As our understanding of human health grows, we often see that similar biological dysfunction underlies the co-occurrence of various complex diseases. It remains difficult to determine if there are common genetic mechanisms contributing to clinically distinct conditions or if expression of both conditions relates to other shared risk factors. For example, in some situations, genetic variation may increase risk for one condition, and expression of this condition then increases risk for another disease. Identifying potentially pleiotropic genes is crucial for advancing the development of more effective treatment options, especially in instances where current therapies are insufficient. Genome-wide association studies (GWAS) provide cross-trait associations but do not provide the full functionality of how dysfunction in genes being tagged by GWAS hits are contributing to two or more distinct phenotypes. Fortunately, as other types of available data continue to grow exponentially (e.g., RNA-seq, mass spectrometry, mouse knock-out phenotype associations), these can be leveraged to help process GWAS results into meaningful information. The aim of this protocol is to provide clear instructions for using various databases and available software tools to identify key pleiotropic genes contributing to two distinct phenotypes of interest. The protocol uses information from various publicly available databases, including GWAS Catalog, Functional Mapping and Annotation (FUMA), Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT), International Mouse Phenotype Consortium (IMPC), STRINGdb, Pharos, and Cytoscape for network visualization. This pipeline, with code written in R and RStudio software, helps the user identify and generate hypotheses about shared genetic mechanisms contributing to their selected phenotypes of interest as well as prioritize genes of interest to functionally follow up in model systems that are more likely to be clinically relevant. © 2025 Wiley Periodicals LLC. Basic Protocol: Pleiotropic gene prioritization pipeline for studies in model systems.
Balanced translocation carriers experience elevated reproductive risks, including pregnancy loss and children with anomalies due to generating chromosomally unbalanced gametes. While understanding the likelihood of producing unbalanced conceptuses is critical for individuals to make reproductive decisions, risk estimates are difficult to obtain as most balanced translocations are unique. To improve reproductive risk estimates, Drs. Trunca and Mendell created models based on a logistic regression analysis of a dataset of over 6000 individuals from over 1000 translocation families. While risk assessments using these models have been offered as a free service for years, this protocol aims to create a sustainable model for genetics professionals to obtain risk estimates for their patients directly. This protocol guides the user through collecting clinical information, using a risk-generating calculator based on the models, and interpreting the calculator outputs. This version of the protocol has been updated from the initial publication to introduce an additional resource to the community that is intended to improve accessibility and reduce error in calculating risks. In addition to the previously offered custom Java program, a web-based calculator that calculates age-adjusted miscarriage risks is now available. A practice tutorial is provided for both versions of the calculator to ensure competency in interpretation prior to use. © 2025 Wiley Periodicals LLC. Basic Protocol 1 : Estimation of reproductive risks for balanced translocation carriers Basic Protocol 2 : Practical examples of typical patient encounters with instructive interpretations
Omics biomarkers play a pivotal role in personalized medicine by providing molecular-level insights into the etiology of diseases, guiding precise diagnostics, and facilitating targeted therapeutic interventions. Recent advancements in omics technologies have resulted in an increasing abundance of multimodal omics data, providing unprecedented opportunities for identifying novel omics biomarkers for human diseases. Mendelian randomization (MR) is a practically useful causal inference method that uses genetic variants as instrumental variables (IVs) to infer causal relationships between omics biomarkers and complex traits/diseases by removing hidden confounding bias. In this article, we first present current challenges in performing MR analysis with omics data, and then describe four MR methods for analyzing multi-omics data including epigenomics, transcriptomics, proteomics, and metabolomics data, all executable within the R software environment.
DNA copy number variants (CNVs) are routinely evaluated as part of clinical diagnosis in both the prenatal and postnatal genetic settings. Current guidelines for interpreting the potential clinical significance of these CNVs, typically identified by chromosomal microarray, focus entirely on genes localized within the CNV region. However, recent work has suggested that some CNVs can actually produce clinical impacts by influencing transcription of genes outside the CNV region. These alterations of transcription appear to occur by disrupting the composition of DNA topologically associated domains (TADs), which strongly influence contacts between gene promoters and their associated enhancers. Here we present a set of detailed protocols for the use of the free software tool ClinTAD ( https://www.clintad.com ). This decision-support software allows for prediction as to whether a given CNV may potentially disrupt a TAD boundary, and offers phenotype matching to genes near, but not within the CNV region, whose expression could be influenced by altered TAD architecture and that have phenotypic impacts related to that reported in a given patient. Our protocols here provide specific examples of how to implement these tools. In addition, the software has the capability to impact genomic research by evaluating multiple cases in parallel. We propose that this decision-support tool can benefit and improve genetic diagnosis. © 2020 Wiley Periodicals LLC. Basic Protocol 1 : Evaluating a single case using ClinTAD Basic Protocol 2 : Evaluating a single case with multiple variants using ClinTAD Basic Protocol 3 : Evaluating multiple cases using ClinTAD Basic Protocol 4 : Creating tracks with custom data
Genetic research often utilizes or generates information that is potentially sensitive to individuals, families, or communities. For these reasons, genetic research may warrant additional scrutiny from investigators and governmental regulators, compared to other types of biomedical research. The informed consent process should address the range of social and psychological issues that may arise in genetic research. This article addresses a number of these issues, including recruitment of participants, disclosure of results, psychological impact of results, insurance and employment discrimination, community engagement, consent for tissue banking, and intellectual property issues. Points of consideration are offered to assist in the development of protocols and consent processes in light of contemporary debates on a number of these issues. © 2020 Wiley Periodicals LLC.
