Gene Interactions in Human Disease Studies-Evidence Is Mounting

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits

Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving the power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large Biobank data sets or lack interpretability. We propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (quadratic effects) within small to medium-sized sets of genetic variants (window size ≤100) on a trait and provide quantified interpretation of these effects. Comprehensive simulations show that QuadKAST is well-calibrated. Additionally, QuadKAST is highly sensitive in detecting loci with epistatic signals and accurate in its estimation of quadratic effects. We applied QuadKAST to 52 quantitative phenotypes measured in ≈300,000 unrelated white British individuals in the UK Biobank to test for quadratic effects within each of 9515 protein-coding genes. We detect 32 trait-gene pairs across 17 traits and 29 genes that demonstrate statistically significant signals of quadratic effects (accounting for the number of genes and traits tested). Across these trait-gene pairs, the proportion of trait variance explained by quadratic effects is comparable to additive effects, with five pairs having a ratio >1. Our method enables the detailed investigation of epistasis on a large scale, offering new insights into its role and importance.

Variance quantitative trait loci reveal gene-gene interactions which alter blood traits

Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the "missing heritability" of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ~450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ~140,000 NIH All of Us and ~70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

Functional polymorphisms of NOS3 and GUCY1A3 affect both nitric oxide formation and association with hypertensive disorders of pregnancy

Impaired nitric oxide (NO) formation may be associated with endothelial dysfunction and increased cardiovascular disease risk in preeclampsia (PE). Functional single-nucleotide polymorphisms (SNPs) of nitric oxide synthase 3 (NOS3) (rs3918226) and guanylate cyclase 1, soluble, alpha 3 (GUCY1A3) (rs7692387) increase susceptibility to the adverse consequences due to inadequate generation of NO by the endothelium. However, no previous study has examined whether these SNPs affect NO formation in healthy pregnancy and in gestational hypertension (GH) and PE. Here, we compared the alleles and genotypes of NOS3 (rs3918226) and GUCY1A3 (rs7692387) SNPs in normotensive pregnant women (NP, n = 153), in GH (n = 96) and PE (n = 163), and examined whether these SNPs affect plasma nitrite concentrations (a marker of NO formation) in these groups. We further examined whether the interaction among SNP genotypes is associated with GH and PE. Genotypes were determined using TaqMan allele discrimination assays, and plasma nitrite concentrations were determined by an ozone-based chemiluminescence assay. Multifactor dimensionality reduction was used to examine the interactions among SNP genotypes. Regarding NOS3 rs3918226, the CT genotype (p = 0.046) and T allele (p = 0.020) were more frequent in NP than in GH, and GH patients carrying the CT+TT genotypes showed lower nitrite concentrations than NP carrying the CT+TT genotypes (p < 0.05). Regarding GUCY1A3 rs7692387, the GA genotype (p = 0.013) and A allele (p = 0.016) were more frequent in PE than in NP, and NP women carrying the GG genotype showed higher nitrite concentrations than GH or PE patients carrying the GG genotype (p < 0.05). However, we found no significant interactions among genotypes for these functional SNPs to be associated with GH or PE. Our novel findings suggest that NOS3 rs3918226 and GUCY1A3 rs7692387 may affect NO formation and association with hypertensive disorders of pregnancy.

Identification of intergenerational epigenetic inheritance by whole genome DNA methylation analysis in trios

Genome-wide association studies have identified thousands of loci associated with common diseases and traits. However, a large fraction of heritability remains unexplained. Epigenetic modifications, such as the observed in DNA methylation have been proposed as a mechanism of intergenerational inheritance. To investigate the potential contribution of DNA methylation to the missing heritability, we analysed the methylomes of four healthy trios (two parents and one offspring) using whole genome bisulphite sequencing. Of the 1.5 million CpGs (19%) with over 20% variability between parents in at least one family and compatible with a Mendelian inheritance pattern, only 3488 CpGs (0.2%) lacked correlation with any SNP in the genome, marking them as potential sites for intergenerational epigenetic inheritance. These markers were distributed genome-wide, with some preference to be located in promoters. They displayed a bimodal distribution, being either fully methylated or unmethylated, and were often found at the boundaries of genomic regions with high/low GC content. This analysis provides a starting point for future investigations into the missing heritability of simple and complex traits.

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p < 5 × 10 - 8 53 ). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.