IntroductionLate-onset Alzheimer's disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly 1 , and risk is partially driven by genetics 2 . Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS) [3][4][5][6][7][8] . To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 x 10 -7 ) indicating that additional rare variants remain to be identified. Main TextOur previous work identified 19 genome-wide significant common variant signals in addition to APOE 9 , that influence risk for LOAD. These signals, combined with 'subthreshold' common variant associations, account for ~31% of the genetic variance of LOAD 2 , leaving the majority of genetic risk uncharacterized 10 . To search for additional signals, we conducted a GWAS metaanalysis of non-Hispanic Whites (NHW) using a larger sample (17 new, 46 total datasets) from our group, the International Genomics of Alzheimer's Project (IGAP) (composed of four AD consortia: ADGC, CHARGE, EADI, and GERAD). This sample increases our previous discovery sample (Stage 1) by 29% for cases and 13% for controls (N=21,982 cases; 41,944 controls) ( Supplementary Table 1 and 2, and Supplementary Note). To sample both common and rare variants (minor allele frequency MAF ≥ 0.01, and MAF < 0.01, respectively), we imputed the discovery datasets using a 1000 Genomes reference panel consisting of . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a 11 36,648,992 single-nucleotide variants, 1,380,736 insertions/deletions, and 13,805 structural variants. After quality control, 9,456,058 common variants and 2,024,574 rare variants were selected for analysis (a 63% increase from our previous common variant analysis in 2013).Genotype dosages were analyzed within each dataset, and then combined with meta-analysis ( Supplementary Figures 1 and 2 and Supplementary Table 3). The Stage 1 discovery metaanalysis was first followed by Stage 2 using the I-select chip we previously developed in Lambert et al (including 11,632 variants, N=18,845) and finally stage 3A (N=6,998). The final sample was 33,692 clinical AD cases and 56,077 controls.Meta-analysis of Stages 1 and 2 produced 21 associations with P ≤ 5x10 -8 (Table 1 and Figure 1). Of these, 18 were previously reported as genome-wide significant and three of them are signals not initially described in Lambert et al: the rare R47H TREM2 coding va...

Multiple sclerosis (MS), an inflammatory autoimmune demyelinating disorder of the central nervous system, is the most common cause of acquired neurological dysfunction arising in the second to fourth decades of life. A genetic component to MS is indicated by an increased relative risk of 20-40 to siblings compared to the general population (lambda s), and an increased concordance rate in monozygotic compared to dizygotic twins. Association and/or linkage studies to candidate genes have produced many reports of significant genetic effects including those for the major histocompatability complex (MHC; particularly the HLA-DR2 allele), immunoglobulin heavy chain (IgH), T-cell receptor (TCR) and myelin basic protein (MBP) loci. With the exception of the MHC, however, these results have been difficult to replicate and/or apply beyond isolated populations. We have therefore conducted a two-stage, multi-analytical genomic screen to identify genomic regions potentially harbouring MS susceptibility genes. We genotyped 443 markers and 19 such regions were identified. These included the MHC region on 6p, the only region with a consistently reported genetic effect. However, no single locus generated overwhelming evidence of linkage. Our results suggest that a multifactorial aetiology, including both environmental and multiple genetic factors of moderate effect, is more likely than an aetiology consisting of simple mendelian disease gene(s).

Deciphering the genetic landscape of Alzheimer disease (AD) is essential to define the pathophysiological pathways involved and to successfully translate genomics to potential tailored medical care. To generate the most complete knowledge of the AD genetics, we developed through the European Alzheimer Disease BioBank (EADB) consortium a discovery meta-analysis of genome-wide association studies (GWAS) based on a new large case-control study and previous GWAS (in total 39,106 clinically diagnosed cases, 46,828 proxy-AD cases and 401,577 controls) with the most promising signals followed-up in independent samples (18,063 cases and 23,207 controls). In addition to 34 known AD loci, we report here the genome-wide significant association of 31 new loci with the risk of AD. Pathway-enrichment analyses strongly indicated the involvement of gene sets related to amyloid and Tau, but also highlighted microglia, in which increased gene expression corresponds to more significant AD risk. In addition, we successfully prioritized candidate genes in the majority of our new loci, with nine being primarily expressed in microglia. Finally, we observed that a polygenic risk score generated from this new genetic landscape was strongly associated with the risk of progression from mild cognitive impairment (MCI) to dementia (4,609 MCI cases of whom 1,532 converted to dementia), independently of age and the APOE e4 allele.

To provide a definitive linkage map for multiple sclerosis, we have genotyped the Illumina BeadArray linkage mapping panel (version 4) in a data set of 730 multiplex families of Northern European descent. After the application of stringent quality thresholds, data from 4,506 markers in 2,692 individuals were included in the analysis. Multipoint nonparametric linkage analysis revealed highly significant linkage in the major histocompatibility complex (MHC) on chromosome 6p21 (maximum LOD score [MLS] 11.66) and suggestive linkage on chromosomes 17q23 (MLS 2.45) and 5q33 (MLS 2.18). This set of markers achieved a mean information extraction of 79.3% across the genome, with a Mendelian inconsistency rate of only 0.002%. Stratification based on carriage of the multiple sclerosis-associated DRB1*1501 allele failed to identify any other region of linkage with genomewide significance. However, ordered-subset analysis suggested that there may be an additional locus on chromosome 19p13 that acts independent of the main MHC locus. These data illustrate the substantial increase in power that can be achieved with use of the latest tools emerging from the Human Genome Project and indicate that future attempts to systematically identify susceptibility genes for multiple sclerosis will have to involve large sample sizes and an association-based methodology.

Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.