BACKGROUND: In recent years, thanks to the rapid development of next-generation sequencing (NGS) technology, an entire human genome can be sequenced in a short period. As a result, NGS technology is now being widely introduced into clinical diagnosis practice, especially for diagnosis of hereditary disorders. Although the exome data of single-nucleotide variant (SNV) can be generated using these approaches, processing the DNA sequence data of a patient requires multiple tools and complex bioinformatics pipelines.
OBJECTIVE: This study aims to assist physicians to automatically interpret the genetic variation information generated by NGS in a short period. To determine the true causal variants of a patient with genetic disease, currently, physicians often need to view numerous features on every variant manually and search for literature in different databases to understand the effect of genetic variation.
METHODS: We constructed a machine learning model for predicting disease-causing variants in exome data. We collected sequencing data from whole-exome sequencing (WES) and gene panel as training set, and then integrated variant annotations from multiple genetic databases for model training. The model built ranked SNVs and output the most possible disease-causing candidates. For model testing, we collected WES data from 108 patients with rare genetic disorders in National Taiwan University Hospital. We applied sequencing data and phenotypic information automatically extracted by a keyword extraction tool from patient\'s electronic medical records into our machine learning model.
RESULTS: We succeeded in locating 92.5% (124/134) of the causative variant in the top 10 ranking list among an average of 741 candidate variants per person after filtering. AI Variant Prioritizer was able to assign the target gene to the top rank for around 61.1% (66/108) of the patients, followed by Variant Prioritizer, which assigned it for 44.4% (48/108) of the patients. The cumulative rank result revealed that our AI Variant Prioritizer has the highest accuracy at ranks 1, 5, 10, and 20. It also shows that AI Variant Prioritizer presents better performance than other tools. After adopting the Human Phenotype Ontology (HPO) terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108).
CONCLUSIONS: We successfully applied sequencing data from WES and free-text phenotypic information of patient\'s disease automatically extracted by the keyword extraction tool for model training and testing. By interpreting our model, we identified which features of variants are important. Besides, we achieved a satisfactory result on finding the target variant in our testing data set. After adopting the HPO terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108). The performance of the model is similar to that of manual analysis, and it has been used to help National Taiwan University Hospital with a genetic diagnosis.

BACKGROUND: Genetic disorders significantly affect patients in neonatal intensive care units, where establishing a diagnosis can be challenging through routine tests and supplementary examinations. Whole-exome sequencing offers a molecular-based approach for diagnosing genetic disorders. This study aimed to assess the importance of whole-exome sequencing for neonates in intensive care through a retrospective observational study within a Chinese cohort.
METHODS: We gathered data from neonatal patients at Tianjin Children\'s Hospital between January 2018 and April 2021. These patients presented with acute illnesses and were suspected of having genetic disorders, which were investigated using whole-exome sequencing. Our retrospective analysis covered clinical data, genetic findings, and the correlation between phenotypes and genetic variations.
RESULTS: The study included 121 neonates. Disorders affected multiple organs or systems, predominantly the metabolic, neurological, and endocrine systems. The detection rate for whole-exome sequencing was 52.9% (64 out of 121 patients), identifying 84 pathogenic or likely pathogenic genetic variants in 64 neonates. These included 13 copy number variations and 71 single-nucleotide variants. The most frequent inheritance pattern was autosomal recessive (57.8%, 37 out of 64), followed by autosomal dominant (29.7%, 19 out of 64). In total, 40 diseases were identified through whole-exome sequencing.
CONCLUSIONS: This study underscores the value and clinical utility of whole-exome sequencing as a primary diagnostic tool for neonates in intensive care units with suspected genetic disorders. Whole-exome sequencing not only aids in diagnosis but also offers significant benefits to patients and their families by providing clarity in uncertain diagnostic situations.

OBJECTIVE: Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions.
METHODS: A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed.
RESULTS: The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations.
CONCLUSIONS: This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients\' response to immunotherapy.

Parkinson\'s disease is a neurological disorder that affects motor function, autonomic functions, and cognitive abilities. It is likely that both genetic and environmental factors, along with age, contribute to the cause. However, there is no comprehensive guideline for genetic testing for Parkinson\'s disease, and more research is needed to understand genetic variations in different populations. There has been no research on the genetic background of Parkinson\'s disease in Croatia so far. Therefore, with the GiOPARK project, we aimed to investigate the genetic variants responsible for Parkinson\'s disease in 153 Croatian patients with early onset, familial onset, and sporadic late-onset using whole-exome sequencing, along with multiplex ligation-dependent probe amplification and Sanger sequencing in select patients. We found causative variants in 7.84% of the patients, with GBA being the most common gene (4.58%), followed by PRKN (1.96%), ITM2B (0.65%), and MAPT (0.65%). Moreover, variants of uncertain significance were identified in 26.14% of the patients. The causative variants were found in all three subgroups, indicating that genetic factors play a role in all the analyzed Parkinson\'s disease subtypes. This study emphasizes the need for more inclusive research and improved guidelines to better understand the genetic basis of Parkinson\'s disease and facilitate more effective clinical management.

BACKGROUND: The etiology of premature ovarian insufficiency, that is, the loss of ovarian activity before 40 years of age, is complex. Studies suggest that genetic factors are involved in 20-25% of cases. The aim of this study was to explore the oligogenic basis of premature ovarian insufficiency.
RESULTS: Whole-exome sequencing of 93 patients with POI and whole-genome sequencing of 465 controls were performed. In the gene-burden analysis, multiple genetic variants, including those associated with DNA damage repair and meiosis, were more common in participants with premature ovarian insufficiency than in controls. The ORVAL-platform analysis confirmed the pathogenicity of the RAD52 and MSH6 combination.
CONCLUSIONS: The results of this study indicate that oligogenic inheritance is an important cause of premature ovarian insufficiency and provide insights into the biological mechanisms underlying premature ovarian insufficiency.

BACKGROUND: The contribution of genetic variants to congenital heart defects (CHDs) has been investigated in many postnatal cohorts but described in few prenatal fetus cohorts. Overall, specific genetic variants especially copy number variants (CNVs) leading to CHDs are somewhat diverse among different prenatal cohort studies. In this study, a total of 1118 fetuses with confirmed CHDs were recruited from three units over a 5-year period, composing 961 of singleton pregnancies and 157 of twin pregnancies. We performed chromosomal microarray analysis on all cases to detect numerical chromosomal abnormalities (NCAs) and pathogenic/likely pathogenic CNVs (P/LP CNVs) and employed whole-exome sequencing for some cases without NCAs and P/LP CNVs to detect P/LP sequence variants (P/LP SVs).
RESULTS: Overall, NCAs and P/LP CNVs were identified in 17.6% (197/1118) of cases, with NCA accounting for 9.1% (102/1118) and P/LP CNV for 8.5% (95/1118). Nonisolated CHDs showed a significantly higher frequency of NCA than isolated CHD (27.3% vs. 4.4%, p < 0.001), but there was no significant difference in the frequency of P/LP CNVs between isolated and nonisolated CHD (11.7% vs. 7.7%). A total of 109 P/LP CNVs were identified in 95 fetuses, consisting of 97 (89.0%) de novo, 6 (5.5%) parental inherited and 6 (5.5%) with unavailable parental information. The 16p11.2 proximal BP4-BP5 deletion was detected in 0.9% (10/1118) of all cases, second only to the most common 22q11.21 proximal A-D deletion (2.1%, 23/1118). Most of the 16p11.2 deletions (8/10) detected were de novo, and were enriched in CHD cases compared with a control cohort from a previous study. Additionally, SV was identified in 12.9% (8/62) of cases without NCA and P/LP CNV, most of which were de novo with autosomal dominant inheritance.
CONCLUSIONS: Our cohort study provides a deep profile of the contribution of genetic variants to CHDs in both singleton and twin fetuses; NCA and P/LP CNV contribute to 9.1% and 8.5% of CHD in fetuses, respectively. We confirmed the 16p11.2 deletion as a CHD-associated hotspot CNV, second only to the 22q11.21 deletion in frequency. Most 16p11.2 deletions detected were de novo. Additionally, P/LP SV was identified in 12.9% (8/62) of fetuses without NCA or P/LP CNV.

OBJECTIVE: To comprehensively evaluate the contributions of numerical chromosomal abnormality, copy number variant (CNV), and sequence variant (SV) to fetuses with small head circumference in a Chinese cohort using chromosome microarray analysis and whole exome sequencing.
METHODS: A total of 157 fetuses with small heads defined as head circumference < - 2 standard deviation (SD) were recruited between October 2014 and March 2023. We used the ultrasonic measurement parameter Z-score to define small head as possible microcephaly (3 < Z ≤ -2), microcephaly (-5 < Z ≤ -3), or pathologic microcephaly (Z ≤ -5). Ultrasound findings and genetic results were analyzed.
RESULTS: The overall diagnostic yield of chromosomal abnormalities by microarray analysis was 13 %. Whole exome sequencing revealed eight novel variants and two interesting candidate genes and provided a 25.4 % incremental yield compared with microarray analysis. Of the detected SVs, 56 % were de novo and the most common inheritance pattern was autosomal dominant inheritance presented in 11/16 fetuses. Compared with isolated small heads, non-isolated small heads had a significantly higher detection rate of chromosomal abnormalities (16 % vs. 3.0 %, P = 0.049) but not SVs (24 % vs. 5.5 %, P = 0.126). Subgroup analysis showed that intracranial anomalies had a similar high detection rate of SVs in fetuses with all small heads subgroups while no chromosomal abnormalities and causative SVs were found in fetuses with isolated possible microcephaly.
CONCLUSIONS: Ultrasound findings of small fetal head circumference < 3 SD below the mean, especially those with intracranial structural abnormalities, indicate the need for genetic counseling. Genetic variants, mainly copy number variants and SV, may be responsible for the substantial proportion of small fetal head circumference, while most are de novo. Whole exome sequencing and microarray analysis are effective diagnostic approaches for this population.

Fetal digestive system malformations (DSMs) are correlated with chromosomal anomalies. The prenatal diagnosis of DSMs allows for timely treatment and reduces perinatal morbidity and mortality. However, genetic screening for fetal DSMs is rarely reported. This study aimed to investigate genetic etiology and pregnancy outcomes in cases of fetal DSM by analyzing correlations between DSM types and chromosomal anomalies. This retrospective single-center study included 126 fetuses in whom DSMs were detected via prenatal ultrasonography. Genetic etiology was investigated using conventional karyotyping, chromosome microarray analysis (CMA), and whole-exome sequencing (WES). DSMs were categorized as simple DSM (Group A), DSM combined with abnormal ultrasound soft markers (Group B), and DSM combined with comorbidities of other systems (Group C). Abnormal karyotypes were detected in 11/126 (8.7 %) fetuses. Four more pathogenic copy number variants (CNVs) were detected using CMA, increasing the detection rate to 11.9 %. The detection rates significantly differed between the three DSM types (1.78 %, 8.11 %, and 33.33 % in Groups A, B, and C, respectively). The overall adverse pregnancy outcome rate was 33.9 %, and 11.5 %, 23.5 %, and 81.3 %, (P < 0.001), respectively, in Groups A, B, and C. Out of 83 live births, three neonates died, 26 underwent postnatal surgery with 24 favorable outcomes, and 54 did not undergo surgery and were basically normal. Two neonates who underwent WES were diagnosed with CHD7-associated Charge syndrome and JAG1-associated Alagille syndrome, respectively. Our findings demonstrate that fetal DSM is closely related to chromosome aneuploidies, CNVs, and point mutations. The prognoses of most fetuses with simple DSM and those with comorbid abnormal ultrasound soft markers were favorable in the absence of chromosomal anomalies and severe structural malformations, provided they underwent timely surgery as neonates. These findings provide guidance for the prenatal diagnosis and clinical management of fetal DSMs and the genetic counseling of parents.

Canine lymphoma (CL) is one of the most common malignant tumors in dogs. The cause of CL remains unclear. Genetic mutations that have been suggested as possible causes of CL are not fully understood. Whole-exome sequencing (WES) is a time- and cost-effective method for detecting genetic variants targeting only the protein-coding regions (exons) that are part of the entire genome region. A total of eight patients with B-cell lymphomas were recruited, and WES analysis was performed on whole blood and lymph node aspirate samples from each patient. A total of 17 somatic variants (GOLIM4, ITM2B, STN1, UNC79, PLEKHG4, BRF1, ENSCAFG00845007156, SEMA6B, DSC1, TNFAIP1, MYLK3, WAPL, ADORA2B, LOXHD1, GP6, AZIN1, and NCSTN) with moderate to high impact were identified by WES analysis. Through a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of 17 genes with somatic mutations, a total of 16 pathways were identified. Overall, the somatic mutations identified in this study suggest novel candidate mutations for CL, and further studies are needed to confirm the role of these mutations.

Sudden unexplained death (SUD) constitutes a considerable portion of unexpected sudden death in the young. Molecular autopsy has proved to be an efficient diagnostic tool in the multidisciplinary management of SUD. Yet, many cases remain undiagnosed using the widely adopted targeted genetic screening strategies. Here, we investigated the genetic substrates of a young SUD cohort (18-40 years old) from China using whole-exome sequencing (WES), with the primary aim to identify novel SUD susceptibility genes. Within 255 previously acknowledged SUD-associated genes, 21 variants with likely functional effects (pathogenic/likely pathogenic) were identified in 51.9% of the SUD cases. More importantly, a set of 33 candidate genes associated with myopathy were identified to be novel susceptibility genes for SUD. Comparative analysis of the cumulative PHRED-scaled CADD score and polygenetic burden score showed that the amount and deleteriousness of variants in the 255 SUD-associated genes and the 33 candidate genes identified by this study were significantly higher compared with 289 randomly selected genes. A significantly higher genetic burden of rare variants (MAF < 0.1%) in the 33 candidate genes also highlighted putative roles of these genes in SUD. After incorporating these novel genes, the genetic testing yields of the current SUD cohort elevated from 51.9 to 66.7%. Our study expands understanding of the genetic variants underlying SUD and presents insights that improve the utility of genetic screenings.