Biology subjects

Worrall, B. B.

Publications and source records attributed to Worrall, B. B..

3 recordsLinked to original sources

bioRxiv2025

Prediction of Polygenic Risks by Screening Thousands of Polygenic Scores

RationaleA polygenic score (PGS) summarizes a persons genetic information in a single number for a trait, and its utility in genomic medicine is well-recognized. Although PGS models have been generated for many traits, they are not broadly available for certain traits due to limited sample sizes in studies of infrequent outcomes. Often, prediction of these not well-studied traits (e.g., treatment/drug responses) would be of great clinical utility and have been underutilized due to statistical power limitations. To support more versatile trait prediction, we present a method of developing PGS models that can be used in studies of any size with genome-wide SNP data. MethodWe first generate thousands of PGSs for each study participant in a given data set using their genome-wide SNP data and public resources. A PGS-wide scan involves evaluating the Area Under the Curve (AUC) of prediction for a binary trait (or the R-squared of association for a quantitative trait) at each PGS. We present two methods for the PGS model development, SECRET-Best, which selects the most predictive PGS from the PGS scan for prediction, and SECRET-WTSUM, which considers a combined score from multiple correlation-pruned PGSs. This algorithm is scalable and implemented in a user-friendly software tool, SECRET (Screen and Evaluate Catalogued Risk scores to Enhance Trait predictions). ResultsWe applied SECRET to a binary outcome (type 1 diabetes [T1D]) and a dataset of 2,100 samples, each with 12 laboratory test-related continuous traits. For nine traits with existing PGSs available in the PGS catalog, eight of the traits were predicted correctly, with the same trait-related PGS identified as the top predictor. We showed that the existing PGS methods had rather limited power to predict trait values in a validation set when only 1,500 samples were used to develop a PGS model, while the SECRET methods were able to maintain the prediction power for under-powered GWAS studies even when the sample size of the study was in hundreds. ConclusionThe SECRET methods and tool provide a valuable resource for studies with genomic data but of limited sample sizes. This approach enables systematic development and evaluation of PGS models.

bioinformatics↗

bioRxiv2019

Alternate approach to stroke phenotyping identifies a genetic risk locus for small vessel stroke

Stroke causes approximately 1 in every 20 deaths in the United States. Most strokes are ischemic, caused by a blockage of blood flow to the brain. While neurologists agree on the delineation of ischemic stroke (IS) into the three most common subtypes (cardioembolic stroke (CES), large artery stroke (LAS), and small vessel stroke (SVS)), several different subtyping systems exist. The two most commonly-used clinical subtyping systems are TOAST (Trial of Org 10172 in Acute Stroke Treatment) and CCS (Causative Classification System for Stroke), but agreement between these two systems is only moderate. Here, we have compared two approaches to combining the existing subtyping systems for a phenotype suited for a genome-wide association study (GWAS).\n\nWe used the NINDS Stroke Genetics Network dataset (SiGN, 13,390 cases and 28,026 controls), which includes cases with both CCS and TOAST subtypes. We defined two new phenotypes: 1) the intersect, for which an individual must be assigned the same subtype by CCS and TOAST; and 2) the union, for which an individual must be assigned a subtype by either CCS or TOAST. The union yields the largest sample size while the intersect may yield a phenotype with less potential misclassification.\n\nWe performed GWAS for all subtypes, using the original subtyping systems, the intersect, and the union as phenotypes. In each subtype, heritability was higher for the intersect phenotype compared to the union, CCS (alone), and TOAST (alone) phenotypes. We observed stronger effects at known IS variants with the intersect compared to the other phenotype definitions. In GWAS of the intersect, we identify rs10029218 as an associated variant with small vessel stroke. We conclude that in the absence of a golden standard for phenotyping, taking this alternate approach yields more power to detect genetic associations in ischemic stroke.\n\nAuthor summaryAround one in five people will have a stroke at some point in their life. Most strokes (~80%) are ischemic, caused by a blockage of blood supply to the brain. Ischemic stroke risk is partly influenced by lifestyle, and partly by genetics. There are different ischemic stroke subtypes, and genome-wide association studies (GWAS) indicate that the genetic risk for these subtypes is influenced by different genetic factors. Genetic studies of ischemic stroke are therefore typically performed by analyzing each subtype separately. There are several methods to determine someones subtype based on clinical features. To find more genetic factors that influence ischemic stroke risk, we aimed to find a group of patients that are phenotypically similar by using information from all subtyping methods. We compared a group of patients assigned the same subtype by all subtyping methods (the intersect) to a group of patients assigned that subtype by at least one subtyping method (the union). Even though the intersect sample size is smaller, we find genetic factors in the intersect GWAS have stronger genetic effects, likely explained by the fact that we are more certain of the subtype in the intersect. Using the intersect, we find new risk-associated genetic factors.

genetics↗

bioRxiv2019

White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95) and Pearson correlation of total brain volume (r=0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N=2783) and identify a decrease in total brain volume of -2.4cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2,533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

neuroscience↗