schizophrenia.^[18]^[31] Seminal studies from the mid-20th century, such as Franz Kallmann's twin investigations in the 1930s and 1940s, provided early evidence of familial clustering by demonstrating markedly higher concordance rates for monozygotic twins (approximately 69%) compared to dizygotic twins (around 10%). These findings underscored a strong genetic component in transmission. Complementing this, the Israeli Kibbutz high-risk study, initiated in the 1970s with follow-ups through the 1980s and beyond, explored the influence of shared rearing environments in communal settings versus traditional family homes among offspring of parents with schizophrenia, revealing that environmental factors like collective child-rearing played a notable role in modulating outcomes, though genetic liability remained predominant.^[32]^[33] Family aggregation in schizophrenia extends beyond the core diagnosis to encompass the broader spectrum, including schizoaffective disorder and schizotypal personality disorder, where relatives show elevated rates of these conditions compared to the general population. Modern genomic approaches, such as polygenic risk scores (PRS) derived from large-scale genome-wide association studies (GWAS), capture a portion of this familial liability, explaining approximately 7-10% of the variance in schizophrenia risk among relatives. Recurrence risks highlight the transmission patterns, with the sibling relative risk (λ_s) estimated at 8-10, indicating that siblings of affected individuals are about 8 to 10 times more likely to develop the disorder than the general population; parent-offspring transmission mirrors this elevated risk for first-degree pairs.^[34]^[35]^[36]

Mood and Anxiety Disorders

Family aggregation of mood and anxiety disorders demonstrates patterns of increased risk among relatives, though generally lower in magnitude compared to psychotic disorders like schizophrenia.^[37] In bipolar disorder, first-degree relatives of affected individuals face a substantially elevated risk, estimated at 10-25% lifetime prevalence compared to approximately 1% in the general population.^[38] Monozygotic twin concordance rates range from 40% to 70%, underscoring a strong genetic component while indicating incomplete penetrance.^[39] Early family studies have suggested anticipation effects, with evidence of decreasing age at onset across generations in affected pedigrees.^[40] Major depressive disorder exhibits more modest familial aggregation, with sibling relative risk (λ_s) approximately 2-3, reflecting a twofold to threefold increase over population rates. This risk is notably higher among female relatives, consistent with sex-specific patterns in prevalence and transmission.^[41] Twin studies from the Norwegian Twin Registry estimate heritability at around 37%, suggesting additive genetic influences account for a moderate portion of liability, with shared and non-shared environmental factors also contributing.^[37] Anxiety disorders, including generalized anxiety disorder and panic disorder, show clear familial clustering, with first-degree relatives experiencing a roughly fourfold increased risk compared to the general population.^[42] The Yale Family Study in the 1990s provided key evidence of this aggregation alongside comorbidity patterns, particularly noting elevated rates of anxiety disorders among relatives of probands with co-occurring conditions.^[43] Cross-disorder aggregation is evident in mood and anxiety conditions, with shared familial liability extending to substance use disorders, as demonstrated by co-transmission patterns in family studies.^[43] Additionally, earlier age of onset in affected individuals is associated with stronger familial loading in pedigrees, influencing transmission dynamics across generations.^[44]

Neurological Disorders

Parkinson's Disease

Family aggregation in Parkinson's disease (PD) is observed in approximately 10-15% of cases, where individuals report at least one affected first-degree relative.^[45] The risk to first-degree relatives of PD patients is elevated, with relative risks ranging from 1.7- to 3-fold higher compared to the general population, indicating a moderate familial component.^[46] Twin studies further support this aggregation but highlight the limited role of genetic factors in typical PD; for instance, a longitudinal Swedish twin study reported monozygotic concordance rates of about 11-13%, compared to 4-5% in dizygotic pairs, yielding heritability estimates around 34-40% and suggesting dominant environmental influences.^[47] A 1999 twin follow-up study similarly found low monozygotic concordance (under 20%), reinforcing that shared genetics alone do not fully explain disease occurrence in most cases.^[48] Monogenic forms account for 5-10% of PD cases and demonstrate clear familial patterns through pedigree analyses. Mutations in genes such as SNCA (encoding alpha-synuclein), LRRK2, and PRKN (encoding parkin) are primary contributors, with SNCA and LRRK2 following autosomal dominant inheritance and PRKN showing autosomal recessive patterns in early-onset families.^[49] These mutations lead to protein aggregation and dopaminergic neuron loss, observable in family pedigrees with multi-generational affected members. For example, LRRK2 variants are the most common monogenic cause, prevalent in 1-2% of sporadic cases but up to 40% in certain populations like Ashkenazi Jews or North African Arabs.^[50] In idiopathic PD, which comprises the majority of cases, familial aggregation is subtler, with sibling relative risk (λ_s) estimated at approximately 2.2, reflecting age-dependent penetrance and polygenic influences.^[46] Genome-wide association studies (GWAS), including a prominent 2010 Icelandic analysis by deCODE genetics, have identified risk loci such as the MAPT region (involved in tau protein regulation), confirming shared genetic signals with other tauopathies and contributing to familial clustering beyond monogenic forms.^[51] Familial PD cases typically exhibit earlier disease onset by 5-10 years compared to sporadic cases, often in the 40s or 50s versus the 60s, alongside more consistent Lewy body pathology—intracellular aggregates of alpha-synuclein—in affected family members.^[45] This earlier onset and pathological uniformity in pedigrees underscore a gradient of genetic risk, distinguishing familial from sporadic presentations while both share core nigrostriatal degeneration.^[52]

Alzheimer's Disease

Family aggregation in Alzheimer's disease (AD) manifests distinctly between early-onset familial forms and late-onset sporadic cases. Early-onset familial AD, accounting for 1-5% of all AD cases, is primarily caused by autosomal dominant mutations in the amyloid precursor protein (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes.^[53]^[54] These mutations lead to increased production of amyloid-beta peptides, resulting in a 50% risk of disease transmission to offspring of affected individuals.^[55] Pioneering studies in the 1980s on Volga German kindreds identified a specific PSEN2 mutation (N141I), highlighting the role of such large pedigrees in mapping genetic contributions to early-onset AD.^[56] In contrast, late-onset sporadic AD, which comprises the majority of cases, exhibits more modest familial aggregation, with sibling relative risk (λ_s) estimated at approximately 1.5-2, indicating a mildly elevated risk among first-degree relatives compared to the general population.^[57] The apolipoprotein E (APOE) ε4 allele serves as the primary genetic risk factor, increasing the relative risk of AD by 3- to 15-fold in carriers depending on the number of alleles, as evidenced by longitudinal data from the Framingham Heart Study showing accelerated cognitive decline and higher incidence in ε4-positive individuals.^[58]^[59] Twin studies further underscore the genetic underpinnings of late-onset AD, with monozygotic (MZ) probandwise concordance rates of 67%, significantly higher than dizygotic pairs at 22%, based on data from the Swedish Twin Registry (Gatz et al., 1997).^[60] These analyses estimate heritability (h²) at 60-80% for late-onset forms, emphasizing a substantial genetic influence moderated by age.^[60] Risk gradients are steeper in multiplex families, where multiple affected members show enhanced aggregation and patterns of amyloid-beta deposition that correlate with accelerated plaque formation and disease progression.^[61]^[62]

Cardiovascular and Metabolic Conditions

Cardiovascular Diseases

Family aggregation of cardiovascular diseases (CVDs) is well-documented, with evidence indicating that genetic factors contribute substantially to the familial clustering of conditions such as coronary artery disease (CAD), hypertension, and related disorders. Studies have consistently shown that individuals with affected first-degree relatives face elevated risks, often independent of traditional environmental risk factors like smoking or diet. This aggregation underscores the importance of family history in risk assessment and preventive strategies, highlighting both monogenic and polygenic influences on disease susceptibility. In coronary artery disease, the Framingham Heart Study, initiated in 1948, has provided seminal evidence of familial risk, demonstrating that individuals with a parental history of CAD experience approximately a 1.3-fold increased risk compared to those without such history, with the association strengthening to about 1.7-fold for early-onset cases (diagnosed before age 60).^[63]^[64] Sibling relative risk (λ_s) estimates for CAD range from 3 to 5, particularly in families with premature disease, indicating significant clustering beyond sporadic cases. Early-onset CAD shows even greater familial concentration, where multiple affected relatives amplify the risk, emphasizing the role of shared genetic predispositions in accelerating atherosclerosis. Hypertension exhibits strong familial aggregation, with heritability estimates (h²) typically ranging from 30% to 50%, reflecting a substantial genetic component in blood pressure regulation. Familial patterns vary by ancestry, with higher aggregation observed in populations of African descent, where first-degree relatives of affected individuals show elevated prevalence and earlier onset compared to other groups. Familial hypercholesterolemia (FH) represents a monogenic paradigm within CVD aggregation, primarily caused by mutations in the LDLR gene, which impair low-density lipoprotein clearance and lead to severe hypercholesterolemia. Heterozygous FH carriers have a 50% chance of transmitting the mutation to each offspring, resulting in a markedly increased risk of premature CAD. Cascade screening protocols, recommended by international guidelines, systematically test at-risk relatives of index cases to identify and treat undiagnosed FH, preventing early cardiovascular events. Beyond monogenic forms, polygenic contributions drive much of CVD aggregation, with genome-wide association studies (GWAS) identifying over 300 genetic loci associated with CAD susceptibility.^[65] These loci, often involving lipid metabolism and inflammation pathways, explain a portion of familial risk not attributable to single mutations, supporting the use of polygenic risk scores in family-based screening.

Type 2 Diabetes

Family aggregation of type 2 diabetes is evident through elevated risks among relatives, particularly first-degree family members, who face a 2- to 6-fold increased likelihood of developing the condition compared to those without such history.^[66] This pattern underscores the interplay of genetic susceptibility and shared metabolic factors within families. Longitudinal studies in high-prevalence populations, such as the Pima Indians of Arizona since the 1960s, have highlighted pronounced familial clustering, with monozygotic twin concordance rates approaching 74% in select cohorts, far exceeding dizygotic rates and emphasizing genetic contributions amid environmental influences like obesity and insulin resistance.^[67] These findings illustrate how diabetes manifests in pedigrees through intergenerational transmission, often linked to beta-cell dysfunction and impaired glucose homeostasis. Heritability estimates for type 2 diabetes typically range from 40% to 70%, reflecting a substantial genetic component modulated by lifestyle factors.^[68] The Framingham Offspring Study has provided key insights into this transmission, demonstrating that offspring of parents with type 2 diabetes exhibit a markedly higher incidence, with risks elevated regardless of whether the affected parent is maternal or paternal, though maternal history may correlate with higher birth weights and metabolic traits in progeny.^[69] This multigenerational pattern supports models where polygenic risks accumulate across family lines, contributing to the disease's clustering beyond sporadic cases. Certain monogenic subtypes exemplify extreme familial aggregation within the broader spectrum of type 2 diabetes. Maturity-onset diabetes of the young (MODY), especially forms caused by mutations in the HNF1A gene, follows an autosomal dominant pattern with high penetrance, resulting in approximately 50% risk to first-degree relatives and often presenting as early-onset, non-insulin-dependent diabetes misdiagnosed as type 2.^[70] These subtypes highlight how single-gene defects can drive strong pedigree-based inheritance, contrasting with the polygenic nature of typical type 2 diabetes but reinforcing overall family risk profiles. Ethnic variations further accentuate family aggregation patterns, with higher clustering observed in South Asian and African ancestry populations, where first-degree relative risks can exceed those in European groups.^[71] This disparity is partly attributed to the thrifty gene hypothesis, which suggests evolutionary selection for genes promoting efficient energy storage in ancestors facing famine, leading to heightened susceptibility in modern high-calorie environments and amplified transmission in affected lineages.^[72] Such variations emphasize the need for culturally tailored screening in high-risk families, where shared genetic and metabolic factors converge.

Genetic and Environmental Influences

Heritability and Genetic Models

Heritability estimation in family aggregation of disorders typically focuses on narrow-sense heritability (h²), which quantifies the proportion of phenotypic variance attributable to additive genetic effects, excluding dominance and epistasis.^[73] Segregation analysis, a classical method applied to family pedigrees, estimates h² by fitting parametric models to observed transmission patterns across generations, allowing decomposition of variance into genetic and non-genetic components.^[21] For binary traits common in disorders like psychiatric and neurological conditions, polygenic inheritance models employ a liability threshold approach, where underlying liability is assumed to follow a normal distribution, and affection status occurs when liability exceeds a population-specific threshold determined by prevalence. Key genetic models for family aggregation include the multifactorial threshold model, originally proposed by Edwards in 1960, which posits that disease risk arises from the combined effects of multiple genetic and environmental factors, with familial clustering explained by correlated liabilities among relatives. This model underpins the common disease-common variant (CD/CV) hypothesis, which suggests that widespread diseases result from relatively frequent alleles (minor allele frequency >1-5%) with small individual effects, contributing to population-level aggregation through cumulative polygenic burden.^[74] Complementing this, rare variants—often with larger effects—play a significant role in familial aggregation, as evidenced by exome sequencing studies identifying low-frequency coding mutations enriched in affected families for conditions like multiple sclerosis^[75] and alcohol use disorder.^[76] Cross-disorder analyses reveal pleiotropy as a driver of shared family aggregation between psychiatric and neurological disorders, with genome-wide studies identifying overlapping loci, such as those near MHC and MAPT regions implicated in both schizophrenia and Parkinson's disease.^[77] Polygenic risk scores (PRS), which aggregate effects across many variants, demonstrate modest predictive accuracy for familial aggregation, typically explaining 5-20% of liability variance in these disorders, highlighting their utility in quantifying shared genetic architecture despite limited individual-level precision.^[78] Molecular tools for dissecting these models include linkage disequilibrium (LD) mapping, which leverages non-random allele associations in populations or families to localize causal variants by tracing historical recombination events in pedigrees.^[79] A core computation in such analyses is the PRS, formulated as:

\text{PRS} = \sum_i \beta_i G_i

where \beta_i represents the effect size of the i-th variant from genome-wide association studies, and G_i is the genotype dosage (0, 1, or 2) for that variant, enabling summation across thousands of loci to predict aggregation risk.^[80]

Shared Environmental Factors

Shared environmental factors refer to non-genetic influences that are experienced similarly by family members, contributing to the familial aggregation of diseases beyond purely heritable components. These factors encompass exposures within the family unit that can shape risk profiles across generations, such as prenatal conditions, household dynamics, and broader socio-cultural norms. In the context of disease aggregation, shared environments explain a portion of the variance in liability that is not attributable to individual unique experiences or genetics alone.^[22] Key types of shared environmental influences include intrauterine effects, where maternal exposures during pregnancy affect multiple offspring. For instance, maternal smoking during pregnancy has been linked to increased schizophrenia risk in exposed children, persisting even after accounting for familial confounding factors like genetic predispositions. Household exposures, such as shared dietary patterns, also play a role; high availability of saturated fats like lard in the home environment elevates cardiovascular disease risk among family members through consistent consumption habits. Additionally, cultural practices within families can perpetuate aggregation, as seen in type 2 diabetes management, where traditional food norms and family meal structures in certain ethnic groups hinder adherence to healthier diets and contribute to shared metabolic risks.^[81]^[82]^[83] The magnitude of shared environmental contributions is quantified in twin and family studies using the ACE model, which decomposes phenotypic variance into additive genetic (A), shared environmental (C), and unique environmental (E) components. Here, the shared environmental variance (c²) is estimated as the monozygotic twin correlation minus the heritability (h²), capturing familial influences like parenting styles or socioeconomic status that make siblings more similar. In mood disorders, such as major depression, twin studies indicate that shared environmental factors account for approximately 10-20% of the variance, highlighting their modest but significant role alongside genetics. These estimates underscore how shared environments interact with genetic models to amplify familial patterns without dominating the overall liability.^[84]^[85] Gene-environment correlations further complicate the attribution of familial aggregation to shared environments, as genetic predispositions can shape the family milieu in ways that mimic environmental effects. Passive gene-environment correlation occurs when children inherit both risk alleles and environments from parents, such as a family history of substance use fostering similar exposures. Evocative correlation arises when an individual's genetically influenced traits elicit shared family responses, like aggressive behaviors prompting consistent parenting strategies that affect siblings alike. Assortative mating, where partners with similar genetic risks pair up, can inflate apparent shared environmental variance by concentrating liabilities within families and enhancing intergenerational transmission of disease susceptibility.^[86]^[87]^[88] Epigenetic mechanisms provide a molecular basis for the familial transmission of shared environmental influences, particularly through heritable changes in gene expression without altering DNA sequence. DNA methylation patterns, which can be modified by environmental exposures like diet or stress, are transmitted across generations and have been implicated in Alzheimer's disease risk at loci associated with amyloid processing. For example, altered methylation at promoters of genes like APP or PSEN1 in affected families correlates with increased neurodegeneration vulnerability, illustrating how shared familial environments can epigenetically "tag" risk in offspring. These processes bridge environmental exposures and long-term disease aggregation, offering insights into non-genetic inheritance pathways.^[89]^[90]

Limitations and Future Directions

Methodological Challenges

One major methodological challenge in studying family aggregation is recall bias, where individuals inaccurately report family medical histories due to memory lapses, lack of knowledge about relatives' health, or emotional factors. Validation studies have shown that self-reported family histories often have positive predictive values of 60-70%, meaning a significant portion of reported cases cannot be confirmed through medical records or registries. For instance, in a large cohort assessing colorectal cancer family history, sensitivity for first-degree relatives ranged from 52.9% to 56.6%, with underreporting more common for distant relatives.^[91] Confounding factors further complicate the interpretation of family aggregation patterns. Assortative mating, where individuals pair with partners sharing similar genetic or environmental risk profiles, can inflate apparent familial clustering by increasing shared liability across generations without reflecting true genetic transmission. Population stratification, arising from ancestral differences in allele frequencies across subpopulations, introduces spurious associations in genetic studies unless controlled for using methods like principal component analysis. Additionally, Berkson's bias affects clinic-based samples, as patients seeking care for one condition are more likely to report or be ascertained for related familial conditions, leading to overestimation of aggregation; for example, clinic-recruited cases of essential tremor were 3.79 times more likely to report affected first-degree relatives than community samples.^[92]^[93]^[94]^[95] Small sample sizes pose significant issues for detecting rare familial forms of diseases, resulting in low statistical power to identify causal variants or aggregation patterns. Family-based studies of rare variants often require larger cohorts than case-control designs to achieve adequate power, as the infrequency of events limits the ability to discern signals from noise. In genome-wide association studies (GWAS) examining familial traits, multiple testing across millions of variants exacerbates this by necessitating stringent corrections (e.g., Bonferroni thresholds of p < 5 × 10^{-8}), which can obscure true associations in underpowered analyses of rare forms.^[96]^[97]^[98] Ethical concerns arise particularly in high-aggregation families, where genetic counseling must balance informing at-risk relatives with respecting autonomy and avoiding coercion. Counselors face dilemmas in disclosing actionable risks, such as the duty to warn family members of hereditary threats like BRCA mutations, which may strain family dynamics or lead to unintended psychological harm. Privacy issues in pedigree data compound these challenges, as detailed family trees can inadvertently reveal sensitive health information about unconsenting relatives, with surveys showing that up to 78% of investigators do not obtain explicit consent for pedigree publication. Such disclosures risk stigma, discrimination, or re-identification in genomic databases.^[99]^[100]^[101]

Implications for Precision Medicine

Family aggregation informs precision medicine by enabling personalized risk stratification, particularly through the integration of familial history into clinical guidelines and predictive models. For atherosclerotic cardiovascular disease (ASCVD), major guidelines from the American College of Cardiology and American Heart Association recommend incorporating family history of premature ASCVD—defined as onset before age 55 in male relatives or 65 in female relatives—into risk assessment tools like the ASCVD Risk Estimator Plus. This factor refines 10-year and lifetime risk predictions, guiding decisions on statin therapy and lifestyle modifications for individuals aged 40-79. Similarly, polygenic risk scores (PRS) for schizophrenia, derived from genome-wide association studies, identify individuals at elevated risk; those in the top 1% of PRS distribution exhibit a sixfold increased likelihood of developing the disorder, supporting early screening in high-familial-risk groups.^[102]^[103]^[104] Cascade screening exemplifies how family aggregation facilitates targeted genetic testing in relatives of probands with monogenic conditions, enhancing precision diagnostics. In familial hypercholesterolemia (FH), a common monogenic cause of premature ASCVD, cascade screening involves systematic cholesterol and genetic testing of first-degree relatives, identifying undiagnosed cases at a cost-effective rate and enabling early lipid-lowering interventions. Studies demonstrate that this approach reduces ASCVD morbidity and mortality by promoting timely statin initiation and family-wide management, with uptake rates improving through national registries and genetic counseling.^[105]^[106] Preventive strategies tailored to high-aggregation families leverage family history to prioritize interventions that address both genetic and modifiable risks. For cardiovascular conditions like FH, family-based lifestyle programs emphasizing healthy diet, regular physical activity, smoking cessation, and weight management have been associated with up to a 50% relative risk reduction in coronary events, independent of mutation status. In type 2 diabetes, where familial clustering indicates shared polygenic and environmental burdens, pharmacogenomic profiling guides drug selection; for instance, variants in SLC22A1 and ATM genes predict metformin efficacy, allowing personalized glycemic control in at-risk relatives to prevent progression.^[107]00319-3/fulltext)^[108] Emerging trends in precision medicine extend family aggregation insights through polygenic counseling and AI-driven predictions. Polygenic counseling integrates PRS with family history to provide probabilistic risk estimates, motivating preventive behaviors in conditions like cardiovascular disease and type 2 diabetes; for schizophrenia, it complements clinical assessments to inform early interventions. In the 2020s, AI models analyzing EHR-derived family pedigrees have improved risk forecasting for thousands of diagnoses, achieving superior accuracy over traditional methods by automating aggregation patterns and enabling scalable, personalized care pathways.^[109]^[110]^[111]

References

[1]
Methods for Assessing Familial Aggregation - NIH
The clustering of disease in close family members may be explained by sharing of either environmental or genetic factors or both. Familial clustering is often ...
[2]
Family Aggregation - an overview | ScienceDirect Topics
Family aggregation refers to the clustering of a disease within families, measured by the relative recurrence risk (RRR) or familial risk ratio (FRR), ...Genetic Risk Studies · Genomes And Evolution · The Origins And Evolution Of...
[3]
Analysis of familial aggregation studies with complex ascertainment ...
Familial aggregation refers to this clustering of disease within families. This clustering may be due to genetic and/or environmental factors, or even ...
[4]
Familial Aggregation Studies: A Valuable Tool in the Genetic Toolbox
Aug 2, 2022 · Familial aggregation studies provided recurrence risk estimates used in genetic counseling and informed understanding of the genetic contribution to disease.
[5]
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(05)67570-8/fulltext
[6]
The Relationship between the Sibling Recurrence-Risk Ratio and ...
The recurrence-risk ratio of disease in siblings, λS, is a standard parameter used in genetic analysis to estimate the statistical power for detection of a ...
[7]
“Familiality” or Heritability - PMC - NIH
... in a trait in a population that results from additive genetic effects. ... familiality rather than the more specific term heritability. Footnotes.
[8]
The Prehistory of Psychiatric Genetics: 1780–1910 - Psychiatry Online
Oct 15, 2020 · To provide such a perspective, the author reviews 48 representative texts, published from 1780 to 1910, examining the inheritance of insanity.
[9]
Emil Kraepelin: A pioneer of scientific understanding of psychiatry ...
Emil Kraepelin was an influential German psychiatrist who lived in the late 19 th and the early 20 th century. His work had a major impact on modern psychiatry.
[10]
From R.A. Fisher's 1918 Paper to GWAS a Century Later - PMC - NIH
Apr 3, 2019 · A century ago, a paper from RA Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation ...Missing: aggregation | Show results with:aggregation
[11]
Seymour S. Kety and the Genetics of Schizophrenia - Nature
Sep 1, 2001 · If Lidz and Laing were correct, the rate of schizophrenia in the adoptive parents of schizophrenics should be just as high as the rate of ...
[12]
Genome-wide association studies | Nature Reviews Methods Primers
Aug 26, 2021 · Genome-wide association studies (GWAS) aim to identify associations of genotypes with phenotypes by testing for differences in the allele ...Missing: rise | Show results with:rise<|control11|><|separator|>
[13]
Pedigree and Family History Taking - Understanding Genetics - NCBI
In addition, a family history can even help to exclude genetic diseases, particularly for common diseases in which lifestyle and environment play strong roles.
[14]
Pedigree Drawing - Progeny
Premium Account users can import existing pedigree data from delimited text, Excel, XML or Cyrillic FAM files. Pedigrees are automatically built after import.Missing: construction | Show results with:construction
[15]
Cyrillic Downloads | AP Benson
Cyrillic is a powerful program that brings together all the tools you need for drawing family pedigrees, and managing and analyzing pedigree data. The following ...Missing: construction Progeny
[16]
APPLICATION OF ODDS RATIO REGRESSION MODELS FOR ...
A regression model for estimating covariate effects on odds ratios to test for familial aggregation of common disease in first-degree relatives of cases and ...
[17]
Irma Weinberg's 1928 paper “on the problem of the determination of ...
Apr 17, 2023 · The main aim of Weinberg's study was to generate an “Hereditary Prognosis” (e.g., empirical risk) for DP in the full cousins of DP probands.
[18]
Morbid risk of schizophrenia amongst relatives of ... - PMC
Lifetime morbid risk is usually defined and determined by the Kaplan-Meier product limit estimator and Weinberg method as the probability of a person developing ...
[19]
Family history assessment significantly enhances delivery of ...
Jan 7, 2021 · Family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs.
[20]
The Family History — More Important Than Ever
The family history has been shown to help predict the risk of such varied health concerns as heart disease, colorectal cancer, breast cancer, ovarian cancer, ...
[21]
Assessing the Heritability of Complex Traits in Humans - NIH
Falconer's formula is applied to twin studies which provides an estimate of broad sense heritability (H2) by comparing the correlation of the phenotype in ...Missing: aggregation | Show results with:aggregation
[22]
Analytic approaches to twin data using structural equation models
In family studies familial aggregation of a disease or trait is investigated ... classical twin method, Falconer's formula was used to estimate ...
[23]
Adoption Studies - an overview | ScienceDirect Topics
Adoption studies are used to estimate the degree to which variation in a trait is due to environmental and genetic influences.
[24]
Adoption Studies - PMC
Researchers use adoption studies to determine the contributions of genetic and environmental factors to the development of alcohol problems.
[25]
Family environment and the malleability of cognitive ability - PNAS
Mar 23, 2015 · The resulting four groups approximated the “cross-fostering” design used in genetic studies of experimental animals, including a very unusual ...
[26]
Segregation Analysis Using the Unified Model - PubMed
It is a statistical method to determine if a trait, continuous or binary, has a transmission pattern in pedigrees that is consistent with Mendelian segregation.
[27]
Segregation analysis using the unified model - PubMed - NIH
It is a statistical method to determine if a trait, continuous or binary, has a transmission pattern in pedigrees that is consistent with Mendelian segregation.
[28]
[PDF] Statistical Analysis for Genetic Epidemiology (S.A.G.E.) Version 6.4 ...
SEGREGation models : This program can be used to fit mixtures of ... RELative Pair AnaLysis : Performs a regression-based univariate or multivariate model ...
[29]
Estimating disease heritability from complex pedigrees allowing for ...
Summary. We propose TetraHer, a method for estimating the liability heritability of binary phenotypes. TetraHer has five key features.
[30]
A Twin Study into the Genetic and Environmental Influences ... - NIH
The shared environmental influence is the variance that makes MZ and DZ twins similar, but is not explained by additive genetic effects. It is estimated by ...
[31]
Risk of schizophrenia in relatives of individuals affected by ...
For first-degree relatives of one proband with schizophrenia, the risk is eight-fold. For first-degree relatives of two probands with schizophrenia, the risk ...
[32]
Genetics of Schizophrenia: Overview of Methods, Findings and ...
In this context, it merits special attention that Gottesman and Shields (1967) already proposed a polygenic model for schizophrenia. ... threshold of significance ...
[33]
Twenty-five-year followup of the Israeli High-Risk Study - PubMed
Current and lifetime psychopathology was assessed in 50 Israeli children of parents with schizophrenia who were either of kibbutz families and raised ...Missing: 1980s | Show results with:1980s
[34]
Evaluating the spectrum concept of schizophrenia in the ...
OBJECTIVE: The authors sought to evaluate whether the pattern of schizophrenia and related disorders in probands and their relatives can be explained by a ...
[35]
Schizophrenia liability shares common molecular genetic risk factors ...
The PGC report a variance explained in schizophrenia by the polygenic risk score at a p-value threshold of 0.05 of 7%. This approach has previously shown that ...Phenotypic Measures · Polygenic Risk Score... · Data Availability
[36]
Finding schizophrenia genes - JCI
Jun 1, 2005 · In the children and siblings of individuals with schizophrenia, the increase in risk is approximately 10-fold; it is somewhat less than this in ...
[37]
Genetic epidemiology of major depression: review and meta-analysis
Five twin studies met the inclusion criteria, and their statistical summation suggested that familial aggregation was due to additive genetic effects (point ...Missing: lambda_s Norwegian
[38]
Is Bipolar Disorder a genetic condition? - Best Psychiatry
Family studies indicate that if one parent has a mood disorder, their child will have a risk of between 10 and 25 percent for a mood disorder.
[39]
The Genetics of Bipolar Disorder - PMC - PubMed Central - NIH
The three largest recent twin studies have reported a concordance rate of 38.5–43% for MZ twins compared with 4.5 – 5.6% for DZ twins (Kendler et al., 1995, ...Missing: 1978 | Show results with:1978
[40]
A Danish twin study of manic-depressive disorders - PubMed
This finding is in accordance with previous twin studies of manic-depressive disorders and confirms the evidence of a strong genetic factor.Missing: pdf | Show results with:pdf
[41]
A Swedish national twin study of lifetime major depression - PubMed
This study suggests both that the heritability of major depression is higher in women than in men and that some genetic risk factors for major depression are ...Missing: family aggregation lambda_s Norwegian
[42]
[PDF] GENETIC AND OTHER VULNERABILITY FACTORS FOR ANXIETY ...
ders converge in demonstrating a 3- to 5-fold increased risk of anxiety disorders among first-degree relatives of affected probands compared to controls ...
[43]
Co-morbidity and familial aggregation of alcoholism and anxiety ...
This study examined the patterns of familial aggregation and co-morbidity of alcoholism and anxiety disorders in the relatives of 165 probands selected for ...Missing: 1990s | Show results with:1990s
[44]
Familial traits of bipolar disorder: A systematic review and meta ...
May 15, 2023 · These studies indicated 14 potentially familial traits of BD: age of onset (OR: 4.50; 95% CI: [3.25, 6.22]), bipolar type (OR: 2.05 [1.50, 2.79]) ...
[45]
The Genetics of Parkinson's Disease and Implications for Clinical ...
Meta-analysis suggests that the presence of a family history of Parkinson's confers a 3–4× increase in PD risk, implying a significant effect of shared genetic ...
[46]
Familial aggregation of Parkinson's disease: a meta-analysis
Jun 15, 2008 · We sought to determine the relative risk (RR) of Parkinson's disease (PD) for having a first-degree relative with PD versus having no first-degree relative ...
[47]
Heritability of Parkinson disease in Swedish twins: a longitudinal study
Concordance rates for PD were 11% for monozygotic and 4% for same-sexed dizygotic twin pairs, with a heritability estimate of 34%.
[48]
Parkinson's disease in twins: a follow-up study - PubMed
Based on concordance rates only, the findings in our twin sample do not support a major genetic impact for the motor expression of PD.
[49]
Genetics of Parkinson's disease - PubMed
Approximately 5-10% of patients suffer from a monogenic form of PD where autosomal dominant mutations in SNCA, LRRK2, and VPS35 and autosomal recessive ...Missing: prevalence | Show results with:prevalence
[50]
Genetics of Parkinson's Disease - PMC - PubMed Central
Mutations in the LRRK2 gene are the most frequent known cause of late-onset autosomal-dominant and sporadic PD, with a mutation frequency ranging from 2% to 40% ...
[51]
Genetic Neuropathology of Parkinson's Disease - PMC
Parkinson's disease (PD) has long been considered to be a sporadic entity, perhaps with an environmental etiology. However, recent genetic discoveries have ...
[52]
[PDF] Genetic study of familial cases of Alzheimer's disease
Jan 23, 2004 · A small number (1–5%) of Alzheimer's disease (AD) cases associated with the early-onset form of the disease (EOAD) appears to be transmitted ...
[53]
APP, PSEN1, and PSEN2 mutations in early-onset Alzheimer disease
Mar 28, 2017 · APP, PSEN1, or PSEN2 mutations were identified in 53 novel AD-EOAD families. Of the 129 sporadic cases screened, 17 carried a PSEN1 mutation and ...Missing: Volga kindred 1980s
[54]
Entry - #104300 - ALZHEIMER DISEASE, FAMILIAL, 1; AD1 - OMIM
... PSEN1 and APP mutations account for 71% of autosomal ... Early-onset autosomal dominant Alzheimer disease: prevalence, genetic heterogeneity, and mutation ...
[55]
Genetics of Alzheimer Disease - PMC - PubMed Central
To date, as many as 14 PSEN2 mutations have been identified. One of the first mutations to be identified was a point mutation located within the second ...Missing: offspring | Show results with:offspring
[56]
Alzheimer Disease Overview - GeneReviews® - NCBI Bookshelf
Dec 20, 2018 · Late-onset familial 1 (age >60-65 years), 15%-25% ; Early-onset familial 1 (age <60-65 years), <2% ; Down syndrome · <1% ; Unknown (includes genetic ...
[57]
ApoE in Alzheimer's disease: pathophysiology and therapeutic ...
Nov 8, 2022 · APOE4 is a major genetic risk factor for AD in a gene dose-dependent manner increasing risk by up to 15 fold in homozygotes [13], whereas APOE2 ...Missing: Framingham | Show results with:Framingham
[58]
s disease progression: Framingham Heart Study
Oct 6, 2020 · ApoE ε4 raised the risk for AD in both genders similarly. Sex ... Plasma amyloid-beta and risk of Alzheimer's disease in the Framingham Heart ...
[59]
Heritability for Alzheimer's disease: the study of dementia ... - PubMed
In this study, our purpose was to detect all cases of dementia in an established twin registry and to estimate total extent of genetic contribution to liability ...Missing: studies MZ 30-40% 2000s h² 60-80%
[60]
The multiplex model of the genetics of Alzheimer's disease
Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58–79% and early-onset AD showing over 90%.
[61]
Familial Alzheimer's Disease Mutations within the Amyloid Precursor ...
These results indicate that FAD mutations falling within the Aβ sequence lead to dramatic changes in aggregation kinetics and influence the ability of Aβ to ...
[62]
Association between Family History Risk Categories and ...
Individuals with a positive family history of diabetes experience 3- to 4-fold higher risks for this disease than those with a negative family history of ...<|control11|><|separator|>
[63]
Genetic Studies of the Etiology of Type 2 Diabetes in Pima Indians
May 1, 2004 · Studies in twins, and particularly in twins reared apart, have also produced high heritability estimates for BMI, ranging between 0.6 and 0.9 (7) ...Missing: MZ | Show results with:MZ
[64]
Genetics of Type 2 Diabetes—Pitfalls and Possibilities - MDPI
However, heritability estimates have varied between 25%–80% in different studies; the highest estimates are seen in those studies with the longest follow-up ...Missing: h2 | Show results with:h2
[65]
Parental transmission of type 2 diabetes: the Framingham Offspring ...
Study of parental transmission of diabetes provides insight into the relative contributions of underlying maternal and paternal influences.
[66]
Maturity-Onset Diabetes of the Young: Rapid Evidence Review - AAFP
Up to 80% of MODY cases are misdiagnosed as type 1 or 2 diabetes. MODY should be considered in nonobese patients who have diabetes that was diagnosed at a ...Abstract · Epidemiology and Genetics · Diagnosis · Treatment
[67]
Genetics of Type 2 Diabetes in African Americans
Aug 19, 2015 · The thrifty gene hypothesis proposed by Neel [69] suggests that genetic variants that promote efficient energy absorption and storage at time ...
[68]
The Elevated Susceptibility to Diabetes in India: An Evolutionary ...
Jul 7, 2016 · The influential “thrifty genotype” hypothesis ... FTO gene variants are strongly associated with type 2 diabetes in South Asian Indians.Missing: aggregation | Show results with:aggregation
[69]
How to estimate heritability: a guide for genetic epidemiologists - NIH
'Narrow-sense heritability' refers to phenotypic variation explained by additive genetic variation only. Heritability may be used to estimate both continuous ...Missing: h² segregation
[70]
Common vs. Rare Allele Hypotheses for Complex Diseases - PMC
The 'Common Disease, Common Variant (CDCV)' hypothesis argues that genetic variations with appreciable frequency in the population at large, but relatively low ...
[71]
Contribution of Common Genetic Variants to Familial Aggregation of ...
Nov 15, 2019 · We then show that Polygenic Risk Scores (PRS) may be useful for identifying families likely to carry such a rare variant and introduce a related ...
[72]
Exome sequencing reveals low-frequency and rare variant ... - Nature
Apr 5, 2025 · In this study, we evaluated the contribution of low-frequency and rare genetic variants to MS susceptibility within one of the largest family-based MS cohorts ...
[73]
Genome-wide association analysis of Parkinson's disease and ... - NIH
Genome-wide association analysis of Parkinson's disease and schizophrenia reveals shared genetic architecture and identifies novel risk loci.
[74]
Polygenic risk scores: from research tools to clinical instruments
May 18, 2020 · Polygenic risk scores may be used to estimate an individual's lifetime genetic risk of disease, but the current discriminative ability is low in the general ...
[75]
Linkage disequilibrium mapping in isolated populations - PNAS
Linkage disequilibrium analysis can provide high resolution in the mapping of disease genes because it incorporates information on recombinations that have ...Missing: aggregation | Show results with:aggregation
[76]
A guide to performing Polygenic Risk Score analyses - PMC
Here we provide a guide to performing polygenic risk score analysis, outlining the standard quality control steps required, options for PRS calculation and ...
[77]
Familial Confounding of the Association Between Maternal Smoking ...
We observed a significantly increased risk of schizophrenia in offspring exposed to maternal smoking during pregnancy, even after adjusting for a variety of ...
[78]
Household availability of dietary fats and cardiovascular disease ...
Jul 30, 2021 · Our results indicated higher risk of CVD among those who had access to high amounts of lard in their household, but there was no clear ...
[79]
Cultural and Family Challenges to Managing Type 2 Diabetes in ...
Jul 23, 2009 · We describe cultural and family challenges to illness management in foreign-born Chinese American patients with type 2 diabetes and their spouses.Missing: aggregation | Show results with:aggregation
[80]
A Meta-Analysis of Shared Environmental Influences - ResearchGate
Oct 9, 2025 · The author examined twin and adoption studies (n = 490) of internalizing and externalizing psychopathology prior to adulthood. Analyses revealed ...
[81]
Shared Genetics and Couple-Associated Environment Are Major ...
Depression has a pattern of familial aggregation, which implies the influence of genetic effects, common environmental effects shared by relatives, or both. The ...
[82]
Testing different types of genotype-environment correlation - NIH
Typically, three types of rGE are distinguished: passive, evocative, and active (Moffitt, Caspi, & Rutter, 2005; Scarr & McCartney, 1983).Missing: aggregation | Show results with:aggregation
[83]
Gene–Environment Correlation Underlying the Association Between ...
Results suggest that evocative rGE, not passive rGE or direct environmental effects of parenting on adolescent externalizing, explains associations between ...Missing: aggregation | Show results with:aggregation
[84]
Cross-trait assortative mating is widespread and inflates genetic ...
Nov 17, 2022 · We observe that xAM affects many phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates.
[85]
DNA methylation: The epigenetic mechanism of Alzheimer's disease
Nov 15, 2023 · This study discusses possible pathways that may affect the brain after methylation of gene loci highly associated with Alzheimer's disease (AD).
[86]
Epigenetics Mechanisms in Alzheimer's disease - PMC - NIH
DNA methylation appears to be one of these epigenetic mechanisms, and may be of particular importance given the unusually high frequency of CpG dinucleotides ...
[87]
https://pmc.ncbi.nlm.nih.gov/articles/PMC3928634/
[88]
Genetic similarity between relatives provides evidence on ... - Nature
Mar 26, 2024 · We also described how assortative mating can induce and increase gene-environment correlations, which mimic higher heritability and thereby ...Missing: aggregation | Show results with:aggregation
[89]
Assortative mating and parental genetic relatedness contribute to ...
Dec 7, 2023 · Assortative mating can lead to increases in disease liability over generations and the appearance of genetic anticipation in families carrying rare variants.
[90]
Population Stratification in Genetic Association Studies - PMC - NIH
Here we review historical and current approaches for addressing PS when performing genetic association studies in human populations.
[91]
Family history information on essential tremor: Potential biases ...
Feb 28, 2001 · In univariate logistic regression models, clinic cases were 3.79 times more likely to report an affected first-degree relative, 12.69 times more ...
[92]
Increasing Generality and Power of Rare-Variant Tests by Utilizing ...
Sep 22, 2016 · Statistical power to detect an association of rare variants could be higher in family-based studies than in case-control studies because a ...
[93]
The challenges of researching rare genetic diseases
To obtain statistical power, GWAS rely on large sample sizes, which are only available for common diseases. Additionally, these databases are often ...
[94]
Weighted multiple testing procedures in genome-wide association ...
Jun 15, 2023 · In this work, we evaluate some recent weighted multiple testing procedures in the specific context of GWAS through a simulation study.Missing: aggregation | Show results with:aggregation
[95]
Ethics in genetic counselling - PMC - PubMed Central
Difficult ethical issues arise for patients and professionals in medical genetics, and often relate to the patient's family or their social context.
[96]
Duty to Warn At-Risk Family Members of Genetic Disease
Physicians and counselors must address the importance of communicating genetic test results to family members in the pre-test counseling and ...
[97]
Privacy and Confidentiality in the Publication of Pedigrees: A Survey ...
Jun 10, 1998 · Sixty-one investigators (36%) stated that family members were not informed that their pedigree would be published; 131 (78%) do not obtain ...
[98]
Power of the Pedigree: The Family History Variable for ASCVD Risk ...
Feb 1, 2021 · Current prevention guidelines recommend the assessment of family history (FamHx) to guide ASCVD risk prediction, particularly when ...<|control11|><|separator|>
[99]
ASCVD Risk Estimator + - Mobile and Web Apps
This calculator only provides 10-year risk estimates for individuals 40-79 years of age. Click here to view brief suggestions for younger patients. ... Lifetime ...
[100]
Polygenic Scores in Psychiatry: On the Road From Discovery to ...
Nov 1, 2022 · Across ancestries, those with a polygenic score in the top 1% of the distribution were at a sixfold increased risk of schizophrenia compared ...
[101]
Cascade Screening for Familial Hypercholesterolemia and the Use ...
Jul 25, 2017 · Cascade screening for FH is an evidence-based intervention that can reduce the burden of morbidity and mortality from ASCVD in populations.
[102]
The Power of the Pedigree: Cascade Screening in Familial ... - JACC
Cascade screening represents a simple, cost-effective method for identifying additional cases in a family. This ultimately leads to earlier diagnoses of FH.
[103]
Impact of Healthy Lifestyle in Patients With Familial ...
A healthy lifestyle was associated with a reduced risk of CVD among patients with FH regardless of their FH mutation status. Therefore, lifestyle modifications ...Original Research · Clinical Characteristics Of... · Discussion
[104]
The Pharmacogenetics of Type 2 Diabetes: A Systematic Review
Feb 11, 2014 · We performed a systematic review to identify which genetic variants predict response to diabetes medications.Missing: family aggregation
[105]
Polygenic Risk Scores for Cardiovascular Disease: A Scientific ...
Jul 18, 2022 · Personalized genetic risk counseling to motivate diabetes ... Polygenic risks and cardiovascular treatment effects in severe mental illness ...
[106]
Recent advances in polygenic scores: translation, equitability ...
Feb 19, 2024 · Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically ...Disease Risk Prediction... · Risk Stratification · Use In Clinical Trials And...
[107]
Automated Family Histories Significantly Improve Risk Prediction in ...
May 31, 2024 · In this study, we devised a family pedigree-driven high-throughput machine learning pipeline to simultaneously predict risks for thousands of diagnosis codes.