Microtia is a congenital malformation that encompasses mild hypoplasia to complete loss of the external ear, or pinna. Although the contribution of genetic variation and environmental factors to microtia remains elusive, Amerindigenous populations have the highest reported incidence. Here, using both transmission disequilibrium tests and association studies in microtia trios (parents and affected child) and microtia cohorts enrolled in Latin America, we map an ∼10-kb microtia locus (odds ratio = 4.7; P = 6.78e-18) to the intergenic region between Roundabout 1 (ROBO1) and Roundabout 2 (ROBO2) (chr3: 78546526 to 78555137). While alleles at the microtia locus significantly increase the risk of microtia, their penetrance is low (<1%). We demonstrate that the microtia locus contains a polymorphic complex repeat element that is expanded in affected individuals. The locus is located near a chromatin loop region that regulates ROBO1 and ROBO2 expression in induced pluripotent stem cell–derived neural crest cells. Furthermore, we use single nuclear RNA sequencing to demonstrate ROBO1 and ROBO2 expression in both fibroblasts and chondrocytes of the mature human pinna. Because the microtia allele is enriched in Amerindigenous populations and is shared by some East Asian subjects with craniofacial malformations, we propose that both populations share a mutation that arose in a common ancestor prior to the ancient migration of Eurasian populations into the Americas and that the high incidence of microtia among Amerindigenous populations reflects the population bottleneck that occurred during the migration out of Eurasia.
Pubmed ID: 35584116 RIS Download
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A software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm. It uses a block relaxation approach to alternately update allele frequency and ancestry fraction parameters. Each block update is handled by solving a large number of independent convex optimization problems, which are tackled using a fast sequential quadratic programming algorithm. Convergence of the algorithm is accelerated using a novel quasi-Newton acceleration method.
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