Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease

p53 target ANKRA2 cooperates with RFX7 to regulate tumor suppressor genes

The transcription factor regulatory factor X 7 (RFX7) has been identified as a tumor suppressor that is recurrently mutated in lymphoid cancers and appears to be dysregulated in many other cancers. RFX7 is activated by the well-known tumor suppressor p53 and regulates several other known tumor suppressor genes. However, what other factors regulate RFX7 and its target genes remains unclear. Here, reporter gene assays were used to identify that RFX7 regulates the tumor suppressor gene PDCD4 through direct interaction with its X-box promoter motif. We utilized mass spectrometry to identify factors that bind to DNA together with RFX7. In addition to RFX7, we also identified RFX5, RFXAP, RFXANK, and ANKRA2 that bind to the X-box motif in the PDCD4 promoter. We demonstrate that ANKRA2 is a bona fide direct p53 target gene. We used transcriptome analyses in two cell systems to identify genes regulated by ANKRA2, its sibling RFXANK, and RFX7. These results revealed that ANKRA2 functions as a critical cofactor of RFX7, whereas RFXANK regulates largely distinct gene sets.

Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study.

Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins

BACKGROUND

Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis.

METHODS

Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables.

RESULTS

We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways.

CONCLUSIONS

Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

Sequencing-based genome-wide association studies reporting standards

Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.

Transcription factor RFX7 governs a tumor suppressor network in response to p53 and stress

Despite its prominence, the mechanisms through which the tumor suppressor p53 regulates most genes remain unclear. Recently, the regulatory factor X 7 (RFX7) emerged as a suppressor of lymphoid neoplasms, but its regulation and target genes mediating tumor suppression remain unknown. Here, we identify a novel p53-RFX7 signaling axis. Integrative analysis of the RFX7 DNA binding landscape and the RFX7-regulated transcriptome in three distinct cell systems reveals that RFX7 directly controls multiple established tumor suppressors, including PDCD4, PIK3IP1, MXD4, and PNRC1, across cell types and is the missing link for their activation in response to p53 and stress. RFX7 target gene expression correlates with cell differentiation and better prognosis in numerous cancer types. Interestingly, we find that RFX7 sensitizes cells to Doxorubicin by promoting apoptosis. Together, our work establishes RFX7’s role as a ubiquitous regulator of cell growth and fate determination and a key node in the p53 transcriptional program.

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants

Gene-based tests of association (e.g., variance components and burden tests) are now common practice for analyses attempting to elucidate the contribution of rare genetic variants on common disease. As sequencing datasets continue to grow in size, the number of variants within each set (e.g., gene) being tested is also continuing to grow. Pathway-based methods have been used to allow for the initial aggregation of gene-based statistical evidence and then the subsequent aggregation of evidence across the pathway. This “multi-set” approach (first gene-based test, followed by pathway-based) lacks thorough exploration in regard to evaluating genotype–phenotype associations in the age of large, sequenced datasets. In particular, we wonder whether there are statistical and biological characteristics that make the multi-set approach optimal vs. simply doing all gene-based tests? In this paper, we provide an intuitive framework for evaluating these questions and use simulated data to affirm us this intuition. A real data application is provided demonstrating how our insights manifest themselves in practice. Ultimately, we find that when initial subsets are biologically informative (e.g., tending to aggregate causal genetic variants within one or more subsets, often genes), multi-set strategies can improve statistical power, with particular gains in cases where causal variants are aggregated in subsets with less variants overall (high proportion of causal variants in the subset). However, we find that there is little advantage when the sets are non-informative (similar proportion of causal variants in the subsets). Our application to real data further demonstrates this intuition. In practice, we recommend wider use of pathway-based methods and further exploration of optimal ways of aggregating variants into subsets based on emerging biological evidence of the genetic architecture of complex disease.

The Radiogenomics of Late-onset Alzheimer Disease

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that causes problems related to brain function. To some extent it is understood on a molecular level how AD arises, however there are a lack of biomarkers that can be used for early diagnosis. Two popular methods to identify AD-related biomarkers use genetics and neuroimaging. Genes and neuroimaging phenotypes have provided some insights as to the potential for AD biomarkers. While the field of imaging-genomics has identified genetic features associated with structural and functional neuroimaging phenotypes, it remains unclear how variants that affect splicing could be important for understanding the genetic etiology of AD.

METHODS

In this study, rare variants (minor allele frequency < 0.01) in splicing regulatory element (SRE) loci from whole genome sequencing (WGS) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, were used to identify genes that are associated with global brain cortical glucose metabolism in AD measured by FDG PET-scans. Gene-based associated analyses of rare variants were performed using the program BioBin and the optimal Sequence Kernel Association Test (SKAT-O).

RESULTS

The gene, EXOC3L4, was identified as significantly associated with global cortical glucose metabolism (FDR (false discovery rate) corrected p < 0.05) using SRE coding variants only. Three loci that may affect splicing within EXOC3L4 contribute to the association.

CONCLUSION

Based on sequence homology, EXOC3L4 is likely a part of the exocyst complex. Our results suggest the possibility that variants which affect proper splicing of EXOC3L4 via SREs may impact vesicle transport, giving rise to AD related phenotypes. Overall, by utilizing WGS and functional neuroimaging we have identified a gene significantly associated with an AD related endophenotype, potentially through a mechanism that involves splicing.

FANCC localizes with UNC5A at neurite outgrowth and promotes neuritogenesis

Objective

The Uncoordinated 5A (UNC5A) protein is part of a family of receptors that play roles in axonal pathfinding and cell migration. We previously showed that the Fanconi anemia C protein (FANCC) interacts with UNC5A and delays UNC5A-mediated apoptosis. FANCC is a predominantly cytoplasmic protein that has multiple functions including DNA damage signaling, oxygen radical metabolism, signal transduction, transcriptional regulation and apoptosis. Given the direct interaction between FANCC and UNC5A and that FANCC interferes with UNC5A-mediated apoptosis, we explored the possibility that FANCC might play a role in axonal-like growth processes.

Results

Here we show that FANCC and UNC5A are localized to regions of neurite outgrowth during neuronal cell differentiation. We also show that absence of FANCC is required for neurite outgrowth. In addition, FANCC seems required for UNC5A expression. Results from this study combined with our previous report suggest that FANCC plays a role in tissue development through the regulation of UNC5A-mediated functions.

Codon bias among synonymous rare variants is associated with Alzheimer's disease imaging biomarker

Alzheimer's disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD. Some synonymous codons are preferred over others leading to a codon bias. The bias can arise with respect to codons that are more or less frequently used in the genome. A bias can also result from optimal and non-optimal codons, which have stronger and weaker codon anti-codon interactions, respectively. Although association tests have been utilized before to identify genes associated with AD, it remains unclear how codon bias plays a role and if it can improve rare variant analysis. In this work, rare variants from whole-genome sequencing from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were binned into genes using BioBin. An association analysis of the genes with AD-related neuroimaging biomarker was performed using SKAT-O. While using all synonymous variants we did not identify any genomewide significant associations, using only synonymous variants that affected codon frequency we identified several genes as significantly associated with the imaging phenotype. Additionally, significant associations were found using only rare variants that contains an optimal codon in among minor alleles and a non-optimal codon in the major allele. These results suggest that codon bias may play a role in AD and that it can be used to improve detection power in rare variant association analysis.