OBJECTIVE: The field of genetics and genomics continues to expand at an unprecedented pace. As scientific knowledge is translated to clinical practice, genomic information is routinely being used in preventive, diagnostic, and therapeutic decision-making across a variety of clinical practice areas. As adoption of genomic medicine further evolves, health professionals will be required to stay abreast of new genetic discoveries and technologies and implementation of these advances within their scope of practice will be indicated.
METHODS: The Association of Professors of Human and Medical Genetics previously developed medical school genetics core competencies, last updated in 2013. The competencies were reviewed and updated through a structured approach incorporating a modified Delphi method.
RESULTS: The updated Association of Professors of Human and Medical Genetics core competencies are presented. Current revisions include competencies that are concise, specific, and assessable. In addition, they incorporate recent advances in clinical practice and promote equity and inclusion in clinical care.
CONCLUSIONS: The 2022 competencies will serve as a guide for medical school leadership and educators involved in curriculum development, implementation, and assessment. Use of these competencies across the undergraduate medical curricula will foster knowledge, skills, and behaviors required in medical practice across a wide range of specialties.

Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP \"strength of evidence categories\" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has the practical attribute of user-friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.

We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning.
The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.
We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).
By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

This is a guide for fieldwork in Population Medical Genetics research projects. Data collection, handling, and analysis from large pedigrees require the use of specific tools and methods not widely familiar to human geneticists, unfortunately leading to ineffective graphic pedigrees. Initially, the objective of the pedigree must be decided, and the available information sources need to be identified and validated. Data collection and recording by the tabulated method is advocated, and the involved techniques are presented. Genealogical and personal information are the two main components of pedigree data. While the latter is unique to each investigation project, the former is solely represented by gametic links between persons. The triad of a given pedigree member and its two parents constitutes the building unit of a genealogy. Likewise, three ID numbers representing those three elements of the triad is the record field required for any pedigree analysis. Pedigree construction, as well as pedigree and population data analysis, varies according to the pre-established objectives, the existing information, and the available resources.