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RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type–specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity—regulated by RNA binding proteins—in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders. 

The number of variants that have a non-zero effect on a trait ( i.e . polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions ( N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs. 

Abstract While variance components analysis has emerged as a powerful tool in complex trait genetics, existing methods for fitting variance components do not scale well to large-scale datasets of genetic variation. Here, we present a method for variance components analysis that is accurate and efficient: capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating and partitioning variation in a trait explained by genotyped SNPs (SNP-heritability). Analyzing 22 traits with genotypes from 300,000 individuals across about 8 million common and low frequency SNPs, we observe that per-allele squared effect size increases with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD) consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.