Genome-wide association studies (GWAS) have identified several risk variants for late-onset Alzheimer's disease (LOAD)1,2. These common variants have replicable but small effects on LOAD risk and generally do not have obvious functional effects. Low-frequency coding variants, not detected by GWAS, are predicted to include functional variants with larger effects on risk. To identify low frequency coding variants with large effects on LOAD risk, we performed whole exome-sequencing (WES) in 14 large LOAD families and follow-up analyses of the candidate variants in several large case-control datasets. A rare variant in PLD3 (phospholipase-D family, member 3, rs145999145; V232M) segregated with disease status in two independent families and doubled risk for AD in seven independent case-control series (V232M meta-analysis; OR= 2.10, CI=1.47-2.99; p= 2.93×10-5, 11,354 cases and controls of European-descent). Gene-based burden analyses in 4,387 cases and controls of European-descent and 302 African American cases and controls, with complete sequence data for PLD3, indicate that several variants in this gene increase risk for AD in both populations (EA: OR= 2.75, CI=2.05-3.68; p=1.44×10-11, AA: OR= 5.48, CI=1.77-16.92; p=1.40×10-3). PLD3 is highly expressed in brain regions vulnerable to AD pathology, including hippocampus and cortex, and is expressed at lower levels in neurons from AD brains compared to control brains (p=8.10×10-10). Over-expression of PLD3 leads to a significant decrease in intracellular APP and extracellular Aβ42 and Aβ40, while knock-down of PLD3 leads to a significant increase in extracellular Aβ42 and Aβ40. Together, our genetic and functional data indicate that carriers of PLD3 coding variants have a two-fold increased risk for LOAD and that PLD3 influences APP processing. This study provides an example of how densely affected families may be used to identify rare variants with large effects on risk for disease or other complex traits.

A genome scan meta-analysis (GSMA) was carried out on 32 independent genome-wide linkage scan analyses that included 3255 pedigrees with 7413 genotyped cases affected with schizophrenia (SCZ) or related disorders. The primary GSMA divided the autosomes into 120 bins, rank-ordered the bins within each study according to the most positive linkage result in each bin, summed these ranks (weighted for study size) for each bin across studies and determined the empirical probability of a given summed rank (PSR) by simulation. Suggestive evidence for linkage was observed in two single bins, on chromosomes 5q (142-168 Mb) and 2q (103-134 Mb). Genome-wide evidence for linkage was detected on chromosome 2q (119-152 Mb) when bin boundaries were shifted to the middle of the previous bins. The primary analysis met empirical criteria for ‘aggregate’ genome-wide significance, indicating that some or all of 10 bins are likely to contain loci linked to SCZ, including regions of chromosomes 1, 2q, 3q, 4q, 5q, 8p and 10q. In a secondary analysis of 22 studies of European-ancestry samples, suggestive evidence for linkage was observed on chromosome 8p (16-33 Mb). Although the newer genome-wide association methodology has greater power to detect weak associations to single common DNA sequence variants, linkage analysis can detect diverse genetic effects that segregate in families, including multiple rare variants within one locus or several weakly associated loci in the same region. Therefore, the regions supported by this meta-analysis deserve close attention in future studies.

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.

Rare variants in TREM2 cause susceptibility to late-onset Alzheimer's disease. Here we use microarray-based expression data generated from 101 neuropathologically normal individuals and covering 10 brain regions, including the hippocampus, to understand TREM2 biology in human brain. Using network analysis, we detect a highly preserved TREM2-containing module in human brain, show that it relates to microglia, and demonstrate that TREM2 is a hub gene in 5 brain regions, including the hippocampus, suggesting that it can drive module function. Using enrichment analysis we show significant overrepresentation of genes implicated in the adaptive and innate immune system. Inspection of genes with the highest connectivity to TREM2 suggests that it plays a key role in mediating changes in the microglial cytoskeleton necessary not only for phagocytosis, but also migration. Most importantly, we show that the TREM2-containing module is significantly enriched for genes genetically implicated in Alzheimer's disease, multiple sclerosis, and motor neuron disease, implying that these diseases share common pathways centered on microglia and that among the genes identified are possible new disease-relevant genes.

Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P SR =0.00034, P OR =0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.

IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide, but its etiologic mechanisms are still poorly understood. Different prevalences among ethnic groups and familial aggregation, together with an increased familial risk, suggest important genetic influences on its pathogenesis. A locus for familial IgAN, called "IGAN1," on chromosome 6q22-23 has been described, without the identification of any responsible gene. The partners of the European IgAN Consortium organized a second genomewide scan in 22 new informative Italian multiplex families. A total of 186 subjects (59 affected and 127 unaffected) were genotyped and were included in a two-stage genomewide linkage analysis. The regions 4q26-31 and 17q12-22 exhibited the strongest evidence of linkage by nonparametric analysis (best P=.0025 and .0045, respectively). These localizations were also supported by multipoint parametric analysis, in which peak LOD scores of 1.83 ( alpha =0.50) and 2.56 ( alpha =0.65) were obtained using the affected-only dominant model, and by allowance for the presence of genetic heterogeneity. Our results provide further evidence for genetic heterogeneity among families with IgAN. Evidence of linkage to multiple chromosomal regions is consistent with both an oligo/polygenic and a multiple-susceptibility-gene model for familial IgAN, with small or moderate effects in determining the pathological phenotype. Although we identified new candidate regions, replication studies are required to confirm the genetic contribution to familial IgAN.

Myoclonus-dystonia (M-D) is a rare movement disorder characterized by a combination of non-epileptic myoclonic jerks and dystonia. SGCE mutations represent a major cause for familial M-D being responsible for 30%-50% of cases. After excluding SGCE mutations, we identified through a combination of linkage analysis and whole-exome sequencing KCTD17 c.434 G>A p.(Arg145His) as the only segregating variant in a dominant British pedigree with seven subjects affected by M-D. A subsequent screening in a cohort of M-D cases without mutations in SGCE revealed the same KCTD17 variant in a German family. The clinical presentation of the KCTD17-mutated cases was distinct from the phenotype usually observed in M-D due to SGCE mutations. All cases initially presented with mild myoclonus affecting the upper limbs. Dystonia showed a progressive course, with increasing severity of symptoms and spreading from the cranio-cervical region to other sites. KCTD17 is abundantly expressed in all brain regions with the highest expression in the putamen. Weighted gene co-expression network analysis, based on mRNA expression profile of brain samples from neuropathologically healthy individuals, showed that KCTD17 is part of a putamen gene network, which is significantly enriched for dystonia genes. Functional annotation of the network showed an over-representation of genes involved in post-synaptic dopaminergic transmission. Functional studies in mutation bearing fibroblasts demonstrated abnormalities in endoplasmic reticulum-dependent calcium signaling. In conclusion, we demonstrate that the KCTD17 c.434 G>A p.(Arg145His) mutation causes autosomal dominant M-D. Further functional studies are warranted to further characterize the nature of KCTD17 contribution to the molecular pathogenesis of M-D.

Genetic isolates represent exceptional resources for the mapping of complex traits but not all isolates are similar. We have selected a genetic and cultural isolate, the village of Talana from an isolated area of Sardinia, and propose that this population is suitable for the mapping of complex traits. A wealth of historical and archive data allowed the reconstruction of the demographic and genealogical history of the village. Key features of the population, which has grown slowly with no significant immigration, were defined by using a combination of historical, demographic and genetic studies. The genealogy of each Talana inhabitant was reconstructed and the main maternal and paternal lineages of the village were defined. Haplotype and phylogenetic analyses of the Y chromosome and characterisation of mitochondrial DNA haplogroups were used to determine the number of ancestral village founders. The extent of linkage disequilibrium (LD) was evaluated by the analysis of several microsatellites in chromosomal region Xq13.3, which was previously used to asses the extension of LD. Genealogical reconstructions were confirmed and reinforced by the genetic analyses, since some lineages were found to have merged prior to the beginning of the archival records, suggesting an even smaller number of founders than initially predicted. About 80% of the present-day population appears to derive from eight paternal and eleven maternal ancestral lineages. LD was found to span, on average, a 5-Mb region in Xq13.3. This suggests the possibility of identifying identical-by-descent regions associated with complex traits in a genome-wide search by using a low-density marker map. The present study emphasises the importance of combining genetic studies with genealogical and historical information.