Francesco Vallania

Rare Variants in APP, PSEN1 and PSEN2 Increase Risk for AD in Late-Onset Alzheimer's Disease Families

Pathogenic mutations in APP, PSEN1, PSEN2, MAPT and GRN have previously been linked to familial early onset forms of dementia. Mutation screening in these genes has been performed in either very small series or in single families with late onset AD (LOAD). Similarly, studies in single families have reported mutations in MAPT and GRN associated with clinical AD but no systematic screen of a large dataset has been performed to determine how frequently this occurs. We report sequence data for 439 probands from late-onset AD families with a history of four or more affected individuals. Sixty sequenced individuals (13.7%) carried a novel or pathogenic mutation. Eight pathogenic variants, (one each in APP and MAPT, two in PSEN1 and four in GRN) three of which are novel, were found in 14 samples. Thirteen additional variants, present in 23 families, did not segregate with disease, but the frequency of these variants is higher in AD cases than controls, indicating that these variants may also modify risk for disease. The frequency of rare variants in these genes in this series is significantly higher than in the 1,000 genome project (p = 5.09×10−5; OR = 2.21; 95%CI = 1.49–3.28) or an unselected population of 12,481 samples (p = 6.82×10−5; OR = 2.19; 95%CI = 1.347–3.26). Rare coding variants in APP, PSEN1 and PSEN2, increase risk for or cause late onset AD. The presence of variants in these genes in LOAD and early-onset AD demonstrates that factors other than the mutation can impact the age at onset and penetrance of at least some variants associated with AD. MAPT and GRN mutations can be found in clinical series of AD most likely due to misdiagnosis. This study clearly demonstrates that rare variants in these genes could explain an important proportion of genetic heritability of AD, which is not detected by GWAS.

Quantification of rare allelic variants from pooled genomic DNA

We report a targeted, cost-effective method to quantify rare single-nucleotide polymorphisms from pooled human genomic DNA using second-generation sequencing. We pooled DNA from 1,111 individuals and targeted four genes to identify rare germline variants. Our base-calling algorithm, SNPSeeker, derived from large deviation theory, detected single-nucleotide polymorphisms present at frequencies below the raw error rate of the sequencing platform.

Performance of Common Analysis Methods for Detecting Low-Frequency Single Nucleotide Variants in Targeted Next-Generation Sequence Data

Next-generation sequencing (NGS) is becoming a common approach for clinical testing of oncology specimens for mutations in cancer genes. Unlike inherited variants, cancer mutations may occur at low frequencies because of contamination from normal cells or tumor heterogeneity and can therefore be challenging to detect using common NGS analysis tools, which are often designed for constitutional genomic studies. We generated high-coverage (>1000×) NGS data from synthetic DNA mixtures with variant allele fractions (VAFs) of 25% to 2.5% to assess the performance of four variant callers, SAMtools, Genome Analysis Toolkit, VarScan2, and SPLINTER, in detecting low-frequency variants. SAMtools had the lowest sensitivity and detected only 49% of variants with VAFs of approximately 25%; whereas the Genome Analysis Toolkit, VarScan2, and SPLINTER detected at least 94% of variants with VAFs of approximately 10%. VarScan2 and SPLINTER achieved sensitivities of 97% and 89%, respectively, for variants with observed VAFs of 1% to 8%, with >98% sensitivity and >99% positive predictive value in coding regions. Coverage analysis demonstrated that >500× coverage was required for optimal performance. The specificity of SPLINTER improved with higher coverage, whereas VarScan2 yielded more false positive results at high coverage levels, although this effect was abrogated by removing low-quality reads before variant identification. Finally, we demonstrate the utility of high-sensitivity variant callers with data from 15 clinical lung cancers.

High-throughput discovery of rare insertions and deletions in large cohorts

Pooled-DNA sequencing strategies enable fast, accurate, and cost-effect detection of rare variants, but current approaches are not able to accurately identify short insertions and deletions (indels), despite their pivotal role in genetic disease. Furthermore, the sensitivity and specificity of these methods depend on arbitrary, user-selected significance thresholds, whose optimal values change from experiment to experiment. Here, we present a combined experimental and computational strategy that combines a synthetically engineered DNA library inserted in each run and a new computational approach named SPLINTER that detects and quantifies short indels and substitutions in large pools. SPLINTER integrates information from the synthetic library to select the optimal significance thresholds for every experiment. We show that SPLINTER detects indels (up to 4 bp) and substitutions in large pools with high sensitivity and specificity, accurately quantifies variant frequency (r = 0.999), and compares favorably with existing algorithms for the analysis of pooled sequencing data. We applied our approach to analyze a cohort of 1152 individuals, identifying 48 variants and validating 14 of 14 (100%) predictions by individual genotyping. Thus, our strategy provides a novel and sensitive method that will speed the discovery of novel disease-causing rare variants.

TATA is a modular component of synthetic promoters

The expression of most genes is regulated by multiple transcription factors. The interactions between transcription factors produce complex patterns of gene expression that are not always obvious from the arrangement of cis-regulatory elements in a promoter. One critical element of promoters is the TATA box, the docking site for the RNA polymerase holoenzyme. Using a synthetic promoter system coupled to a thermodynamic model of combinatorial regulation, we analyze the effects of different strength TATA boxes on various aspects of combinatorial cis-regulation. The thermodynamic model explains 75% of the variance in gene expression in synthetic promoter libraries with different strength TATA boxes, suggesting that many of the salient aspects of cis-regulation are captured by the model. Our results demonstrate that the effect of changing the TATA box on gene expression is the same for all synthetic promoters regardless of the arrangement of cis-regulatory sites we studied. Our analysis also showed that in our synthetic system the strength of the RNA polymerase-TATA interaction does not alter the combinatorial interactions between transcription factors, or between transcription factors and RNA polymerase. Finally, we show that although stronger TATA boxes increase expression in a predictable fashion, stronger TATA boxes have very little effect on noise in our synthetic promoters, regardless of the arrangement of cis-regulatory sites. Our results support a modular model of promoter function, where cis-regulatory elements can be mixed and matched (programmed) with outcomes on expression that are predictable based on the rules of simple protein-protein and protein-DNA interactions.

Genome-wide discovery of functional transcription factor binding sites by comparative genomics: the case of Stat3.

The identification of direct targets of transcription factors is a key problem in the study of gene regulatory networks. However, the use of high throughput experimental methods, such as ChIP-chip and ChIP-sequencing, is limited by their high cost and strong dependence on cellular type and context. We developed a computational method for the genome-wide identification of functional transcription factor binding sites based on positional weight matrices, comparative genomics, and gene expression profiling. The method was applied to Stat3, a transcription factor playing crucial roles in inflammation, immunity and oncogenesis, and able to induce distinct subsets of target genes in different cell types or conditions. A newly generated positional weight matrix enabled us to assign affinity scores of high specificity, as measured by EMSA competition assays. Phylogenetic conservation with 7 vertebrate species was used to select the binding sites most likely to be functional. Validation was carried out on predicted sites within genes identified as differentially expressed in the presence or absence of Stat3 by microarray analysis. Twelve of the fourteen sites tested were bound by Stat3 in vivo, as assessed by Chromatin Immunoprecipitation, allowing us to identify 9 Stat3 transcriptional targets. Given its high validation rate, and the availability of large transcription factor-dependent gene expression datasets obtained under diverse experimental conditions, our approach appears to be a valid alternative to high-throughput experimental assays for the discovery of novel direct targets of transcription factors.

Cardiac signaling genes exhibit unexpected sequence diversity in sporadic cardiomyopathy, revealing HSPB7 polymorphisms associated with disease.

Sporadic heart failure is thought to have a genetic component, but the contributing genetic events are poorly defined. Here, we used ultra-high-throughput resequencing of pooled DNAs to identify SNPs in 4 biologically relevant cardiac signaling genes, and then examined the association between allelic variants and incidence of sporadic heart failure in 2 large Caucasian populations. Resequencing of DNA pools, each containing DNA from approximately 100 individuals, was rapid, accurate, and highly sensitive for identifying common and rare SNPs; it also had striking advantages in time and cost efficiencies over individual resequencing using conventional Sanger methods. In 2,606 individuals examined, we identified a total of 129 separate SNPs in the 4 cardiac signaling genes, including 23 nonsynonymous SNPs that we believe to be novel. Comparison of allele frequencies between 625 Caucasian nonaffected controls and 1,117 Caucasian individuals with systolic heart failure revealed 12 SNPs in the cardiovascular heat shock protein gene HSPB7 with greater proportional representation in the systolic heart failure group; all 12 SNPs were confirmed in an independent replication study. These SNPs were found to be in tight linkage disequilibrium, likely reflecting a single genetic event, but none altered amino acid sequence. These results establish the power and applicability of pooled resequencing for comparative SNP association analysis of target subgenomes in large populations and identify an association between multiple HSPB7 polymorphisms and heart failure.

Rare missense variants in CHRNB4 are associated with reduced risk of nicotine dependence

Genome-wide association studies have identified common variation in the CHRNA5-CHRNA3-CHRNB4 and CHRNA6-CHRNB3 gene clusters that contribute to nicotine dependence. However, the role of rare variation in risk for nicotine dependence in these nicotinic receptor genes has not been studied. We undertook pooled sequencing of the coding regions and flanking sequence of the CHRNA5, CHRNA3, CHRNB4, CHRNA6 and CHRNB3 genes in African American and European American nicotine-dependent smokers and smokers without symptoms of dependence. Carrier status of individuals harboring rare missense variants at conserved sites in each of these genes was then compared in cases and controls to test for an association with nicotine dependence. Missense variants at conserved residues in CHRNB4 are associated with lower risk for nicotine dependence in African Americans and European Americans (AA P = 0.0025, odds-ratio (OR) = 0.31, 95% confidence-interval (CI) = 0.31-0.72; EA P = 0.023, OR = 0.69, 95% CI = 0.50-0.95). Furthermore, these individuals were found to smoke fewer cigarettes per day than non-carriers (AA P = 6.6 × 10(-5), EA P = 0.021). Given the possibility of stochastic differences in rare allele frequencies between groups replication of this association is necessary to confirm these findings. The functional effects of the two CHRNB4 variants contributing most to this association (T375I and T91I) and a missense variant in CHRNA3 (R37H) in strong linkage disequilibrium with T91I were examined in vitro. The minor allele of each polymorphism increased cellular response to nicotine (T375I P = 0.01, T91I P = 0.02, R37H P = 0.003), but the largest effect on in vitro receptor activity was seen in the presence of both CHRNB4 T91I and CHRNA3 R37H (P = 2 × 10(-6)).

The RhoU/Wrch1 Rho GTPase gene is a common transcriptional target of both the gp130/STAT3 and Wnt-1 pathways

STAT3 (signal transducer and activator of transcription 3) is a transcription factor activated by cytokines, growth factors and oncogenes, whose activity is required for cell survival/proliferation of a wide variety of primary tumours and tumour cell lines. Prominent among its multiple effects on tumour cells is the stimulation of cell migration and metastasis, whose functional mechanisms are however not completely characterized. RhoU/Wrch1 (Wnt-responsive Cdc42 homologue) is an atypical Rho GTPase thought to be constitutively bound to GTP. RhoU was first identified as a Wnt-1-inducible mRNA and subsequently shown to act on the actin cytoskeleton by stimulating filopodia formation and stress fibre dissolution. It was in addition recently shown to localize to focal adhesions and to Src-induced podosomes and enhance cell migration. RhoU overexpression in mammary epithelial cells stimulates quiescent cells to re-enter the cell cycle and morphologically phenocopies Wnt-1-dependent transformation. In the present study we show that Wnt-1-mediated RhoU induction occurs at the transcriptional level. Moreover, we demonstrate that RhoU can also be induced by gp130 cytokines via STAT3, and we identify two functional STAT3-binding sites on the mouse RhoU promoter. RhoU induction by Wnt-1 is independent of beta-catenin, but does not involve STAT3. Rather, it is mediated by the Wnt/planar cell polarity pathway through the activation of JNK (c-Jun N-terminal kinase). Both the so-called non-canonical Wnt pathway and STAT3 are therefore able to induce RhoU, which in turn may be involved in mediating their effects on cell migration.

Methods to increase reproducibility in differential gene expression via meta-analysis

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.