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Dental size traits within families: path analysis for first molar and lateral incisor.

Previous works on the inheritance of dental traits have traditionally followed Fisher's model for family data under the assumption that phenotypic similarity between relatives is due solely to genetic factors. This study uses recent causal models that incorporate the contribution of nonrandom environmental sources of variation to familial resemblance on dental size. Path analysis was applied to observed interclass and intraclass correlations of sex-specific parent-offspring and sib pairs in 293 Pima Indian families from the southwest United States. The mesiodistal dimension of an early-forming and stable tooth (first molar) was contrasted with a late-forming and variable tooth (upper lateral incisor) for genetic and familial environmental components of variation. Parameters were estimated according to the XTAU models of Rice et al. (1980) and linear constraints placed upon the parameters were tested. The proportion of variance accounted for by genetic and environmental transmissible factors is estimated to be 52% for the first molar and 35% for the lateral incisor. Neither X-linkage nor sex-specific environmental effects are required to explain the transmission of dental size. Nontransmissible environmental effects that account for sibling correlations are detectable. Furthermore, sex differences are found in correlated sibling environments for the lateral incisor but not the first molar, to explain in part male-female differences in the distributions of the upper lateral incisor size.

Female↗

Variation at the hepatic lipase and apolipoprotein AI/CIII/AIV loci is a major cause of genetically determined variation in plasma HDL cholesterol levels.

Genetic factors have been shown to play an important role in determining interindividual variation in plasma HDL-C levels, but the specific genetic determinants of HDL cholesterol (HDL-C) levels have not been elucidated. In this study, the effects of variation in the genomic regions encoding hepatic lipase, apolipoprotein AI/CIII/AIV, and the cholesteryl ester transfer protein on plasma HDL-C levels were examined in 73 normotriglyceridemic, Caucasian nuclear families. Genetic factors accounted for 56.5 +/- 13% of the interindividual variation in plasma HDL-C levels. For each candidate gene, adjusted plasma HDL-C levels of sibling pairs who shared zero, one, or two parental alleles identical-by-descent were compared using sibling-pair linkage analysis. Allelic variation in the genes encoding hepatic lipase and apolipoprotein AI/CIII/AIV accounted for 25 and 22%, respectively, of the total interindividual variation in plasma HDL-C levels. In contrast, none of the variation in plasma HDL-C levels could be accounted for by allelic variation in the cholesteryl ester transfer protein. These findings indicate that a major fraction of the genetically determined variation in plasma HDL-C levels is conferred by allelic variation at the hepatic lipase and the apolipoprotein AI/CIII/AIV gene loci.

Apolipoprotein A-I↗

Progress towards identifying inflammatory bowel disease susceptibility genes.

Genomewide searches have identified about a dozen genomic regions potentially containing inflammatory bowel disease (IBD) susceptibility loci. Although some were observed in more than one genome scan or in replication studies, no one locus has been identified in all studies. This apparent lack of consistency is explained by the modest effect of most genetic loci in complex traits and by the modest power and the sampling variance of individual family-based studies. Despite these challenges, the field of IBD genetics found convincing evidence of linkage and, more recently, of genetic sequence variants that are strongly associated with disease. The first example was that of the IBD1 locus on chromosome 16, a linkage region confirmed by a large multicentre study, for which the CARD 15 gene was identified--using independent association strategies-as being a gene contributing to Crohn's disease susceptibility. Despite these advances, a number of questions still remain: (1) What is the best approach to identify the remaining IBD susceptibility loci? (2) How do we prove causality for any given gene or genetic variant? (3) How can we best use this information to obtain an understanding of the biological mechanisms underlying disease susceptibility and to identify useful markers of disease progression and response to therapy?

Chromosomes, Human, Pair 16↗

Establishment of genetic associations for complex diseases is independent of early study findings.

Numerous genetic association studies for complex diseases are performed. Investigators place emphasis on formal statistical significance (P-values < 0.05), but the predictive ability of early statistically significant ('positive') findings is unclear. We scrutinized 55 cumulative meta-analyses of genetic associations (579 studies), in order to assess whether having statistical significance in the earliest (first) published study or in at least half among several (> or =3) early-published studies, or high statistical significance in early studies had any predictive ability for establishing or refuting the presence of the genetic association in subsequent research. In 35 associations, a first study was 'positive' and in 15 associations more than half of the early-published reports were 'positive'. The average publication rate of subsequent studies increased 1.71-fold with a 'positive' first report. When compared against the summary results of subsequent research, sensitivity and specificity were 0.65 and 0.38 for the first reports, and 0.40 and 0.73, respectively, when at least three early studies were considered. First studies also had poor predictive ability, when we considered the estimated attributable fraction and coverage of the 95% confidence interval thereof or higher levels of statistical significance. We conclude that although 'positive' findings in the very first reports provide strong incentive for conducting more studies on a putative genetic epidemiological association, the statistical significance or even the magnitude of the effect of early studies cannot adequately predict eventual establishment of an association. Conversely, many genuine epidemiological associations would be missed, if research were abandoned after early underpowered 'negative' studies.

Causality↗

Evaluating the causal effect of mitochondrial dysfunction on Alzheimer's and Parkinson's disease using Polygenic Risk Scores and Mendelian Randomization.

INTRODUCTION: Mitochondrial DNA copy number (mtDNAcn), a measure of mitochondrial genomes per nucleated cell, has an unclear causal relationship with AD and PD. We integrate genetic correlation, Polygenic Risk Scores (PRS), and Mendelian Randomization (MR) to assess whether mtDNAcn influences the risk of AD and PD, and evaluate how study-specific factors in mtDNAcn genome-wide association studies (GWAS) may distort these causal estimates. METHODS: Using GWAS of four mtDNAcn measures, AD, AD/dementia, and PD, we evaluated genetic correlations, generated ancestry-normalized PRS in the AD Genetics Consortium (N=27,383), and applied MR methods including Latent Heritable Confounder MR (LHC-MR). RESULTS: Across the four mtDNAcn GWAS, only one was consistently associated with AD/dementia and PD, with genetic correlations and PRS analysis showing negative correlations and MR indicating that higher mtDNAcn reduced AD/dementia and PD risk. DISCUSSION: Higher blood-based mtDNAcn was causally associated with reduced risk of AD/dementia and PD, with limited evidence to suggest a bidirectional effect.

Alzheimer&#x2019;s disease↗

Post-genome-wide association study variant-to-function challenges in asthma research.

Genome-wide association studies of asthma have identified nearly 200 independent loci, yet the mechanisms through which individual loci influence asthma risk remain largely unknown. A growing array of computational and experimental tools has begun to fill these gaps by identifying causal variants and effector genes and characterizing their functions. In parallel, emerging studies are exploring the translational applications of genetic and multiomics data in asthma, including defining molecular endotypes and predicting disease risk. Here we review the strengths and limitations of current approaches for addressing the post-genome-wide association study challenges and discuss the next tier of questions and directions for the field.

Humans↗

[From the genetics of disease to the genetics of behavior].

Apparently a kind of antimony exists between the psychopathological approach which implies a continuum between normality and abnormality and genetic studies which intend to give a causal explanation of the mental illness. The choice of the behavioral phenotype requires nevertheless some psychopathological and clinical criteria. Furthermore, when we consider the mode of action of the genetic factor we are dealing with a behavioral target as much as with physiological mechanisms. When we discover a genetic factor in the determinism of a particular mental disorder, we must admit that the same factor is involved in the determinism of the normal behavior which corresponds to this disorder. It follows that the genetic analysis of mental illness appears as a particular methodology for the genetic approach of behavior in a general way. But it needs that we build up a new phenomenology of human behavior which fits with this approach.

Bipolar Disorder↗

Genetic heterogeneity between the three components of the autism spectrum: a twin study.

OBJECTIVE: This study investigated the etiology of autistic-like traits in the general population and the etiological overlap between the three aspects of the triad of impairments (social impairments, communication impairments, restricted repetitive behaviors and interests) that together define autism spectrum disorders. METHOD: Parents of 3,400 8-year-old twin pairs from the Twins Early Development Study completed the Childhood Asperger Syndrome Test, a screening instrument for autism spectrum symptoms in mainstream samples. Genetic model-fitting of categorical and continuous data is reported. RESULTS: High heritability was found for extreme autistic-like traits (0.64-0.92 for various cutoffs) and autistic-like traits as measured on a continuum (0.78-0.81), with no significant shared environmental influences. All three subscales were highly heritable but showed low covariation. In the genetic modeling, distinct genetic influences were identified for the three components. CONCLUSIONS: These results suggest the triad of impairments that define autism spectrum disorders is heterogeneous genetically. Molecular genetic research examining the three components separately may identify different causal pathways for the three components. The analyses give no indication that different genetic processes affect extreme autistic impairments and autistic impairments as measured on a continuum, but this can only be directly tested once genes are identified.

Asperger Syndrome↗

Structural variant discovery and diagnostic impact in rare diseases from short-read and long-read sequencing.

Rare diseases collectively affect 1 in 10 individuals, yet current genetic testing fails to identify a causal variant for most cases. At present, cytogenetic methods and/or sequencing approaches such as exome (ES) or short-read genome sequencing (srGS) represent the state-of-the-art for comprehensive clinical discovery of sequence and structural variants (SVs), including copy number variants, balanced SVs, complex SVs, and tandem repeats (TRs). Recently, long-read genome sequencing (lrGS), coupled with multiomics data, has presented great promise to resolve variation in genomic regions recalcitrant to characterization by srGS such as highly repetitive simple repeat sequences and segmental duplications. However, there are few guidelines to enable clinical interpretation of genetic variation in these highly repetitive genomic regions, and the enthusiasm of the field in adopting lrGS has made it difficult to assess the true added diagnostic yield of this technology due to widely variable and inconsistently applied analytic pipelines and variable degrees of pre-screening by ES or srGS. Here, we investigated the contribution of SVs to rare diseases using srGS as a front-line strategy when paired with highly sensitive SV discovery and evaluate the added diagnostic yield of incorporating lrGS for a subset of cases. Our srGS analysis encompassed 1,462 families (3,450 individuals) recruited through the Broad Institute Center for Mendelian Genetics and the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) programs. Diagnostic SVs were identified in 5.4% of cases (79/1,462), of which 80% were uniquely detectable by srGS compared to standard cytogenetic techniques. For 96 families (including 10 families with a heterozygous variant observed in a known recessive gene of clinical relevance), we performed lrGS with methylation profiling, as well as long-read transcriptomic analyses in a subset of 20 trios. Analyses with lrGS yielded over 25,000 SVs per genome, 63% of which were not captured by srGS, along with an additional ~200 rare SNV/indels per genome not previously captured and 12 differentially methylated regions per genome. Among these, we identified only one diagnostic variant not interpreted by srGS, an apparently mosaic de novo SNV in CASK that was absent in the srGS callset due to allelic imbalance. No new diagnoses were supported by long-read transcriptomics or episignatures. In this well characterized rare disease cohort, the added diagnostic yield was thus 1.04% (1/96 families). Following a systematic literature review of prior lrGS studies, we find that most reported diagnoses were detectable by srGS and that our added diagnostic yield is consistent with those prior studies. These studies emphasize the significant impact of comprehensive SV discovery in rare disease cases and further demonstrate the power for increased discovery of novel genomic variation and episignatures from lrGS. Nonetheless, they also serve to temper expectations of dramatic diagnostic advances in rare disease patients until there is more extensive annotation of the functional and clinical impact of all coding and noncoding variation uniquely accessible to lrGS with extensive reference databases spanning highly repetitive genomic sequencing that could be enabled by this transformative technology.

Journal Article↗

Genetic epidemiology and public health: hope, hype, and future prospects.

Genetic epidemiology is a rapidly expanding research field, but the implications of findings from such studies for individual or population health are unclear. The use of molecular genetic screening currently has some legitimacy in certain monogenic conditions, but no established value with respect to common complex diseases. Personalised medical care based on molecular genetic testing is also as yet undeveloped for common diseases. Genetic epidemiology can contribute to establishing the causal nature of environmentally modifiable risk factors, through the application of mendelian randomisation approaches and thus contribute to appropriate preventive strategies. Technological and other advances will allow the potential of genetic epidemiology to be revealed over the next few years, and the establishment of large population-based resources for such studies (biobanks) should contribute to this endeavour.

Adult↗

Genetic analysis of a complex disease.

A complex disease may be defined as a disease that has a complex phenotype, no single causal agent and a range of influencing factors, both genetic and environment. The paper considers how the aetiology of such disorders may be unravelled using recently developed genetical and statistical methods. Problems are illustrated using ischaemic heart disease, congenital neural tube defects and depression as examples.

Coronary Disease↗

Understanding the aetiology of childhood obesity: implications for treatment.

Childhood obesity poses one of the greatest challenges to paediatric health in the 21st century. Developing effective strategies for treatment and prevention is therefore a priority for clinical medicine and public health. This process is taking place at a time of unprecedented change in the understanding of the role of genetic factors in human health and disease, and genetic research into obesity has challenged assumptions about causal processes. In the spirit of the conference theme, the present paper will discuss the implications of the understanding of genetic and environmental influences on obesity for the development of effective behavioural treatments.

Child↗

Multifactorial dependence of congenital malformations.

Birth prevalence of congenital malformations ranges between 2 and 6%, depending on the population. Congenital malformations significantly contribute to the causes of early and late infant mortality as well as to the development of severe handicaps. These facts make congenital malformations a serious medical and social problem. In many cases, the type and the severity of the defect together with anatomical location determine the fate of an infant in the very beginning of life. And this is the case independently of the quality and the method of treatment applied. These facts are the major motivation for the attempt to develop an effective method of primary prevention of congenital malformations. The effectiveness of this approach is strictly related to the amount of information on the etiology of congenital anomalies. The clinical and etiologic heterogeneity of congenital malformations is the major factor responsible for the inability to provide a clear and unequivocal definition of causes and to eliminate them from the environment. In addition, in a substantial number of malformations no etiologic factor could be identified. A special problem is created by malformations with a "threshold effect", where the disease liability results from genetic susceptibility and environmental triggering factors. If both components, genetic and environmental, exceed a certain threshold, the malformation appears. The paper discusses the major etiologic factors and inheritance patterns of congenital malformations and the practical implications for genetic counselling.

Causality↗

TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.

The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.

Humans↗

The trajectory of suicidal behavior over time.

Some empirical work on genetic and prenatal factors in suicidality is presented. These factors may represent enduring predispositions that comprise risk for initial as well as later suicidal behavior. The existence of enduring predispositions does not preclude the possibility, however, that initial suicidal behavior sets processes into motion that spur later suicidal behavior. Based on past conceptual and empirical work, I suggest two psychological processes--cognitive sensitization and opponent processes--that may partly explain the link between past and future suicidal behavior.

Causality↗

Mitochondrial dysfunction in the pathogenesis of intervertebral disc herniation: a mitochondrial related genome-wide Mendelian randomization analysis.

BACKGROUND: As a degenerative disease, the pathophysiology of intervertebral disc herniation (IDH) closely related to mitochondrial dysfunction. However, the specific molecular mechanisms involved have yet to be precisely established. METHODS: Here, we employed a two-sample, two-step Mendelian Randomization (MR) approach, along with summary-Data-Based MR, genetic colocalization, full phenomenon association analysis, and GO and KEGG enrichment analysis to investigate the genetical effects of mitochondrial dysfunction on IDH. RESULTS: From the intercross between mitochondrial-related genes and eQTLGen genes, we obtained seven genes (DMPK, EHHADH, SLC25A16, ME3, METTL17, TUFM, NDUFA13) with strong causal association with disc herniation. By SMR and genetic colocalization analysis, we further identify five key genes (EHHADH, METTL17, TUFM, NDUFA13, DMPK) as the direct causal tartgeted genes with no heterogeneity and pleiotropy in the SNPs of the genes. Full phenomenon Mendelian randomization was performed to determine possible side effects of the 5 targeting genes. Finally, we validated the expressions of key genes in the degenerative intervertebral discs tissues by qRT-PCR and double-immunofluorescence examinations, and the results were consistent with the MR analysis. CONCLUSIONS: Our study provided genetic support for the relationship between mitochondrial related genes and IDH, implicating potential therapeutic targets for future development. However, more basic experiments need to be finished to validate the role of key genes in the development of IDH.

Intervertebral Disc Displacement↗

The importance of family-based sampling for biobanks.

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges.

Humans↗