ReviewIndian journal of pediatrics2026
Approach to Disorders of Sex Development in the Genomic Era.
Review in Indian journal of pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Disorders of or differences in sex development (DSD) are congenital conditions characterized by a discrepancy between chromosomal, gonadal and/or anatomic sex. They are broadly classified on the basis of karyotype into 46,XX, 46,XY and sex chromosomal DSDs. While the most common cause of 46,XX DSD is congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency, the genetics of 46,XY DSD is more complex with over 100 genes implicated. A precise genetic diagnosis of DSD ends the diagnostic odyssey and helps predict the future course with respect to gender development, puberty and extragenital features. With advancements in genetic testing including next-generation sequencing (NGS) and chromosomal microarray (CMA), it is possible to get a precise genetic diagnosis in up to 40-50% patients with 46,XY DSD. The choice of genetic tests depends on the expected pathophysiology inferred from the clinical phenotype, aiming to maximize diagnostic yields while minimizing costs. However, genetic investigations are not without their own shortcomings, some of which include high cost and complex data analysis, identification of 'variants of uncertain significance (VUS)', and negative results despite exhaustive testing. Therefore, genetic testing should always be undertaken following pre-test counseling, and in consultation with a pediatric endocrinologist and a geneticist. This article reviews the genetic mechanisms underlying DSD, the various genetic testing modalities along with their indications, prerequisites and pitfalls; and outlines a broad algorithmic approach to genetic diagnosis in DSD.
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