Brain MRI Abnormalities, Epilepsy and Intellectual Disability in LAMA2 Related Dystrophy - a Genotype/Phenotype Correlation.

BACKGROUND: LAMA2-related muscular dystrophy is a disorder that causes muscle weakness and varies in severity, from a severe, congenital type to a milder, late-onset form. However, the disease does not only affect the muscles, but has systemic involvement and can lead to alterations such as brain malformation, epilepsy and intellectual disability. OBJECTIVE: Describe the frequency of cortical malformations, epilepsy and intellectual disability in LAMA2-RD in a Brazilian cohort and correlate the neurological findings to genetic and motor function. METHODS: This is an observational study of 52 LAMA2-RD patients, who were divided into motor function subgroups and compared based on brain MRI findings, epilepsy, intellectual disability, and type of variants and variant domains. RESULTS: 44 patients (84.6%) were only able to sit, and 8 patients (15.4%) were able to walk. 10 patients (19.2%) presented with cortical malformations (polymicrogyria, lissencephaly-pachygyria, and cobblestone),10 patients (19.2%) presented with epilepsy, and 8 (15.4%) had intellectual disability. CNS manifestations correlated with a more severe motor phenotype and none of the patients able to walk presented with cortical malformation or epilepsy. There was a relation between gene variants affecting the laminin-α2 LG-domain and the presence of brain malformation (P = 0.016). There was also a relation between the presence of null variants and central nervous system involvement. A new brazilian possible founder variant was found in 11 patients (21,15%) (c.1255del; p. Ile419Leufs*4). CONCLUSION: Cortical malformations, epilepsy and intellectual disability are more frequent among LAMA2-RD patients than previously reported and correlate with motor function severity and the presence of variants affecting the laminin-α2 LG domain. This brings more insight fore phenotype-genotype correlations, shows the importance of reviewing the brain MRI of patients with LAMA2-RD and allows greater attention to the risk of brain malformation, epilepsy, and intellectual disability in those patients with variants that affect the LG domain.

Focused goodness of fit tests for gene set analyses.

Gene set-based signal detection analyses are used to detect an association between a trait and a set of genes by accumulating signals across the genes in the gene set. Since signal detection is concerned with identifying whether any of the genes in the gene set are non-null, a goodness-of-fit (GOF) test can be used to compare whether the observed distribution of gene-level tests within the gene set agrees with the theoretical null distribution. Here, we present a flexible gene set-based signal detection framework based on tail-focused GOF statistics. We show that the power of the various statistics in this framework depends critically on two parameters: the proportion of genes within the gene set that are non-null and the degree of separation between the null and alternative distributions of the gene-level tests. We give guidance on which statistic to choose for a given situation and implement the methods in a fast and user-friendly R package, wHC (https://github.com/mqzhanglab/wHC). Finally, we apply these methods to a whole exome sequencing study of amyotrophic lateral sclerosis.