Genetic Epidemiology最新文献

{"title":"Identifying Causal Genotype–Phenotype Relationships for Population-Sampled Parent–Child Trios","authors":"Yushi Tang,&nbsp;Irineo Cabreros,&nbsp;John D. Storey","doi":"10.1002/gepi.70027","DOIUrl":"10.1002/gepi.70027","url":null,"abstract":"<p>The process by which genes are transmitted from parent to child provides a source of randomization preceding all other factors that may causally influence any particular child phenotype. Because of this, it is natural to consider genetic transmission as a source of experimental randomization. In this work, we show how parent–child trio data can be leveraged to identify causal genetic loci by modeling the randomization during genetic transmission. We develop a new test, the transmission mean test (TMT), together with its unbiased estimator of the average causal effect, and derive its causal properties within the potential outcomes framework. We also prove that the transmission disequilibrium test (TDT) is a test of causality as a complementary case of the TMT for the affected-only design. The TMT and the TDT differ in the types of traits that they can handle and the study designs for which they are appropriate. The TMT handles arbitrarily distributed traits and is appropriate when trios are randomly sampled; the TDT handles dichotomous traits and is appropriate when sampling is based on a child's trait status. We compare the transmission-based methods with established approaches for genotype–phenotype analyses to clarify conditions appropriate for each method, what conclusions can be drawn by each one, and how these methods can be used together.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12793723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moderating the Heritability of Body Mass Index by Age and Sex With Genomic Data","authors":"Sarah E. Benstock,&nbsp;Elizabeth Prom-Wormley,&nbsp;Brad Verhulst","doi":"10.1002/gepi.70026","DOIUrl":"https://doi.org/10.1002/gepi.70026","url":null,"abstract":"<div>\u0000 \u0000 <p>Environmental contexts may increase or decrease the heritability of a phenotype. Or equivalently, people with some genotypes may be more (or less) sensitive to environmental differences. Such genetic sensitivity to environmental contexts is called gene-environment interaction (G × E). While G × E has been robustly detected in twin and model organism studies for numerous phenotypes, there is a lingering perception that existing genome-wide G × E methods struggle to identify substantively significant and replicable interactions. We propose a novel method for examining G × E heritability using genetic marginal effects from genome-wide G × E analyses and Linkage Disequilibrium Score Regression (LDSC). We demonstrate the effectiveness of our method for body mass index (BMI) using biological sex (binary) and age (continuous) as moderators. Using the same procedures for both binary and continuous moderators, we detect robust evidence for G × E. Our results are consistent with findings from twin G × E studies of BMI and are more sensitive to environmental moderation than other LDSC-based methods. We conclude that BMI heritability is substantially more sensitive to variation in sex and age than is currently appreciated. Extending this method to other phenotype-moderator combinations has the potential to reveal G × E across numerous outcomes and moderators.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145905041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Archipelago Method for Variant Set Association Test Statistics","authors":"Dylan Lawless,&nbsp;Ali Saadat,&nbsp;Mariam Ait Oumelloul,&nbsp;Luregn J. Schlapbach,&nbsp;Jacques Fellay","doi":"10.1002/gepi.70025","DOIUrl":"https://doi.org/10.1002/gepi.70025","url":null,"abstract":"<p>Variant set association tests (VSAT), especially those incorporating rare variants via variant collapse, are invaluable in genetic studies. However, unlike Manhattan plots for single-variant tests, VSAT statistics lack intrinsic genomic coordinates, hindering visual interpretation. To overcome this, we developed the Archipelago method, which assigns a meaningful genomic coordinate to VSAT P values so that both set-level and individual variant associations can be visualised together. This results in an intuitive and information rich illustration akin to an Archipelago of clustered islands, enhancing the understanding of both collective and individual impacts of variants. We conducted three validation studies spanning simulated and real datasets across small and biobank-scale cohorts, from 504 individuals up to 490,640 UK Biobank participants. We integrated single-variant genome-wide association studies (GWAS) with gene- and protein pathway-level rare-variant collapse. These studies included the 1KG GWAS cohort, the Pan-UK Biobank GWAS with DeepRVAT WES gene-level study, and the UKBB WGS gene-level UTR collapsing PheWAS. The Archipelago plot is applicable in any genetic association study that uses variant collapse to evaluate both individual variants and variant sets, and its customisability facilitates clear communication of complex genetic data. By integrating at least two dimensions of genetic data into a single visualisation, VSAT results can be easily read and aid in identification of potential causal variants in variant sets such as protein pathways.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145905040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variant Classification Using Proteomics-Informed Large Language Models Increases Power of Rare Variant Association Studies and Enhances Target Discovery","authors":"Christopher E. Gillies,&nbsp;Joelle Mbatchou,&nbsp;Lukas Habegger,&nbsp;Michael D. Kessler,&nbsp;Suying Bao,&nbsp;Suganthi Balasubramanian,&nbsp;Olivier Delaneau,&nbsp;Jack A. Kosmicki,&nbsp;Cristen J. Willer,&nbsp;Hyun Min Kang,&nbsp;Aris Baras,&nbsp;Jeffrey G. Reid,&nbsp;Jonathan Marchini,&nbsp;Gonçalo R. Abecasis,&nbsp;Maya Ghoussaini","doi":"10.1002/gepi.70023","DOIUrl":"10.1002/gepi.70023","url":null,"abstract":"<p>Rare variant association analysis, which assesses the aggregate effect of rare damaging variants within a gene, is a powerful strategy for advancing knowledge of human biology. Numerous models have been proposed to identify damaging coding variants, with the most recent ones employing deep learning and large language models (LLMs) to predict the impact of changes in coding sequences. Here, we use newly available proteomics data on 2898 proteins across 46,665 individuals to evaluate and refine LLM predictors of damaging variants. Using one of these refined models, we evaluate the association between rare damaging variants and human phenotypes at 241 positive control gene-trait pairs. Among these gene-trait pairs, our proteomics-guided model outperforms an ensemble of conventional approaches including PolyPhen2, MutationTaster, SIFT, and LRT, as well as newer machine learning approaches for identifying damaging missense variants, such as CADD, ESM-1v, ESM-1b, and AlphaMissense. When attempting to recover known associations by correctly separating damaging singleton missense variants from other singleton variants, our approach recapitulates 36.5% of gene-trait pairs with known associations, exceeding all the alternatives we considered. Furthermore, when we apply our model to 10 example traits from the UK Biobank, we identify 177 gene–trait associations—again exceeding all other approaches. Our results demonstrate that summary statistics from large-scale human proteomics data enable evaluation and refinement of coding variant classification LLMs, improving discovery potential in human genetic studies.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 8","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145431214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family-Based Association Testing","authors":"Tyler R. C. Day,&nbsp;Joshua C. Bis,&nbsp;Nicola Chapman,&nbsp;Alejandro Q. Nato Jr.,&nbsp;Andrea R. V. R. Horimoto,&nbsp;Harkirat Sohi,&nbsp;Rafael Nafikov,&nbsp;Elizabeth E. Blue,&nbsp;Mohamad Saad,&nbsp;Ellen M. Wijsman","doi":"10.1002/gepi.70021","DOIUrl":"10.1002/gepi.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>Genotype imputation is a widely-used data augmentation approach that is applied to samples of related and/or unrelated individuals. Association testing may then be carried out on the complete data with commonly-used methods. This approach has typically not accounted for the mix of observed and imputed data, although recent work has noted the potential for introduction of confounding in case-control studies. In the Alzheimer's Disease Sequencing Project family sample we found severe inflation of the test statistics in logistic regression analysis following genotype imputation, even after standard covariate adjustments. Here we dissect sources of this inflation, which is driven by three factors: frequency-dependent bias in imputation-induced allele frequencies, differential measurement error, and differential genotyping rates in cases versus controls that introduces confounding. To address the problem, we propose a statistic, imputation deviance (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${mathscr{D}}$</annotation>\u0000 </semantics></math>), which can be easily computed from the observed and imputed genotype probabilities. We show that <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${mathscr{D}}$</annotation>\u0000 </semantics></math>, as an additional fixed-effect covariate, controls the genome-wide inflation in analysis of this family-based sample, and we speculate that use of imputation deviance may also provide a practical approach to correct for genotype imputation effects in other settings, particularly when a data set is unbalanced and includes related individuals.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 8","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145400586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Random Forest in Genetic Risk Score Construction","authors":"Vaishnavi Venkat,&nbsp;Kaylyn Clark,&nbsp;X. Jessie Jeng,&nbsp;Tsung-Chieh Yao,&nbsp;Hui-Ju Tsai,&nbsp;Tzu-Pin Lu,&nbsp;Tzu-Hung Hsiao,&nbsp;Ching-Heng Lin,&nbsp;Shannon Holloway,&nbsp;Cathrine Hoyo,&nbsp;Shin-Yi Chou,&nbsp;Hui Wang,&nbsp;Wan-Ping Lee,&nbsp;Li-San Wang,&nbsp;Jung-Ying Tzeng","doi":"10.1002/gepi.70022","DOIUrl":"https://doi.org/10.1002/gepi.70022","url":null,"abstract":"<p>Genetic risk scores (GRS) are crucial tools for estimating an individual's genetic liability to various traits and diseases, computed as a weighted sum of trait-associated allele counts. Traditionally, GRS models assume additive, linear effects of risk variants. However, complex traits often involve nonadditive interactions, such as epistasis, which are not captured by these conventional methods. In this study, we investigate the use of random forest (RF) models as a model-free approach for constructing GRS, leveraging RF's capacity to capture complex, nonlinear interactions among genetic variants. Specifically, we introduce two new RF-based GRS strategies to boost RF performance and to incorporate base data information if available, including (1) ctRF, which optimizes linkage disequilibrium (LD) clumping and <i>p</i>-value thresholds within RF; and (2) wRF, which adjusts the chance of SNP inclusion in tree nodes based on their association strength. Through simulation studies and real data applications of Alzheimer's disease, body mass index, and atopy, we find that ctRF consistently outperforms other RF-based methods and classical additive models when traits exhibit complex genetic architectures. Additionally, incorporating informative base data into RF-GRS construction can enhance predictive accuracy. Our findings suggest that RF-based GRS can effectively capture intricate genetic interactions, and offer a robust alternative to traditional GRS methods, especially for complex traits with nonlinear genetic effects.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 8","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations of Herpes Simplex Virus Type 1/2 IgG Seropositivity and Arthritis Subtypes: Integrating Cross-Sectional Epidemiology and Genetic Association Analyses","authors":"Haining Li,&nbsp;Zhen Shen,&nbsp;Changzhou Feng","doi":"10.1002/gepi.70020","DOIUrl":"10.1002/gepi.70020","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 <p>Herpes simplex viruses (HSV) have been detected within the synovial joint cavity, a secluded area of inflammation that may harbor etiological agents. However, the role of HSV-1/2 infection in arthritis pathogenesis remains ambiguous. In this study, we integrate cross-sectional epidemiology and genetic associations to elucidate their relationships and uncover causal mechanisms. We analyzed cross-sectional data from 18,292 NHANES participants (1999–2016) using multivariable-adjusted logistic regression to assess associations between anti-HSV-1/2 IgG seropositivity and arthritis-related risks. Complementary analyses included linkage disequilibrium score regression (LDSC) and bidirectional Mendelian randomization (MR) using genetic instruments for anti-HSV IgG levels to explore genetic correlations and infer causality. Initial observational findings demonstrated significant positive associations between HSV-1/2 IgG seropositivity and arthritis risk (all <i>p</i> &lt; 0.001); however, these associations lost significance after multivariable adjustment. Notably, after multivariable adjustment, subtype analyses revealed that HSV-2 IgG seropositivity was linked to increased risks of rheumatoid arthritis (RA) (OR: 1.40, 95% CI: 1.04–1.88) and osteoarthritis (OA) (OR: 1.39, 95% CI: 1.07–1.81), while HSV-1 IgG seropositivity correlated with an unknown-arthritis subtype (OR: 1.38, 95% CI: 1.08–1.75). Moreover, MR analyses uncovered divergent causal effects: anti-HSV-1 IgG levels were protective against OA (OR: 0.90, 95% CI: 0.82–0.98), whereas anti-HSV-2 IgG levels modestly increased OA risk (OR: 1.05, 95% CI: 1.01–1.09). No reverse causation or genetic correlation was observed. This study's innovative integration of epidemiological and genetic methodologies not only clarifies the distinct roles of HSV sub-types in arthritis but also identifies HSV-2 as a potential causal factor in OA, thereby opening new avenues for therapeutic targeting.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 8","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145345061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Complex Relationship of Genetic Ancestry With Self-Reported Race/Ethnicity","authors":"Yambazi Banda,&nbsp;Neil Risch","doi":"10.1002/gepi.70019","DOIUrl":"https://doi.org/10.1002/gepi.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>Race and ethnicity are demographic constructs used to characterize individuals in biomedical research, and in particular to assess health disparities. Their use in medicine and research has been discussed and challenged, as well as the degree to which they represent strictly social constructs, or ones also with biological meaning. The relationship of race and ethnicity with genetic ancestry has also been described, and how genetic ancestry reflects historical continental isolation, migration, and mating structure. Race and ethnicity are currently most often assessed by self-report in epidemiology and biomedical applications. Here we further interrogate the relationship between how people self-report their race and ethnicity and their genetic ancestry by examining self-report patterns of 97,671 individuals who are participants in the Kaiser Permanente Northern California Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetic ancestry was determined from a set of 43,988 SNPs from genome-wide genotyping arrays. We observed that rates of self-identification as African American, East Asian and Latino(a) rise dramatically with a modest amount of African, East Asian and Native American genetic ancestry, respectively. By contrast, the rate of self-identification as White rises only when the European/West Asian genetic ancestry is substantial. This indicates that the majority of people who are genetically admixed, even those with primarily European/West Asian genetic ancestry, self-identify with the minority race/ethnicity group. By contrast, self-report as Native American did not increase with Native American genetic ancestry; instead, it was positively correlated with European genetic ancestry, with only a small minority of individuals self-reporting Native American race/ethnicity having Native American genetic ancestry. These results differ dramatically from the other minority race/ethnicity groups. These findings have important implications on how the different self-report race/ethnicity groups are considered in epidemiologic and biomedical research.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 8","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Importance of Sensitivity Analyses for the MR Steiger Approach","authors":"Sharon M. Lutz,&nbsp;Kirsten Voorhies,&nbsp;John E. Hokanson,&nbsp;Stijn Vansteelandt,&nbsp;Christoph Lange","doi":"10.1002/gepi.70018","DOIUrl":"https://doi.org/10.1002/gepi.70018","url":null,"abstract":"&lt;p&gt;An extension to Mendelian randomization (MR), MR Steiger uses single nucleotide polymorphisms (SNPs) in an instrumental variables framework to infer the causal direction between two phenotypes (Hemani et al. &lt;span&gt;2017&lt;/span&gt;). In 2021 and 2022, we explored the role of unmeasured confounding, pleiotropy, and measurement error on the performance of the MR Steiger approach (Lutz et al. &lt;span&gt;2021&lt;/span&gt;) as well as selection bias (Lutz et al. &lt;span&gt;2022a&lt;/span&gt;). In 2022, we used simulation studies to further examine the role of unmeasured confounding on the general performance of the MR Steiger approach to show that unmeasured confounding can increase the variance of phenotype 1 as compared to phenotype 2 such that the wrong causal direction between the two phenotypes will be inferred by the approach. We moreover created an R package UCRMS to reproduce these simulation studies (Lutz et al. &lt;span&gt;2022b&lt;/span&gt;). However, in a 2023 paper by Hemani at el., the authors incorrectly stated that “Lutz et al. (2022) propose an R package (UCRMS) for performing sensitivity analysis of the MR Steiger method” (Hemani et al. &lt;span&gt;2023&lt;/span&gt;), where a sensitivity analysis examines how different values of an independent variable affect a dependent variable under a given set of assumptions. The purpose of our R package (UCRMS) was to examine the general performance of the MR Steiger approach in the presence of unmeasured confounding, not as a package for sensitivity analyses. In the 2023 paper by Hemani et al. they state that “If [Lutz et al.] were presenting a simulation of the general performance of MR Steiger under unmeasured confounding then it would not matter that the simulated parameters are not tied to those observed in a particular empirical analysis” (Hemani et al. &lt;span&gt;2023&lt;/span&gt;), illustrating the correct original purpose of our R package as a simulation to assess the performance of the MR Steiger approach and not as a sensitivity analysis.&lt;/p&gt;&lt;p&gt;Here, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 &lt;mi&gt;OLS&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${beta }_{{OLS}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is the “observed effect” of phenotype X on phenotype Y, which may differ from the true effect &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;β&lt;/mi&gt;\u0000 &lt;mi&gt;xy&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${beta }_{{xy}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; as a result of confounding by U.&lt;/p&gt;&lt;p&gt;As stated by the Hemani et al. estimates of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 ","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 7","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correcting for Genomic Inflation Leads to Loss of Power in Large-Scale Genome-Wide Association Study Meta-Analysis","authors":"Archit Singh,&nbsp;Lorraine Southam,&nbsp;Konstantinos Hatzikotoulas,&nbsp;Nigel W. Rayner,&nbsp;Ken Suzuki,&nbsp;Henry J. Taylor,&nbsp;Xianyong Yin,&nbsp;Ravi Mandla,&nbsp;Alicia Huerta-Chagoya,&nbsp;Andrew P. Morris,&nbsp;Eleftheria Zeggini,&nbsp;Ozvan Bocher","doi":"10.1002/gepi.70016","DOIUrl":"https://doi.org/10.1002/gepi.70016","url":null,"abstract":"<p>Inflation in genome-wide association studies (GWAS) summary statistics represents a major challenge, for which correction methods have been developed. These include the genomic control (GC) method, which uses the λ-value to correct summary statistics, and the linkage disequilibrium score regression (LDSR) method, which uses the LDSR intercept. By using type 2 diabetes (T2D) as an exemplar, we explore factors influencing λ-values and the impact of these corrections on association signals. We find that larger sample sizes increase λ-values due to increased captured polygenicity, while including lower frequency variants decreases λ-values due to reduced power. Comparing T2D genetic associations described in overlapping GWAS meta-analyses of increasing sample size, we find that GC correction reduces the false positive rate and leads to the loss of robust associations. In one of the largest meta-analysis, GC correction results in 39.7% loss of independent loci, substantially reducing the number of detected associations. In comparison, the LDSR intercept correction leads to a loss of up to 25.2% of the independent loci, being therefore less conservative than the GC correction. We conclude that in large, well-powered GWAS meta-analysis of polygenic traits, both GC and LDSR intercept correction leads to power loss, highlighting the need for improved genomic inflation correction methods.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 6","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}