Biology subjects

Herrick, N.

Publications and source records attributed to Herrick, N..

4 recordsLinked to original sources

bioRxiv2024

Automated 3D Landmarking of the Skull: A Novel Approach for Craniofacial Analysis

Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising 9,999 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (>0.988), and automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.

bioinformatics↗

bioRxiv2023

ILIAD: A suite of automated Snakemake workflows for processing genomic data for downstream applications

BackgroundProcessing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability of biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data. ResultsThe Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster. ConclusionIliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists who are less familiar with programming can manage their own big data with this open-source suite of workflows.

bioinformatics↗

bioRxiv2022

Joint Multi-Ancestry and Admixed GWAS Reveals the Complex Genetics behind Human Cranial Vault Shape

The cranial vault - the portion of the skull surrounding the brain and cerebellum - is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conducted a joint multi-ancestry and admixed multivariate GWAS on 3D cranial vault shape extracted from magnetic resonance images of 6,772 children from the ABCD study cohort, identifying 30 genome-wide significant genetic loci and replicating 20 of these signals in 16,947 additional individuals of the UK Biobank. This joint multi-ancestry GWAS was enriched for genetic components of cranial vault shape shared across ancestral groups and yielded a greater discovery than a European-only GWAS. We present supporting evidence for parietal versus frontal bone localization for several of the identified genes based on expression patterns in E15.5 mice. Collectively, our GWAS loci were enriched for processes related to skeletal development and showed elevated activity in cranial neural crest cells, suggesting a role during early craniofacial development. Among the identified genes, were RUNX2 and several of its upstream and downstream actors, highlighting the prominent role of intramembranous ossification - which takes place at the cranial sutures - in influencing cranial vault shape. We found that mutations in many genes associated with craniosynostosis exert their pathogenicity by modulating the same pathways involved in normal cranial vault development. This was further demonstrated in a non-syndromic sagittal craniosynostosis case-parent trio dataset of 63 probands (n = 189), where our GWAS signals near BMP2, BBS9, and ZIC2 contributed significantly to disease risk. Moreover, we found strong evidence of overlap with genes influencing the morphology of the face and the brain, suggesting a common genetic architecture connecting these developmentally adjacent structures. Overall, our study provides a comprehensive overview of the genetics underlying normal cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.

genetics↗

bioRxiv2022

Hybrid Autoencoder with Orthogonal Latent Space for Robust Population Structure Inference

BackgroundAnalysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. ResultsIn this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. I.e., it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. ConclusionWe introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

bioinformatics↗