Biology subjects

Denault, W. R. P.

Publications and source records attributed to Denault, W. R. P..

6 recordsLinked to original sources

bioRxiv2025

mfSuSiE enables multi-cell-type fine-mapping and multi-omic integration of chromatin accessibility QTLs in aging brain.

Molecular quantitative trait locus (QTL) studies increasingly profile chromatin accessibility, histone modifications, DNA methylation, RNA modifications such as N6-methyladenosine (m6A), and transcription across multiple cell types using high-throughput sequencing, generating dense base-pair-resolved measurements. The conventional approach of testing each variant against each molecular feature independently suffers from severe multiple testing burden and ignores linkage disequilibrium and spatial correlation. Existing fine-mapping methods only partially address these challenges and are sub-optimal for analyzing such datasets: multivariate approaches such as mvSuSiE jointly analyze multiple molecular contexts but are designed for a single trait value per context and cannot accommodate thousands of base-resolution measurements per context, while functional approaches such as fSuSiE model spatial structure across thousands of measurements but analyze each context separately. Here, we introduce mfSuSiE, which integrates multivariate analysis with wavelet-based functional regression to jointly fine-map thousands of base-resolution traits across multiple cell types. In simulations, mfSuSiE identified causal variants and affected molecular features more accurately than fSuSiE, while mvSuSiE cannot be applied to this type of data. Applied to single-nucleus chromatin accessibility data from six brain cell types from postmortem aging human brains, mfSuSiE substantially increased discovery and resolution, with substantial power gains for cell types with limited samples. Multi-cell-type analysis revealed extensive sharing of regulatory effects on chromatin accessibility (caQTL). Importantly, mfSuSiE produces Bayesian inference compatible with the SuSiE framework, enabling systematic multi-omic integration. Applied to Alzheimers disease loci, we integrated caQTL with expression QTLs, epigenomic QTLs, and GWAS, observing regulatory patterns suggesting complex mechanisms at loci including EARS2, CHRNE, SCIMP, and RABEP1.

genetics↗

bioRxiv2025

fSuSiE enables fine-mapping of QTLs from genome-scale molecularprofiles

Molecular quantitative trait locus (QTL) studies seek to identify the causal variants affecting molecular traits like DNA methylation and histone modifications. However, existing fine-mapping tools are not well suited to high-dimensional molecular traits, and so analyses of these traits typically proceed by considering each variant and each molecular measurement independently, ignoring the LD among variants and the spatial correlation in effects between nearby sites. This severely limits accuracy in identifying causal variants and quantifying their molecular trait effects. Here, we introduce fSuSiE ("functional Sum of Single Effects"), a fine-mapping method that addresses these challenges by explicitly modeling the spatial structure of genetic effects on molecular traits. fSuSiE integrates wavelet-based functional regression with the computationally efficient "Sum of Single Effects" framework to simultaneously finemap causal variants and identify the molecular traits they affect. In simulations, fSuSiE identified causal variants and affected CpGs more accurately than methods that ignore spatial structure. In applications to DNA methylation and histone acetylation (H3K9ac) data from the ROSMAP study of the dorsolateral prefrontal cortex, fSuSiE achieved dramatically higher resolution than existing methods (e.g., identifying 6,355 single-variant methylation credible sets compared to only 328 from an existing approach). Applied to Alzheimers disease (AD) risk loci, fSuSiE identified potential causal variants colocalizing with AD GWAS signals for established genes, including CASS4 and CR1/CR2, suggesting specific potential regulatory mechanisms underlying these AD risk loci.

genetics↗

bioRxiv2025

Accounting for uncertainty in residual variances improves calibration of the Sum of Single Effects model for small sample sizes

The Sum of Single Effects (SuSiE) model is a widely adopted method for genetic fine-mapping. We show that, in small-sample studies, the original SuSiE fitting procedure produces substantially higher rates of false positive findings. We show that a simple modification to SuSiE improves performance, especially in small-sample studies. This modification is particularly important for emerging molecular QTL applications in rare cell types and primary tissues where sample sizes are inherently limited.

bioinformatics↗

bioRxiv2022

The X-factor in ART: does the use of Assisted Reproductive Technologies influence DNA methylation on the X chromosome?

BackgroundAssisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate DNAm differences on the X chromosome. Materials and MethodsThe discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). The replication cohort consisted of 149 ART and 58 non-ART neonates from the Australian "Clinical review of the Health of adults conceived following Assisted Reproductive Technologies" (CHART) study. The Illumina EPIC array was used to measure DNA methylation (DNAm) in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ("XWASs" hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. For replication purposes, we ran an analogous model to our main model on the CHART dataset. Results and conclusionsIn the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted even after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental infertility as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. Overall, genes that co-localized with the significant CpGs and DMRs are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism. These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to naturally-conceived.

genetics↗

bioRxiv2021

Cross-fitted instrument: a blueprint for one-sample Mendelian Randomization

Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a simple solution for handling weak instrument bias by introducing a new type of instrumental variable called cross-fitted instrument (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. The estimates are then used to perform an IVR on each partition. We adapt CFI to Mendelian randomization (MR) and term this adaptation Cross-Fitting for Mendelian Randomization (CFMR). A major advantage of CFMR is its use of all the available data to select genetic instruments, as opposed to traditional two-sample MR where a large part of the data is only used for instrument selection. Consequently, CFMR has the potential to enhance the power of MR in a meta-analysis setting by enabling an unbiased one-sample MR to be performed in each cohort prior to meta-analyzing the results across all the cohorts. In a similar fashion, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in consortia-led meta-analyses where the participating cohorts might be of different ethnicities. To our knowledge, there are currently no MR approach that can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting. Key messagesO_LIWe develop a new method to enable an unbiased one-sample Mendelian Randomization. C_LIO_LIThe new method provides the same power as the standard two-sample Mendelian Randomization approach and does not require summary statistics from a genome-wide association study in an independent cohort. C_LIO_LIOur approach enables a cross-ethnic instrumental variable regression to account for heterogeneity in a sample consisting of multiple ethnicities. C_LI

bioinformatics↗

bioRxiv2020

Wavelet Screening: a novel approach to analysing GWAS data

We present here an alternative method for genome-wide association study (GWAS) that is more powerful than traditional GWAS methods for locus detection. Single-variant GWAS methods incur a substantial multiple-testing burden because of the vast number of single nucleotide polymorphisms (SNPs) being tested simultaneously. Furthermore, these methods do not consider the functional genetic effect on the outcome because they ignore more complex joint effects of nearby SNPs within a region. By contrast, our method reduces the number of tests to be performed by screening the entire genome for associations using a sliding-window approach based on wavelets. In this context, a sequence of SNPs represents a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modelled using the posterior distribution of the wavelet coefficients. We enhance our decision procedure by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex signals than single-SNP associations, we show through simulations that Wavelet Screening provides a substantial gain in power compared to both the traditional GWAS modelling as well as another popular regional-based association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we re-analysed data from the large Norwegian HARVEST cohort.

bioinformatics↗