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      Technical standards for the interpretation and reporting of constitutional copy number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)

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          Abstract

          Copy number analysis to detect disease-causing losses and gains across the genome is recommended for the evaluation of individuals with neurodevelopmental disorders and/or multiple congenital anomalies, as well as for fetuses with ultrasound abnormalities. In the decade that this analysis has been in widespread clinical use, tremendous strides have been made in understanding the effects of copy number variants (CNVs) in both affected individuals and the general population. However, continued broad implementation of array- and next-generation sequencing-based technologies will expand the types of CNVs encountered in the clinical setting, as well as our understanding of their impact on human health. To assist clinical laboratories in the classification and reporting of CNVs, irrespective of the technology used to identify them, the American College of Medical Genetics and Genomics has developed the following professional standards in collaboration with the NIH-funded Clinical Genome Resource (ClinGen) project. This update introduces a quantitative, evidence-based scoring framework; encourages the implementation of the 5-tier classification system widely used in sequence variant classification; and recommends “uncoupling” the evidence-based classification of a variant from its potential implications for a particular individual. These professional standards will guide the evaluation of constitutional CNVs and encourage consistency and transparency across clinical laboratories.

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          Most cited references14

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          Diet and the evolution of human amylase gene copy number variation.

          Starch consumption is a prominent characteristic of agricultural societies and hunter-gatherers in arid environments. In contrast, rainforest and circum-arctic hunter-gatherers and some pastoralists consume much less starch. This behavioral variation raises the possibility that different selective pressures have acted on amylase, the enzyme responsible for starch hydrolysis. We found that copy number of the salivary amylase gene (AMY1) is correlated positively with salivary amylase protein level and that individuals from populations with high-starch diets have, on average, more AMY1 copies than those with traditionally low-starch diets. Comparisons with other loci in a subset of these populations suggest that the extent of AMY1 copy number differentiation is highly unusual. This example of positive selection on a copy number-variable gene is, to our knowledge, one of the first discovered in the human genome. Higher AMY1 copy numbers and protein levels probably improve the digestion of starchy foods and may buffer against the fitness-reducing effects of intestinal disease.
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            Modeling the ACMG/AMP Variant Classification Guidelines as a Bayesian Classification Framework

            Purpose We evaluated the ACMG/AMP variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning. Methods The ACMG/AMP criteria were translated into a naïve 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. Results 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 VUS. Conclusion By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only two 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.
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              Array-based technology and recommendations for utilization in medical genetics practice for detection of chromosomal abnormalities

              Laboratory evaluation of patients with developmental delay/intellectual disability, congenital anomalies, and dysmorphic features has changed significantly in the last several years with the introduction of microarray technologies. Using these techniques, a patient’s genome can be examined for gains or losses of genetic material too small to be detected by standard G-banded chromosome studies. This increased resolution of microarray technology over conventional cytogenetic analysis allows for identification of chromosomal imbalances with greater precision, accuracy, and technical sensitivity. A variety of array-based platforms are now available for use in clinical practice, and utilization strategies are evolving. Thus, a review of the utility and limitations of these techniques and recommendations regarding present and future application in the clinical setting are presented in this study.
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                Author and article information

                Journal
                9815831
                22061
                Genet Med
                Genet. Med.
                Genetics in medicine : official journal of the American College of Medical Genetics
                1098-3600
                1530-0366
                23 May 2020
                06 November 2019
                February 2020
                24 June 2020
                : 22
                : 2
                : 245-257
                Affiliations
                [1 ]Autism & Developmental Medicine Institute, Geisinger, Danville, PA, USA
                [2 ]ARUP Laboratories, Salt Lake City, UT, USA; University of Utah, Salt Lake City, UT, USA
                [3 ]Stanford University School of Medicine, Stanford, CA, USA
                [4 ]Quest Diagnostics Nichols Institute, San Juan Capistrano, CA, USA
                [5 ]Genomics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
                [6 ]Lineagen, Salt Lake City, UT, USA
                [7 ]Children’s Hospital Los Angeles, Los Angeles, CA, USA
                [8 ]Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA
                [9 ]AncestryDNA, Lehi, UT, USA
                [10 ]Invitae, San Francisco, CA, USA
                Author notes
                Corresponding author: Erin Rooney Riggs; 770-313-9087; eriggs@ 123456geisinger.edu
                Article
                NIHMS1596793
                10.1038/s41436-019-0686-8
                7313390
                31690835
                da84f045-8674-4315-8a74-f3d9fb635d98

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                Article

                Genetics
                copy number variant,interpretation,classification,cnv,scoring metric
                Genetics
                copy number variant, interpretation, classification, cnv, scoring metric

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