Biology subjects

Chiu, A. M.

Publications and source records attributed to Chiu, A. M..

5 recordsLinked to original sources

bioRxiv2023

A multivariate outcome test of covariance

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance, and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1,000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

bioinformatics↗

bioRxiv2021

Inferring population structure in biobank-scale genomic data

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. While a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

bioinformatics↗

bioRxiv2020

Quantifying the contribution of dominance effects to complex trait variation in biobank-scale data

The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of Biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method that can partition the variation in complex traits into variance that can be attributed to additive (additive heritability) and dominance (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency, MAF > 1%) and 50 quantitative traits measured in 291, 273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no evidence for dominance heritability ([Formula] accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

genetics↗

bioRxiv2019

Scalable probabilistic PCA for large-scale genetic variation data

Principal component analysis (PCA) is a key tool for understanding population structure and controlling for population stratification in genome-wide association studies (GWAS). With the advent of large-scale datasets of genetic variation, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. We present ProPCA, a highly scalable method based on a probabilistic generative model, which computes the top PCs on genetic variation data efficiently. We applied ProPCA to compute the top five PCs on genotype data from the UK Biobank, consisting of 488,363 individuals and 146,671 SNPs, in less than thirty minutes. Leveraging the population structure inferred by ProPCA within the White British individuals in the UK Biobank, we scanned for SNPs that are not well-explained by the PCs to identify several novel genome-wide signals of recent putative selection including missense mutations in RPGRIP1L and TLR4.\n\nAuthor SummaryPrincipal component analysis is a commonly used technique for understanding population structure and genetic variation. With the advent of large-scale datasets that contain the genetic information of hundreds of thousands of individuals, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. In this study, we present ProPCA, a highly scalable statistical method to compute genetic PCs efficiently. We systematically evaluate the accuracy and robustness of our method on large-scale simulated data and apply it to the UK Biobank. Leveraging the population structure inferred by ProPCA within the White British individuals in the UK Biobank, we identify several novel signals of putative recent selection.

bioinformatics↗

bioRxiv2019

NMDAR-activated PP1 dephosphorylates GluN2B to modulate NMDAR plasticity.

In mature neurons, postsynaptic NMDARs are segregated into two populations, synaptic and extrasynaptic, which differ in localization, function, and associated intracellular cascades. These two pools are connected via lateral diffusion, and receptor exchange between them modulates synaptic NMDAR content. Here, we identify the phosphorylation of the PDZ-ligand of the GluN2B subunit of NMDARs (at S1480) as a critical determinant in dynamically controlling NMDAR synaptic content. We find that phosphorylation of GluN2B at S1480 maintains NMDARs at extrasynaptic membranes as part of a protein complex containing Protein Phosphatase 1 (PP1). Global activation of NMDARs leads to the activation of PP1, which mediates dephosphorylation of GluN2B at S1480 to promote an increase in synaptic NMDAR content. Thus, PP1-mediated dephosphorylation of the GluN2B PDZ-ligand modulates the synaptic expression of NMDARs in mature neurons in an activity-dependent manner, a process with profound consequences for synaptic and structural plasticity, metaplasticity, and synaptic neurotransmission.\n\nHIGHLIGHTSO_LIPhosphorylation of the PDZ-ligand of the GluN2B subunit of NMDARs (GluN2B-pS1480) maintains NMDARs at extrasynaptic sites.\nC_LIO_LIExtrasynaptic NMDARs form a stable protein complex containing PP1.\nC_LIO_LIGlobal NMDAR activation increases NMDAR synaptic content by promoting PP1-mediated dephosphorylation of GluN2B-pS1480.\nC_LIO_LIGluN2B-pS1480 dephosphorylation is mediated by a PP1 subpopulation not involved in LTD and is enhanced by age.\nC_LI

neuroscience↗