Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma.

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study.

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Proteome-wide association studies have predicted that the protein abundance of LSM6, GMPPB, ICA1L, and CISD2 is associated with attention-deficit/hyperactivity disorder.

Identification of changes in protein abundance for attention-deficit/hyperactivity disorder (ADHD) is important for potential disease mechanisms and therapeutic study for ADHD. In order to identify candidate proteins that confer risk for ADHD, a proteome-wide association study (PWAS) for ADHD was conducted by integrating two human brain proteome datasets and the ADHD genome-wide association study (GWAS) summary statistics released by the Psychiatric Genomics Consortium (PGC). A total of 11 risk proteins were identified as significant candidates that passed the bonferroni corrected proteome-wide significant (PWS) level. The predicted protein abundance level of LSM6, GMPPB, ICA1L and CISD2 are shown significantly associated with ADHD in both proteome datasets, highlighting their potential role in ADHD pathogenesis. A transcriptome-wide association study (TWAS) of ADHD was also conducted, and 13 genes with predicted expression changes related to ADHD were identified. GMPPB, ICA1L and NAT6 were supported by both TWAS and PWASs analysis. This study uncovers the predicted protein abundance changes that confer risk for ADHD and pinpoints a number of high-confidence protein candidates (e.g. LSM6, GMPPB, ICA1L, CISD2) for further functional exploration studies and drug development targeting these proteins.

Temperature Hotspot Detection on Printed Circuit Boards (PCBs) Using Ultrasonic Guided Waves-A Machine Learning Approach.

This paper addresses the challenging issue of achieving high spatial resolution in temperature monitoring of printed circuit boards (PCBs) without compromising the operation of electronic components. Traditional methods involving numerous dedicated sensors such as thermocouples are often intrusive and can impact electronic functionality. To overcome this, this study explores the application of ultrasonic guided waves, specifically utilising a limited number of cost-effective and unobtrusive Piezoelectric Wafer Active Sensors (PWAS). Employing COMSOL multiphysics, wave propagation is simulated through a simplified PCB while systematically varying the temperature of both components and the board itself. Machine learning algorithms are used to identify hotspots at component positions using a minimal number of sensors. An accuracy of 97.6% is achieved with four sensors, decreasing to 88.1% when utilizing a single sensor in a pulse-echo configuration. The proposed methodology not only provides sufficient spatial resolution to identify hotspots but also offers a non-invasive and efficient solution. Such advancements are important for the future electrification of the aerospace and automotive industries in particular, as they contribute to condition-monitoring technologies that are essential for ensuring the reliability and safety of electronic systems.

Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease.

Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.

New insights into the hypothalamic-pituitary-thyroid axis: a transcriptome- and proteome-wide association study.

Introduction: Thyroid hormones have systemic effects on the human body and play a key role in the development and function of virtually all tissues. They are regulated via the hypothalamic-pituitary-thyroid (HPT) axis and have a heritable component. Using genetic information, we applied tissue-specific transcriptome-wide association studies (TWAS) and plasma proteome-wide association studies (PWAS) to elucidate gene products related to thyrotropin (TSH) and free thyroxine (FT4) levels. Results: TWAS identified 297 and 113 transcripts associated with TSH and FT4 levels, respectively (25 shared), including transcripts not identified by genome-wide association studies (GWAS) of these traits, demonstrating the increased power of this approach. Testing for genetic colocalization revealed a shared genetic basis of 158 transcripts with TSH and 45 transcripts with FT4, including independent, FT4-associated genetic signals within the CAPZB locus that were differentially associated with CAPZB expression in different tissues. PWAS identified 18 and ten proteins associated with TSH and FT4, respectively (HEXIM1 and QSOX2 with both). Among these, the cognate genes of five TSH- and 7 FT4-associated proteins mapped outside significant GWAS loci. Colocalization was observed for five plasma proteins each with TSH and FT4. There were ten TSH and one FT4-related gene(s) significant in both TWAS and PWAS. Of these, ANXA5 expression and plasma annexin A5 levels were inversely associated with TSH (PWAS: P = 1.18 × 10-13, TWAS: P = 7.61 × 10-12 (whole blood), P = 6.40 × 10-13 (hypothalamus), P = 1.57 × 10-15 (pituitary), P = 4.27 × 10-15 (thyroid)), supported by colocalizations. Conclusion: Our analyses revealed new thyroid function-associated genes and prioritized candidates in known GWAS loci, contributing to a better understanding of transcriptional regulation and protein levels relevant to thyroid function.

Revealing the genetic complexity of hypothyroidism: integrating complementary association methods.

Hypothyroidism is a common endocrine disorder whose prevalence increases with age. The disease manifests itself when the thyroid gland fails to produce sufficient thyroid hormones. The disorder includes cases of congenital hypothyroidism (CH), but most cases exhibit hormonal feedback dysregulation and destruction of the thyroid gland by autoantibodies. In this study, we sought to identify causal genes for hypothyroidism in large populations. The study used the UK-Biobank (UKB) database, reporting on 13,687 cases of European ancestry. We used GWAS compilation from Open Targets (OT) and tuned protocols focusing on genes and coding regions, along with complementary association methods of PWAS (proteome-based) and TWAS (transcriptome-based). Comparing summary statistics from numerous GWAS revealed a limited number of variants associated with thyroid development. The proteome-wide association study method identified 77 statistically significant genes, half of which are located within the Chr6-MHC locus and are enriched with autoimmunity-related genes. While coding GWAS and PWAS highlighted the centrality of immune-related genes, OT and transcriptome-wide association study mostly identified genes involved in thyroid developmental programs. We used independent populations from Finland (FinnGen) and the Taiwan cohort to validate the PWAS results. The higher prevalence in females relative to males is substantiated as the polygenic risk score prediction of hypothyroidism relied mostly from the female group genetics. Comparing results from OT, TWAS, and PWAS revealed the complementary facets of hypothyroidism's etiology. This study underscores the significance of synthesizing gene-phenotype association methods for this common, intricate disease. We propose that the integration of established association methods enhances interpretability and clinical utility.