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Trio-based GWAS reveals loci associated with different forms of isolated cleft lip.

Orofacial clefts (OFCs) are the most common craniofacial birth defect and comprise a diverse group of traits with complex and heterogeneous etiologies. Genetic studies of OFCs typically approach this diversity by stratifying cases into broad diagnostic classes, including cleft lip (CL), cleft palate (CP), and cleft lip with palate (CLP). Although this strategy has yielded important insights into OFC risk, it ignores the phenotypic heterogeneity within each subtype. CL exhibits marked phenotypic variability, involving differences in alveolar involvement, laterality, and sidedness that may reflect distinct etiologies. Given this phenotypic diversity within CL, we assembled a multi-ancestry cohort of 837 nonsyndromic CL case-parent trios with whole-genome sequencing and detailed phenotyping. We performed genome-wide association scans (GWAS) via transmission disequilibrium tests for CL overall and for 14 CL subtypes defined by involvement of the alveolus (with and without), laterality (uni- and bilateral), and sidedness (left and right). We identified four genome-wide significant loci. Two loci, IRF6 and 8q24.21, were both detected in the overall CL GWAS. PLCB1/PLCB4 and MAFB were detected in GWASs of alveolar cleft involvement and CL left sidedness, respectively. These subtype-specific associations were followed by case-only comparisons that reflect the presence or absence of alveolus cleft or left-sided bias of CL to confirm the specificity of the association signal to the particular subtype. Our results provide evidence of within-class CL subtype-specific genetic links for loci previously discussed in the context of primary OFC classes and demonstrate the value of granular OFC subtype characterization to capture trait-specific associations.

Alveolus Cleft

Fundamentals of FAIR biomedical data analyses in the cloud using custom pipelines.

As the biomedical data ecosystem increasingly embraces the findable, accessible, interoperable, and reusable (FAIR) data principles to publish multimodal datasets to the cloud, opportunities for cloud-based research continue to expand. Besides the potential for accelerated and diverse biomedical discovery that comes from a harmonized data ecosystem, the cloud also presents a shift away from the standard practice of duplicating data to computational clusters or local computers for analysis. However, despite these benefits, researcher migration to the cloud has lagged, in part due to insufficient educational resources to train biomedical scientists on cloud infrastructure. There exists a conceptual lack especially around the crafting of custom analytic pipelines that require software not pre-installed by cloud analysis platforms. We here present three fundamental concepts necessary for custom pipeline creation in the cloud. These overarching concepts are workflow and cloud provider agnostic, extending the utility of this education to serve as a foundation for any computational analysis running any dataset in any biomedical cloud platform. We illustrate these concepts using one of our own custom analyses, a study using the case-parent trio design to detect sex-specific genetic effects on orofacial cleft (OFC) risk, which we crafted in the biomedical cloud analysis platform CAVATICA.

Cloud Computing

Methods for modeling gene-environment interplay using polygenic risk scores.

Polygenic risk scores (PRS) are increasingly recognized as pivotal tools for quantifying disease risk through the aggregation of multiple genetic variants. As sample sizes in genome-wide association studies (GWAS) continue to expand and PRS become more powerful, they are set to play a key role in translational research and personalized medicine. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in diverse contexts. This interplay manifests in two forms: PRS-by-environment interaction (PRS × E) and gene-environment correlation (rGE). However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS × E interaction analyses while controlling for rGE, which can lead to inconsistencies across studies and misinterpretation of results. Here we provide a review of different methods for performing PRSxE interaction in various epidemiological study designs, propose recommendations for best-practice, and discuss future challenges.

Gene-Environment Interaction