Scan-statistic approach identifies clusters of rare disease variants in LRP2, a gene linked and associated with autism spectrum disorders, in three datasets

Cluster-detection approaches, commonly used in epidemiology and astronomy, can be applied in the context of genetic sequence data for the identification of genetic regions significantly enriched with rare disease-risk variants (DRVs). Unlike existing association tests for sequence data, the goal of cluster-detection methods is to localize significant disease mutation clusters within a gene or region of interest. Here, we focus on a chromosome 2q replicated linkage region that is associated with autism spectrum disorder (ASD) and that has been sequenced in three independent datasets. We found that variants in one gene, LRP2, residing on 2q are associated with ASD in two datasets (the combined variable-threshold-test p value is 1.2 × 10(-5)). Using a cluster-detection method, we show that in the discovery and replication datasets, variants associated with ASD cluster preponderantly in 25 kb windows (adjusted p values are p(1) = 0.003 and p(2) = 0.002), and the two windows are highly overlapping. Furthermore, for the third dataset, a 25 kb region similar to those in the other two datasets shows significant evidence of enrichment of rare DRVs. The region implicated by all three studies is involved in ligand binding, suggesting that subtle alterations in either LRP2 expression or LRP2 primary sequence modulate the uptake of LRP2 ligands. BMP4 is a ligand of particular interest given its role in forebrain development, and modest changes in BMP4 binding, which binds to LRP2 near the mutation cluster, might subtly affect development and could lead to autism-associated phenotypes.