The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson's disease data

Open science and collaboration are necessary to facilitate the advancement of Parkinson's disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities, thus we coordinated a virtual 3-day hackathon event, during which 49 early-career scientists from 12 countries built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools. Each team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools. Hackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early-career researchers. The resources generated can be used to accelerate research on the genetics of PD.This project was supported by the Global Parkinson’s Genetics Program (GP2). GP2 is funded by the Aligning Science Against Parkinson’s (ASAP) initiative and implemented by The Michael J. Fox Foundation for Parkinson’s Research (https://gp2.org).Open Access funding provided by the National Institutes of Health (NIH).This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Services; project numbers ZO1 AG000535 and ZO1 AG000949, as well as the National Institute of Neurological Disorders and StrokePeer reviewe

Whole Genome Sequence, Variant Discovery and Annotation in Mapuche-Huilliche Native South Americans

Whole human genome sequencing initiatives help us understand population history and the basis of genetic diseases. Current data mostly focuses on Old World populations, and the information of the genomic structure of Native Americans, especially those from the Southern Cone is scant. Here we present annotation and variant discovery from high-quality complete genome sequences of a cohort of 11 Mapuche-Huilliche individuals (HUI) from Southern Chile. We found approximately 3.1 × 10 6 single nucleotide variants (SNVs) per individual and identified 403,383 (6.9%) of novel SNVs events. Analyses of large-scale genomic events detected 680 copy number variants (CNVs) and 4,514 structural variants (SVs), including 398 and 1,910 novel events, respectively. Global ancestry composition of HUI genomes revealed that the cohort represents a sample from a marginally admixed population from the Southern Cone, whose main genetic component derives from Native American ancestors. A

Overrepresentation of Glutamate Signaling in Alzheimer's Disease: Network-Based Pathway Enrichment Using Meta-Analysis of Genome-Wide Association Studies

<div><p>Genome-wide association studies (GWAS) have successfully identified several risk loci for Alzheimer's disease (AD). Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls) derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp) associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW), defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES) procedure. Comparison of these strategies revealed that ontological sub-networks (SNs) involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10⁻¹¹, p<1.9×10⁻¹¹; GW and GATES, respectively). Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10⁻⁸) in the Alzheimer's disease Neuroimaging Initiative (ADNI) study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.</p></div

Meta-analysis 25 top hits.

<p>Chr: Chromosome; BP: Physical Position (Base Pair, NCBI 36.3/Hg18); A1: Allele 1 (Affected); A2: Allele 2 (Reference); N: Number of datasets with information; p-value: Fixed effect model p-value; OR: Odd Ratio.</p

Gene expression analysis of glutamate signaling components in selected human brain regions.

<p>Heatmap and dendrogram of normalized expression levels of the 20 genes of interest displaying significant clustering in: (A) hippocampal formation (HIF); (B) hypothalamus (HY); (C) Dorsal Thalamus (DT); and (D) white matter (WM). Heatmaps were generated using normalized Z score gene-wise expression values, which were averaged from 6 brain donor individuals (ids. H0351.2001, H0351.2002, H0351.1009, H0351.1012, H0351.1015 and H0351.1016). Bright red and green color indicates high (Z>2) and low expression (Z<2). Highly correlated gene clusters (Euclidean distance correlation coefficient r>0.7) are denoted by colored lines in the dendrograms: green clusters, indicates low expression patterns; red clusters show high levels of expression of correlated genes. Gene expression patterns in the corresponding substructures are shown for HIF: Dentate Gyrus (DG); Cornu Ammonis 1 (CA1); Cornu Ammonis 2 (CA2); Cornu Ammonis 3 (CA3); Cornu Ammonis 4 (CA4) and Subiculum (S). For HY: Anterior Hypothalamic Area (AHA); Lateral hypothalamic Area (LHA); Paraventricular Nucleus of the Hypothalamus (PVH); Supraoptic Nucleus (SO); Lateral Hypothalamic Area, Mammillary Region (LHM); Mammillary Body (MB); Posterior Hypothalamic Area (PHA); Supramammillary Nucleus (SuM); Tuberomammillary Nucleus (TM); Preoptic Region (PrOR); Arcuate Nucleus of the Hypothalamus (ARH); Dorsomedial Hypothalamic Nucleus (DMH); Lateral Hypothalamic Area, Tuberal Region (LHT); Lateral Tuberal Nucleus (LTu); Perifornical Nucleus (PeF); Ventromedial Hypothalamic Nucleus (VMH). For DT: Anterior Group of Nuclei (DTA); Caudal Group of intralaminar Nuclei (ILc); Dorsal Lateral Geneiculate Nucleus (LGd); Lateral Group of Nuclei, Dorsal Division (DTLd); Lateral Group of Nuclei, Ventral Division (DTLv); Medial Geniculate Complex (MG); Medial Group of Nuclei (DTM); Posterior Group of Nuclei (DTP); Rostral Group of Intralaminar Nuclei (ILr). For WM: Cc: Corpus callosum; Cgb: Cingulum bundle.</p

Genome-Wide Association Studies main features.

a<p>Imputed. ^bSNPs shared between the 3 studies. ^cAD + mild cognitive impairment (MCI) individuals.</p

Genome wide meta-analysis results in AD.

<p>Manhattan plot showing the p-values obtained in the meta-analysis. The end and beginning of a chromosome is denoted by the change of color pattern of the SNPs (black, grey and brown dots). Genome-wide significance threshold is denoted by a red line (5.0×10⁻⁸). The Y-axis has been truncated to show all associated SNPs inside the APOE loci and to improve visualization of suggestive associations.</p

Glutamate positive sub-networks in ADNI-GW analysis.

<p>SN: Sub-network; GO ID: Gene ontology term ID; GIP: Genes in population; GISN: Genes in sub-network; TG: Total genes in SN; BP: Biological process; CC: Cellular component; MF: Molecular function.</p

GO terms enriched in Meta-GW and Meta-GATES top 3 Sub-Networks.

<p>SN: Sub-network; GO ID:Gene ontology term ID; GIP: Genes in population; GISN: Genes in sub-network; TG: Total genes in SN; BP: Biological process; CC:Cellular component; MF: Molecular function.</p

SN search results.

<p>(A) The number of significant SNs (size <50 and score>3) in Meta-GW (light green) and Meta-GATES (dark green) is shown compared with same values permuted across the FPAN: Meta-GW-Permuted (light grey) and Meta-GATES-Permuted (dark grey). (B) Score comparison of the top 10 SNs obtained in the corresponding module searches presented in (A). (C) The number of significant SNs in the replication step for ADNI-GW (light blue) and ADNI-GATES (dark blue) analysis, in comparison with their corresponding permuted controls: ADNI-GW-Permuted (light grey) and ADNI-GW-Permuted (dark grey). (D) Score comparison of the top 10 SNs obtained for each module searches presented in (C). Caped bar/points denote SD; Significant differences between real and permuted data observed in GW and GATES analysis are denoted by an asterisk and those between real and permuted data observed only in GW analysis are denoted by a plus sign (two-sided Student's t-test; p<0.01).</p