Quantifying gene selection in cancer through protein functional alteration bias

PWAS Hub: exploring gene-based associations of complex diseases with sex dependency

The Proteome-Wide Association Study (PWAS) is a protein-based genetic association approach designed to complement traditional variant-based methods like GWAS. PWAS operates in two stages: first, machine learning models predict the impact of genetic variants on protein-coding genes, generating effect scores. These scores are then aggregated into a gene-damaging score for each individual. This score is then used in case-control statistical tests to significantly link to specific phenotypes. PWAS Hub (v1.2) is a user-friendly platform that facilitates the exploration of gene-disease associations using clinical and genetic data from the UK Biobank (UKB), encompassing 500k individuals. PWAS Hub reports on 819 diseases and phenotypes determined by PheCode and ICD-10 clinical codes, each with a minimum of 400 affected individuals. PWAS-derived gene associations were reported for 72% of the tested phenotypes. The PWAS Hub also analyzes gene associations separately for males and females, considering sex-specific genetic effects, inheritance patterns (dominant and recessive), and gene pleiotropy. We illustrated the utility of the PWAS Hub for primary (essential) hypertension (I10), type 2 diabetes mellitus (E11), and specified haematuria (R31) that showed sex-dependent genetic signals. The PWAS Hub, available at pwas.huji.ac.il, is a valuable resource for studying genetic contributions to common diseases and sex-specific effects.

Multiome-wide Association Studies: Novel Approaches for Understanding Diseases

The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

PWAS Hub for exploring gene-based associations of common complex diseases

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects.

Post-implantation analysis of genomic variations in the progeny from developing fetus to birth

The analysis of genomic variations in offspring after implantation has been infrequently studied. In this study, we aim to investigate the extent of de novo mutations in humans from developing fetus to birth. Using high-depth whole-genome sequencing, 443 parent-offspring trios were studied to compare the results of de novo mutations (DNMs) between different groups. The focus was on fetuses and newborns, with DNA samples obtained from the families' blood and the aspirated embryonic tissues subjected to deep sequencing. It was observed that the average number of total DNMs in the newborns group was 56.26 (54.17-58.35), which appeared to be lower than that the multifetal reduction group, which was 76.05 (69.70-82.40) (F = 2.42, P = 0.12). However, after adjusting for parental age and maternal pre-pregnancy body mass index (BMI), significant differences were found between the two groups. The analysis was further divided into single nucleotide variants (SNVs) and insertion/deletion of a small number of bases (indels), and it was discovered that the average number of de novo SNVs associated with the multifetal reduction group and the newborn group was 49.89 (45.59-54.20) and 51.09 (49.22-52.96), respectively. No significant differences were noted between the groups (F = 1.01, P = 0.32). However, a significant difference was observed for de novo indels, with a higher average number found in the multifetal reduction group compared to the newborn group (F = 194.17, P < 0.001). The average number of de novo indels among the multifetal reduction group and the newborn group was 26.26 (23.27-29.05) and 5.17 (4.82-5.52), respectively. To conclude, it has been observed that the quantity of de novo indels in the newborns experiences a significant decrease when compared to that in the aspirated embryonic tissues (7-9 weeks). This phenomenon is evident across all genomic regions, highlighting the adverse effects of de novo indels on the fetus and emphasizing the significance of embryonic implantation and intrauterine growth in human genetic selection mechanisms.

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.

Discovering predisposing genes for hereditary breast cancer using deep learning

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

Gene-based association study reveals a distinct female genetic signal in primary hypertension

Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

The function of LncRNA-ATB in cancer

Cancer as a progressive and complex disease is caused by early chromosomal changes and stimulated cellular transformation. Previous studies reported that long non-coding RNAs (lncRNAs) play pivotal roles in the initiation, maintenance, and progression of cancer cells. LncRNA activated by TGF-β (ATB) has been shown to be dysregulated in different types of cancer. Aberrant expression of lncRNA-ATB plays an important role in the progression of diverse malignancies. High expression of LncRNA-ATB is associated with cancer cell growth, proliferation, metastasis, and EMT. LncRNA-ATB by targeting various signaling pathways and microRNAs (miRNAs) can trigger cancer pathogenesis. Therefore, lncRNA-ATB can be a novel target for cancer prediction and diagnosis. In this review, we will focus on the function of lncRNA-ATB in various types of human cancers.

Evolutionary and functional lessons from human-specific amino acid substitution matrices

Human genetic variation in coding regions is fundamental to the study of protein structure and function. Most methods for interpreting missense variants consider substitution measures derived from homologous proteins across different species. In this study, we introduce human-specific amino acid (AA) substitution matrices that are based on genetic variations in the modern human population. We analyzed the frequencies of >4.8M single nucleotide variants (SNVs) at codon and AA resolution and compiled human-centric substitution matrices that are fundamentally different from classic cross-species matrices (e.g. BLOSUM, PAM). Our matrices are asymmetric, with some AA replacements showing significant directional preference. Moreover, these AA matrices are only partly predicted by nucleotide substitution rates. We further test the utility of our matrices in exposing functional signals of experimentally-validated protein annotations. A significant reduction in AA transition frequencies was observed across nine post-translational modification (PTM) types and four ion-binding sites. Our results propose a purifying selection signal in the human proteome across a diverse set of functional protein annotations and provide an empirical baseline for interpreting human genetic variation in coding regions.

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

The FABRIC Cancer Portal: A Ranked Catalogue of Gene Selection in Tumors Over the Human Coding Genome

Contemporary catalogues of cancer driver genes rely primarily on high mutation rates as evidence for gene selection in tumors. Here, we present The Functional Alteration Bias Recovery In Coding-regions Cancer Portal, a comprehensive catalogue of gene selection in cancer based purely on the biochemical functional effects of mutations at the protein level. Gene selection in the portal is quantified by combining genomics data with rich proteomic annotations. Genes are ranked according to the strength of evidence for selection in tumor, based on rigorous and robust statistics. The portal covers the entire human coding genome (∼18,000 protein-coding genes) across 33 cancer types and pan-cancer. It includes a selected set of cross-references to the most relevant resources providing genomics, proteomics, and cancer-related information. We showcase the portal with known and overlooked cancer genes, demonstrating the utility of the portal via its simple visual interface, which allows users to pivot between gene-centric and cancer type views. The portal is available at fabric-cancer.huji.ac.il. SIGNIFICANCE: A new cancer portal quantifies and presents gene selection in tumor over the entire human coding genome across 33 cancer types and pan-cancer.

PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins

We introduce Proteome-Wide Association Study (PWAS), a new method for detecting gene-phenotype associations mediated by protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein's function using machine learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. PWAS can capture complex modes of heritability, including recessive inheritance. A comparison with GWAS and other existing methods proves its capacity to recover causal protein-coding genes and highlight new associations. PWAS is available as a command-line tool.

Serine substitutions are linked to codon usage and differ for variable and conserved protein regions

Serine is the only amino acid that is encoded by two disjoint codon sets (TCN & AGY) so that a tandem substitution of two nucleotides is required to switch between the two sets. We show that these codon sets underlie distinct substitution patterns at positions subject to purifying and diversifying selections. We found that in humans, positions that are conserved among ~100 vertebrates, and thus subjected to purifying selection, are enriched for substitutions involving serine (TCN, denoted S'), proline, and alanine, (S'PA). In contrast, the less conserved positions are enriched for serine encoded with AGY codons (denoted S″), glycine and asparagine, (GS″N). We tested this phenomenon in the HIV envelope glycoprotein (gp120), and the V-gene that encodes B-cell receptors/antibodies. These fast evolving proteins both have hypervariable positions, which are under diversifying selection, closely adjacent to highly conserved structural regions. In both instances, we identified an opposite abundance of two groups of serine substitutions, with enrichment of S'PA in the conserved positions, and GS″N in the hypervariable regions. Finally, we analyzed the substitutions across 60,000 individual human exomes to show that, when serine has a specific functional constraint of phosphorylation capability, S' codons are 32-folds less prone than S″ to substitutions to Threonine or Tyrosine that could potentially retain the phosphorylation site capacity. Combined, our results, that cover evolutionary signals at different temporal scales, demonstrate that through its encoding by two codon sets, serine allows for the existence of alternating substitution patterns within positions of functional maintenance versus sites of rapid diversification.