Clinical application of targeted long read sequencing in prenatal beta-thalassemia testing and genetic counseling.

BACKGROUND: Beta thalassemia, related to HBB mutation and associated with elevated hemoglobin A2 (HbA2), is an important genetic hemoglobinopathy with high incidences of disease and carrier rates in Singapore. Carrier screening is essential to facilitate prenatal counseling and testing. However, when individuals with elevated HbA2 do not have an identifiable HBB disease-associated variant, there is ambiguity on risk to their offspring. METHODS: We describe a case report of a proband with elevated HbA2, no identifiable HBB disease-associated variant, whose partner was a beta thalassemia carrier. Through clinical HBB gene sequencing, multiplex ligation-dependent probe amplification (MLPA) analysis, as well as targeted Nanopore long read sequencing of selected genes, we performed a complete analysis of HBB including the promoter region, 5'UTR and coding gene sequence, as well as evaluation for potential modifier variants and other rare structural variants. RESULTS: This process identified that the proband was heterozygous for KLF1:c.544T>C (p.Phe182Leu), a potential functional polymorphism previously known to be associated with benign elevated HbA2 levels. The presence of disease variants in the HBB locus was excluded. CONCLUSION: This finding provided clarity and enabled family planning for the proband and her family.

Molecular and computational characterization of ABCB11 and ABCG5 variants in Tunisian patients with neonatal/infantile low-GGT intrahepatic cholestasis: Genetic diagnosis and genotype-phenotype correlation assessment.

Many inherited conditions cause hepatocellular cholestasis in infancy, including progressive familial intrahepatic cholestasis (PFIC), a heterogeneous group of diseases with highly overlapping symptoms. In our study, six unrelated Tunisian infants with PFIC suspicion were the subject of a panel-target sequencing followed by an exhaustive bioinformatic and modeling investigations. Results revealed five disease-causative variants including known ones: (the p.Asp482Gly and p.Tyr354 * in the ABCB11 gene and the p.Arg446 * in the ABCC2 gene), a novel p.Ala98Cys variant in the ATP-binding cassette subfamily G member 5 (ABCG5) gene and a first homozygous description of the p.Gln312His in the ABCB11 gene. The p.Gln312His disrupts the interaction pattern of the bile salt export pump as well as the flexibility of the second intracellular loop domain harboring this residue. As for the p.Ala98Cys, it modulates both the interactions within the first nucleotide-binding domain of the bile transporter and its accessibility. Two additional potentially modifier variants in cholestasis-associated genes were retained based on their pathogenicity (p.Gly758Val in the ABCC2 gene) and functionality (p.Asp19His in the ABCG8 gene). Molecular findings allowed a PFIC2 diagnosis in five patients and an unexpected diagnosis of sisterolemia in one case. The absence of genotype/phenotype correlation suggests the implication of environmental and epigenetic factors as well as modifier variants involved directly or indirectly in the bile composition, which could explain the cholestasis phenotypic variability.

Machine learning approaches to explore digenic inheritance.

Some rare genetic disorders, such as retinitis pigmentosa or Alport syndrome, are caused by the co-inheritance of DNA variants at two different genetic loci (digenic inheritance). To capture the effects of these disease-causing variants and their possible interactive effects, various statistical methods have been developed in human genetics. Analogous developments have taken place in the field of machine learning, particularly for the field that is now called Big Data. In the past, these two areas have grown independently and have started to converge only in recent years. We discuss an overview of each of the two fields, paying special attention to machine learning methods for uncovering the combined effects of pairs of variants on human disease.