Biology subjects

Liang, J. W.

Publications and source records attributed to Liang, J. W..

4 recordsLinked to original sources

bioRxiv2022

A framework evaluating the utility of multi-gene, multi-disease population-based panel testing that accounts for uncertainty in penetrance estimates

Panel germline testing allows for efficient detection of deleterious variants for multiple conditions, but the benefits and harms of identifying these variants are not always well-understood. We present a multi-gene, multi-disease aggregate utility formula that allows the user to consider adding or removing each gene in a panel based on variant frequency; estimated penetrances; and subjective disutilities for testing positive but not developing the disease and testing negative but developing the disease. We provide credible intervals for utility that reflect uncertainty in penetrance estimates. Rare, highly-penetrant deleterious variants tend to contribute positive net utilities for a wide variety of user-specified disutilities, even when accounting for parameter estimation uncertainty. However, the clinical utility of deleterious variants with moderate, uncertain penetrance depends more on assumed disutilities. The decision to include a gene on a panel depends on variant frequency, penetrance, and subjective utilities, and should account for uncertainties around these factors.

genetics↗

bioRxiv2019

Longitudinal Study of Leukocyte DNA Methylation and Biomarkers for Cancer Risk in Older Adults

BackgroundChanges in DNA methylation over the course of life may provide an indicator of risk for cancer. We explored longitudinal changes in CpG methylation from blood leukocytes, and likelihood of a future cancer diagnosis.\n\nMethodsPeripheral blood samples were obtained at baseline and at follow-up visit from 20 participants in the Health, Aging and Body Composition prospective cohort study. Genome-wide CpG methylation was assayed using the Illumina Infinium Human MethylationEPIC (HM850K) microarray.\n\nResultsGlobal patterns in DNA methylation from CpG-based analyses showed extensive changes in cell composition over time in participants who developed cancer. By visit year 6, the proportion of CD8+ T-cells decreased (p-value = 0.02), while granulocytes cell levels increased (p-value = 0.04) among participants diagnosed with cancer compared to those who remained cancer-free (cancer-free vs. cancer-present: 0.03 {+/-} 0.02 vs. 0.003 {+/-} 0.005 for CD8+ T-cells; 0.52 {+/-} 0.14 vs. 0.66 {+/-} 0.09 for granulocytes). Epigenome-wide analysis identified three CpGs with suggestive p-values [≤] 10-5 for differential methylation between cancer-free and cancer-present groups, including a CpG located in MTA3, a gene linked with metastasis. At a lenient statistical threshold (p-value [≤] 3 x 10-5), the top 10 cancer-associated CpGs included a site near RPTOR that is involved in the mTOR pathway, and the candidate tumor suppressor genes REC8, KCNQ1, and ZSWIM5. However, only the CpG in RPTOR (cg08129331) was replicated in an independent data set. Analysis of within-individual change from baseline to Year 6 found significant correlations between the rates of change in methylation in RPTOR, REC8 and ZSWIM5, and time to cancer diagnosis.\n\nConclusionThe results show that changes in cellular composition explains much of the cross-sectional and longitudinal variation in CpG methylation. Additionally, differential methylation and longitudinal dynamics at specific CpGs could provide powerful indicators of cancer development and/or progression. In particular, we highlight CpG methylation in the RPTOR gene as a potential biomarker of cancer that awaits further validation.

cancer biology↗

bioRxiv2018

Matrix linear models for high-throughput chemical genetic screens

We develop a flexible and computationally efficient approach for analysing high throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. The goal is to detect interactions between genes and stresses. Typically, this is achieved by grouping the mutants and stresses into categories, and performing modified t-tests for each combination. This approach does not have a natural extension if mutants or stresses have quantitative or non-overlapping annotations (eg. if conditions have doses, or a mutant falls into more than one category simultaneously). We develop a matrix linear model framework that allows us to model relationships between mutants and conditions in a simple, yet flexible multivariate framework. It encodes both categorical and continuous relationships to enhance detection of associations. To handle large datasets, we develop a fast estimation approach that takes advantage of the structure of matrix linear models. We evaluate our methods performance in simulations and in an E. coli chemical genetic screen, comparing it with an existing univariate approach based on modified t-tests. We show that matrix linear models perform slightly better than the univariate approach when mutants and conditions are classified in non-overlapping categories, and substantially better when conditions can be ordered in dosage categories. Our approach is much faster computationally and is scalable to larger datasets. It is an attractive alternative to current methods, and provides a natural framework extensible to larger, and more complex chemical genetic screens. A Julia implementation of matrix linear models and the code used for the analysis in this paper can be found at https://bitbucket.org/jwliang/mlm_packages and https://bitbucket.org/jwliang/mlm_gs_supplement, respectively.

bioinformatics↗

bioRxiv2017

Profiling DNA Methylation Differences Between Inbred Mouse Strains on the Illumina Human Infinium MethylationEPIC Microarray

The Illumina Infinium MethylationEPIC provides an efficient platform for profiling DNA methylation in humans at over 850,000 CpGs. Model organisms such as mice do not currently benefit from an equivalent array. Here we used this array to measure DNA methylation in mice. We defined probes targeting conserved regions and performed a comparison between the array-based assay and affinity-based DNA sequencing of methyl-CpGs (MBD-seq). Mouse samples consisted of 11 liver DNA from two strains, C57BL/6J (B6) and DBA/2J (D2), that varied widely in age. Linear regression was applied to detect differential methylation. In total, 13,665 probes (1.6% of total probes) aligned to conserved CpGs. Beta-values ({beta}-value) for these probes showed a distribution similar to that in humans. Overall, there was high concordance in methylation signal between the EPIC array and MBD-seq (Pearson correlation r = 0.70, p-value < 0.0001). However, the EPIC probes had higher quantitative sensitivity at CpGs that are hypo-({beta}-value < 0.3) or hypermethylated ({beta}-value > 0.7). In terms of differential methylation, no EPIC probe detected significant difference between age groups at a Benjamini-Hochberg threshold of 10%, and the MBD-seq performed better at detecting age-dependent change in methylation. However, the top most significant probe for age (cg13269407; uncorrected p-value = 1.8 x 10-5) is part of the clock CpGs used to estimate the human epigenetic age. For strain, 219 Infinium probes detected significant differential methylation (FDR cutoff 10%) with [~]80% CpGs associated with higher methylation in D2. This higher methylation profile in D2 compared to B6 was also replicated by the MBD-seq data. To summarize, we found only a small subset of EPIC probes that target conserved sites. However, for this small subset the array provides areliable assay of DNA methylation and can be effectively used to measure differential methylation in mice.

bioinformatics↗