Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis

CNV-Profile Regression: A New Approach for Copy Number Variant Association Analysis in Whole Genome Sequencing Data

Copy number variants (CNVs) are DNA gains or losses involving >50 base pairs. Assessing CNV effects on disease risk requires consideration of several factors. First, there are no natural definitions for CNV loci. Second, CNV effects can depend on dosage and length. Third, CNV effects can be more accurately estimated when all CNV events in a genomic region are analyzed together to assess their joint effects. We propose a new framework for association analysis that directly models an individual's entire CNV profile within a genomic region. This framework represents an individual's CNVs using a CNV profile curve to capture variations in CNV length and dosage and to bypass the need to predefine CNV loci. CNV effects are estimated at each genome position, making the results comparable across different studies. To jointly estimate the effects of all CNVs, we use a Lasso penalty to select CNVs associated with the trait and integrate a weighted L2-fusion penalty to encourage similar effects of adjacent CNVs when supported by the data. Simulations show that the proposed model can more effectively identify causal CNVs while maintaining false positive rates comparable to baseline methods and yield more precise effect-size estimates across different settings. When applied to CNV derived from whole genome sequencing data of the Alzheimer's Disease Sequencing Project, the proposed methods identify additional CNVs associated with Alzheimer's Disease (AD). These identified CNVs overlap with several known AD-risk genes and are significantly enriched by biological processes related to neuron structures and functions crucial in AD development.

Genetic and epigenetic signatures associated with plasma oxytocin levels in children and adolescents with autism spectrum disorder

Oxytocin (OT), the brain's most abundant neuropeptide, plays an important role in social salience and motivation. Clinical trials of the efficacy of OT in autism spectrum disorder (ASD) have reported mixed results due in part to ASD's complex etiology. We investigated whether genetic and epigenetic variation contribute to variable endogenous OT levels that modulate sensitivity to OT therapy. To carry out this analysis, we integrated genome-wide profiles of DNA-methylation, transcriptional activity, and genetic variation with plasma OT levels in 290 participants with ASD enrolled in a randomized controlled trial of OT. Our analysis identified genetic variants with novel association with plasma OT, several of which reside in known ASD risk genes. We also show subtle but statistically significant association of plasma OT levels with peripheral transcriptional activity and DNA-methylation profiles across several annotated gene sets. These findings broaden our understanding of the effects of the peripheral oxytocin system and provide novel genetic candidates for future studies to decode the complex etiology of ASD and its interaction with OT signaling and OT-based interventions. LAY SUMMARY: Oxytocin (OT) is an abundant chemical produced by neurons that plays an important role in social interaction and motivation. We investigated whether genetic and epigenetic factors contribute to variable OT levels in the blood. To this, we integrated genetic, gene expression, and non-DNA regulated (epigenetic) signatures with blood OT levels in 290 participants with autism enrolled in an OT clinical trial. We identified genetic association with plasma OT, several of which reside in known autism risk genes. We also show statistically significant association of plasma OT levels with gene expression and epigenetic across several gene pathways. These findings broaden our understanding of the factors that influence OT levels in the blood for future studies to decode the complex presentation of autism and its interaction with OT and OT-based treatment.

SMCKAT, a Sequential Multi-Dimensional CNV Kernel-Based Association Test

Copy number variants (CNVs) are the most common form of structural genetic variation, reflecting the gain or loss of DNA segments compared with a reference genome. Studies have identified CNV association with different diseases. However, the association between the sequential order of CNVs and disease-related traits has not been studied, to our knowledge, and it is still unclear that CNVs function individually or whether they work in coordination with other CNVs to manifest a disease or trait. Consequently, we propose the first such method to test the association between the sequential order of CNVs and diseases. Our sequential multi-dimensional CNV kernel-based association test (SMCKAT) consists of three parts: (1) a single CNV group kernel measuring the similarity between two groups of CNVs; (2) a whole genome group kernel that aggregates several single group kernels to summarize the similarity between CNV groups in a single chromosome or the whole genome; and (3) an association test between the CNV sequential order and disease-related traits using a random effect model. We evaluate SMCKAT on CNV data sets exhibiting rare or common CNVs, demonstrating that it can detect specific biologically relevant chromosomal regions supported by the biomedical literature. We compare the performance of SMCKAT with MCKAT, a multi-dimensional kernel association test. Based on the results, SMCKAT can detect more specific chromosomal regions compared with MCKAT that not only have CNV characteristics, but the CNV order on them are significantly associated with the disease-related trait.

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.