The probability of edge existence due to node degree: a baseline for network-based predictions.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease.

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aß) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; ß = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; ß = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; ß = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; ß = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, ß = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, ß = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Hetnet connectivity search provides rapid insights into how two biomedical entities are related.

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

The probability of edge existence due to node degree: a baseline for network-based predictions.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree's predictive performance diminishes when the networks used for training and testing-despite measuring the same biological relationships-were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

The genetic regulation of protein expression in cerebrospinal fluid.

Studies of the genetic regulation of cerebrospinal fluid (CSF) proteins may reveal pathways for treatment of neurological diseases. 398 proteins in CSF were measured in 1,591 participants from the BioFINDER study. Protein quantitative trait loci (pQTL) were identified as associations between genetic variants and proteins, with 176 pQTLs for 145 CSF proteins (P < 1.25 × 10-10 , 117 cis-pQTLs and 59 trans-pQTLs). Ventricular volume (measured with brain magnetic resonance imaging) was a confounder for several pQTLs. pQTLs for CSF and plasma proteins were overall correlated, but CSF-specific pQTLs were also observed. Mendelian randomization analyses suggested causal roles for several proteins, for example, ApoE, CD33, and GRN in Alzheimer's disease, MMP-10 in preclinical Alzheimer's disease, SIGLEC9 in amyotrophic lateral sclerosis, and CD38, GPNMB, and ADAM15 in Parkinson's disease. CSF levels of GRN, MMP-10, and GPNMB were altered in Alzheimer's disease, preclinical Alzheimer's disease, and Parkinson's disease, respectively. These findings point to pathways to be explored for novel therapies. The novel finding that ventricular volume confounded pQTLs has implications for design of future studies of the genetic regulation of the CSF proteome.

Hetnet connectivity search provides rapid insights into how biomedical entities are related.

BACKGROUND: Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes-including genes, diseases, drugs, pathways, and anatomical structures-with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia. FINDINGS: We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. CONCLUSION: We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy.

Lymphangiogenic therapy prevents cardiac dysfunction by ameliorating inflammation and hypertension.

The lymphatic vasculature is involved in the pathogenesis of acute cardiac injuries, but little is known about its role in chronic cardiac dysfunction. Here, we demonstrate that angiotensin II infusion induced cardiac inflammation and fibrosis at 1 week and caused cardiac dysfunction and impaired lymphatic transport at 6 weeks in mice, while co-administration of VEGFCc156s improved these parameters. To identify novel mechanisms underlying this protection, RNA sequencing analysis in distinct cell populations revealed that VEGFCc156s specifically modulated angiotensin II-induced inflammatory responses in cardiac and peripheral lymphatic endothelial cells. Furthermore, telemetry studies showed that while angiotensin II increased blood pressure acutely in all animals, VEGFCc156s-treated animals displayed a delayed systemic reduction in blood pressure independent of alterations in angiotensin II-mediated aortic stiffness. Overall, these results demonstrate that VEGFCc156s had a multifaceted therapeutic effect to prevent angiotensin II-induced cardiac dysfunction by improving cardiac lymphatic function, alleviating fibrosis and inflammation, and ameliorating hypertension.

Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer's disease.

To date, the development of disease-modifying therapies for Alzheimer's disease (AD) has largely focused on the removal of amyloid beta Aß fragments from the CNS. Proteomic profiling of patient fluids may help identify novel therapeutic targets and biomarkers associated with AD pathology. Here, we applied the Olink™ ProSeek immunoassay to measure 270 CSF and plasma proteins across 415 Aß- negative cognitively normal individuals (Aß- CN), 142 Aß-positive CN (Aß+ CN), 50 Aß- mild cognitive impairment (MCI) patients, 75 Aß+ MCI patients, and 161 Aß+ AD patients from the Swedish BioFINDER study. A validation cohort included 59 Aß- CN, 23 Aß- + CN, 44 Aß- MCI and 53 Aß+ MCI. To compare protein concentrations in patients versus controls, we applied multiple linear regressions adjusting for age, gender, medications, smoking and mean subject-level protein concentration, and corrected findings for false discovery rate (FDR, q < 0.05). We identified, and replicated, altered levels of ten CSF proteins in Aß+ individuals, including CHIT1, SMOC2, MMP-10, LDLR, CD200, EIF4EBP1, ALCAM, RGMB, tPA and STAMBP (- 0.14 < d < 1.16; q < 0.05). We also identified and replicated alterations of six plasma proteins in Aß+ individuals OSM, MMP-9, HAGH, CD200, AXIN1, and uPA (- 0.77 < d < 1.28; q < 0.05). Multiple analytes associated with cognitive performance and cortical thickness (q < 0.05). Plasma biomarkers could distinguish AD dementia (AUC = 0.94, 95% CI = 0.87-0.98) and prodromal AD (AUC = 0.78, 95% CI = 0.68-0.87) from CN. These findings reemphasize the contributions of immune markers, phospholipids, angiogenic proteins and other biomarkers downstream of, and potentially orthogonal to, Aß- and tau in AD, and identify candidate biomarkers for earlier detection of neurodegeneration.

Genome-wide RNAi screen reveals ALK1 mediates LDL uptake and transcytosis in endothelial cells.

In humans and animals lacking functional LDL receptor (LDLR), LDL from plasma still readily traverses the endothelium. To identify the pathways of LDL uptake, a genome-wide RNAi screen was performed in endothelial cells and cross-referenced with GWAS-data sets. Here we show that the activin-like kinase 1 (ALK1) mediates LDL uptake into endothelial cells. ALK1 binds LDL with lower affinity than LDLR and saturates only at hypercholesterolemic concentrations. ALK1 mediates uptake of LDL into endothelial cells via an unusual endocytic pathway that diverts the ligand from lysosomal degradation and promotes LDL transcytosis. The endothelium-specific genetic ablation of Alk1 in Ldlr-KO animals leads to less LDL uptake into the aortic endothelium, showing its physiological role in endothelial lipoprotein metabolism. In summary, identification of pathways mediating LDLR-independent uptake of LDL may provide unique opportunities to block the initiation of LDL accumulation in the vessel wall or augment hepatic LDLR-dependent clearance of LDL.

Identification of 15 genetic loci associated with risk of major depression in individuals of European descent.

Despite strong evidence supporting the heritability of major depressive disorder (MDD), previous genome-wide studies were unable to identify risk loci among individuals of European descent. We used self-report data from 75,607 individuals reporting clinical diagnosis of depression and 231,747 individuals reporting no history of depression through 23andMe and carried out meta-analysis of these results with published MDD genome-wide association study results. We identified five independent variants from four regions associated with self-report of clinical diagnosis or treatment for depression. Loci with a P value <1.0 × 10(-5) in the meta-analysis were further analyzed in a replication data set (45,773 cases and 106,354 controls) from 23andMe. A total of 17 independent SNPs from 15 regions reached genome-wide significance after joint analysis over all three data sets. Some of these loci were also implicated in genome-wide association studies of related psychiatric traits. These studies provide evidence for large-scale consumer genomic data as a powerful and efficient complement to data collected from traditional means of ascertainment for neuropsychiatric disease genomics.

The 4p16.3 Parkinson Disease Risk Locus Is Associated with GAK Expression and Genes Involved with the Synaptic Vesicle Membrane.

Genome-wide association studies (GWAS) have identified the GAK/DGKQ/IDUA region on 4p16.3 among the top three risk loci for Parkinson's disease (PD), but the specific gene and risk mechanism are unclear. Here, we report transcripts containing the 3' clathrin-binding domain of GAK identified by RNA deep-sequencing in post-mortem human brain tissue as having increased expression in PD. Furthermore, carriers of 4p16.3 PD GWAS risk SNPs show decreased expression of one of these transcripts, GAK25 (Gencode Transcript 009), which correlates with the expression of genes functioning in the synaptic vesicle membrane. Together, these findings provide strong evidence for GAK clathrin-binding- and J-domain transcripts' influence on PD pathogenicity, and for a role for GAK in regulating synaptic function in PD.

Multiple genes influence BMI on chromosome 7q31-34: the NHLBI Family Heart Study.

The National Heart, Lung, and Blood Institute Family Heart Study (FHS) genome-wide linkage scan identified a region of chromosome 7q31-34 with a lod score of 4.9 for BMI at D7S1804 (131.9 Mb). We report the results of linkage and association to BMI in this region for two independent FHS samples. The first sample includes 225 FHS pedigrees with evidence of linkage to 7q31-34, using 1,132 single-nucleotide polymorphisms (SNPs) and 7 microsatellites. The second represents a case-control sample (318 cases; BMI >25 and 325 controls; BMI <25) derived from unrelated FHS participants who were not part of the genome scan. The latter set was genotyped for 606 SNPs, including 37 SNPs with prior evidence for association in the linked families. Although variance components linkage analysis using only SNPs generated a peak lod score that coincided with the original linkage scan at 131.9 Mb, a conditional linkage analysis showed evidence of a second quantitative trait locus (QTL) near 143 cM influencing BMI. Three SNPs (rs161339, rs12673281, and rs1993068) located near the three genes pleiotrophin (PTN), diacylglycerol (DAG) kinase iota (DGK iota), and cholinergic receptor, muscarinic 2 (CHRM2) demonstrated significant association in both linked families (P = 0.0005, 0.002, and 0.03, respectively) and the case-control sample (P = 0.01, 0.0003, and 0.03, respectively), regardless of the genetic model tested. These findings suggest that several genes may be associated with BMI in the 7q31-34 region.

A genome-wide association study of pulmonary function measures in the Framingham Heart Study.

The ratio of forced expiratory volume in one second to forced vital capacity (FEV(1)/FVC) is a measure used to diagnose airflow obstruction and is highly heritable. We performed a genome-wide association study in 7,691 Framingham Heart Study participants to identify single-nucleotide polymorphisms (SNPs) associated with the FEV(1)/FVC ratio, analyzed as a percent of the predicted value. Identified SNPs were examined in an independent set of 835 Family Heart Study participants enriched for airflow obstruction. Four SNPs in tight linkage disequilibrium on chromosome 4q31 were associated with the percent predicted FEV(1)/FVC ratio with p-values of genome-wide significance in the Framingham sample (best p-value = 3.6e-09). One of the four chromosome 4q31 SNPs (rs13147758; p-value 2.3e-08 in Framingham) was genotyped in the Family Heart Study and produced evidence of association with the same phenotype, percent predicted FEV(1)/FVC (p-value = 2.0e-04). The effect estimates for association in the Framingham and Family Heart studies were in the same direction, with the minor allele (G) associated with higher FEV(1)/FVC ratio levels. Results from the Family Heart Study demonstrated that the association extended to FEV(1) and dichotomous airflow obstruction phenotypes, particularly among smokers. The SNP rs13147758 was associated with the percent predicted FEV(1)/FVC ratio in independent samples from the Framingham and Family Heart Studies producing a combined p-value of 8.3e-11, and this region of chromosome 4 around 145.68 megabases was associated with COPD in three additional populations reported in the accompanying manuscript. The associated SNPs do not lie within a gene transcript but are near the hedgehog-interacting protein (HHIP) gene and several expressed sequence tags cloned from fetal lung. Though it is unclear what gene or regulatory effect explains the association, the region warrants further investigation.

The Gly2019Ser mutation in LRRK2 is not fully penetrant in familial Parkinson's disease: the GenePD study.

BACKGROUND: We report age-dependent penetrance estimates for leucine-rich repeat kinase 2 (LRRK2)-related Parkinson's disease (PD) in a large sample of familial PD. The most frequently seen LRRK2 mutation, Gly2019Ser (G2019S), is associated with approximately 5 to 6% of familial PD cases and 1 to 2% of idiopathic cases, making it the most common known genetic cause of PD. Studies of the penetrance of LRRK2 mutations have produced a wide range of estimates, possibly due to differences in study design and recruitment, including in particular differences between samples of familial PD versus sporadic PD. METHODS: A sample, including 903 affected and 58 unaffected members from 509 families ascertained for having two or more PD-affected members, 126 randomly ascertained PD patients and 197 controls, was screened for five different LRRK2 mutations. Penetrance was estimated in families of LRRK2 carriers with consideration of the inherent bias towards increased penetrance in a familial sample. RESULTS: Thirty-one out of 509 families with multiple cases of PD (6.1%) were found to have 58 LRRK2 mutation carriers (6.4%). Twenty-nine of the 31 families had G2019S mutations while two had R1441C mutations. No mutations were identified among controls or unaffected relatives of PD cases. Nine PD-affected relatives of G2019S carriers did not carry the LRRK2 mutation themselves. At the maximum observed age range of 90 to 94 years, the unbiased estimated penetrance was 67% for G2019S families, compared with a baseline PD risk of 17% seen in the non-LRRK2-related PD families. CONCLUSION: Lifetime penetrance of LRRK2 estimated in the unascertained relatives of multiplex PD families is greater than that reported in studies of sporadically ascertained LRRK2 cases, suggesting that inherited susceptibility factors may modify the penetrance of LRRK2 mutations. In addition, the presence of nine PD phenocopies in the LRRK2 families suggests that these susceptibility factors may also increase the risk of non-LRRK2-related PD. No differences in penetrance were found between men and women, suggesting that the factors that influence penetrance for LRRK2 carriers are independent of the factors which increase PD prevalence in men.

Replication of association between ELAVL4 and Parkinson disease: the GenePD study.

Genetic variants in embryonic lethal, abnormal vision, Drosophila-like 4 (ELAVL4) have been reported to be associated with onset age of Parkinson disease (PD) or risk for PD affection in Caucasian populations. In the current study we genotyped three single nucleotide polymorphisms in ELAVL4 in a Caucasian study sample consisting of 712 PD patients and 312 unrelated controls from the GenePD study. The minor allele of rs967582 was associated with increased risk of PD (odds ratio = 1.46, nominal P value = 0.011) in the GenePD population. The minor allele of rs967582 was also the risk allele for PD affection or earlier onset age in the previously studied populations. This replication of association with rs967582 in a third cohort further implicates ELAVL4 as a PD susceptibility gene.

Polymorphisms near EXOC4 and LRGUK on chromosome 7q32 are associated with Type 2 Diabetes and fasting glucose; the NHLBI Family Heart Study.

BACKGROUND: The chromosome 7q32 region is linked to metabolic syndrome and obesity related traits in the Family Heart Study. As part of a fine mapping study of the region, we evaluated the relationship of polymorphisms to fasting glucose levels and Type 2 diabetes. METHODS: Thirty-nine HapMap defined tag SNPs in a 1.08 Mb region and a novel deletion polymorphism were genotyped in 2,603 participants of the NHLBI Family Heart Study (FHS). Regression modeling, adjusting for BMI, age, sex, smoking and the TCF7L2 polymorphism, was used to evaluate the association of these polymorphisms with T2D and fasting glucoses levels. RESULTS: The deletion polymorphism confers a protective effect for T2D, with homozygous deletion carriers having a 53% reduced risk compared to non-deleted carriers. Among non-diabetics, the deletion was significantly associated with lower fasting glucose levels in men (p = 0.038) but not women (p = 0.118). In addition, seven SNPs near the deletion were significantly associated (p < 0.01) to diabetes. CONCLUSION: Chromosome 7q32 contains both SNPs and a deletion that were associated to T2D. Although the deletion region contains several islands of strongly conserved sequence, it is not known to contain a transcribed gene. The closest nearby gene, EXOC4, is involved in insulin-stimulated glucose transport and may be a candidate for this association. Further work is needed to determine if the deletion represents a functional variant or may be in linkage disequilibrium with a functional mutation influencing EXOC4 or another nearby gene.