Biology subjects

Lamkin, M.

Publications and source records attributed to Lamkin, M..

5 recordsLinked to original sources

bioRxiv2026

Haplotype-based fine-mapping of variant associations for complex traits

Genome-wide association studies (GWAS) have identified tens of thousands of associations between genomic regions and complex traits in humans, but identifying the causal variants underlying these associations is challenging. Statistical fine-mapping techniques attempt to quantify the probability of causality of individual variants while accounting for the correlation induced by linkage disequilibrium (LD) between nearby variants. Existing fine-mapping solutions are limited to considering additive and independent contributions of individual variants, and also assume the causal variant is included in the analysis. Here, we introduce a novel fine-mapping method, happler, which models the effects of variant haplotypes (sets of alleles of two or more variants in cis) in addition to individual variants. By identifying haplotype proxies for causal variants, happler can infer additional effects including those driven by combinations of variants in cis and ungenotyped causal variants, such as complex structural variants, which are in some cases better tagged by one or more haplotypes than by individual SNPs. Simulation experiments show that whereas happler can correctly identify causal haplotype effects, traditional methods based on individual variants can produce inconclusive results in such cases. We applied happler to identify 32 haplotypes associated with gene expression using 359 individuals from the Geuvadis dataset. Of these, five are confidently fine-mapped, with stronger posterior inclusion probabilities than any individual variant, in an external cohort from the Genotype Tissue Expression (GTEx) project. Finally, we used the AoU dataset to demonstrate the utility of haplotypes for tagging rare causal variants that are absent from the input dataset.

bioinformatics↗

bioRxiv2025

Systematic evaluation of the impact of promoter proximal short tandem repeats on expression

Genetic variation at thousands of short tandem repeats (STRs), which consist of consecutive repeated sequences of 1-6bp, has been statistically associated with gene expression and other molecular phenotypes in humans. However, the causality and regulatory mechanisms for most of these STRs remains unknown. Massively parallel reporter assays (MPRA) enable testing the regulatory activity of a large number of synthesized variants, but have not been applied to STRs due to experimental and computational challenges. Here, we optimized an MPRA framework based on random barcoding to study the impact of variation in repeat copy number on expression. We first performed an MPRA on sequences derived from 30,516 promoter-proximal STR loci along with up to 152bp of genomic context, testing 3-4 variants with differing repeat copy numbers for each locus in HEK293T cells. We identified 1,366 loci with significant associations between repeat copy number and expression, which were enriched for positive effect sizes (P=2.08e-110). We then designed a second MPRA in which we performed deeper perturbations, including systematic manipulation of the repeat unit sequence, orientation, and copy number, with 200-300 perturbations for each of the 300 loci with the strongest signals. Our results revealed that the repeat unit sequence is the primary driver of differences in the relationship between copy number and expression across loci, whereas orientation and flanking sequence have weaker effects, primarily for AT-rich repeat units. The high resolution of these perturbations enabled us to detect non-linear effects, most notably for AAAC/GTTT repeats, which emerge only beyond a certain copy number threshold. Finally, we observed that a subset of STRs in our library show expression levels that are tightly linked with predicted DNA secondary structure formation. We repeated our perturbation MPRA in HeLa S3 cells under wildtype and RNase H1 knockdown conditions, which, via reduction in RNase H1 activity, are expected to hinder resolution of R-loops. This demonstrated that associations between copy number and expression at G-quadruplex-forming CCCCG/CGGGG repeats are particularly sensitive to loss of RNase H1, providing support for an R-loop mediated mechanism for these repeats. Altogether, we establish STRs as a critical component of the non-coding regulatory grammar and provide a framework for understanding how this dynamic form of genetic variation shapes gene expression.

genomics↗

bioRxiv2020

The contribution of de novo tandem repeat mutations to autism spectrum disorders

Autism Spectrum Disorder (ASD) is an early onset developmental disorder characterized by deficits in communication and social interaction and restrictive or repetitive behaviors1,2. Family studies demonstrate that ASD has a significant genetic basis3 with contributions both from inherited and de novo variants. While the majority of variance in liability to ASD is estimated to arise from common genetic variation4, it has been estimated that de novo mutations may contribute to 30% of all simplex cases, in which only a single child is affected per family5. Tandem repeats (TRs), consisting of approximately 1-20bp motifs repeated in tandem, comprise one of the largest sources of de novo mutations in humans6. Yet, largely due to technical challenges they present, de novo TR mutations have not yet been characterized on a genome-wide scale, and their contribution to ASD remains unexplored. Here, we develop novel bioinformatics tools for identifying and prioritizing de novo TR mutations from whole genome sequencing (WGS) data and use these to perform a genome-wide characterization of de novo TR mutations in ASD-affected probands and unaffected siblings. Compared to recent work on TRs in ASD7, we explicitly infer mutation events and their precise changes in repeat copy number, and primarily focus on more prevalent stepwise copy number changes rather than large or complex expansions. Our results demonstrate a significant genome-wide excess of TR mutations in ASD probands. TR mutations in probands tend to be larger, enriched in fetal brain regulatory regions, and predicted to be more evolutionarily deleterious compared to mutations observed in unaffected siblings. Overall, our results highlight the importance of considering repeat variants in future studies of de novo mutations.

genomics↗

bioRxiv2020

Deep neural networks identify context-specific determinants of transcription factor binding affinity

Transcription factors (TFs) bind DNA by recognizing highly specific DNA sequence motifs, typically of length 6-12bp. A TF motif can occur tens of thousands of times in the human genome, but only a small fraction of those sites are actually bound. Despite the availability of genome-wide TF binding maps for hundreds of TFs, predicting whether a given motif occurrence is bound and identifying the influential context features remain challenging. Here we present a machine learning framework leveraging existing convolutional neural network architectures and state of the art model interpretation techniques to identify, visualize, and interpret context features most important for determining binding activity for a particular TF. We apply our framework to predict binding at motifs for 38 TFs in a lymphoblastoid cell line and achieve superior classification performance compared to existing frameworks. We compute importance scores for context regions at single base pair resolution and uncover known and novel determinants of TF binding. Finally, we demonstrate that important context bases are under increased purifying selection compared to nearby bases and are enriched in disease-associated variants identified by genome-wide association studies.

genomics↗

bioRxiv2019

A flexible simulation toolkit for designing and evaluating ChIP-sequencing experiments

A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. We present Tulip, a toolkit for rapidly simulating ChIP-seq data using statistical models of the experimental steps. Tulip may be used for a range of applications, including power analysis for experimental design, benchmarking of analysis tools, and modeling effects of processes such as replication on ChIP-seq signals.

genomics↗