Next-generation Phenotyping Research Articles

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings

Individuals with ultrarare disorders pose a structural challenge for healthcare systems since expert clinical knowledge is required to establish diagnoses. In TRANSLATE NAMSE, a 3-year prospective study, we evaluated a novel diagnostic concept based on multidisciplinary expertise in Germany. Here we present the systematic investigation of the phenotypic and molecular genetic data of 1,577 patients who had undergone exome sequencing and were partially analyzed with next-generation phenotyping approaches. Molecular genetic diagnoses were established in 32% of the patients totaling 370 distinct molecular genetic causes, most with prevalence below 1:50,000. During the diagnostic process, 34 novel and 23 candidate genotype–phenotype associations were identified, mainly in individuals with neurodevelopmental disorders. Sequencing data of the subcohort that consented to computer-assisted analysis of their facial images with GestaltMatcher could be prioritized more efficiently compared with approaches based solely on clinical features and molecular scores. Our study demonstrates the synergy of using next-generation sequencing and phenotyping for diagnosing ultrarare diseases in routine healthcare and discovering novel etiologies by multidisciplinary teams.

Speed Breeding: A Transformative Approach to Accelerate Crop Improvement

Speed breeding has emerged as a transformative approach to expedite crop improvement by optimizing environmental conditions to achieve rapid generation turnover. This technique manipulates factors such as photoperiod, temperature, light intensity, and nutrition to hasten plant growth and reproduction cycles. Through the production of 4-6 generations per year, compared to 1-2 generations in traditional breeding, speed breeding unlocks the potential for rapid development of crop varieties with enhanced yield potential, biotic and abiotic stress resilience, improved nutritional quality, and climate adaptation. The key principles involve tailoring photoperiods, controlling temperatures, employing specialized lighting, creating controlled environments, and formulating targeted nutrition. Speed breeding has diverse applications across cereals, legumes, vegetables, and other crops, enabling accelerated introgression of desirable traits, efficient hybrid breeding, and integration with contemporary genomics technologies. The primary advantages of speed breeding include rapid genetic gain, year-round breeding, precision phenotyping and selection, flexibility across diverse species, and seamless integration with molecular tools. However, challenges such as infrastructure costs, protocol optimization needs, genetic diversity implications, phenotyping data management, and accessibility constraints, particularly in developing countries, require attention. Future prospects encompass the integration of advanced genomics techniques, next-generation phenotyping, sustainable approaches, global consortiums for collaboration, capacity building initiatives, and responsible governance frameworks. Realizing the immense potential of speed breeding through collaborative efforts, cutting-edge innovations, and participatory approaches can contribute significantly to global food and nutritional security in the face of climate change.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study.

While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score's levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729–0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.

865. Same Shift Susceptibility Testing of Cefepime-Taniborbactam and Cefiderocol Using The Selux Next-Generation Phenotyping System

Abstract Background Robust, efficient, and reliable antimicrobial susceptibility testing (AST) systems are an integral component of effective antibiotic treatment. Multi-drug resistant (MDR) bacteria continue to emerge at alarming rates, requiring continuous innovation and development of new antibiotics. Among the new promising treatments for severe bacterial infections caused by carbapenem-resistant gram-negative bacteria, Cefiderocol (FDC) and Cefepime-Taniborbactam (FTB) have emerged as two frontrunners. FDC is the only FDA-approved siderophore cephalosporin, and FTB pairs the fourth-generation cephalosporin Cefepime, with Taniborbactam, a novel beta-lactamase inhibitor. AST results are paramount when directing therapy for patients with MDR infections, yet there is an average 3-year delay between the approval of new antibiotics and 510k submissions for new agents on automated AST systems. This is typified by FDC, for which there are currently no FDA-cleared automated AST systems. Here we assess the accuracy of FDC and FTB AST results using the Selux Next-Generation Phenotyping System. Methods For both FTB and FDC susceptibility, AST was performed using the Selux AST system and compared to CLSI broth microdilution (BMD) reference method in either cation-adjusted Muller Hinton broth (CAMHB) or its Iron depleted counterpart (ID-CAMHB). FDC, was tested in ID-CAMHB across 44 total CDC AR-Bank carbapenem-resistant K. pneumoniae, A. baumannii, and P. aeruginosa species. 34 CDC AR-Bank Strains predominantly resistant (30/34) to Cefepime were used to assess FTB. For FDC Essential Agreement (EA) and Categorical Agreement (CA) relative to ID-CAMHB were determined according to FDA breakpoints. FTB breakpoints were based on FDA breakpoints for Cefepime. For both agents, a comparison of BMD and the Selux system was accessed by EA and CA. Results The Selux AST system produced MICs to FDC with an average accuracy of 98% EA and 98% CA in ID-CAMHB (Table 1). In FTB, the Selux AST system produced MICs with an average accuracy of 94% EA and 100% CA in CAMHB (Table 2). Conclusion These data demonstrate the ability of the Selux NGP system to provide accurate results for FDC and FTB. This is the first known demonstration of an automated AST system for these important new drugs. Disclosures David Jimenez, B.S., Selux Diagnostics: Employee Kristin Baker, PhD, Selux Diagnostics: Employee Kelly Flentie, PhD, Selux Diagnostics: Employee Kristen Roberts, BS, Selux Diagnostics: Stocks/Bonds Gabriel Michaels, n/a, Selux Diagnostics: Employee Eric Stern, PhD, Selux Diagnostics: Board Member|Selux Diagnostics: Ownership Interest

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. phecodeX is available at https://github.com/PheWAS/phecodeX.

Next-generation phenotyping in rare diseases with facial dysmorphism

Next-generation phenotyping in rare diseases with facial dysmorphism

Computational facial analysis for rare Mendelian disorders.

With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.

Facial clues to the photosensitive trichothiodystrophy phenotype in childhood.

Among genodermatoses, trichothiodystrophies (TTDs) are a rare genetically heterogeneous group of syndromic conditions, presenting with skin, hair, and nail abnormalities. An extra-cutaneous involvement (craniofacial district and neurodevelopment) can be also a part of the clinical picture. The presence of photosensitivity describes three forms of TTDs: MIM#601675 (TTD1), MIM#616390 (TTD2) and MIM#616395 (TTD3), that are caused by variants afflicting some components of the DNA Nucleotide Excision Repair (NER) complex and with more marked clinical consequences. In the present research, 24 frontal images of paediatric patients with photosensitive TTDs suitable for facial analysis through the next-generation phenotyping (NGP) technology were obtained from the medical literature. The pictures were compared to age and sex-matched to unaffected controls using 2 distinct deep-learning algorithms: DeepGestalt and GestaltMatcher (Face2Gene, FDNA Inc., USA). To give further support to the observed results, a careful clinical revision was undertaken for each facial feature in paediatric patients with TTD1 or TTD2 or TTD3. Interestingly, a distinctive facial phenotype emerged by the NGP analysis delineating a specific craniofacial dysmorphic spectrum. In addition, we tabulated every single detail within the observed cohort. The novelty of the present research includes the facial characterization in children with the photosensitive types of TTDs through the 2 different algorithms. This result can become additional criteria for early diagnosis, and for subsequent targeted molecular investigations as well as a possible tailored multidisciplinary personalized management.

Next-generation phenotyping contributing to the identification of a 4.7 kb deletion in KANSL1 causing Koolen-de Vries syndrome.

Next-generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results in gestalt scores that can be used for the selection of appropriate genetic tests, and for the interpretation of the molecular data. Here, we report on an exceptional case of a young girl that was presented at the age of 8 and 15 and enrolled in NGP diagnostics on the latter occasion. The girl had clinical features associated with Koolen-de Vries syndrome (KdVS) and a suggestive facial gestalt. However, chromosomal microarray (CMA), Sanger sequencing, multiplex ligation-dependent probe analysis (MLPA), and trio exome sequencing remained inconclusive. Based on the highly indicative gestalt score for KdVS, the decision was made to perform genome sequencing to also evaluate noncoding variants. This analysis revealed a 4.7 kb de novo deletion partially affecting intron 6 and exon 7 of the KANSL1 gene. This is the smallest reported structural variant to date for this phenotype. The case illustrates how NGP can be integrated into the iterative diagnostic process of test selection and interpretation of sequencing results.

Craniofacial features of 3q29 deletion syndrome: Application of next-generation phenotyping technology.

3q29 deletion syndrome (3q29del) is a recurrent deletion syndrome associated with neuropsychiatric disorders and congenital anomalies. Dysmorphic facial features have been described but not systematically characterized. This study aims to detail the 3q29del craniofacial phenotype and use a machine learning approach to categorize individuals with 3q29del through analysis of 2D photos. Detailed dysmorphology exam and 2D facial photos were ascertained from 31 individuals with 3q29del. Photos were used to train the next‐generation phenotyping algorithm DeepGestalt (Face2Gene by FDNA, Inc, Boston, MA) to distinguish 3q29del cases from controls and all other recognized syndromes. Area under the curve of receiver operating characteristic curves (AUC‐ROC) was used to determine the capacity of Face2Gene to identify 3q29del cases against controls. In this cohort, the most common observed craniofacial features were prominent forehead (48.4%), prominent nose tip (35.5%), and thin upper lip vermillion (25.8%). The FDNA technology showed an ability to distinguish cases from controls with an AUC‐ROC value of 0.873 (p = 0.006) and led to the inclusion of 3q29del as one of the supported syndromes. This study found a recognizable facial pattern in 3q29del, as observed by trained clinical geneticists and next‐generation phenotyping technology. These results expand the potential application of automated technology such as FDNA in identifying rare genetic syndromes, even when facial dysmorphology is subtle.

Microbial phenomics linking the phenotype to fonction: The potential of Raman spectroscopy.

Raman spectroscopy is a promising tool for identifying microbial phenotypes based on single cell Raman spectra reflecting cellular biochemical biomolecules. Recent studies using Raman spectroscopy have mainly analyzed phenotypic changes caused by microbial interactions or stress responses (e.g., antibiotics) and evaluated the microbial activity or substrate specificity under a given experimental condition using stable isotopes. Lack of labelling and the nondestructive pretreatment and measurement process of Raman spectroscopy have also aided in the sorting of microbial cells with interesting phenotypes for subsequently conducting physiology experiments through cultivation or genome analysis. In this review, we provide an overview of the principles, advantages, and status of utilization of Raman spectroscopy for studies linking microbial phenotypes and functions. We expect Raman spectroscopy to become a next-generation phenotyping tool that will greatly contribute in enhancing our understanding of microbial functions in natural and engineered systems.

Success of Face Analysis Technology in Rare Genetic Diseases Diagnosed by Whole-Exome Sequencing: A Single-Center Experience

The diagnosis of rare genetic diseases is one of the most difficult areas in medicine. Whole-exome sequencing (WES) technology makes it easier to diagnose these diseases. In addition, next-generation phenotyping can help to diagnose computer-based algorithms. Detailed dysmorphologic findings of 25 patients diagnosed by WES in our center were described. The success of this technology in diagnosing rare genetic diseases was investigated by scanning the photographs of 25 patients with Face2Gene application. The application listed possible preliminary diagnoses (30 disease suggestion). Of these, 12 (48%) cases were correctly matched. The most common disease group in the patients was neurological disease (96%). The most common mode of inheritance in the patients was autosomal recessive. The rate of consanguineous marriages was determined in 80% of the patients. Ten patients had microcephaly and 7 patients had corpus callosum anomaly. In our study, we found that the success of Face2Gene was lower than described in the literature. We think that the probable cause of this condition is that the cases are very rare, and there is not enough data about these diseases in the application. Therefore, it is recommended that applications should be used more frequently by pediatricians and clinical geneticists. The diagnosis of rare diseases still is quite difficult. Nowadays, WES is a successful method. However, applications such as Face2Gene help to make a clinical prediagnosis and create a larger database.

2159. Accurate Carbapenem Susceptibility Testing Within 5–6 Hours

BackgroundPatients infected with multi-drug-resistant (MDR) pathogens may experience long delays to targeted therapies due to the incomplete antimicrobial menus and/or breakpoints tested on current commercial antimicrobial susceptibility testing (AST) systems. Detection of carbapenem resistance poses a challenge to rapid, accurate, minimum inhibitory concentrations (MIC) determinations because some resistant organisms may be inhibited by a carbapenem antibiotic until sufficient carbapenemase production has been achieved and traditional AST platforms must wait to make MIC calls. More accurate carbapenem MICs can be determined by implementing a carbapenemase test alongside rapid AST.MethodsWe demonstrate a novel, proprietary test to detect carbapenemase production that enables rapid MIC testing for carbapenem antibiotics. The test is processed in parallel with the Selux next-generation phenotyping (NGP) AST method, enabling rapid, <6-hour, accurate MIC determinations. The carbapenemase assay utilizes high concentrations of intact bacteria. After 3 hours of incubation, a fluorescent pH indicator is read spectroscopically. The solution pH is lowered by carbapenemase-mediated imipenem degradation and is indicative of enzyme activity.ResultsThis assay accurately identifies carabapenemases across multiple enzyme classes and bacterial species. Figure 1 shows the accuracy and speed of NGP AST at determining MICs for representative isolates from the FDA-CDC antimicrobial resistance bank compared with results from overnight broth microdilution (BMD). To date, over 100 challenge strains of Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii have been tested with no very major errors and an average time-to-result of 5.3 hours.ConclusionBy incorporating a rapid, on-board carbapenemase activity assay, the NGP AST platform rapidly delivers accurate carbapenem results. Combined with NGP’s comprehensive antibiotic menus, this platform will therefore ensure prompt delivery of personalized antibiotic therapies for all patients, including those infected with MDROs, and enable streamlined antibiotic stewardship coordination. Disclosures All authors: No reported disclosures.

PIGT-CDG, a disorder of the glycosylphosphatidylinositol anchor: description of 13 novel patients and expansion of the clinical characteristics

PurposeTo provide a detailed electroclinical description and expand the phenotype of PIGT-CDG, to perform genotype–phenotype correlation, and to investigate the onset and severity of the epilepsy associated with the different genetic subtypes of this rare disorder. Furthermore, to use computer-assisted facial gestalt analysis in PIGT-CDG and to the compare findings with other glycosylphosphatidylinositol (GPI) anchor deficiencies. MethodsWe evaluated 13 children from eight unrelated families with homozygous or compound heterozygous pathogenic variants in PIGT. ResultsAll patients had hypotonia, severe developmental delay, and epilepsy. Epilepsy onset ranged from first day of life to two years oF.A.ge. Severity of the seizure disorder varied from treatable seizures to severe neonatal onset epileptic encephalopathies. The facial gestalt of patients resembled that of previously published PIGT patients as they were closest to the center of the PIGT cluster in the clinical face phenotype space and were distinguishable from other gene-specific phenotypes. ConclusionWe expand our knowledge of PIGT. Our cases reaffirm that the use of genetic testing is essential for diagnosis in this group of disorders. Finally, we show that computer-assisted facial gestalt analysis accurately assigned PIGT cases to the multiple congenital anomalies–hypotonia–seizures syndrome phenotypic series advocating the additional use of next-generation phenotyping technology.

Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders

PurposeThe interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance. MethodsFacial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population. ResultsWe validated our model using images from 71 individuals with Koolen–de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10−4), PPM1D (p = 4.65 × 10−2), and PHIP (p = 6.3 × 10−3). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p < 1.52 × 10−2). ConclusionOur results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.

Next generation phenotyping in Emanuel and Pallister-Killian syndrome using computer-aided facial dysmorphology analysis of 2D photos.

High throughput approaches are continuously progressing and have become a major part of clinical diagnostics. Still, the critical process of detailed phenotyping and gathering clinical information has not changed much in the last decades. Forms of next generation phenotyping (NGP) are needed to increase further the value of any kind of genetic approaches, including timely consideration of (molecular) cytogenetics during the diagnostic quest. As NGP we used in this study the facial dysmorphology novel analysis (FDNA) technology to automatically identify facial phenotypes associated with Emanuel (ES) and Pallister-Killian Syndrome (PKS) from 2D facial photos. The comparison between ES or PKS and normal individuals expressed a full separation between the cohorts. Our results show that NPG is able to help in the clinic, and could reduce the time patients spend in diagnostic odyssey. It also helps to differentiate ES or PKS from each other and other patients with small supernumerary marker chromosomes, especially in countries with no access to more sophisticated genetic approaches apart from banding cytogenetics. Inclusion of more facial pictures of patient with sSMC, like isochromosome-18p-, cat-eye-syndrome or others may contribute to higher detection rates in future.

The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in dairy cows was 0.88 (20.0MJ), 0.78 (18.6MJ), and 0.63 (22.0MJ), respectively, based on cross-validation. These studies, however, are limited to results from one research group based on data from 2 contrasting production systems in the United Kingdom and Ireland and would need to be replicated, especially in a range of production systems because the prediction equations are not accurate when the variability used in validation is not represented in the calibration data set. Heritable genetic variation exists for all predicted traits. Phenotypic differences in energy intake also exists among animals stratified based on genetic merit for energy intake predicted from milk FTIRS, substantiating the usefulness of such FTIR-predicted phenotypes not only for day-to-day herd management, but also as part of a breeding strategy to improve cow performance.