Check out all the tutorials available to help you master Face2Gene:

How to Change the View in Face2Gene

Learn how to choose between the Geneticist and Pediatrician views in Face2Gene.

Face2Gene Mobile Demo

The Pediatrician View

The Pediatrician View is a technology recently developed by FDNA and available on Face2Gene that allows physicians to receive guidance on the level of facial dysmorphology a patient may have. This may support the decision to refer the patient for a genetic diagnostic study.

How to use Face2Gene as a Team

Face2Gene is used by both individuals and by teams who manage and share cases as a group. The Face2Gene Teams feature is designed for teams and genetics departments to streamline case and workflow management. If your team would like to use the Teams feature, create your free Face2Gene accounts and contact us at success@fdna.com to set up your team.

How to use the Ultra-Rare and Undiagnosed tab?

Face2Gene now has the GestaltMatcher algorithm for you to match Ultra-rare and Undiagnosed Cases.

Under the Ultra-rare tab, with the new algorithm GestalMatcher, the patient’s photos are matched to a molecular diagnosis even when the disorder was not part of the DeepGestalt (Rare Tab) training set.

Similarities among patients with previously unknown disease genes can also be detected, and these are displayed in the Undiagnosed tab. If a patient can be a possible match, you can connect to the clinician.

Assigning a diagnosis in Face2Gene

Assigning a diagnosis to your cases, helps the patient community worldwide to benefit from your experience today in the clinic.

Sequencing Upfront

In just one click, find the geno/ phenotype correlation for your patient. Search for a specific gene:

Or look up the top genes within Face2Gene results.

Instantly extract features from a dictated clinical note

No time to write the clinical note? Simply say it, and it will be written down. By clicking “Extract Features”, the relevant traits/features (HPO terms) will be suggested for you to add to your patient’s analysis. Coming soon to your mobile app.

View a Graphical display of your patient’s top syndrome-matches  

What syndromes clusters are close to my patient? This graphical view provides a t-SNE visualization: a 2D projection of your case in the Clinical Face Phenotype Space as compared to the top-10 syndromes analyzed in Rare and Ultra-Rare. Check this Nature publication for more information

Sort syndrome-matches by facial analysis only

Sometimes we need to analyze results based on their source:  the facial photo or the patient’s clinical features? This new sorting capability allows you to do just that,  By unclicking, the combined results will return.

Add facial analysis to your London Medical Database (LMD) results

Conducted an extensive feature search in LMD and now wonder what the facial analysis would add to the results? Now it is possible. After using the advanced search- capabilities of the LMD Library, you can now simply add a photo by exporting the search criteria used. More info on LMD.

Face2Gene Clinic Overview

New to Face2Gene on the web?

Signing In | Case List | New Case | Add Features | Extract Features | Feature Meter | Share Phenotype

The post All the tutorials available to help you master Face2Gene appeared first on FDNA™.

Cara O’Neill, MD, FAAP

VP, Scientific Director, Cure Sanfilippo Foundation

Cara O’Neill’s daughter, Eliza, was diagnosed with Sanfilippo about five years ago, which thrust her abruptly into the advocacy sphere.

Sanfilippo, also known as mucopolysaccharidosis III (MPS III), is a lysosomal storage disease caused by an enzyme deficiency that leads to a buildup of heparan sulfate, a glycosaminoglycan. The buildup impacts the central nervous system and causes progressive neurodegeneration; Sanfilippo has been referred to as childhood Alzheimer’s because of the symptoms it causes.

O’Neill, a pediatrician herself, quickly recognized the issue of diagnostic delay in identifying the syndrome in patients.

“Newborn screening wasn’t just right around the corner,” she said, which is why she was excited to meet FDNA at a genetics conference. “We thought, wow. What a great tool to help us find kids earlier.”

Cure Sanfilippo Foundation (co-founded by O’Neill and her husband) and FDNA set to work training the Face2Gene system on Sanfilippo syndrome. After creating a patient portal for parents to submit photos, the artificial intelligence powering Face2Gene eventually learned to differentiate the faces of patients with MPS III from faces of unaffected controls, as well as distinguish between MPS III and other MPS syndromes.

O’Neill also wanted to see how patient faces changed over time, so she gathered cohorts based on age and created average faces for each.

“What was really exciting to me about this was that we were able to pull out that early age group in the one to three-year-old range where symptoms are starting to become more apparent to doctors and parents. We thought this is where we could possibly pick up children early and the software was able to distinctly, with 95% accuracy, recognize MPS IIIB patients in the 1-3-year-old age range from other syndromes and from unaffected controls. That really validated what we had hoped this tool might be for us,” she said.

O’Neill said her experience as a parent and pediatrician impacted her desire to make other pediatricians more comfortable with disorders like MPS III.

“We would like to identify these kids before they have so many symptoms that they make it to the specialists.”

As part of that effort, Cure Sanfilippo and Greenwood Genetic Center partnered to create a set of trigger parameters prompting pediatricians to use Face2Gene to seek diagnostic consultation with genetic experts.

The process has been extremely rewarding for O’Neill, who had not done this kind of research work before. “It was surprisingly easy to move these projects forward,” she said, encouraging others to give it a try.

Karen Gripp, MD, FAAP, FACMG

Chief, Division of Medical Genetics, A.I. DuPont Hospital for Children

Chief Medical Officer, FDNA

Dr. Karen Gripp uses Face2Gene in both clinical and research settings. With one particular patient, she described how with just a single photo and seven features, Face2Gene presented Smith-Lemli-Opitz as a potential match, which allowed Gripp to order targeted testing rather than whole exome sequencing.

Gripp also used Face2Gene to identify a “face” for Ayme-Gripp, a syndrome previously described as “Down-syndrome-like.” “There was a long ongoing debate about how distinct a condition is it,” Gripp said.

Gripp noted how a facial composite of Ayme-Gripp syndrome is distinct: small eyes, short nasal tips, and full eyebrows are clearly evident. She was able to use Face2Gene to prove that these “average faces” are indeed distinct, different from those of children with Down syndrome.The system analyzed the facial phenotypes of the three separate cohorts with binary comparisons and multiclass comparisons. “For each photograph, the system has to decide which bucket to place it in,” she explained.

Gripp said she appreciated the RESEARCH application of Face2Gene for the way it streamlines hypothesis testing. “You get statistical data that is very helpful in terms of proving your impression in an objective manner.”

Ilana Jacqueline

Patient Advocacy Manager & Genomics Collaborative Project Coordinator, FDNA

Ilana Jacqueline said after the 2017 Year of Discovery that she and her colleagues realized “a year of discovery is not enough” and used the momentum from their monthly deep dives into specific diseases to build the Genomic Collaborative.

“Our technology continues to learn by way of collaboration,” Jacqueline said, explaining how in Face2Gene, “facial features become a mathematical descriptor,” which maintains patient privacy while optimising system learning. “Ultimately that reduces the amount of testing that may need to be done to confirm a diagnosis,” she said.

Clinicians and researchers who join the Genomics Collaborative can access forums to discuss difficult cases and gain the support of the FDNA research team to support the creation and publications of scientific papers, while of course maintaining ownership of their discoveries. As a bonus, because the technology is free, doctors and advocacy groups can devote their resources to other projects instead.

As a result, patients and families can see growing communities from improved diagnostic rates. “Patients will gain a greater understanding of their diseases as more experts become involved in the research,” she said.

“The need is urgent always,” said Jacqueline, who understands this from personal experience as a rare disease patient. “You’re dealing with families and children whose survival is a ticking clock. You know the stakes are high.” That’s why, she said, “We should always be learning.”

The post The Genomics Collaborative: How to Design the Future of Health—Together 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.

The post Dr. Karen Gripp: Face2Gene RESEARCH for Deep Phenotyping of Novel Syndromes appeared first on FDNA™.

Dr. Carey. a highly-practiced clinician, currently at the University of Utah’s Department of Medical Genetics, kicked off the webinar with a comprehensive background on phenotyping, including a discussion of the important role phenotyping plays in the process of diagnosing a patient.

Dr. Carey noted colleagues’ worries that there would be a decline of phenotypic analysis as next-generation sequencing (NGS) rises in popularity and accessibility, but went on to agree with the sentiments from a 2012 paper by Hennekam & Bisecker that, in fact, this new era of genome sequencing has led us to enter a “new epoch of phenotyping.” Rather than replacing the need for phenotyping, NGS has instead led to a new way of phenotyping.

Over the last few years, Dr. Carey noted there has been a “proliferation of resources which speaks to this notion of deep phenotyping.” Among such resources is FDNA’s Face2Gene technology. Using case studies as examples, Dr. Carey demonstrates the benefit Face2Gene can play in picking up on subtle facial patterns and, in turn, deciding on testing.

“We [clinicians] would benefit by having expanded knowledge and tools truly at our fingertips, but there is no question that the patients will benefit with the increased chance of making a diagnosis, or at least helping us in our genome analyses”

Dr. Stanley built on this point by sharing insights into the importance of phenotyping from a lab perspective. Drawing on her extensive background, most recently as the Head of Clinical Labs, US at WuXi NextCODE, Dr. Stanley was able to provide a thorough look into what exactly is required for a successful genetic test. Previously, phenotypic information was not required when ordering testing, but now, “the justification for testing is dependent on a complete and accurate clinical intake.”

According to Dr. Stanley, the clinical phenotype is very important as a line of evidence in variant classification. She goes on to say that “the more limited the phenotype, the greater the risk the disease causing variant will be filtered out of the data set.” In order to avoid the accidental omittance of the causal variant from the report, Dr. Stanley suggests increasing the use of the PP4 line of evidence—which is associated with the patient phenotype—in testing, possibly by way of tools that help to incorporate phenotypic information into clinical reports.

“The phenotypic information combined with the gene sequence information is a powerful combination.”

According to Dr. Stanley, with the use of FDNA’s Face2Gene technology, a “dynamic feedback loop of clinical symptoms & genomic information can be achieved,” which leads to improved variant prioritization and speedier diagnosis. Ultimately, Dr. Stanley summarized that “phenotyping is critical for diagnostics and diagnostics are critical for patient management.”

“Every aspect of healthcare can be impacted by understanding the phenotype.”

Listen in as Dr. Carey and Dr. Stanley share their respective clinical and lab perspectives about genomic medicine and NGS ushering in a new era of phenotyping.

Continue the Discussion With These Thought Leadership Talks at ACMG

Visit FDNA (Booth #1015) to learn more

Delineating Genetic Syndromes and Next-Generation Phenotyping

John C. Carey, MD, MPH
Professor, Department of Pediatrics, University of Utah
Emeritus Editor in Chief, American Journal of Medical Genetics
Thursday, 4/12 10:15AM

Face2Gene RESEARCH for Deep Phenotyping of Novel Syndromes

Karen Gripp, MD
Chief, Division of Medical Genetics, A.I. DuPont Hospital for Children
Thursday, 4/12 11:30AM

Face2Gene LABS at WuXi NextCODE: Phenotyping for Improved Variant Prioritization

Christine Stanley, PhD, FACMG
Head of Clinical Laboratory, US, WuXi NextCODE
Medical Director, QNA Diagnostics
Thursday, 4/12 3:45PM

Next-Generation Phenotyping in the Era of Next-Generation Sequencing

Dekel Gelbman
Chief Executive Officer, FDNA
Friday, 4/13 10:15AM

The post Redefining Phenotyping for Clinical Advancements and Variant Prioritization appeared first on FDNA™.