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Results for “genetic confounding”

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At least 19 recordsLinked to original sources

PubMed1992

Factors confounding genetic linkage between atopy and chromosome 11q.

The results of testing for linkage between atopy and the chromosome 11 marker D11S97 is shown for all the 723 subjects genotyped by us up to January 1992. Lod score estimations were confounded by the high population prevalence of atopy, maternal inheritance of atopy at the 11q locus, genetic heterogeneity, and excess of atopy in families not ascertained through a single proband. Affected sib-pair analysis shows evidence for linkage which is not dependent on the definition of atopy or model specification. We suggest that presentation of sib-pair data will be suitable for meta-analysis of the different studies of genetic linkage and atopy.

Alleles↗

PubMed2026

Genetic risk scores, perceived neighborhood disorder, and sleep duration.

STUDY OBJECTIVES: Most studies of neighborhood context and sleep health emphasize direct effects and fail to account for the role of genetics. In this paper, we draw on the socioecological model to examine the interplay of genetics, neighborhood context, and sleep health. We specifically examine the independent and joint effects of genetic risk scores (GRS) and perceived neighborhood disorder on sleep duration. METHODS: We combine genomic and cross-sectional survey data from the All of Us Research Program, a non-probability sample of 22 575 adults of European ancestry living in the United States. We use the sleep duration-increasing risk allele count for 78 genome-wide single nucleotide polymorphisms (SNPs) to construct weighted genetic risk scores. Our analyses include an index of perceived neighborhood disorder and an objective measure of sleep duration based on wrist actigraphy. RESULTS: Genetic risk scores are inversely associated with neighborhood disorder, positively associated with continuous sleep duration, and inversely associated with the odds of short sleep. Neighborhood disorder is inversely associated with continuous sleep duration and positively associated with the odds of short and long sleep. The association between genetic risk scores and sleep duration (continuous and categorical) is invariant across levels of neighborhood disorder. CONCLUSIONS: Our analyses confirm the independent direct effects of genetic risk scores and neighborhood disorder on sleep duration. Our findings extend the socioecological model by assessing the role of genetics in the study of neighborhood context and sleep health. Although we observed a gene-environment correlation between genetic risk scores and perceived neighborhood disorder, there was little indication of genetic confounding and no evidence of gene-environment interaction.

Humans↗

PubMed1992

Recombinant-inbred strains: general methodological considerations relevant to the study of complex characters.

If appropriately determined, recombinant-inbred (RI) strain means provide an excellent method for determining genetic correlations among complex characters. However, little systematic attention has been paid to important environmental influences on strain means such as random effects due to litter membership or systematic maternal influences, which are inevitably confounded with genetic effects. It is suggested that users of RI strains would do well to control for litter effects by sampling appropriately from many litters and assessing the potential role of maternal influences by appropriate fostering procedures. Concern for these and other environmental sources of variation has caused reliability of strain means to emerge as an important issue in studies with RIs which focus on complex characters. Examples of estimating the reliability of RI strain means are provided to draw attention to the value of this kind of information in both gene-mapping studies and genetic correlational analyses. In addition, particularly in the case of behavioral tests which are susceptible to considerable day-to-day variation, repeated testing of the same animals can serve to diminish the influence of extreme deviations which are due to random variations in the manner in which the test is conducted on any given day. The advantages of RIs for gene mapping are well established. However, via the power of genetic correlational analysis, the RI methodology is emerging as a major alternative method, e.g., as distinct from lesion studies, pharmacological interventions, etc., in the bio-behavioral sciences to explore relationships between different domains of inquiry. Via its cumulative and integrative power, it is likely to make a major contribution to investigations of relationships between complex characters at various levels of and this application which should be considered separately from its application to gene mapping.

Animals↗

PubMed2025

Investigating the interplay between prematurity and genetic variation in the context of rare developmental disorders.

BACKGROUND: Rare damaging genetic variation accounts for a substantial proportion of the risk of rare developmental disorders (DDs), but common genetic variants as well as environmental factors, including prematurity, also contribute. Little is known about the interplay between prematurity and genetic variation in influencing phenotypic outcomes in DDs, nor about how genetic factors may contribute to risk of preterm birth in DDs. METHODS: We leveraged phenotypic and genetic data from 21,712 patients with DDs recruited for clinical sequencing, 16% of whom were born prematurely. Using multivariable regression models, we compared phenotypic features and the prevalence of diagnostic genetic variation in specific genes between preterm and term individuals with DDs. We tested whether the fraction of cases attributable to de novo mutations differed between term and preterm probands. Additionally, we assessed whether associations between common variant contributions to education-related traits and prematurity are explained by direct genetic effects. RESULTS: Prematurity was associated with more severe clinical phenotypes among these DD patients, including more affected organ systems and more delayed developmental milestones. Prematurity and the presence of a monogenic diagnosis contributed additively to severity. We found that genes associated with fetal anomalies were enriched for diagnostic mutations among preterm individuals (p = 7.83 × 10-5). We also demonstrated an exome-wide enrichment of de novo mutations (DNMs) in both term and preterm probands; the fraction of cases explained by DNMs in known DD-associated genes was higher in term than preterm cases (25% versus 20%) but DNMs in as-yet-undiscovered genes likely contribute approximately equally to both groups (14% versus 13%). Finally, we showed that the positive association between polygenic predisposition to education-related traits and gestational duration is likely to be the result of genetically influenced parental traits or confounders, rather than direct genetic effects in the child, and that a monogenic diagnosis modifies this association. CONCLUSIONS: Our findings emphasise the importance of considering environmental factors like prematurity in understanding outcomes in DDs suspected to have a genetic component, and motivate further exploration of the role that genetic variation plays in influencing prematurity.

Humans↗

PubMed2025

SPC: a SPectral Component approach leveraging Identity-by-Descent graphs to address recent population structure in genomic analysis.

Population structure is a well-known confounder in statistical genetics, particularly in genome-wide association studies (GWAS), where it can lead to inflated test statistics and spurious associations. Traditional methods, such as principal components (PCs), commonly used to adjust for population structure, are limited in capturing fine-scale, non-linear patterns that arise from recent demographic events - patterns that are crucial for understanding rare variant effects. To address this challenge, we propose a novel method called SPectral Components (SPCs), which leverages identity-by-descent (IBD) graphs to capture and transform local, non-linear fine-scale population structure into continuous representations that can be seamlessly integrated into genetic analysis pipelines. Using both simulated datasets and empirical data from the UK Biobank (N ≈ 420,000), we demonstrate that SPCs outperform PCs in adjusting for fine-scale population structure. In simulations, SPCs explained over 90% of the fine-scale population structure with fewer components, while PCs captured less than 5%. In the UK Biobank, SPCs reduced the inflation of p-values in the GWAS of an environmental-driven phenotype by 12% compared to PCs, while maintaining a similar performance to PCs in height, a highly heritable phenotype. Additionally, SPCs improved rare variant association analyses, reducing genomic inflation (e.g., from 7.6 to 1.2 in one analysis), and provided more accurate heritability estimates. Spatial autocorrelation analysis further confirmed the ability of SPCs to account for environmental effects, reducing Moran's I for both environmental and heritable phenotypes more effectively than PCs. Overall, our findings demonstrate that SPCs provide a robust, scalable adjustment for recent population structure, offering a powerful alternative or complement to PCs in large-scale biobank studies.

GWAS↗

PubMed2026

Robust inference and correlates from genetic associations with personality.

Personality traits describe stable differences in how people think, feel and behave, and how they interact with and experience their social and physical environments1,2. Many questions remain unanswered about associations between DNA and personality traits, such as their robustness, their generalizability and the biological and social pathways through which they act. Here we meta-analyse data across 46 cohorts comprising 611,037 to 1.14 million participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism and openness to experience), and data from up to 50,725 participants for within-family GWAS. We identify 1,260 lead genetic variants associated with personality, including 824 novel variants3. Common genetic variants explain a moderate 4.8-9.3% of the variance in measures of each trait, and 9.3-13.3% among instruments with typical measurement reliability. Genetic associations with personality are highly consistent but not identical across geography, reporter (self versus close other), age group and measurement instrument, and we find minimal spousal assortment for personality in recent history. In contrast to many other social and behavioural traits4,5, within-family GWAS and polygenic index analyses indicate that genetic associations with personality are minimally confounded by the shared family environment. Polygenic prediction, genetic correlation and Mendelian randomization analyses indicate that personality traits have widespread, potentially causal associations with consequential behaviours and life outcomes. Overall, we find that the genetic architecture of personality is robustly generalizable, minimally confounded and widely relevant to human experience.

Journal Article↗

PubMed2025

Exploring the shared genetic basis of attention-deficit/hyperactivity disorder and obstructive sleep apnea: A multi-omics analysis.

BACKGROUND: Observational studies have suggested an association between attention-deficit/hyperactivity disorder (ADHD) and obstructive sleep apnea (OSA), but these findings are often inconsistent due to potential biases from medication use, and varying diagnostic criteria. Genetic analyses can help mitigate these confounding factors, providing additional evidence. METHODS: This study evaluated the genetic correlations between ADHD and OSA using Genome-wide association study (GWAS) summary data, applying linkage disequilibrium score regression (LDSC) and SUPER GeNetic cOVariance Analyzer (SUPERGNOVA). Cross-trait association and colocalization analysis identify potential pleiotropic loci. Tissue enrichment analysis and gene-level analysis of shared genes between OSA and ADHD was conducted. Additionally, bidirectional Mendelian randomization was used to assess potential causal relationships. RESULTS: We found significant genetic correlations between ADHD and OSA (rg = 0.309, p = 3.252E-27), and identified 8 novel pleiotropic loci through cross-trait association analysis. Tissue enrichment analysis showed that these shared genes were primarily concentrated in brain tissues, particularly in deep gray matter regions, and were associated with immune and inflammatory pathways. Forward Mendelian Randomization analysis showed that ADHD was significantly associated with the risk of OSA (OR 1.070, 95 % CI 1.013-1.130, p = 0.016), and reverse analysis showed that OSA was significantly associated with the risk of ADHD (OR 1.240, 95 % CI 1.106-1.390, p = 2.213E-4). CONCLUSION: The findings of this study show a significant positive genetic correlation between ADHD and OSA and each is a risk factor for the other. Inflammation in specific brain regions may be the underlying mechanism for their comorbidity.

Humans↗

PubMed1992

Use of biomarkers in epidemiology: quantitative aspects.

Cancer initiators (mutagens) present, due to the absence of definable no effect threshold, a special problem in toxicology, requiring a high sensitivity of detection methods. Disease epidemiology aiming at identification of carcinogens and quantification of associated risks has a low resolving power, the detectable incidence or mortality increments being often orders of magnitude larger than those which are of public concern. Other drawbacks of disease epidemiology is the long latency times and the influence of confounders. The use of genetic endpoints as biomarkers suffers from low cause specificity, although this drawback seems to be overcome, partly at least, by emerging methods for determination of mutation spectra at the DNA level. Proximal cancer initiators/mutagens are electrophilic compounds or metabolites that can react with nucleophilic atoms in nucleic acids and proteins. These reactions lead to 'adducts' that can be identified and quantified, e.g. in lymphocytes and erythrocytes in blood samples. The shift from biological observations to chemical analysis permits sufficient sensitivity, and measurement can be done shortly after onset of exposure. The well-defined life span of the adducts to hemoglobin (Hb) offer possibilities of dose calculation and risk estimation. For these reasons the measurement of adducts to Hb and DNA constitutes a powerful epidemiological tool, applications of which has been initiated in work environments and the general environment and also in the search for a priori unknown carcinogens.

Animals↗

PubMed2026

Evolution and domestication-trait associations of ultra-long centromere haplotypes in pepper plants.

Centromeric and pericentromeric regions of most eukaryotic genomes are highly repetitive and strongly recombination-suppressed, confounding efforts to resolve genetic variation, population structure and phenotypic associations. Pepper (Capsicum annuum) centromeres are nearly devoid of satellite repeats, facilitating assembly and population-level comparison of centromeric regions. Here we integrate 9 near-complete genome assemblies, CENH3 ChIP-seq profiles from 26 diverse accessions, and resequencing and phenotypic data from ~400 cultivated and wild accessions to investigate population-level diversity and phenotypic relevance of pepper peri/centromeric regions. Functional centromere positions are largely fixed on 8 of 12 chromosomes, whereas the remaining 4 carry distinct centromeric epialleles shaped mainly by centromere repositioning and pericentromeric inversions. Pepper centromeres are embedded within ultra-long centromere-spanning haplotype (cenhap) blocks, ranging from 29.8 to 112.9 Mb and collectively covering 23.96% of the genome; each block contains only 1-4 major haplotypes. Some cenhaps may act as supergene-like units and are strongly associated with fruit traits, probably because recombination-suppressed intervals harbour multiple fruit-related genes, including OFP and F-box genes. F2 segregation assays further reveal transmission distortion of chromosomes carrying alternative cenhaps. Together, these findings highlight peri/centromeric regions as underrecognized reservoirs of agronomically important variation.

Centromere↗

PubMed2025

Personality Genomics.

Recent research advances have precipitated the era of personality genomics: the study of how variation in human DNA sequence predicts individual differences in characteristic patterns of thinking, feeling, and behaving. Here, we introduce personality and genomics, and we review key findings from recent genome-wide association studies of personality traits. These findings support five key observations: (a) sizable genetic effects on personality arise from a vast number of genetic variants with individually miniscule effects; (b) genetic variants associated with personality have widespread associations with other attributes, including social, economic, and medical outcomes; (c) genetic effects on personality generalize across groupings of people; (d) genetic effects on personality are minimally confounded by familial environmental effects; and (e) many recent genomic findings were anticipated by classic twin genetic research. For personality psychologists, embracing genomics provides unique and powerful inferential tools. For genomics researchers, incorporating unifying personality frameworks enables an integrative understanding of core behavioral dimensions.

Humans↗

PubMed2026

Lineage-associated differences in adenine methylation patterns of mammalian-associated Campylobacter fetus isolates: a possible role for epigenetic factors in host tropism and pathogenesis.

Mammalian Campylobacter fetus (CF) is divided into two subspecies, C. fetus fetus (CFF) and C. fetus venerealis (CFV), the latter being bovine-adapted and responsible for the notifiable disease bovine genital campylobacteriosis (BGC). Differentiation between CF subspecies has traditionally been undertaken by a few biochemical tests, but these are complicated by the existence of a biotype, C. fetus venerealis intermedius (CFVi), which shares attributes of both CFF and CFV. Molecular methods targeting specific genes have gained acceptance for more accurate subtype identification and align well with whole-genome analysis. However, limited genomic diversity between subtypes has confounded efforts to understand the genetic basis for differential host tropism and pathogenesis of these organisms. A previous study of a small cohort of C. fetus isolates suggested that dam gene coding variations might correlate with CF subtype. Accordingly, this study examines a cohort of 331 C. fetus genomes, representative of all seven phylogenetic groups for their complement of adenine methylases and the genomic motifs they target in representative isolates. All CF isolates retained a cfeM1 gene, the presence of which correlates with RAATTY methylation, while seven other adenine methylase genes exhibited distinct cladal distributions. Notably, a cjeM1 gene appears to target the CCAN7TAG/CTAN7TGG motif in CFV and CFVi isolates only. Given the increasing recognition of the impact of adenine methylation on bacterial-host interactions, further exploration of the role of adenine methylation in C. fetus pathogenesis could reveal mechanisms contributing to BGC and thus aid in its eradication.IMPORTANCECampylobacter fetus remains an important zoonotic pathogen, for which a better understanding of its host tropism and pathogenesis is sought. However, the limited genomic variation observed between subtypes has to date confounded efforts in this regard. This study suggests that an alternative approach that examines epigenetic differences between subtypes, specifically adenine methylation patterns, may reveal mechanisms critical to the pathologies of these organisms.

Animals↗

PubMed1977

Generality of learning differences in brain-weight-selected mice.

Eight lines of mice from two independent selection programs for high, medium, and low brain weight were raised in enriched environments and tested on active avoidance, water maze, operant discrimination, and passive avoidance tasks. Although statistically reliable performance differences were found on each task within at least one selection, there was no consistent relation within selections and across tests or within tests and across selections between brain weight and performance. The results emphasize the necessity of eliminating confounded performance variables in behavior genetic research on learning phenotypes and suggest that brain weight-learing corrleations in mice are either small or nonexistent.

Animals↗

PubMed2025

Exploring the causal relationship between plasma proteins and postherpetic neuralgia: a Mendelian randomization study.

BACKGROUND: The proteome represents a valuable resource for identifying therapeutic targets and clarifying disease mechanisms in neurological disorders. This study investigated potential causal relationships between plasma proteins and postherpetic neuralgia (PHN). METHODS: We conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary statistics from the Decode Genetics dataset (4,907 plasma proteins) and the FinnGen database (490 PHN cases and 435,371 controls). Instrumental variables (IVs) were selected based on relevance, independence, and exclusivity. Causal associations were assessed using inverse-variance weighted (IVW), MR-Egger regression, simple mode, weighted mode, and weighted median methods. Sensitivity analyses, including leave-one-out tests, evaluated result robustness, while colocalization analysis examined shared causal variants between traits. RESULTS: Eight plasma proteins showed significant associations with PHN (PFDR < 0.05). Higher levels of ATRN, PIANP, and CD48 correlated with increased PHN risk, whereas elevated KIR2DL5A, GPI, SEMG2, EIF4B, and HFE2 levels were associated with reduced risk. Sensitivity analyses supported these findings and excluded genetic pleiotropy as a major confounding factor. Colocalization analysis did not detect shared causal variants (PPH4 < 0.8). CONCLUSION: These results suggest a potential causal role for eight plasma proteins in PHN pathogenesis. While these proteins may serve as biomarkers or therapeutic candidates, further validation is required. This study advances understanding of PHN pathophysiology and supports future investigations into diagnostic and therapeutic strategies.

Mendelian randomization↗

PubMed2025

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

MOTIVATION: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). RESULTS: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. AVAILABILITY AND IMPLEMENTATION: INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Cluster Analysis↗

PubMed1992

Genetic control of diabetes mellitus.

Genetic inheritance predisposing individuals to diabetes mellitus was discussed in this work group. The two forms of the disease, Type 1 (insulin-dependent) and Type 2 (non-insulin-dependent) were discussed separately since the pattern of inheritance and genes involved appear to be distinctly different. Within these subtypes there is considerable genetic heterogeneity, and superimposed environmental factors confound the analysis. New technologies that will allow finer molecular analysis, as well as new candidates genes, were presented.

Animals↗

PubMed1976

Statistical resolution of genetic heterogeneity in familial disease.

If a disease can be split into two or more groups on any criterion (clinical, biochemical, physiological or statistical) then the grouping can be tested to establish if genetically independent forms of the disease have been identified. The data required are simply the frequencies of the two disease groups in relatives of probands for each of the disease groups. A systematic search for such distinct groups is proposed in searches for genetic heterogeneity in familial diseases. In disease forms with overlapping, correlated genetic liabilities, the method of Falconer (1967) can be used to estimate the genetic correlation. However, when the groupings of the disease are confounded (such as one form precluding the other as in early and late onset diabetes) Falconer's method will be biased. Special methods of analysis to estimate the genetic parameters have been developed and are presented here. However, even when the groupings are confounded the Falconer method still gives reasonable estimates of the genetic correlation, in that they are unlikely to seriously mislead the investigator in the analysis and interpretation of observed data. In practice Falconer's simple method may be preferred to the more complex methods developed here because it involves fewer assumptions and can be applied over a wider range of circumstances.

Anencephaly↗

PubMed2026

Patterns and implications of co-use between vaping and hallucinogens: a systematic review and meta-analysis.

BACKGROUND: The co-occurrence use of e-cigarettes and hallucinogens has become increasingly common, particularly among youth and young adults. However, evidence regarding the association between these behaviors remains limited and fragmented. This systematic review and meta-analysis aimed to synthesize current evidence, examining the correlation between hallucinogen use and the likelihood of being an e-cigarette user. METHODS: A comprehensive search was conducted in PubMed, Scopus, Web of Science, EMBASE, and Cochrane CENTRAL up to June 2025. Eligible studies measured both hallucinogen and e-cigarette use and reported quantitative associations between these behaviors. Data extraction and risk-of-bias assessments were performed independently by three reviewers using the Newcastle-Ottawa Scale. Pooled effect sizes were calculated using a random-effects model (REML). Certainty of evidence was evaluated with the GRADE approach. RESULTS: Eleven studies met the inclusion criteria (n = 247,904), and seven were included in the meta-analysis (n = 217,478). The pooled analysis demonstrated that hallucinogen users had 4.47 times higher odds of being e-cigarette users (OR: 4.47, 95% CI 2.72 to 7.34; p < 0.001; I2 = 95.7%, n = 7). The certainty of evidence was rated as low. CONCLUSIONS: Hallucinogen use is directionally and strongly associated with e-cigarette use across diverse populations. Although the direction of association was consistent across studies, the magnitude of effect was heterogeneous. These behaviors likely share psychosocial and environmental determinants, although alternative explanations, including shared genetic liability, recall bias, and residual confounding, cannot be excluded. Further longitudinal studies are needed to clarify the underlying mechanisms of this association and establish temporality. The findings also support integrating hallucinogen-use screening into e-cigarette prevention and harm-reduction programs targeting youth and young adults.

Humans↗

PubMed2026

Polygenic score for sleep duration in relation to the risk of Alzheimer's disease: results from the UK biobank.

Studies have suggested that sleep duration may be associated with Alzheimer's disease risk; however, findings based on self-reported sleep duration are likely to be influenced by reverse causation and residual confounding bias. We derived weights for genetic variants associated with wearable-derived sleep duration using the LDpred2-auto method in 77,770 white British participants from the UK Biobank, following the generation of new genome-wide association summary statistics. We then used these weights to generate polygenic scores (PGSs) for the remaining 264,746 white British participants for the association analysis, independent of the sample used to develop PGS weights. We assessed the association of fifths between genetically predicted sleep duration and the risk of Alzheimer's disease (1,451 cases/264,746 individuals over a median 12.5 years of follow-up). The PGS explained approximately 2% of the variation in device-measured sleep duration. Compared with individuals in the middle fifth of PGSs, those in the highest fifth (indicating approximately 15 min/day longer sleep) had a lower risk of Alzheimer's disease (hazard ratio (HR) = 0.79[95%CI, 0.67-0.94]). Our results indicate that genetic predisposition to relatively long sleep duration is associated with a lower Alzheimer's disease risk.

Alzheimer’s disease↗