Gerard Schellenberg and colleagues report a genome-wide association study of late-onset Alzheimer's disease (LOAD), as part of the Alzheimer Disease Genetics Consortium. They identify common variants in MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 associated with LOAD. The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study of late-onset Alzheimer disease using a three-stage design consisting of a discovery stage (stage 1) and two replication stages (stages 2 and 3). Both joint analysis and meta-analysis approaches were used. We obtained genome-wide significant results at MS4A4A (rs4938933; stages 1 and 2, meta-analysis P (PM) = 1.7 × 10−9, joint analysis P (PJ) = 1.7 × 10−9; stages 1, 2 and 3, PM = 8.2 × 10−12), CD2AP (rs9349407; stages 1, 2 and 3, PM = 8.6 × 10−9), EPHA1 (rs11767557; stages 1, 2 and 3, PM = 6.0 × 10−10) and CD33 (rs3865444; stages 1, 2 and 3, PM = 1.6 × 10−9). We also replicated previous associations at CR1 (rs6701713; PM = 4.6 × 10−10, PJ = 5.2 × 10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9 × 10−8), BIN1 (rs7561528; PM = 4.0 × 10−14, PJ = 5.2 × 10−14) and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0 × 10−10), but not at EXOC3L2, to late-onset Alzheimer's disease susceptibility1,2,3.

Alzheimer’s disease (AD) is a complex disorder influenced by environmental and genetic factors. Recent work has identified 11 AD markers in 10 loci. We used Genome-wide Complex Trait Analysis to analyze >2 million SNPs for 10,922 individuals from the Alzheimer’s Disease Genetics Consortium to assess the phenotypic variance explained first by known late-onset AD loci, and then by all SNPs in the Alzheimer’s Disease Genetics Consortium dataset. In all, 33% of total phenotypic variance is explained by all common SNPs. APOE alone explained 6% and other known markers 2%, meaning more than 25% of phenotypic variance remains unexplained by known markers, but is tagged by common SNPs included on genotyping arrays or imputed with HapMap genotypes. Novel AD markers that explain large amounts of phenotypic variance are likely to be rare and unidentifiable using genome-wide association studies. Based on our findings and the current direction of human genetics research, we suggest specific study designs for future studies to identify the remaining heritability of Alzheimer’s disease.

Alzheimer's disease (AD) is a complex genetic disorder with no effective treatments. More than 20 common markers have been identified, which are associated with AD. Recently, several rare variants have been identified in Amyloid Precursor Protein (APP), Triggering Receptor Expressed On Myeloid Cells 2 (TREM2) and Unc-5 Netrin Receptor C (UNC5C) that affect risk for AD. Despite the many successes, the genetic architecture of AD remains unsolved. We used Genome-wide Complex Trait Analysis to (1) estimate phenotypic variance explained by genetics; (2) calculate genetic variance explained by known AD single nucleotide polymorphisms (SNPs); and (3) identify the genomic locations of variation that explain the remaining unexplained genetic variance. In total, 53.24% of phenotypic variance is explained by genetics, but known AD SNPs only explain 30.62% of the genetic variance. Of the unexplained genetic variance, approximately 41% is explained by unknown SNPs in regions adjacent to known AD SNPs, and the remaining unexplained genetic variance outside these regions.

The human genome contains “dark” gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions. Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer’s Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer’s disease gene, found in disease cases but not in controls. While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer’s disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.