The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification.

/pdf/large-scale-multifactorial-likelihood-quantitative-analysis-4n941qqrua.pdf

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants : An ENIGMA resource to support clinical variant classification

Noninvasive prenatal diagnosis for pregnancies at risk for β‐thalassaemia: a retrospective study

Labormedizinische Diagnostik ist fur die Diagnosestellung, Therapieeinleitung und Uberwachung von Patienten unverzichtbar. Unberucksichtigte oder fehlerhaft interpretierte Laborergebnisse haben unerwunschte Auswirkungen und gefahrden die Patientensicherheit. „Clinical decision support systems“ (CDSS) konnten helfen, eine angemessene Interpretation und medizinische Reaktion zu unterstutzen. Das Forschungsprojekt zur digitalen Labormedizin (Analyse- und Meldesystem zur Verbesserung der Patientensicherheit durch Echtzeitintegration von Laborbefunden [AMPEL]) hat zum Ziel, auf Basis der Diagnostik am Institut fur Laboratoriumsmedizin der Universitatsmedizin Leipzig ein CDSS zu entwickeln, das die Behandler dabei unterstutzt, notwendige medizinische Masnahmen sicherzustellen. In einer Literaturrecherche zu CDSS wird der aktuelle Stand der Technik beschrieben. Hierauf aufbauend wird das AMPEL-Projekt mit seinen Zielen, Herausforderungen und ersten Ergebnissen vorgestellt. Die Entwicklung der Regel- und Meldesysteme wird am klinischen Beispiel der schweren Hypokaliamie erlautert. Durch die interdisziplinare Entwicklung von Regel- und Meldesystemen auf Basis von klinischen Daten wurden spezifische, fachgebietsubergreifende CDSS erstellt, die auf hohe Akzeptanz stosen. Erste Ergebnisse zur schweren Hypokaliamie belegen einen positiven Effekt auf die Patientenbehandlung. Bei der Entwicklung komplexerer Regelwerke, etwa zur Sepsisdiagnostik oder dem akuten Koronarsyndrom, stellt die begrenzte Verfugbarkeit standardisierter und digital verfugbarer klinischer Daten eine Herausforderung dar. Neben klassischen Entscheidungsbaumen in CDSS bieten Methoden des maschinellen Lernens eine vielversprechende Perspektive fur zukunftige Entwicklungen.

/pdf/improved-patient-safety-through-a-clinical-decision-support-2yfmhmmlh9.pdf

Improved patient safety through a clinical decision support system in laboratory medicine

At present, the proposed network finance technology data risk assessment time is too long, leading to low accuracy. In order to solve the above problems, this paper puts forward the research on the...

/pdf/research-on-risk-assessment-of-science-and-technology-data-22zs9yadtq.pdf

Research on Risk Assessment of Science and Technology Data in Network Finance Based on Portfolio Empowerment

Patients with sickle cell disease (SCD) in Kuwait have elevated HbF levels ranging from ~10-44%; however, the modulating factors are unclear. We investigated the association of single nucleotide polymorphisms (SNPs) at BCL11A, HBS1L-MYB and HBB with HbF levels in 237 Kuwaiti SCD patients, divided into 3 subgroups according to their HbF levels. Illumina Ampliseq custom DNA panel was used for genotyping and confirmed by arrayed primer extension or Sanger sequencing. In the BCL11A locus, the CC genotype of rs7606173 [χ2 = 16.5] and (GG) of rs10195871 [χ2 = 15.0] were associated with Hb-F1 and HbF-2 subgroups, unlike rs1427404-T [χ2 = 17.3], which showed the highest association across the three subgroups. HBS1L-MYB locus revealed 2 previously-described SNPs (rs66650371 [χ2 = 9.5] and rs35795442 [χ2 = 9.2]) and 2 previously-unreported SNPs, (rs13220662 [χ2 = 6.2] and rs1406811 [χ2 = 6.7]) that were associated with the HbF-3 subgroup, making this the key locus elevating HbF to the highest levels. HBB cluster variants were associated with lower levels of HbF (β = -1.1). We report four previously-unpublished variants showing significant association with HbF. Each of the three quantitative trait loci affects HbF levels differently; unique SNPs, especially in HBS1L-MYB, elevate HbF to the highest levels.

https://www.mdpi.com/2075-4426/11/6/567/pdf

Unique Polymorphisms at BCL11A, HBS1L-MYB and HBB Loci Associated with HbF in Kuwaiti Patients with Sickle Cell Disease.

Hemoglobinopathies are the most common monogenic disorders worldwide. Substantial effort has been made to establish databases to record complete mutation spectra causing or modifying this group of diseases. We present a variant database which couples an online auxiliary diagnosis and at-risk assessment system for hemoglobinopathies (DASH). The database was integrated into the Leiden Open Variation Database (LOVD), in which we included all reported variants focusing on a Chinese population by literature peer review-curation and existing databases, such as HbVar and IthaGenes. In addition, comprehensive mutation data generated by high-throughput sequencing of 2,087 hemoglobinopathy patients and 20,222 general individuals from southern China were also incorporated into the database. These sequencing data enabled us to observe disease-causing and modifier variants responsible for hemoglobinopathies in bulk. Currently, 371 unique variants have been recorded; 265 of 371 were described as disease-causing variants, whereas 106 were defined as modifier variants, including 34 functional variants identified by a quantitative trait association study of this high-throughput sequencing data. Due to the availability of a comprehensive phenotype-genotype data set, DASH has been established to automatically provide accurate suggestions on diagnosis and genetic counseling of hemoglobinopathies. LOVD-DASH will inspire us to deal with clinical genotyping and molecular screening for other Mendelian disorders.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.23863

LOVD-DASH: A comprehensive LOVD database coupled with diagnosis and an at-risk assessment system for hemoglobinopathies.

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LOVD-DASH: A comprehensive LOVD database coupled with diagnosis and an at-risk assessment system for hemoglobinopathies.