Interested in learning more about all of the new functionalities in Face2Gene? Visit us at booth #509 or contact us to schedule a meeting. While at the conference, make sure to also check out our platform presentations.

We look forward to seeing you soon in San Antonio! If you have any questions leading up to the conference or would like more details on any of our offerings, please contact us at marketing@fdna.com.

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Next-generation phenotyping (NGP) is characterized by the use of computer learning tools to assess a patient’s presentation (phenotype). For precision medicine to reach its full potential, it requires the combination of NGP with the large data sets derived from next-generation sequencing (NGS).

Before new technologies can be incorporated into the workflow of clinical diagnostic laboratories, validation of results in that setting is necessary. In our presentation at the upcoming ACMG meeting (March 20^th, 5:15 pm CT), we will report on the results of such validation studies in different laboratories.

Improved analysis of NGS data through the application of NGP tools was demonstrated in a research context in the “Prioritization of Exome Data by Image Analysis” (PEDIA)  study as published by Hsieh et al., Genetics in Medicine, 2019. In this study of cases previously solved through exome analysis, the incorporation of the facial features through Face2Gene, FDNA’s flagship NGP tool, allowed improved ranking of the disease-causing gene. These results suggest that incorporation of NGP tools in the workflow of clinical diagnostic laboratories will increase diagnostic efficiency through automated inclusion of relevant phenotypic data.

Face2Gene LABS was used in the PEDIA approach at the Greenwood Genetic Center on a series of cases submitted for exome analysis. A retrospective data analysis on solved cases showed that gene ranking through Face2Gene LABS improves analytic efficiency through prioritization of the disease-causing variant. Most notably, the added benefit from objective facial image analysis was obvious.

Integration in the clinical diagnostic workflow will vary in different laboratories based on the local preferences and regulation. Thus, validation studies have to reflect the lab’s respective structure and workflow. In contrast to Greenwood Genetics Center, which uses automated facial analysis through Face2Gene routinely, the National University of Singapore does not analyze facial features through Face2Gene. Even without the use of the facial analysis, the AI-driven Face2Gene LABS tool efficiently ranked the disease-causing gene for pathogenic or likely pathogenic variants. This validation study was meaningful to the laboratory because it confirms that Face2Gene LABS performs well, even in the absence of facial feature analysis.

We invite you to attend our presentation at ACMG to learn more about this research and to envision how Face2Gene LABS can improve your clinical diagnostic analysis pipeline!

To learn more about our offerings and opportunities to collaborate, contact us at info@fdna.com

The post Precision Medicine and the Integration of Next-Generation Phenotyping appeared first on FDNA™.

Dr. Christine Stanley, Head of Clinical Laboratory for WuXi NextCODE, US and CLIA medical director for Q&A Diagnostics, discusses next-generation phenotyping for improved variant interpretation through integration of Face2Gene LABS with WuXi NextCODE’s variant interpretation system.

According to Dr. Stanley, the ACMG has set standards on the classification and interpretation of genetic variants, including various levels of classifications as benign, unknown or pathogenic.

There are 28 lines of evidence used for classifying these variants.

“When you look at the phenotypic line of evidence, it wasn’t one that was utilized often […] but it really could be if we make it better and more complete,” she said. “In the ACMG publication, to use the phenotype as a line of evidence in variant scoring, you really need to have high clinical sensitivity, the patient must have a well-defined clinical presentation, the gene is not subject to a lot of variation, and the family history is consistent with the mode of inheritance of the disorder.”

“The phenotype information is really critical to performing whole exome and genome interpretation,” she continued.

Dr. Stanley goes on to explain that the phenotypic information allows Wuxi to utilize this phenotypic line of evidence in their variant classification and to help reclassify variants of unknown significance into the pathogenic category.

However, according to Dr. Stanley, phenotypic information is often lacking or not provided to support NGS interpretation.

She goes on to describe how WGS or WES produces a filtered list of variants that have clinical relevance, and that the report is static from what was known at the time. According to Dr. Stanley, “what we need are tools that allow for revisable reporting” because new information—such as new understanding of gene variations, or new patient symptoms—can become known down the road. This new information, ideally, can be used to reanalyze past genomics test results, impacting medical management, clinical research or patient support.

“We really need digital tools to get there,” she said. “We have taken the FDNA tool [Face2Gene] that captures the detailed clinical phenotype, and we’ve incorporated it into WuXi’s clinical sequence analyzer in order to do that real-time variant review.”

Dr. Stanley goes on to describe how the phenotype can be broadened to include other data, such as medical imaging, biometrics, clinical notes, and more, all pulled into Face2Gene and integrated with the variant interpretation.

She demonstrates how clinical users capture photos, anthropometric measurements, phenotypic features, and more within Face2Gene, and the application utilizes artificial intelligence to assist the user in further annotating the phenotype, resulting in a comprehensive phenotype for the patient. From there, the clinician can pass the phenotypic data securely to WuXi by selecting WuXi from the list of labs, resulting in the data being transferred into Wuxi’s clinical sequence analyzer.

Wuxi then reviews the gene list associated with the patient’s genetic sequence, scores the variants based on the ACMG classification criteria, and uses the clinical phenotype to match up to and highlight the variant results, or to highlight variants of unknown significance that relate to the phenotype to help determine if reclassification makes sense.

Watch the recording of the talk On Our Channel.

The post Dr. Christine Stanley: Face2Gene LABS at WuXi NextCODE: Phenotyping for Improved Variant Prioritization appeared first on FDNA™.

HISTORY

Since the 1960s, the field of medical genetics has been evolving rapidly with regard to phenotype delineation and analysis.

A disease phenotype requires a multi-faceted analysis. Besides defining the phenotype with diagnostic criteria, medical geneticists are also faced with the challenge of drawing lines along spectra of manifestations and within disease definitions, allowing for how symptoms may change over time.

To measure these variables, biochemical analysis, detailed histories, anthropometrics, standardized measures, and medical imaging can be applied in concert. As next-generation sequencing evolves the way the medical field examines patients’ genomes, next-generation phenotyping integrates these changes into the analysis of human health, maximizing the impact of new sequencing technologies.

“We’ve actually entered a new golden era of phenotyping,” Dr. Carey said.

PHENOTYPIC DOMAINS

Dr. Carey noted there are three domains to measure disease: diagnostic criteria (a.k.a. definition of the phenotype), the spectrum of manifestations and complications, and the natural history of how the phenotype changes over time.

SYNDROME DELINEATION

Syndrome delineation has three distinct parts, as Dr. Carey reviewed in Charlotte, N.C. The first, the physical examination, involves observations and descriptions of the patients. Then there are two levels of syndrome genesis to consider: formal (e.g., a shortage or malformation of a critical enzyme) and causal (e.g., the genetic source of the formal genesis, such as a deletion or de novo mutation.)

SYNDROME GROUPS & HETEROGENEITY

The broad definition of syndrome classification is misleadingly simple: simply group patterns of anomalies, with at least one being morphologic, thought to be etiologically related. Of course, in practice, this is very different than in theory; groups of syndromes, variant forms, subtypes, or related disorders can be hard to separate into discrete syndromes. Similar phenotypes can be caused by errors in different genes, or by multiple genes, and identical mutations can cause different phenotypes across patients.

“There are syndromes we know quite well that have 17 genes. There’s one gene that has 17 syndromes. Do we go with the gene or do we go with the phenotype?” Dr. Carey asked the audience.

THE AXIS MODEL

To solve these discrepancies, Dr. Carey proposed the use of the “axis model.” Axis I describes the clinical phenotype, Axis II describes the underlying molecular genetics, and Axis III describes non-genetic factors, like the environment.

“It’s never been adapted but I actually like it,” he said.

I would propose that even though you can’t [describe a syndrome in] 3 or 4 words, or less, Dr. Carey said of the sometimes wordy model. Despite its potential complication, its focus on phenotype keeps the naming mechanism patient-centered.

Watch the recording of the talk On our Channel.

The post Dr. John Carey: Delineating Genetic Syndromes and Next-Generation Phenotyping appeared first on FDNA™.

Starting in 1996, a select few clinicians began identifying a small group of patients with similar phenotypic features: Down-syndrome-like facial features, short stature, intellectual disability, cataracts and sensorineural hearing loss. Among these clinicians was Dr. Karen Gripp, who over the next two decades would become one of the namesakes for this MAF transcription factor related rare disease: Aymé-Gripp syndrome (AGS).

In her research, Dr. Gripp (the new Chief Medical Officer for FDNA) asked, “Does Aymé-Gripp syndrome have a facial phenotype that is recognizable using automated facial analysis?” Last week, she shared her investigation at the ACMG annual meeting.

Dr. Gripp used the Face2Gene RESEARCH application to create composite facial images for AGS and for Down syndrome, as well as for individuals with no suspected syndrome.

She ran the analysis twice, using new sets of cases for the Down syndrome and control cohorts for the second analysis to account for the “possibly subtle difference” in averaged facial images.

“There are twenty different images in those cohorts,” Dr. Gripp explained.

Dr. Gripp described the results provided by Face2Gene, including a multiclass comparison that reveals how well the system can successfully classify patients into the correct diagnosis based on facial analysis.

For each analysis, Face2Gene RESEARCH also provides statistics on the performance of the system.

“What you look at here is the mean accuracy for the analysis, and you have to always put that in perspective to the random chance for that particular analysis,” said Dr. Gripp.

Dr. Gripp’s use of the RESEARCH application is easily repeatable by any scientist on their “favorite syndrome.”

First, researchers should upload, to Face2Gene CLINIC, any patients (with at least one frontal facial photo) that they plan to use for research cohorts, being careful to clearly label [in the “case name” field] the study cohort that the patient should be added to, for example: “John Doe, Down Syndrome Cohort.” This makes it easier to find the relevant cases later when creating your study cohorts. If you are interested in analyzing the phenotype distribution for the disease, it is also important to thoroughly annotate a defined set of those phenotypes (present or absent) for every case. Otherwise, just a frontal facial photo for each case is sufficient for the analysis.

Once all the cases are added to Face2Gene CLINIC, you can move to the Face2Gene RESEARCH application by clicking “RESEARCH” in the top right corner of the app. There, you can create a new project, adding the cohorts you want to compare. To do this, select “add cohort,” and select the relevant cases from the list. Repeat for each cohort you want to create, making sure each cohort is roughly the same size.

“If you have a huge imbalance in the cohort size, that by itself introduces a bias,” Dr. Gripp mentioned.

Once cases are uploaded and cohorts are created, users can run their experiment with a click.

“You push the button, you get yourself a cup of coffee; it takes a little bit, literally a few minutes, for the system to run the analysis.”

When the analysis is complete, the user receives an email with summary results and can look in more detail at each item. Results include the sensitivity and specificity reported as the area under the curve of the Receiver Operating Characteristic (ROC) curve, and the p-value to measure significance.

All of the charts and visualizations are easily copied for use in papers and publications by the researcher.

For Dr. Gripp, using the Face2Gene application resulted in the addition of a new syndrome within Face2Gene, paving the way for clinicians globally to better recognize the AGS phenotype in patients through use of  Face2Gene–and any researcher with an internet connection can now do the same for other diseases.

Watch the recording of the talk On our Youtube Channel.

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