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At least 19 recordsLinked to original sources
PubMed2026

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.

Genetic contributions to complex traits are often mediated through coordinated gene-gene interaction networks, yet most existing association frameworks focus on marginal single-gene effects and overlook higher-order dependency structures. Direct modeling of interactions remains challenging due to combinatorial complexity and statistical instability. We introduce Interaction-Bridged Association Study (IBAS), a general framework that incorporates pathway-level interaction patterns into genotype-phenotype association analysis without explicitly enumerating interactions. IBAS leverages transcriptomic reference data to construct low-dimensional representations of pathway activity, which guide SNP-weighting and gene-level association testing within a kernel-based framework. In perturbation-based simulations, IBAS demonstrates improved stability and reproducibility compared to conventional TWAS and gene-based methods, while maintaining well-calibrated Type I error under phenotype permutation. Application to the WTCCC datasets identifies both known and novel genes across multiple complex diseases, including candidates with modest marginal effects missed by standard approaches. These findings are supported by replication in an independent cohort, and analyses across multiple reference tissues revealing both shared and tissue-specific signals. Overall, IBAS provides a statistically robust and computationally tractable framework for incorporating interaction effects into association mapping, extending beyond the single-gene paradigm and enabling more comprehensive characterization of complex trait. IBAS is available on GitHub at: https://github.com/QingrunZhangLab/IBAS.

Polymorphism, Single Nucleotide↗
PubMed2025

Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.

Identifying risk genes associated with complex traits remains challenging. Integrating gene expression data with Genome-Wide Association Study (GWAS) through Transcriptome-Wide Association Study (TWAS) methods has discovered candidate risk genes for various complex traits. Splicing, which explains a comparable heritability of complex traits as gene expression, is under-explored due to its multidimensionality. To leverage multiple splicing events in a gene and shared splicing across tissues, we develop Multi-tissue Splicing Gene (MTSG), which employs tensor decomposition and sparse Canonical Correlation Analysis (sCCA) to extract meaningful information from high-dimensional multiple splicing events across multiple tissues. We build MTSG models using GTEx data and apply them to GWAS summary statistics of Alzheimer's disease (AD) (111,326 cases and 677,663 controls) and schizophrenia (SCZ) (36,989 cases and 113,075 controls). We identify 174 and 497 significant splicing-mediated risk genes for AD and SCZ, respectively, at Bonferroni correction. For AD, our results demonstrate significant enrichment of AD related pathways and identify additional AD risk genes not detected in the single-tissue analysis, while preserving most top genes identified in the brain frontal cortex. Consistently, for SCZ, genes identified by our brain-wide MTSG model, built from a cluster of 13 brain tissues, exhibit stronger enrichment in SCZ-relevant genes and MTSG identifies unique SCZ risk genes compared to single-tissue models. These results showcase that our MTSG models capture distinctive splicing events across tissues, which might be overlooked when using single tissue alone. Our MTSG models can be applied to other complex traits to help identify splicing-mediated disease risk genes.

Humans↗
PubMed2025

Gene dosage architecture across complex traits.

UNLABELLED: Copy number variants (CNVs) have large effects on complex traits, but they are rare and remain challenging to study. As a result, our understanding of biological functions linking gene dosage to complex traits remains limited, and whether these functions sensitive to gene dosage are similar to those underlying the effects of rare single nucleotide variants (SNVs) and common variants remains unknown. METHODS: We developed FunBurd, a functional burden analysis, to test the association of CNVs aggregated within functional gene sets. We applied this approach in 500,000 individuals from the UK Biobank to associate 43 complex traits with CNVs disrupting 172 gene sets across tissues and cell types. We compared CNV findings with those from common variants and LoF (Loss of Function) SNVs in the same cohort using the same functional gene sets. RESULTS: All 43 traits showed FDR significant associations with CNVs. Brain tissue and neuronal cell-types showed the highest levels of pleiotropy. Most of the functional gene set associations could, in part, be explained by genetic constraint, except for brain related processes. Shared genetic contributions between pairs of traits were concordant across types of variants, but on average 2-fold higher, for rare CNVs and SNVs compared to common variants.Functional enrichment across traits found limited overlap between CNVs and common variants. Moreover, the effects of deletions and duplications were negatively correlated for most traits.In conclusion, we present new methods to separate the contributions of genetic constraint and gene function to the associations of CNVs with complex traits. Overall, the functional convergence between different types of variants -even between deletions and duplications-remains limited.

Journal Article↗
PubMed2026

Genetic and environmental interactions outweigh mitonuclear coevolution for complex traits in Drosophila.

The interdependent relationship between mitochondrial and nuclear genomes is a powerful model for understanding how epistasis shapes the architecture and evolution of complex traits. Once considered a neutral marker, mitochondrial DNA variation is now recognized as critical to phenotypic evolution because of its epistatic interactions and history of coevolution with the nuclear genome. A central challenge in evolutionary genetics is to quantify the relative importance of stabilizing and directional selection shaping complex trait distributions within and among species. Both can act on interacting and/or co-evolving genes contributing to quantitative traits, but resolving their relative roles is complicated by the complex architecture of most traits. Here, we use a panel of 90 Drosophila mitonuclear genotypes to quantify the relative contributions of mitochondrial, nuclear, and environmental variation and their interactions to four metabolically demanding complex traits. We sample both within-species and between-species mitochondrial variation and observe stronger interaction effects attributable to within-species variation, consistent with stabilizing selection maintaining mitonuclear function. Additionally, culturing the flies on a mitochondrial Complex I inhibitor, rotenone, reveals significant genotype x environment (G×E and G×G×E) interaction effects, providing insight into how genetic variation can be maintained across changing environments. Our results have broader implications in medicine, where mitochondrial DNA donors with longer purifying selection histories may be safer for mitochondrial replacement therapies.

Journal Article↗
PubMed2025

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

Journal Article↗
PubMed2025

Simple scaling laws control the genetic architectures of human complex traits.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Humans↗
PubMed1990

Linkage strategies for genetically complex traits. I. Multilocus models.

In order to investigate linkage detection strategies for genetically complex traits, multilocus models of inheritance need to be specified. Here, two types of multilocus model are described: (1) a multiplicative model, representing epistasis (interaction) among loci, and (2) an additive model, which is shown to closely approximate genetic heterogeneity, which is characterized by no interlocus interaction. A ratio lambda R of risk for type R relatives that is compared with population prevalence is defined. For a single-locus model, lambda R - 1 decreases by a factor of two with each degree of relationship. The same holds true for an additive multilocus model. For a multiplicative (epistasis) model, lambda R - 1 decreases more rapidly than by a factor of two with degree of relationship. Examination of lambda R values for various classes of relatives can potentially suggest the presence of multiple loci and epistasis. For example, data for schizophrenia suggest multiple loci in interaction. It is shown in the second paper of this series that lambda R is the critical parameter in determining power to detect linkage by using affected relative pairs.

Genetic Linkage↗
PubMed1991

RFLP analysis of complex traits in crop plants.

Detailed genetic maps, based upon molecular markers (in particular, on restriction fragment length polymorphisms-RFLPs) have now been constructed for a number of crop plant species, and permit a range of genetic analyses hitherto considered impossible. The availability of such maps has made it possible to approach the dissection and manipulation of both simply-inherited and complex characteristics. Even those characters that show apparently 'quantitative' inheritance (displaying essentially continuous variation within a segregating family) can frequently be resolved into a handful of major gene effects. Once tagged with molecular markers, the genes can be assembled in any desired combination, permitting the testing of hypotheses on gene action/interaction, or the construction of varieties of plant species with improved agronomic performance. The next technological challenge is to 'walk' from RFLP markers to isolate the actual genes responsible for the complex trait, by a combination of genetical and physical mapping techniques. Such analyses will begin to clarify our picture of the relationship between genetic and physical maps, of recombination, and of the arrangement of diverse families of DNA sequences in plant genomes.

Genes, Plant↗
PubMed2025

Orthobunyavirus neurovirulence is a complex trait involving all three genome segments.

La Crosse orthobunyavirus (LACV) is a tri-segmented negative sense RNA virus and is the leading cause of pediatric arboviral encephalitis in the USA. The viral factors that mediate LACV's ability to replicate and cause damage and disease in the brain (neurovirulence) are not fully understood. We previously characterized the neurovirulence of LACV and closely related Inkoo virus (INKV) and discovered they have opposing neurovirulence phenotypes in mice and human neuronal cells: LACV has high neurovirulence and INKV has low neurovirulence. We therefore generated reassortant viruses between LACV and INKV to map the genome segments that mediate LACV's high neurovirulence phenotype. We recovered all six possible reassortant viruses of the L, M, and S genome segments using coinfection and reverse genetics approaches. We evaluated the neurovirulence of these reassortant viruses in mice in vivo and in human neuronal cells in vitro. Our results show that no single LACV genome segment alone was sufficient to cause wildtype LACV-like neurological disease in mice, and in fact all six reassortant viruses were attenuated from wildtype LACV. We found that the LACV M and S segments together were the primary drivers of neurological disease in mice, whereas the LACV L segment played a minor role. Our in vitro results indicate that the LACV M segment is crucial for efficient replication in neurons, but the LACV L segment appears to mediate slightly more efficient neuronal replication than the INKV L segment. The LACV M and S segments together induced wildtype LACV-like levels of neuronal death, indicating the LACV M and S are the primary mediators of neuronal death, and the L segment is not required. Together, these results indicate that LACV neurovirulence is a complex trait mediated by viral proteins on all three genome segments.

Journal Article↗
PubMed2025

Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.

The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes. Here, we present a statistical method called SDPR_admix for calculating PRS in admixed individuals. SDPR_admix characterizes the joint distribution of the effect sizes of a genetic variant with two ancestries to be both zero, ancestry enriched, or shared with correlation. SDPR_admix outperformed other methods in simulations and improved the prediction of real traits in European-African admixed individuals in UK Biobank when trained on the Population Architecture using Genomics and Epidemiology (PAGE) dataset (N = 13,000). Deployment of SDPR_admix on All of Us (N = 52,000) further increased the prediction accuracy by approximately 5-fold on average compared with training on PAGE. This enhancement was achieved with manageable computational time and cost, demonstrating the feasibility of training PRS models on large-scale All of Us data. We provided several examples demonstrating that both ancestral-enriched and shared effects, as included in the SDPR_admix prediction model, are helpful for improving polygenic prediction in admixed populations. We also applied SDPR_admix to construct PRS for admixed Americans with mixture of European and Amerindigenous ancestries and showed that SDPR_admix overall outperformed other methods.

Humans↗
PubMed1989

HLA and insulin-dependent diabetes: an overview.

The present knowledge of the HLA system and its biological function is summarized as a basis for the subsequent discussion of the associations between this system and insulin-dependent diabetes (IDDM) and some mechanisms that may explain them. Although the serologically detectable DR determinants are still the most handy markers, there is now increasing evidence from studies of restriction enzyme fragment length polymorphism (RFLP) in IDDM that DQ determinants may play a primary role in causing susceptibility and/or resistance to this disease. Thus, it is now evident that about 90% of DR4-positive diabetics carry the DQw8 determinant present in only about 65% of DR4-positive controls. Most recently, it has been claimed that an aspartic acid in position 57 of the DQB1 (DQ-beta-1) chain confers resistance to IDDM. Although this may be true, it does not explain the disproportionate decrease of DR2 or the particularly high risk of DR3/4 heterozygotes, which is still good evidence that several HLA genes are involved. Because Class II antigens show the strongest associations, the most plausible hypothesis about the mechanism(s) involves specific presentation of as yet unknown antigenic peptides to T-helper lymphocytes, which may induced the formation of both anti-islet cell antibodies and T-cytotoxic lymphocytes capable of destroying beta cells. However, T-suppressor lymphocytes also may be involved. If this hypothesis is correct, the most urgent task is to define the antigenic peptides in question, whether they are environmental (e.g., viral) or autologous.

Diabetes Mellitus, Type 1↗
PubMed2025

Genetic effects on chromatin accessibility reveal the molecular mechanisms of complex traits in maize.

Cis-regulatory elements (CREs) are critical for modulating gene expression and phenotypic diversity in maize. While genome-wide association study (GWAS) hits and expression quantitative trait loci (eQTLs) are often enriched in CREs, their molecular mechanisms remain poorly understood. Characterizing CREs within accessible chromatin regions (ACRs) offers a powerful approach to link noncoding variants to chromatin structure alterations and phenotypic variation. Here, we generated ATAC-seq profiles from seedling leaves of 214 maize inbred lines, identifying 82 174 consensus ACRs. Notably, 39.55% of these ACRs exhibited significant population-wide chromatin accessibility variation. By mapping chromatin accessibility quantitative trait loci (caQTLs), we discovered 27 004 loci, including 1398 predicted to disrupt transcription factor (TF)-binding sites. Integration with multi-omics data revealed 7405 caACR-target gene pairs and linked 56 caACRs to GWAS signals for 51 agronomic traits, with significant enrichment in flowering-related pathways. Functional candidates such as ZmZIM30 - putatively regulated by caACRs - emerged as key regulators of flowering time. At the fad7 locus associated with linolenic acid content, allelic variants overlapping a caQTL showed differential chromatin accessibility. Our study provides a high-resolution cis-elements of maize leaves, deciphers the genetic basis of chromatin accessibility variation, and bridges noncoding caQTLs to molecular mechanisms underlying GWAS hits.

Zea mays↗
PubMed1990

Linkage strategies for genetically complex traits. II. The power of affected relative pairs.

The power to detect disease-susceptibility loci through linkage analysis using pairs of affected relatives and affected-unaffected pairs is examined. Allelic identity by descent (ibd) for a completely polymorphic marker for sibling, uncle-nephew, grandparent-grandchild, half-sib, and first-cousin pairs is considered. Affected-unaffected pairs generally represent a poor strategy. For single-locus models, ibd depends on lambda R, the risk ratio for type R relatives compared with population prevalence, and the recombination fraction theta. The ibd for grandparent-grandchild pairs is least affected by recombination, followed by sibs, half-sib, uncle-nephew, and first-cousin pairs. For diseases with large lambda values and for small theta values, distant relatives offer greater power. For larger theta values, grandparent-grandchild pairs are best; for small lambda values, sibs are best. Additive and multiplicative multilocus models are considered. For the multiplicative model, the same formulas as in the single-locus model apply, except that lambda iR (for the ith contributing locus) is substituted for lambda R. For the additive model, the deviation from null expectation for ibd is divided among all contributing loci. Compared with the multiplicative model, for an additive model there is usually greater advantage in distant relationships. Multipoint analysis using linked marker loci for affected relative pairs is described. Simultaneous use of multiple markers diminishes the effect of recombination and allows for localization of the disease-susceptibility locus.

Alleles↗
PubMed1990

Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs.

The results from the second paper of this series are reexamined for markers that are not completely polymorphic. A maximum lod score (MLS) criterion is defined for affected relative pairs. The expected MLS (EMLS) is calculated as a function of the marker polymorphic information content (PIC) for various values of lambda R (relative risk ratio) and different relative types by using simulations. An m-allele model with equal allele frequencies is employed. The EMLS is calculated for two sampling strategies: scheme 1, which uses pairs only, and scheme 2, which also includes additional informative relatives. For scheme 2, the percent of the maximum achievable EMLS (i.e., for a marker with a PIC of 1.0) is approximately equal to the marker PIC value for all relative types. For scheme 1, the EMLS is greatly diminished unless PIC is high, especially for distant relatives. For example, scheme 1 is not cost-effective for sibs unless PIC greater than .7; for second- and third-degree relatives, PIC must be greater than .85. Therefore, in general, it will be worthwhile to type additional relatives in linkage studies using affected pairs. The comparative value of sibs versus distant relatives depends on lambda R, recombination theta, and PIC. For large lambda R and PIC values, distant relatives are preferred. Alternatively, for smaller lambda R and PIC values, sibs are best.

Female↗
PubMed1989

Estimating the power of a proposed linkage study for a complex genetic trait.

Many genetic traits have complex modes of inheritance; they may exhibit incomplete or age-dependent penetrance or fail to show any clear Mendelian inheritance pattern. As primary linkage maps for the human genome near completion, it is becoming increasingly possible to map these traits. Prior to undertaking a linkage study, it is important to consider whether the pedigrees available for the proposed study are likely to provide sufficient information to demonstrate linkage, assuming a linked marker is tested. In the current paper, we describe a computer simulation method to estimate the power of a proposed study to detect linkage for a complex genetic trait, given a hypothesized genetic model for the trait. Our method simulates trait locus genotypes consistent with observed trait phenotypes, in such a way that the probability to detect linkage can be estimated by sample statistics of the maximum lod score distribution. The method uses terms available when calculating the likelihood of the trait phenotypes for the pedigree and is applicable to any trait determined by one or a few genetic loci; individual-specific environmental effects can also be dealt with. Our method provides an objective answer to the question, Will these pedigrees provide sufficient information to map this complex genetic trait?

Genetic Linkage↗
PubMed2026

Beyond exons: Linking noncoding heritability and polygenicity across complex human traits and disorders.

The genetic architecture of complex traits spans a continuum of polygenicity, yet it remains unclear how differences in polygenicity relate to the functional localization of SNP heritability across the genome. We use a MiXeR-based framework to partition heritability across 74 functional annotations covering exonic, intronic, and intergenic regions for 34 complex traits and introduce a likelihood-based annotation contribution score that quantifies annotation-specific impact on heritability. Exons account for a minority of heritability, and their contribution decreases with increasing polygenicity, from an average of 22% in less-polygenic somatic diseases and biomarkers to 13% in highly polygenic psychiatric and cognitive phenotypes. Intergenic fractions show the opposite trend, whereas intronic fractions remain relatively stable. Analysis of the broader set of functional annotations also reveals systematic differences along the polygenicity axis: highly polygenic traits show stronger contributions from comparative genomics and variant-effect scores, whereas less-polygenic traits show stronger contributions from promoter, transcription, and chromatin annotations. Together, these results indicate that the functional partitioning of heritability systematically varies with polygenicity, shifting from gene-proximal regulatory architectures to architectures shaped by numerous dispersed regulatory effects.

MiXeR↗