Biology subjects

Ruczinski, I.

Publications and source records attributed to Ruczinski, I..

13 recordsLinked to original sources

bioRxiv2026

The reliability and accuracy of recombination inferred by Shapeit2 duoHMM on whole genome sequence

Few studies assessed the performance of population-based phasing combined with parental genotypes to infer recombination on whole genome sequence (WGS) data. In this study, our objective was to evaluate whether Shapeit2 duoHMM, a Hidden Markov Model using parental genotypes, infers recombination events reliably on WGS and with narrower intervals than SNP arrays. We based our analysis on the overlap between recombination events inferred by Merlin on SNP genotypes and Shapeit2 on WGS and SNP genotypes. We used a sample of 61 extended families from the GeneSTAR study with TopMED freeze 8 WGS on 580 sequenced subjects (60% of sample). Shapeit2 was run with a window size of 500 kilobases and 200 states on WGS. To mimic a SNP array, we extracted genotypes of 355,112 autosomal markers on the Illumina OmniExpress array. The number of recombination events per meiosis inferred by Shapeit2 on the WGS data (36.8) was aligned with the expected numbers over autosomes (35.7), although Merlin overestimated this number (115.0). 73% of Shapeit2 recombination events on WGS were detected by Merlin, a proportion rising to 91% when restricting to events also inferred by Shapeit2 on OmniExpress genotypes. Furthermore, Shapeit2 recombination intervals were narrower on WGS than OmniExpress genotypes (median of 4,530 bp vs. 49,458 bp). This suggests that Shapeit2 on WGS is a reliable and accurate method for inferring recombination events.

genomics↗

bioRxiv2023

Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn

We investigated a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To enhance this approach, we developed Dolphyn, a novel method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn improves the fraction of gut phage library peptides bound by antibodies from 10% to 31% in healthy individuals, while also reducing the number of synthesized peptides by 78%. In our study on gut phages, we discovered that the immune system develops antibodies to bacteria-infecting viruses in the human gut, particularly E.coli-infecting Myoviridae. Cost-effective PhIP-Seq libraries designed with Dolphyn enable the assessment of a wider range of proteins in a single experiment, thus facilitating the study of the gut-immune axis.

bioengineering↗

bioRxiv2022

A Bayesian Hierarchical Model for Signal Extraction from Protein Microarrays

SO_SCPLOWUMMARYC_SCPLOWProtein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to the high technical variability of these assays and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Using within-array ranks of protein levels for analysis can mitigate the impact of between-sample variation on downstream analysis. Although ranks are sensitive to pre-processing steps, ranking methods that accommodate uncertainty provide robust and loss-function optimal ranks. Such ranking methods require Bayesian modeling that produces full posterior distributions for parameters of interest. Bayesian models that produce such outputs have been developed for other assays, for example DNA microarrays, but those modeling assumptions are not appropriate for protein microarrays. We develop and evaluate a Bayesian model to extract a full posterior distribution of normalized fluorescent signals and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microrarrays from different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

bioinformatics↗

bioRxiv2022

A Random Forest Classifier Uses Antibody Responses to Plasmodium Antigens to Reveal Candidate Biomarkers of the Intensity and Timing of Past Exposure to Plasmodium falciparum

1Important goals of malaria surveillance efforts include accurately quantifying the burden of malaria over time, which can be useful to target and evaluate interventions. The majority of malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden often demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling a large subset of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current difficulties associated with malaria surveillance. However, identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum is challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many P. falciparum antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to P. falciparum and P. vivax antigens as predictors in random forest models to classify adult samples into these three regions of differing historical malaria transmission with high accuracy. Many of the antigens that were most important for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could potentially lead to improved malaria serosurveillance that captures the timing and intensity of past exposure.

immunology↗

bioRxiv2022

Detecting Antibody Reactivities in Phage ImmunoPrecipitation Sequencing Data

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR1 for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments (Bayesian Enrichment Estimation in R, BEER).

bioinformatics↗

bioRxiv2021

A Pre-Processing Pipeline to Quantify, Visualize andReduce Technical Variation in Protein Microarray

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies, and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis. 1 Impact StatementProtein microarrays are in wide use in cancer research, infectious disease diagnostics and biomarker identification. To inform research and practice in these and other fields, technical variation must be corrected using normalization and pre-processing. Current protein microarray studies use a variety of normalization methods, many of which were developed for DNA microarrays, and therefore are based on assumptions and data that are not ideal for protein microarrays. To address this issue, we develop, evaluate, and implement a pre-processing pipeline that corrects for technical variation in protein microarrays. We show that pre-processing and normalization directly impact the validity of downstream analysis, and protein-specific approaches are essential.

bioinformatics↗

bioRxiv2020

Coding de novo mutations identified by WGS reveal novel orofacial cleft genes

While de novo mutations (DNMs) are known to increase risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 case-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

genetics↗

bioRxiv2020

Bayesian copy number detection and association in large-scale studies

Germline copy number variants (CNVs) increase risk for many diseases, yet detection of CNVs and quantifying their contribution to disease risk in large-scale studies is challenging. We developed an approach called CNPBayes to identify latent batch effects, to provide probabilistic estimates of integer copy number across the estimated batches, and to fully integrate the copy number uncertainty in the association model for disease. We demonstrate this approach in a Pancreatic Cancer Case Control study of 7,598 participants where the major sources of technical variation were not captured by study site and varied across the genome. Candidate associations aided by this approach include deletions of 8q24 near regulatory elements of the tumor oncogene MYC and of Tumor Supressor Candidate 3 (TUSC3). This study provides a robust Bayesian inferential framework for estimating copy number and evaluating the role of copy number in heritable diseases.

genetics↗

bioRxiv2019

The protective effect of club cell secretory protein (CC-16) on COPD risk and progression: a Mendelian randomisation study

BackgroundThere are currently no robust biomarkers of chronic obstructive pulmonary disease (COPD) risk or progression. Club cell secretory protein-16 (CC-16) is associated with the clinical expression of COPD. We aimed to determine if there is a causal effect of serum CC-16 level on COPD risk and/or progression using Mendelian randomisation (MR) analysis. MethodsWe performed a genome-wide association meta-analysis for serum CC-16 in two COPD cohorts (Lung Health Study [LHS], n=3,850 and ECLIPSE, n=1,702). We then used the CC-16-associated single-nucleotide polymorphisms (SNPs) in MR analysis to estimate the causal effect of serum CC-16 on COPD risk (International COPD Genetics Consortium/UK-Biobank dataset; n=35,735 cases, n=222,076 controls) and progression (change in forced expiratory volume in 1 s [FEV1] in LHS and ECLIPSE). We also determined the associations between SNPs associated with CC-16 and gene expression using n=1,111 lung tissue samples from the Lung eQTL Study. ResultsWe identified 7 SNPs independently associated (p<5x10-8) with serum CC-16 levels; 6 of these were novel. MR analysis suggested a protective causal effect of increased serum CC-16 on COPD risk (p=0.008) and progression (LHS only, p=0.02). Five of the SNPs were also associated with gene expression in lung tissue, including that of the CC-16-encoding gene SCGB1A1 (false discovery rate<0.1). ConclusionWe have identified several novel genetic variants associated with serum CC-16 level in COPD cohorts. These genetic associations suggest a potential causal effect of serum CC-16 on COPD risk and progression. Further investigation of CC-16 as a biomarker or therapeutic target in COPD is warranted. KEY MESSAGESWhat is the key question? Can genetics help uncover a causal effect of serum CC-16 level on COPD risk and/or progression? What is the bottom line? There is a protective effect of genetically-increased serum CC-16 on both COPD risk and progression (as measured by change in FEV1 over time), which may be due to increased expression of the CC-16-encoding gene SCGB1A1 in the lung. Why read on? This is the first study to demonstrate a possible causal effect of serum CC-16 in people with COPD, and highlights the potential for CC-16 as a biomarker or therapeutic target.

genomics↗

bioRxiv2018

Improved Analysis of Phage ImmunoPrecipitation Sequencing (PhIP-Seq) Data Using a Z-score Algorithm

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a massively multiplexed, phage-display based methodology for analyzing antibody binding specificities, with several advantages over existing techniques, including the uniformity and completeness of proteomic libraries, as well as high sample throughput and low cost. Data generated by the PhIP-Seq assay are unique in many ways. The only published analytical approach for these data suffers from important limitations. Here, we propose a new statistical framework with several improvements. Using a set of replicate mock immunoprecipitations (negative controls lacking antibody input) to generate background binding distributions, we establish a statistical model to quantify antibody-dependent changes in phage clone abundance. Our approach incorporates robust regression of experimental samples against the mock IPs as a means to calculate the expected phage clone abundance, and provides a generalized model for calculating each clones expected abundance-associated standard deviation. In terms of bias removal and detection sensitivity, we demonstrate that this z-score algorithm outperforms the previous approach. Further, in a large cohort of autoantibody-defined Sjogrens Syndrome (SS) patient sera, PhIP-Seq robustly identified Ro52, Ro60, and SSB/La as known autoantigens associated with SS. In an effort to identify novel SS-specific binding specificities, SS z-scores were compared with z-scores obtained by screening Ropositive sera from patients with systemic lupus erythematosus (SLE). This analysis did not yield any commonly targeted SS-specific autoantigens, suggesting that if they exist at all, their epitopes are likely to be discontinuous or post-translationally modified. In summary, we have developed an improved algorithm for PhIP-Seq data analysis, which was validated using a large set of sera with clinically characterized autoantibodies. This z-score approach will substantially improve the ability of PhIP-Seq to detect and interpret antibody binding specificities. The associated Python code is freely available for download here: https://github.com/LarmanLab/PhIP-Seq-Analyzer.

bioinformatics↗

bioRxiv2018

Inferring Disease Risk Genes from Sequencing Data in Multiplex Pedigrees Through Sharing of Rare Variants

We previously demonstrated how sharing of rare variants (RVs) in distant affected relatives can be used to identify variants causing a complex and heterogeneous disease. This approach tested whether single RVs were shared by all sequenced affected family members. However, as with other study designs, joint analysis of several RVs (e.g. within genes) is sometimes required to obtain sufficient statistical power. Further, phenocopies can lead to false negatives for some causal RVs if complete sharing among affecteds is required. Here we extend our methodology (Rare Variant Sharing, RVS) to address these issues. Specifically, we introduce gene-based analyses, refine RV definition based on haplotypes, and introduce a partial sharing test based on RV sharing probabilities for subsets of affected family members. RVS also has the desirable features of not requiring external estimates of variant frequency or control samples, provides functionality to assess and address violations of key assumptions, and is available as open source software for genome-wide analysis. Simulations including phenocopies, based on the families of an oral cleft study, revealed the partial and complete sharing versions of RVS achieved similar statistical power compared to alternative methods (RareIBD and the Gene-Based Segregation Test), and had superior power compared to the pedigree Variant Annotation, Analysis and Search Tool (pVAAST) linkage statistic. In studies of multiplex cleft families, analysis of rare single nucleotide variants in the exome of 151 affected relatives from 54 families revealed no significant excess sharing in any one gene, but highlighted different patterns of sharing revealed by the complete and partial sharing tests.

genetics↗

bioRxiv2018

Detection of de novo copy number deletions from targeted sequencing of trios

De novo copy number deletions have been implicated in many diseases, but there is no formal method to date however that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms. We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability, and is available as open source software at github.com/JMF47/MDTS.

bioinformatics↗

bioRxiv2017

Evolution of Hominin Polyunsaturated Fatty Acid Metabolism: From Africa to the New World

BackgroundThe metabolic conversion of dietary omega-3 and omega-6 18 carbon (18C) to long chain (> 20 carbon) polyunsaturated fatty acids (LC-PUFAs) is vital for human life. Fatty acid desaturase (FADS) 1 and 2 catalyze the rate-limiting steps in the biosynthesis of LC-PUFAs. The FADS region contains two haplotypes; ancestral and derived, where the derived haplotypes are associated with more efficient LC-PUFA biosynthesis and is nearly fixed in Africa. In addition, Native American populations appear to be nearly fixed for the lesser efficient ancestral haplotype, which could be a public health problem due to associated low LC-PUFA levels, while Eurasia is polymorphic. This haplotype frequency distribution is suggestive of archaic re-introduction of the ancestral haplotype to non-African populations or ancient polymorphism with differential selection patterns across the globe. Therefore, we tested the FADS region for archaic introgression or ancient polymorphism. We specifically addressed the genetic architecture of the FADS region in Native American populations to better understand this potential public health impact.\n\nResultsWe confirmed Native American ancestry is nearly fixed for the ancestral haplotype and is under positive selection. The ancestral haplotype frequency is also correlated to Siberian populations geographic location further suggesting the ancestral haplotype s role in cold weather adaptation and leading to the high haplotype frequency within Native American populations. We also find that the Neanderthal is more closely related to the derived haplotypes while the Denisovan clusters closer to the ancestral haplotypes. In addition, the derived haplotypes have a time to the most recent common ancestor of 688,474 years ago which is within the range of the modern-archaic hominin divergence.\n\nConclusionsThese results support an ancient polymorphism forming in the FADS gene region with differential selection pressures acting on the derived and ancestral haplotypes due to the old age of the derived haplotypes and the ancestral haplotype being under positive selection in Native American ancestry populations. Further, the near fixation of the less efficient ancestral haplotype in Native American ancestry suggests the need for future studies to explore the potential health risk of associated low LC-PUFA levels in Native American ancestry populations.

evolutionary biology↗