Biology subjects

Mefford, J. A.

Publications and source records attributed to Mefford, J. A..

4 recordsLinked to original sources

bioRxiv2020

Association Study of Over 200,000 Subjects Detects Novel Rare Variants, Functional Elements, and Polygenic Architecture of Prostate Cancer Susceptibility

The potential association between rare germline genetic variants and prostate cancer (PrCa) susceptibility has been understudied due to challenges with assessing rare variation. Furthermore, although common risk variants for PrCa have shown limited individual effect sizes, their cumulative effect may be of similar magnitude as high penetrance mutations. To identify rare variants associated with PrCa susceptibility, and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we analyzed large population-based cohorts, custom genotyping microarrays, and imputation reference panels in an integrative study of PrCa genetic etiology. In particular, 11,649 men (6,196 PrCa cases, 5,453 controls) of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health, ProHealth Study, and California Mens Health Study were genotyped and meta-analyzed with 196,269 European-ancestry male subjects (7,917 PrCa cases, 188,352 controls) from the UK Biobank. Six novel loci were genome-wide significant in our meta-analysis, including two rare variants (minor allele frequency < 0.01, at 3p21.31 and 8p12). Gene-based rare variant tests implicated a previously discovered PrCa gene (HOXB13) as well as a novel candidate (ILDR1) highly expressed in prostate tissue. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. Furthermore, a polygenic risk score (PRS) of 187 known, largely common PrCa variants was strongly associated with risk in non-Hispanic whites (90th vs. 10th decile OR = 7.66, P = 1.80*10-239). Many of the 187 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a six-fold difference in log-probability of Androgen Receptor binding at the variant rs2680708 (17q22). Our finding of two novel rare variants associated with PrCa should motivate further consideration of the role of low frequency polymorphisms in PrCa, while the considerable effect of PrCa PRS profiles should prompt discussion of their role in clinical practice.

genomics↗

bioRxiv2020

Estimating the rate of cell type degeneration from epigenetic sequencing of cell-free DNA

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death. Here, we develop a method to accurately estimate the relative abundances of cell types contributing to cfDNA. We leverage the distinct DNA methylation profile of each cell type throughout the body. Decomposing the cfDNA mixture is difficult, as fragments from relevant cell types may only be present in a small amount. We propose an algorithm, CelFiE, that estimates cell type proportion from both whole genome cfDNA input and reference data. CelFiE accommodates low coverage data, does not rely on CpG site curation, and estimates contributions from multiple unknown cell types that are not available in reference data. In simulations we show that CelFiE can accurately estimate known and unknown cell type of origin of cfDNA mixtures in low coverage and noisy data. Simulations also demonstrate that we can effectively estimate cfDNA originating from rare cell types composing less than 0.01% of the total cfDNA. To validate CelFiE, we use a positive control: cfDNA extracted from pregnant and non-pregnant women. CelFiE estimates a large placenta component specifically in pregnant women (p = 9.1 x 10-5). Finally, we use CelFiE to decompose cfDNA from ALS patients and age matched controls. We find increased cfDNA concentrations in ALS patients (p = 3.0 x 10-3). Specifically, CelFiE estimates increased skeletal muscle component in the cfDNA of ALS patients (p = 2.6 x 10-3), which is consistent with muscle impairment characterizing ALS. Quantification of skeletal muscle death in ALS is novel, and overall suggests that CelFiE may be a useful tool for biomarker discovery and monitoring of disease progression.

genomics↗

bioRxiv2019

Efficient estimation and applications of cross-validated genetic predictions

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRS). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work we present a novel reference-free method to produce PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial over-fitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues, and can produce informative in-sample PRS over any existing cohort without over-fitting. We then demonstrate several novel applications of reference-free PRS including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

genetics↗

bioRxiv2018

GBAT: a gene-based association method for robust trans-gene regulation detection

Identification of trans-eQTLs has been limited by a heavy multiple testing burden, read-mapping biases, and hidden confounders. To address these issues, we developed GBAT, a powerful gene-based method that allows robust detection of trans gene regulation. Using simulated and real data, we show that GBAT drastically increases detection of trans-gene regulation over standard trans-eQTL analyses.

genetics↗