Surveying the landscape of RNA isoform diversity and expression across 9 GTEx tissues using long-read sequencing data

Even though alternative RNA splicing was discovered in 1977 (nearly 50 years ago), we still understand very little about most isoforms arising from a single gene, including in which tissues they are expressed and if their functions differ. Human gene annotations suggest remarkable transcriptional complexity, with approximately 252,798 distinct RNA isoform annotations from 62,710 gene bodies (Ensembl v109; 2023), emphasizing the need to understand their biological effects. For example, 256 gene bodies have ≥50 annotated isoforms and 30 have ≥100, where one protein-coding gene (MAPK10) even has 192 distinct RNA isoform annotations. Whether such isoform diversity results from biological noise (i.e., spurious alternative splicing) or whether it represents biological intent and specialized functions (even if subtle) remains a mystery. Recent studies by Aguzzoli-Heberle et al., Leung et al., and Glinos et al. demonstrate long-read RNAseq enables improved RNA isoform quantification for essentially any tissue, cell type, or biological condition (e.g., disease, development, aging, etc.) making it possible to better assess individual isoform expression and function. While each study provided important discoveries related to RNA isoform diversity, deeper exploration is needed. We sought, in part, to quantify real isoform usage across tissues (compared to annotations) and explore whether observed diversity is biological noise or intent. We used long-read RNAseq data from 58 GTEx samples across nine tissues (three brain, two heart, muscle, lung, liver, and cultured fibroblasts) generated by Glinos et al. and found considerable isoform diversity within and across tissues. Cerebellar hemisphere was the most transcriptionally complex tissue (22,522 distinct isoforms; 3,726 unique); liver was least diverse (12,435 isoforms; 1,039 unique). We highlight gene clusters exhibiting high tissue-specific isoform diversity per tissue (e.g., TPM1 expresses 19 in heart's atrial appendage), and specific genes (PAX6 and TPM1) that counterintuitively exhibit evidence that their expressed isoform diversity results from both biological noise and intent. We also validated 447 of the 700 new isoforms discovered by Aguzzoli-Heberle et al. and found that 88 were expressed in all nine tissues, while 58 were specific to a single tissue. This study represents a broad survey of the RNA isoform landscape, demonstrating isoform diversity across nine tissues and emphasizes the need to better understand how individual isoforms from a single gene body contribute to human health and disease.

Using deep long-read RNAseq in Alzheimer's disease brain to assess medical relevance of RNA isoform diversity

Due to alternative splicing, human protein-coding genes average over eight RNA isoforms, resulting in nearly four distinct protein coding sequences per gene. Long-read RNAseq (IsoSeq) enables more accurate quantification of isoforms, shedding light on their specific roles. To assess the medical relevance of measuring RNA isoform expression, we sequenced 12 aged human frontal cortices (6 Alzheimer's disease cases and 6 controls; 50% female) using one Oxford Nanopore PromethION flow cell per sample. Our study uncovered 53 new high-confidence RNA isoforms in medically relevant genes, including several where the new isoform was one of the most highly expressed for that gene. Specific examples include WDR4 (61%; microcephaly), MYL3 (44%; hypertrophic cardiomyopathy), and MTHFS (25%; major depression, schizophrenia, bipolar disorder). Other notable genes with new high-confidence isoforms include CPLX2 (10%; schizophrenia, epilepsy) and MAOB (9%; targeted for Parkinson's disease treatment). We identified 1,917 medically relevant genes expressing multiple isoforms in human frontal cortex, where 1,018 had multiple isoforms with different protein coding sequences, demonstrating the need to better understand how individual isoforms from a single gene body are involved in human health and disease, if at all. Exactly 98 of the 1,917 genes are implicated in brain-related diseases, including Alzheimer's disease genes such as APP (Aβ precursor protein; five), MAPT (tau protein; four), and BIN1 (eight). As proof of concept, we also found 99 differentially expressed RNA isoforms between Alzheimer's cases and controls, despite the genes themselves not exhibiting differential expression. Our findings highlight the significant knowledge gaps in RNA isoform diversity and their medical relevance. Deep long-read RNA sequencing will be necessary going forward to fully comprehend the medical relevance of individual isoforms for a "single" gene.

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins

Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10⁻¹⁰⁸) and 1.94 times higher in bacteria (p = 1.22 × 10⁻³⁵). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.

The Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

Curated variation benchmarks for challenging medically relevant autosomal genes

The repetitive nature and complexity of some medically relevant genes poses a challenge for their accurate analysis in a clinical setting. The Genome in a Bottle Consortium has provided variant benchmark sets, but these exclude nearly 400 medically relevant genes due to their repetitiveness or polymorphic complexity. Here, we characterize 273 of these 395 challenging autosomal genes using a haplotype-resolved whole-genome assembly. This curated benchmark reports over 17,000 single-nucleotide variations, 3,600 insertions and deletions and 200 structural variations each for human genome reference GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically relevant genes, including CBS, CRYAA and KCNE1. When masking these false duplications, variant recall can improve from 8% to 100%. Forming benchmarks from a haplotype-resolved whole-genome assembly may become a prototype for future benchmarks covering the whole genome.

Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

The Alzheimer's Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer's disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10-13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Long-read targeted sequencing uncovers clinicopathological associations for C9orf72-linked diseases

To examine the length of a hexanucleotide expansion in C9orf72, which represents the most frequent genetic cause of frontotemporal lobar degeneration and motor neuron disease, we employed a targeted amplification-free long-read sequencing technology: No-Amp sequencing. In our cross-sectional study, we assessed cerebellar tissue from 28 well-characterized C9orf72 expansion carriers. We obtained 3507 on-target circular consensus sequencing reads, of which 814 bridged the C9orf72 repeat expansion (23%). Importantly, we observed a significant correlation between expansion sizes obtained using No-Amp sequencing and Southern blotting (P = 5.0 × 10-4). Interestingly, we also detected a significant survival advantage for individuals with smaller expansions (P = 0.004). Additionally, we uncovered that smaller expansions were significantly associated with higher levels of C9orf72 transcripts containing intron 1b (P = 0.003), poly(GP) proteins (P = 1.3 × 10- 5), and poly(GA) proteins (P = 0.005). Thorough examination of the composition of the expansion revealed that its GC content was extremely high (median: 100%) and that it was mainly composed of GGGGCC repeats (median: 96%), suggesting that expanded C9orf72 repeats are quite pure. Taken together, our findings demonstrate that No-Amp sequencing is a powerful tool that enables the discovery of relevant clinicopathological associations, highlighting the important role played by the cerebellar size of the expanded repeat in C9orf72-linked diseases.

Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

BACKGROUND

The human genome contains "dark" gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions.

RESULTS

Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer's Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer's disease gene, found in disease cases but not in controls.

CONCLUSIONS

While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer's disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

Long-read sequencing across the C9orf72 'GGGGCC' repeat expansion: implications for clinical use and genetic discovery efforts in human disease

BACKGROUND

Many neurodegenerative diseases are caused by nucleotide repeat expansions, but most expansions, like the C9orf72 'GGGGCC' (G4C2) repeat that causes approximately 5-7% of all amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) cases, are too long to sequence using short-read sequencing technologies. It is unclear whether long-read sequencing technologies can traverse these long, challenging repeat expansions. Here, we demonstrate that two long-read sequencing technologies, Pacific Biosciences' (PacBio) and Oxford Nanopore Technologies' (ONT), can sequence through disease-causing repeats cloned into plasmids, including the FTD/ALS-causing G4C2 repeat expansion. We also report the first long-read sequencing data characterizing the C9orf72 G4C2 repeat expansion at the nucleotide level in two symptomatic expansion carriers using PacBio whole-genome sequencing and a no-amplification (No-Amp) targeted approach based on CRISPR/Cas9.

RESULTS

Both the PacBio and ONT platforms successfully sequenced through the repeat expansions in plasmids. Throughput on the MinION was a challenge for whole-genome sequencing; we were unable to attain reads covering the human C9orf72 repeat expansion using 15 flow cells. We obtained 8× coverage across the C9orf72 locus using the PacBio Sequel, accurately reporting the unexpanded allele at eight repeats, and reading through the entire expansion with 1324 repeats (7941 nucleotides). Using the No-Amp targeted approach, we attained > 800× coverage and were able to identify the unexpanded allele, closely estimate expansion size, and assess nucleotide content in a single experiment. We estimate the individual's repeat region was > 99% G4C2 content, though we cannot rule out small interruptions.

CONCLUSIONS

Our findings indicate that long-read sequencing is well suited to characterizing known repeat expansions, and for discovering new disease-causing, disease-modifying, or risk-modifying repeat expansions that have gone undetected with conventional short-read sequencing. The PacBio No-Amp targeted approach may have future potential in clinical and genetic counseling environments. Larger and deeper long-read sequencing studies in C9orf72 expansion carriers will be important to determine heterogeneity and whether the repeats are interrupted by non-G4C2 content, potentially mitigating or modifying disease course or age of onset, as interruptions are known to do in other repeat-expansion disorders. These results have broad implications across all diseases where the genetic etiology remains unclear.

Microglial translational profiling reveals a convergent APOE pathway from aging, amyloid, and tau

Microglia are central players in homeostasis and disease. Kang et al. reveal a novel ApoE-driven microglial pathway shared between aging, amyloidosis, and tauopathy that is exacerbated in females, suggesting a convergent mechanism for altering microglial reactivity during Alzheimer’s disease.

Alzheimer’s disease (AD) is an age-associated neurodegenerative disease characterized by amyloidosis, tauopathy, and activation of microglia, the brain resident innate immune cells. We show that a RiboTag translational profiling approach can bypass biases due to cellular enrichment/cell sorting. Using this approach in models of amyloidosis, tauopathy, and aging, we revealed a common set of alterations and identified a central APOE-driven network that converged on CCL3 and CCL4 across all conditions. Notably, aged females demonstrated a significant exacerbation of many of these shared transcripts in this APOE network, revealing a potential mechanism for increased AD susceptibility in females. This study has broad implications for microglial transcriptomic approaches and provides new insights into microglial pathways associated with different pathological aspects of aging and AD.

Repetitive element transcripts are elevated in the brain of C9orf72 ALS/FTLD patients

Significant transcriptome alterations are detected in the brain of patients with amyotrophic lateral sclerosis (ALS), including carriers of the C9orf72 repeat expansion and C9orf72-negative sporadic cases. Recently, the expression of repetitive element transcripts has been associated with toxicity and, while increased repetitive element expression has been observed in several neurodegenerative diseases, little is known about their contribution to ALS. To assess whether aberrant expression of repetitive element sequences are observed in ALS, we analysed RNA sequencing data from C9orf72-positive and sporadic ALS cases, as well as healthy controls. Transcripts from multiple classes and subclasses of repetitive elements (LINEs, endogenous retroviruses, DNA transposons, simple repeats, etc.) were significantly increased in the frontal cortex of C9orf72 ALS patients. A large collection of patient samples, representing both C9orf72 positive and negative ALS, ALS/FTLD, and FTLD cases, was used to validate the levels of several repetitive element transcripts. These analyses confirmed that repetitive element expression was significantly increased in C9orf72-positive compared to C9orf72-negative or control cases. While previous studies suggest an important link between TDP-43 and repetitive element biology, our data indicate that TDP-43 pathology alone is insufficient to account for the observed changes in repetitive elements in ALS/FTLD. Instead, we found that repetitive element expression positively correlated with RNA polymerase II activity in postmortem brain, and pharmacologic modulation of RNA polymerase II activity altered repetitive element expression in vitro. We conclude that increased RNA polymerase II activity in ALS/FTLD may lead to increased repetitive element transcript expression, a novel pathological feature of ALS/FTLD.

An Epigenetic Spin to ALS and FTD

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two devastating and lethal neurodegenerative diseases seen comorbidly in up to 15% of patients. Despite several decades of research, no effective treatment or disease-modifying strategies have been developed. We now understand more than before about the genetics and biology behind ALS and FTD, but the genetic etiology for the majority of patients is still unknown and the phenotypic variability observed across patients, even those carrying the same mutation, is enigmatic. Additionally, susceptibility factors leading to neuronal vulnerability in specific central nervous system regions involved in disease are yet to be identified. As the inherited but dynamic epigenome acts as a cell-specific interface between the inherited fixed genome and both cell-intrinsic mechanisms and environmental input, adaptive epigenetic changes might contribute to the ALS/FTD aspects we still struggle to comprehend. This chapter summarizes our current understanding of basic epigenetic mechanisms, how they relate to ALS and FTD, and their potential as therapeutic targets. A clear understanding of the biological mechanisms driving these two currently incurable diseases is urgent-well-needed therapeutic strategies need to be developed soon. Disease-specific epigenetic changes have already been observed in patients and these might be central to this endeavor.

Conserved DNA methylation combined with differential frontal cortex and cerebellar expression distinguishes C9orf72-associated and sporadic ALS, and implicates SERPINA1 in disease

We previously found C9orf72-associated (c9ALS) and sporadic amyotrophic lateral sclerosis (sALS) brain transcriptomes comprise thousands of defects, among which, some are likely key contributors to ALS pathogenesis. We have now generated complementary methylome data and combine these two data sets to perform a comprehensive "multi-omic" analysis to clarify the molecular mechanisms initiating RNA misregulation in ALS. We found that c9ALS and sALS patients have generally distinct but overlapping methylome profiles, and that the c9ALS- and sALS-affected genes and pathways have similar biological functions, indicating conserved pathobiology in disease. Our results strongly implicate SERPINA1 in both C9orf72 repeat expansion carriers and non-carriers, where expression levels are greatly increased in both patient groups across the frontal cortex and cerebellum. SERPINA1 expression is particularly pronounced in C9orf72 repeat expansion carriers for both brain regions, where SERPINA1 levels are strictly down regulated across most human tissues, including the brain, except liver and blood, and are not measurable in E18 mouse brain. The altered biological networks we identified contain critical molecular players known to contribute to ALS pathology, which also interact with SERPINA1. Our comprehensive combined methylation and transcription study identifies new genes and highlights that direct genetic and epigenetic changes contribute to c9ALS and sALS pathogenesis.

A novel approach for multi-SNP GWAS and its application in Alzheimer's disease

BACKGROUND

Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype.

RESULTS

Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype.

CONCLUSIONS

Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.

Evaluating the necessity of PCR duplicate removal from next-generation sequencing data and a comparison of approaches

Background

Analyzing next-generation sequencing data is difficult because datasets are large, second generation sequencing platforms have high error rates, and because each position in the target genome (exome, transcriptome, etc.) is sequenced multiple times. Given these challenges, numerous bioinformatic algorithms have been developed to analyze these data. These algorithms aim to find an appropriate balance between data loss, errors, analysis time, and memory footprint. Typical analysis pipelines require multiple steps. If one or more of these steps is unnecessary, it would significantly decrease compute time and data manipulation to remove the step. One step in many pipelines is PCR duplicate removal, where PCR duplicates arise from multiple PCR products from the same template molecule binding on the flowcell. These are often removed because there is concern they can lead to false positive variant calls. Picard (MarkDuplicates) and SAMTools (rmdup) are the two main softwares used for PCR duplicate removal.

Results

Approximately 92 % of the 17+ million variants called were called whether we removed duplicates with Picard or SAMTools, or left the PCR duplicates in the dataset. There were no significant differences between the unique variant sets when comparing the transition/transversion ratios (p = 1.0), percentage of novel variants (p = 0.99), average population frequencies (p = 0.99), and the percentage of protein-changing variants (p = 1.0). Results were similar for variants in the American College of Medical Genetics genes. Genotype concordance between NGS and SNP chips was above 99 % for all genotype groups (e.g., homozygous reference).

Conclusions

Our results suggest that PCR duplicate removal has minimal effect on the accuracy of subsequent variant calls.

Presenilin E318G variant and Alzheimer's disease risk: the Cache County study

Background

Alzheimer's disease is the leading cause of dementia in the elderly and the third most common cause of death in the United States. A vast number of genes regulate Alzheimer’s disease, including Presenilin 1 (PSEN1). Multiple studies have attempted to locate novel variants in the PSEN1 gene that affect Alzheimer's disease status. A recent study suggested that one of these variants, PSEN1 E318G (rs17125721), significantly affects Alzheimer's disease status in a large case–control dataset, particularly in connection with the APOEε4 allele.

Methods

Our study looks at the same variant in the Cache County Study on Memory and Aging, a large population-based dataset. We tested for association between E318G genotype and Alzheimer’s disease status by running a series of Fisher’s exact tests. We also performed logistic regression to test for an additive effect of E318G genotype on Alzheimer’s disease status and for the existence of an interaction between E318G and APOEε4.

Results

In our Fisher’s exact test, it appeared that APOEε4 carriers with an E318G allele have slightly higher risk for AD than those without the allele (3.3 vs. 3.8); however, the 95 % confidence intervals of those estimates overlapped completely, indicating non-significance. Our logistic regression model found a positive but non-significant main effect for E318G (p = 0.895). The interaction term between E318G and APOEε4 was also non-significant (p = 0.689).

Conclusions

Our findings do not provide significant support for E318G as a risk factor for AD in APOEε4 carriers. Our calculations indicated that the overall sample used in the logistic regression models was adequately powered to detect the sort of effect sizes observed previously. However, the power analyses of our Fisher’s exact tests indicate that our partitioned data was underpowered, particularly in regards to the low number of E318G carriers, both AD cases and controls, in the Cache county dataset. Thus, the differences in types of datasets used may help to explain the difference in effect magnitudes seen. Analyses in additional case–control datasets will be required to understand fully the effect of E318G on Alzheimer's disease status.

Variants in ACPP are associated with cerebrospinal fluid Prostatic Acid Phosphatase levels

BACKGROUND

Prostatic Acid Phosphatase (PAP) is an enzyme that is produced primarily in the prostate and functions as a cell growth regulator and potential tumor suppressor. Understanding the genetic regulation of this enzyme is important because PAP plays an important role in prostate cancer and is expressed in other tissues such as the brain.

METHODS

We tested association between 5.8 M SNPs and PAP levels in cerebrospinal fluid across 543 individuals in two datasets using linear regression. We then performed meta-analyses using METAL =with a significance threshold of p < 5 × 10(-8) and removed SNPs where the direction of the effect was different between the two datasets, identifying 289 candidate SNPs that affect PAP cerebrospinal fluid levels. We analyzed each of these SNPs individually and prioritized SNPs that had biologically meaningful functional annotations in wANNOVAR (e.g. non-synonymous, stop gain, 3' UTR, etc.) or had a RegulomeDB score less than 3.

RESULTS

Thirteen SNPs met our criteria, suggesting they are candidate causal alleles that underlie ACPP regulation and expression.

CONCLUSIONS

Given PAP's expression in the brain and its role as a cell-growth regulator and tumor suppressor, our results have important implications in brain health such as cancer and other brain diseases including neurodegenerative diseases (e.g., Alzheimer's disease and Parkinson's disease) and mental health (e.g., anxiety, depression, and schizophrenia).

Variants in CCL16 are associated with blood plasma and cerebrospinal fluid CCL16 protein levels

Background

CCL16 is a chemokine predominantly expressed in the liver, but is also found in the blood and brain, and is known to play important roles in immune response and angiogenesis. Little is known about the gene’s regulation.

Methods

Here, we test for potential causal SNPs that affect CCL16 protein levels in both blood plasma and cerebrospinal fluid in a genome-wide association study across two datasets. We then use METAL to performed meta-analyses with a significance threshold of p < 5x10⁻⁸. We removed SNPs where the direction of the effect was different between the two datasets.

Results

We identify 10 SNPs associated with increased CCL16 protein levels in both biological fluids.

Conclusions

Our results will help understand CCL16’s regulation, allowing researchers to better understand the gene’s effects on human health.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2788-x) contains supplementary material, which is available to authorized users.

SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences

Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, ease-of-use, etc.). Our algorithm addresses these challenges while being the most comprehensive and correct SSR detection software available. SA-SSR is 100% accurate and detected >1000 more SSRs than the second best algorithm, while offering greater control to the user than any existing software.

AVAILABILITY AND IMPLEMENTATION

SA-SSR is freely available at http://github.com/ridgelab/SA-SSR CONTACT: perry.ridge@byu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Liver kinase B1 inhibits the expression of inflammation-related genes postcontraction in skeletal muscle

Skeletal muscle-specific liver kinase B1 (LKB1) knockout mice (skmLKB1-KO) exhibit elevated mitogen-activated protein kinase (MAPK) signaling after treadmill running. MAPK activation is also associated with inflammation-related signaling in skeletal muscle. Since exercise can induce muscle damage, and inflammation is a response triggered by damaged tissue, we therefore hypothesized that LKB1 plays an important role in dampening the inflammatory response to muscle contraction, and that this may be due in part to increased susceptibility to muscle damage with contractions in LKB1-deficient muscle. Here we studied the inflammatory response and muscle damage with in situ muscle contraction or downhill running. After in situ muscle contractions, the phosphorylation of both NF-κB and STAT3 was increased more in skmLKB1-KO vs. wild-type (WT) muscles. Analysis of gene expression via microarray and RT-PCR shows that expression of many inflammation-related genes increased after contraction only in skmLKB1-KO muscles. This was associated with mild skeletal muscle fiber membrane damage in skmLKB1-KO muscles. Gene markers of oxidative stress were also elevated in skmLKB1-KO muscles after contraction. Using the downhill running model, we observed significantly more muscle damage after running in skmLKB1-KO mice, and this was associated with greater phosphorylation of both Jnk and STAT3 and increased expression of SOCS3 and Fos. In conclusion, we have shown that the lack of LKB1 in skeletal muscle leads to an increased inflammatory state in skeletal muscle that is exacerbated by muscle contraction. Increased susceptibility of the muscle to damage may underlie part of this response.

Interaction between variants in CLU and MS4A4E modulates Alzheimer's disease risk

INTRODUCTION

Ebbert et al. reported gene-gene interactions between rs11136000-rs670139 (CLU-MS4A4E) and rs3865444-rs670139 (CD33-MS4A4E). We evaluate these interactions in the largest data set for an epistasis study.

METHODS

We tested interactions using 3837 cases and 4145 controls from Alzheimer's Disease Genetics Consortium using meta-analyses and permutation analyses. We repeated meta-analyses stratified by apolipoprotein E (APOE) ε4 status, estimated combined odds ratio (OR) and population attributable fraction (cPAF), and explored causal variants.

RESULTS

Results support the CLU-MS4A4E interaction and a dominant effect. An association between CLU-MS4A4E and APOE ε4 negative status exists. The estimated synergy factor, OR, and cPAF for rs11136000-rs670139 are 2.23, 2.45, and 8.0, respectively. We identified potential causal variants.

DISCUSSION

We replicated the CLU-MS4A4E interaction in a large case-control series and observed APOE ε4 and possible dominant effect. The CLU-MS4A4E OR is higher than any Alzheimer's disease locus except APOE ε4, APP, and TREM2. We estimated an 8% decrease in Alzheimer's disease incidence without CLU-MS4A4E risk alleles and identified potential causal variants.

Population-based analysis of cholesteryl ester transfer protein identifies association between I405V and cognitive decline: the Cache County Study

Cholesterol has been implicated in the pathogenesis of late-onset Alzheimer's disease (LOAD) and the cholesteryl ester transfer protein (CETP) is critical to cholesterol regulation within the cell, making CETP an Alzheimer's disease candidate gene. Several studies have suggested that CETP I405V (rs5882) is associated with cognitive function and LOAD risk, but findings vary and most studies have been conducted using relatively small numbers of samples. To test whether this variant is involved in cognitive function and LOAD progression, we genotyped 4486 subjects with up to 12 years of longitudinal cognitive assessment. Analyses revealed an average 0.6-point decrease per year in the rate of cognitive decline for each additional valine (p < 0.011). We failed to detect the association between CETP I405V and LOAD status (p < 0.28). We conclude that CETP I405V is associated with preserved cognition over time but is not associated with LOAD status.

Variant Tool Chest: an improved tool to analyze and manipulate variant call format (VCF) files

Background

Since the advent of next-generation sequencing many previously untestable hypotheses have been realized. Next-generation sequencing has been used for a wide range of studies in diverse fields such as population and medical genetics, phylogenetics, microbiology, and others. However, this novel technology has created unanticipated challenges such as the large numbers of genetic variants. Each caucasian genome has more than four million single nucleotide variants, insertions and deletions, copy number variants, and structural variants. Several formats have been suggested for storing these variants; however, the variant call format (VCF) has become the community standard.

Results

We developed new software called the Variant Tool Chest (VTC) to provide much needed tools to work with VCF files. VTC provides a variety of tools for manipulating, comparing, and analyzing VCF files beyond the functionality of existing tools. In addition, VTC was written to be easily extended with new tools.

Conclusions

Variant Tool Chest brings new and important functionality that complements and integrates well with existing software. VTC is available at https://github.com/mebbert/VariantToolChest

Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics

BACKGROUND

Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups.

METHODS

We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase PCR was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier.

RESULTS

In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% luminal A, 20.5% luminal B, 13.0% HER2-enriched, 9.8% basal-like, and 3.6% normal-like. Among low-risk endocrine-positive tumors (i.e., estrogen and progesterone receptor positive by immunohistochemistry, HER2 negative, and low histologic grade), only 76.5% were categorized as luminal A by PAM50. Continuous-scale luminal A, luminal B, HER2-enriched, and normal-like scores from PAM50 were mutually positively correlated. Basal-like score was inversely correlated with other subtypes. The proportion with non-luminal A subtype decreased with older age at diagnosis, P Trend < 0.0001. Compared with non-Hispanic Whites, African American women were more likely to have basal-like tumors, age-adjusted OR = 4.4 [95% confidence intervals (CI), 2.3-8.4], whereas Asian and Pacific Islander women had reduced odds of basal-like subtype, OR = 0.5 (95% CI, 0.3-0.9).

CONCLUSIONS

Our data indicate that over 50% of breast cancers treated in the community have luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathologic criteria to higher-risk subtypes.

IMPACT

Subtyping in a population-based cohort revealed distinct profiles by age and race.

Genetic discoveries in AD using CSF amyloid and tau

The use of cerebrospinal fluid levels of Aβ42 and pTau181 as endophenotypes for genetic studies of Alzheimer's disease (AD) has led to successful identification of both rare and common AD risk variants. In addition, this approach has provided meaningful hypotheses for the biological mechanisms by which known AD risk variants modulate the disease process. In this article we discuss these successes and outline challenges to effective and continued applications of this approach. We contrast the statistical power of this approach with traditional case-control designs and discuss solutions to address challenges in quality control and data analysis for these phenotypes. Finally, we discuss the potential for the use of this approach with larger samples as well as the incorporation of next generation sequencing and for future work with other endophenotypes for AD.

Application of a new informatics tool for contamination screening in the HIV sequencing laboratory

BACKGROUND

Current HIV-1 sequencing-based methods for detecting drug resistance-associated mutations are open and susceptible to contamination. Informatic identification of clinical sequences that are nearly identical to one another may indicate specimen-to-specimen contamination or another laboratory-associated issue.

OBJECTIVES

To design an informatic tool to rapidly identify potential contamination in the clinical laboratory using sequence analysis and to establish reference ranges for sequence variation in the HIV-1 protease and reverse transcriptase regions among a U.S. patient population.

STUDY DESIGN

We developed an open-source tool named HIV Contamination Detection (HIVCD). HIVCD was utilized to make pairwise comparisons of nearly 8000 partial HIV-1 pol gene sequences from patients across the United States and to calculate percent identities (PIDs) for each pair. ROC analysis and standard deviations of PID data were used to determine reference ranges for between-patient and within-patient comparisons and to guide selection of a threshold for identifying abnormally high PID between two unrelated sequences.

RESULTS

The PID reference range for between-patient comparisons ranged from 83.8 to 95.7% while within-patient comparisons ranged from 96 to 100%. Interestingly, 48% of between-patient sequence pairs with a PID>96.5 were geographically related. The selected threshold for abnormally high PIDs was 96 (AUC=0.993, sensitivity=0.980, specificity=0.999). During routine use, HIVCD identified a specimen mix-up and the source of contamination of a negative control.

CONCLUSIONS

In our experience, HIVCD is easily incorporated into laboratory workflow, useful for identifying potential laboratory errors, and contributes to quality testing. This type of analysis should be incorporated into routine laboratory practice.

PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer

To identify a group of patients who might benefit from the addition of weekly paclitaxel to conventional anthracycline-containing chemotherapy as adjuvant therapy of node-positive operable breast cancer. The predictive value of PAM50 subtypes and the 11-gene proliferation score contained within the PAM50 assay were evaluated in 820 patients from the GEICAM/9906 randomized phase III trial comparing adjuvant FEC to FEC followed by weekly paclitaxel (FEC-P). Multivariable Cox regression analyses of the secondary endpoint of overall survival (OS) were performed to determine the significance of the interaction between treatment and the (1) PAM50 subtypes, (2) PAM50 proliferation score, and (3) clinical and pathological variables. Similar OS analyses were performed in 222 patients treated with weekly paclitaxel versus paclitaxel every 3 weeks in the CALGB/9342 and 9840 metastatic clinical trials. In GEICAM/9906, with a median follow up of 8.7 years, OS of the FEC-P arm was significantly superior compared to the FEC arm (unadjusted HR = 0.693, p = 0.013). A benefit from paclitaxel was only observed in the group of patients with a low PAM50 proliferation score (unadjusted HR = 0.23, p < 0.001; and interaction test, p = 0.006). No significant interactions between treatment and the PAM50 subtypes or the various clinical–pathological variables, including Ki-67 and histologic grade, were identified. Finally, similar OS results were obtained in the CALGB data set, although the interaction test did not reach statistical significance (p = 0.109). The PAM50 proliferation score identifies a subset of patients with a low proliferation status that may derive a larger benefit from weekly paclitaxel.

PAM50 breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular markers

Background

Many methodologies have been used in research to identify the “intrinsic” subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions.

Methods

We used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and “intrinsic” subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments.

Results

ESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis.

Conclusions

The standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic.

Agreement in risk prediction between the 21-gene recurrence score assay (Oncotype DX®) and the PAM50 breast cancer intrinsic Classifier™ in early-stage estrogen receptor-positive breast cancer

PURPOSE

To compare risk assignment by PAM50 Breast Cancer Intrinsic Classifier™ and Oncotype DX_Recurrence Score (RS) in the same population.

METHODS

RNA was extracted from 151 estrogen receptor (ER)+ stage I-II breast cancers and gene expression profiled using PAM50 "intrinsic" subtyping test.

RESULTS

One hundred eight cases had complete molecular information; 103 (95%) were classified as luminal A (n = 76) or luminal B (n = 27). Ninety-two percent (n = 98) had a low (n = 59) or intermediate (n = 39) RS. Among luminal A cancers, 70% had low (n = 53) and the remainder (n = 23) had an intermediate RS. Among luminal B cancers, nine were high (33%) and 13 were intermediate (48%) by the RS. Almost all cancers with a high RS were classified as luminal B (90%, n = 9). One high RS cancer was identified as basal-like and had low ER/ESR1 and low human epidermal growth factor receptor 2 (HER2) expression by quantitative polymerase chain reaction in both assays. The majority of low RS cases were luminal A (83%, n = 53). Importantly, half of the intermediate RS cancers were re-categorized as low risk luminal A subtype by PAM50.

CONCLUSION

There is good agreement between the two assays for high (i.e., luminal B or RS > 31) and low (i.e., luminal B or RS < 18) prognostic risk assignment but PAM50 assigns more patients to the low risk category. About half of the intermediate RS group was reclassified as luminal A by PAM50.

Responsiveness of intrinsic subtypes to adjuvant anthracycline substitution in the NCIC.CTG MA.5 randomized trial

PURPOSE

Recent studies suggest that intrinsic breast cancer subtypes may differ in their responsiveness to specific chemotherapy regimens. We examined this hypothesis on NCIC.CTG MA.5, a clinical trial randomizing premenopausal women with node-positive breast cancer to adjuvant CMF (cyclophosphamide-methotrexate-fluorouracil) versus CEF (cyclophosphamide-epirubicin-fluorouracil) chemotherapy.

EXPERIMENTAL DESIGN

Intrinsic subtype was determined for 476 tumors using the quantitative reverse transcriptase PCR PAM50 gene expression test. Luminal A, luminal B, HER2-enriched (HER2-E), and basal-like subtypes were correlated with relapse-free survival (RFS) and overall survival (OS), estimated using Kaplan-Meier plots and log-rank testing. Multivariable Cox regression analyses determined significance of interaction between treatment and intrinsic subtypes.

RESULTS

Intrinsic subtypes were associated with RFS (P = 0.0005) and OS (P < 0.0001) on the combined cohort. The HER2-E showed the greatest benefit from CEF versus CMF, with absolute 5-year RFS and OS differences exceeding 20%, whereas there was a less than 2% difference for non-HER2-E tumors (interaction test P = 0.03 for RFS and 0.03 for OS). Within clinically defined Her2(+) tumors, 79% (72 of 91) were classified as the HER2-E subtype by gene expression and this subset was strongly associated with better response to CEF versus CMF (62% vs. 22%, P = 0.0006). There was no significant difference in benefit between CEF and CMF in basal-like tumors [n = 94; HR, 1.1; 95% confidence interval (CI), 0.6-2.1 for RFS and HR, 1.3; 95% CI, 0.7-2.5 for OS].

CONCLUSION

HER2-E strongly predicted anthracycline sensitivity. The chemotherapy-sensitive basal-like tumors showed no added benefit for CEF over CMF, suggesting that nonanthracycline regimens may be adequate in this subtype although further investigation is required.

TRIM29 functions as a tumor suppressor in nontumorigenic breast cells and invasive ER+ breast cancer

Tripartite motif-containing 29 (TRIM29) is a member of the TRIM protein family that has been implicated in hematologic and solid tumor cancers. We found that TRIM29 functions as a tumor suppressor in both the nontumorigenic MCF10A [estrogen receptor (ER)-/TRIM29+] breast cell line and the invasive MCF7 (ER+/TRIM29-) breast cell line. Silencing TRIM29 in MCF10A cells resulted in preneoplastic changes that included loss of polarity in three-dimensional culture, increased proliferation, anchorage-independent growth, and increased migration and invasion. Conversely, the introduction of TRIM29 into MCF7 cells caused reversion to a less aggressive phenotype by antagonizing the growth effect of 17β-estradiol. The interaction between TRIM29 and ER signaling in MCF7 cells was supported by a reduction in ERE binding in the presence of TRIM29 and suppression of ER-dependent gene expression of TFF1, FOS, and GREB1. By microarray analyses, we showed that younger women (<55 years of age) with early-stage, ER+ breast cancer who were given no adjuvant systemic therapy had a significantly lower risk of relapse when their tumor had high TRIM29 expression (P = 0.02). This effect was not observed in older women (>55 years of age) and thus may be due to menopause and loss of circulating estrogens. Our results suggest that loss of TRIM29 expression in normal breast luminal cells can contribute to malignant transformation and lead to progression of ER+ breast cancer in premenopausal women.

Pharmacogenomics: analysing SNPs in the CYP2D6 gene using amino acid properties

The CYP2D6 gene is responsible for metabolising a large portion of the commonly prescribed drugs. Because of its importance, various approaches have been taken to analyse CYP2D6 and Single Nucleotide Polymorphisms (SNPs) throughout its sequence. This study introduces a novel method to analyse the effects of SNPs on encoded protein complexes by focusing on the biochemical properties of each non-synonymous substitution using the program TreeSAAP. Our results show four SNPs in CYP2D6 that exhibit radical changes in amino acid properties which may cause a lack of functionality in the CYP2D6 gene and contribute to a person's inability to metabolise specific drugs.