Effective discovery of causal disease genes must overcome the statistical challenges of quantitative genetics studies and the practical limitations of human biology experiments. Here we developed diseaseQUEST, an integrative approach that combines data from human genome-wide disease studies with in silico network models of tissue- and cell-type-specific function in model organisms to prioritize candidates within functionally conserved processes and pathways. We used diseaseQUEST to predict candidate genes for 25 different diseases and traits, including cancer, longevity, and neurodegenerative diseases. Focusing on Parkinson's disease (PD), a diseaseQUEST-directed Caenhorhabditis elegans behavioral screen identified several candidate genes, which we experimentally verified and found to be associated with age-dependent motility defects mirroring PD clinical symptoms. Furthermore, knockdown of the top candidate gene, bcat-1, encoding a branched chain amino acid transferase, caused spasm-like 'curling' and neurodegeneration in C. elegans, paralleling decreased BCAT1 expression in PD patient brains. diseaseQUEST is modular and generalizable to other model organisms and human diseases of interest.
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We thank K. Yao, R. Hong, and J. Zhou for assistance with video analysis, G. Laevsky for assistance with confocal microscopy, the CGC for strains, and Z. Gitai and the laboratories of O.G.T. and C.T.M. for valuable discussion. Strain UA44 was generously provided by G. Caldwell (University of Alabama), and strain BY250 was a generous gift from R. Blakely (Vanderbilt University). V.Y. was supported in part by US NIH grant T32 HG003284. O.G.T. is supported as a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR). C.T.M. is supported as the Director of the Glenn Center for Aging Research at Princeton and as an HHMI-Simons Faculty Scholar. This work was supported by the NIH (R01 GM071966 to O.G.T. and Cognitive Aging R01 and DP1 Pioneer Award to C.T.M.).
The authors declare no competing financial interests.
Integrated supplementary information
GO enrichment analysis as performed on PD predictions with a score > 2.0 (n=609 genes). Significant GO terms are shown. Bars represent individual Benjamini p-values derived from GO enrichment analysis.
Neuron-sensitive animals (unc-119p::sid-1) were exposed to adult-only RNAi targeting 45 top candidate PD genes, and tested for thrashing defects on days 2, 5, and 8 of adulthood. Movement was analyzed using CeleST. CeleST quantification of thrashing on day 8 is shown. Control L4440 RNAi (blue), direct GWAS worm orthologs (red), and candidates independently identified using the 23andMe GWAS study (yellow) are shown. Mean ± SEM, unpaired two-sided t-test, Benjamini-Hochberg multiple hypothesis test correction, n ≥ 50 per gene (exact sample sizes per gene in Supplementary Data 13). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.
Supplementary Figure 3 Screen of Parkinson's disease–candidate genes for age-specific motor defects.
Animals were exposed to adult-only RNAi targeting 45 top candidate PD genes, and tested for thrashing defects on days 2, 5, and 8 of adulthood. Movement was analyzed using CeleST. (a) Heatmap of (hierarchically clustered) t-statistics comparing 10 CeleST movement measurements for each of the top 45 top PD gene candidates against the control L4440 RNAi on day 8 of adulthood, n ≥ 50 per gene (exact sample sizes per gene in Supplementary Data 13). (b) Pearson's correlation of t-statistics for each of the 10 CeleST movement measurements between all pairs of genes tested on days 2, 5, and 8 of adulthood. (c) Principal components were calculated using all 13,048 worms (across 45 genes and 3 days). PCA plot of RNAi-treated worms and control (aggregated by gene and day, see sample sizes in Supplementary Data 13). Colors indicate age of worm. PC1 (x-axis) and PC2 (y-axis) respectively account for 39.36% and 11.85% of the total variation. (d) Neuronal RNAi-sensitive animals were exposed to adult-only RNAi individually targeting 13 top cancer and metabolic disease predictions, bcat-1 (red) as a positive control, or the L4440 negative control. Curling was examined on day 8 using an automated analysis program (Sohrabi, et al. in preparation). Mean ± SEM. Control n=351, bcat-1 n=420, cyb-2.1 n=287, pxl-1 n=289, frm-2 n=279, mre-11 n=272, sma-4 n=286, snt-4 n=305, cdh-4 n=285, lbp-2 n=320, ani-3 n=300, hcp-1 n=264, BE0003N10.1 n=229, let-363 n=284, hil-3 n=270. n represents the number of animals per condition. One-way ANOVA with Tukey's multiple comparisons test. Control vs bcat-1i p= 4.33e-8. ****p<0.0001.
CeleST was used to analyze control and bcat-1 RNAi-treated worms on day 2, 5, and 8 of adulthood. Mean ± SEM, two-way ANOVA with Sidak's multiple comparisons test, Control: day 2 n=492, day 5 n=345, day 8 n=573. bcat-1 RNAi: day 2 n=675, day 5 n=714, day 8 n=582. Body wave number day 2 control vs bcat-1i: t=3.075, df=3375, 95% CI: (-0.2648, -0.03323), p=0.0064.
Neuron RNAi insensitive, wild-type (N2) worms treated with control (L4440) or bcat-1 RNAi do not exhibit curling on Day 8 of adulthood compared to neuron-RNAi sensitive animals (unc-119p::sid-1). Mean ± SEM, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, two-way repeated measures ANOVA, Tukey's post hoc tests. Worm thrashing videos were hand counted. (a) Control;unc-119p::sid-1 n=28 animals, bcat-1i:unc-119p::sid-1 n=41 animals, control;wild type n=24 animals, bcat-1i;wild type n=30 animals. Multiple comparisons: Control:unc-119p::sid-1 vs. bcat-1i:unc-119p::sid-1 t=3.156, df=119, 95% CI: (-18.7, -1.491), p=0.0121. Control:wild type vs. bcat-1i:wild type t=0.7787, df=119, 95% CI: (-11.98, 6.577), p=0.9684. bcat-1i:unc-119p::sid-1 vs. bcat-1i:wild type t=3.422, df=119, 95% CI: (2.272, 18.55), p=0.0051. (b) Control;unc-119p::sid-1 n=75 animals, bcat-1i:unc-119p::sid-1 n=86 animals, control;wild type n=73 animals, bcat-1i;wild type n=76 animals. Multiple comparisons: Control:unc-119p::sid-1 vs. bcat-1i:unc-119p::sid-1 t=4.305, df=306, 95% CI: (-10.68, -2.546), p=0.000135. Control:wild type vs. bcat-1i:wild type t=0.8621, df=306, 95% CI: (-5.595, 2.847), p=0.948. bcat-1i:unc-119p::sid-1 vs. bcat-1i:wild type t=4.576, df=306, 95% CI: (2.952, 11.06), p=0.00041.
Supplementary Figure 6 BCAT1 expression in selected brain regions in healthy human subjects from the Allen Brain Atlas.
Average BCAT1 expression in selected brain regions of healthy human individuals, obtained from the Allen Brain Atlas. Expression data for each of three BCAT1 probes is shown for several major brain regions, in addition to four regions that degenerate in PD. Probe A, A_23_P87528; Probe B, A_24_P52921; Probe C, A_24_P935986. Mean ± SEM. n=6 human donors for each sample from the Allen Brain Atlas database for gene expression. Box plots show minimum, first quartile, median, third quartile, and maximum values.
Supplementary Figure 7 bcat-1 knockdown does not alter ADE cell-body numbers in the presence of α-synuclein.
ADE cell bodies were counted on Day 8 in neuron-RNAi sensitive worms expressing α-synuclein and GFP in dopaminergic neurons. Mean ± SEM, unpaired two-sided Student's t-test. L4440 n=45 animals, bcat-1i n=61 animals. t=0.4156, df=104, 95% CI: (-0.3112, 0.2033), p=0.6785. The experiment was repeated three times independently with similar results. Box plots show minimum, first quartile, median, third quartile, and maximum values.
Supplementary Figure 8 The Functional Representation module is robust to data compendium size, amount of prior knowledge, and initialization state.
Semi-supervised network construction approach was applied to (a) ten progressively smaller compendia sub-sampled from the full worm compendium (without replacement) and (b) seven progressively smaller sets of tissue gene annotations subsampled from all previously known tissue genes (without replacement). Each measurement is an average of 10 independent simulations and standard error (shaded regions) is shown.
Supplementary Figures 1–8 (PDF 1540 kb)
203 tissue- and cell-type specific networks. (XLSX 19 kb)
Evaluation of 25 disease predictions. (XLSX 10 kb)
GWAS genes used as gold standard for predictions. (XLSX 39 kb)
Gene Ontology analysis of top ALS disease candidates. (XLSX 139 kb)
Gene Ontology analysis of top schizophrenia candidates. (XLSX 148 kb)
Gene Ontology analysis of top ovarian carcinoma candidates. (XLSX 93 kb)
Gene Ontology analysis of top pancreatic cancer candidates. (XLSX 99 kb)
Evaluation of tissue-specific lifespan gene predictions using human longevity GWAS input. (XLSX 5016 kb)
Parkinson's disease-associated gene predictions. (XLSX 563 kb)
Dopaminergic neuron network clustering of top PD gene predictions and functional enrichment per cluster. (XLSX 85 kb)
KEGG pathway and Gene Ontology enrichment of Parkinson's disease predictions. (XLSX 32 kb)
Prioritized PD candidate genes. (XLSX 33 kb)
CeleST worm movement measures of top candidate genes on days 2, 5, and 8. (XLSX 186 kb)
Top non-PD candidate genes tested for curling defects. (XLSX 10 kb)
Human BCAT1 expression data obtained from the Allen Brain Atlas. (XLS 178 kb)
Sleipnir Library for Computational Functional Genomics (ZIP 1518 kb)
Extending diseaseQUEST to other model organisms and diseases. (PDF 166 kb)
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Yao, V., Kaletsky, R., Keyes, W. et al. An integrative tissue-network approach to identify and test human disease genes. Nat Biotechnol 36, 1091–1099 (2018). https://doi.org/10.1038/nbt.4246
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