Filaggrin loss-of-function mutations are associated with persistence of egg and milk allergy.

BACKGROUND: A genetic defect in the epidermal barrier protein filaggrin (FLG) plays a major role in the etiology of eczema and associated allergic airways diseases. However, it is still controversial to what extend loss-of-function (LOF) mutations in FLG contribute to the development and persistence of food allergies. OBJECTIVES: This study tested association of FLG LOF mutations with allergic reactions to diverse foods and investigated their potential effect on the persistence of early food allergies. METHODS: This study recruited 890 children with challenge-proven food allergy for the German Genetics of Food Allergy Study (GOFA). Longitudinal data were available for 684 children. All children were clinically characterized, including their allergic responses to specific foods, and genotyped for the 4 most common LOF mutations in FLG; R501X, 2282del4, R2447X, and S3247X. Associations between FLG mutations and food allergies were analyzed by logistic regression using the German Multicenter Allergy Study cohort as the control population. RESULTS: FLG mutations were associated with allergies to diverse foods including hen's egg (HE), cow's milk (CM), peanut, hazelnut, fish, soy, cashew, walnut, and sesame with similar risk estimates. Effects remained significant after adjusting for the eczema status. Interestingly, FLG mutations increased the risk of a persistent course of HE and CM allergy. CONCLUSIONS: Using the gold standard for food allergy diagnosis, this study demonstrates that FLG LOF mutations confer a risk of any food allergy independent of eczema. These mutations predispose to the persistence of HE and CM allergy and should be considered in the assessment of tolerance development.

Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance.

PURPOSE: Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. METHODS: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. RESULTS: Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. CONCLUSION: Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.

Correction: Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.