BackgroundWeighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn).ResultsWe assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices.ConclusionsThe results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0420-6) contains supplementary material, which is available to authorized users.

Key Points Question What genes and genomic processes underlie risk of sporadic Parkinson disease? Findings This genetic association study integrated Parkinson disease genome-wide association study data and brain-derived gene regulation data using various complementary bioinformatic tools and identified 11 candidate genes with evidence of disease-associated regulatory changes. Coexpression and protein level analyses of these genes demonstrated a significant functional association with known mendelian Parkinson disease genes. Meaning This study suggests that gene regulation data may be used to identify candidate genes and pathways involved in sporadic Parkinson disease.

Parkinson’s disease is a common incurable neurodegenerative disease. The identification of genetic variants via genome-wide association studies has considerably advanced our understanding of the Parkinson’s disease genetic risk. Understanding the functional significance of the risk loci is now a critical step towards translating these genetic advances into an enhanced biological understanding of the disease. Impaired mitophagy is a key causative pathway in familial Parkinson’s disease, but its relevance to idiopathic Parkinson’s disease is unclear. We used a mitophagy screening assay to evaluate the functional significance of risk genes identified through genome-wide association studies. We identified two new regulators of PINK1-dependent mitophagy initiation, KAT8 and KANSL1, previously shown to modulate lysine acetylation. These findings suggest PINK1-mitophagy is a contributing factor to idiopathic Parkinson’s disease. KANSL1 is located on chromosome 17q21 where the risk associated gene has long been considered to be MAPT. While our data does not exclude a possible association between the MAPT gene and Parkinson’s disease, it provides strong evidence that KANSL1 plays a crucial role in the disease. Finally, these results enrich our understanding of physiological events regulating mitophagy and establish a novel pathway for drug targeting in neurodegeneration.