Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk

Genome-Wide Association Study of Insertions and Deletions Identified Novel Loci Associated with Milk Production Traits in Dairy Cattle

Genome-wide association study (GWAS) have identified a large number of SNPs associated with milk production traits in dairy cattle. Behind SNPs, INDELs are the second most abundant genetic polymorphisms in the genome, which may exhibit an independent association with complex traits in humans and other species. However, there are no reports on GWASs of INDELs for milk production traits in dairy cattle. In this study, using imputed sequence data, we performed INDEL-based and SNP-based GWASs for milk production traits in a Holstein cattle population. We identified 58 unique significant INDELs for one or multiple traits. The majority of these INDELs are in considerable LD with nearby significant SNPs. However, through conditional association analysis, we identified nine INDELs which showed independent associations. Genomic annotations of these INDELs indicated some novel associated genes, i.e., TRNAG-CCC, EPPK1, PPM1K, PTDSS1, and mir-10163, which were not reported in previous SNP-based GWASs. Our findings suggest that INDEL-based GWASs could be valuable complement to SNP-based GWASs for milk production traits.

Identifying new molecular signatures and potential therapeutics for idiopathic pulmonary fibrosis: a network medicine approach

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive collagen deposition and fibrosis of the lung parenchyma, leading to respiratory failure. The molecular mechanisms underlying IPF pathogenesis remain incompletely understood, hindering the development of effective therapeutic strategies. We have used a network medicine approach to comprehensively analyze molecular interactions and identify novel molecular signatures and potential therapeutics associated with IPF progression. Our integrative analysis revealed dysregulated molecular networks that are central to IPF pathophysiology. We have highlighted key molecular players and signaling pathways that are implicated in aberrant fibrotic processes. This systems-level understanding enables the identification of new biomarkers and therapeutic targets for IPF, providing potential avenues for precision medicine. Drug repurposing analysis revealed several drug candidates with anti-fibrotic, anti-inflammatory, and anti-cancer activities that could potentially slow fibrotic progression and improve patient outcomes. This study offers new insights into the molecular underpinnings of IPF and highlights network medicine approaches in uncovering complex disease mechanisms. The molecular signatures and therapeutic targets identified hold promise for developing precision therapies tailored to individual patients, ultimately advancing the management of this debilitating lung disease.

Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications

Insertions and deletions constitute the second most important source of natural genomic variation. Insertions and deletions make up to 25% of genomic variants in humans and are involved in complex evolutionary processes including genomic rearrangements, adaptation, and speciation. Recent advances in long-read sequencing technologies allow detailed inference of insertions and deletion variation in species and populations. Yet, despite their importance, evolutionary studies have traditionally ignored or mishandled insertions and deletions due to a lack of comprehensive methodologies and statistical models of insertions and deletion dynamics. Here, we discuss methods for describing insertions and deletion variation and modeling insertions and deletions over evolutionary time. We provide practical advice for tackling insertions and deletions in genomic sequences and illustrate our discussion with examples of insertions and deletion-induced effects in human and other natural populations and their contribution to evolutionary processes. We outline promising directions for future developments in statistical methodologies that would allow researchers to analyze insertions and deletion variation and their effects in large genomic data sets and to incorporate insertions and deletions in evolutionary inference.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Sorghum Association Panel whole-genome sequencing establishes cornerstone resource for dissecting genomic diversity

Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25-72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5-kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six F_st peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

C2CD4A/B variants in the predisposition of lung cancer in the Chinese Han population

BACKGROUND

The mRNA levels of C2CD4A and C2CD4B were dysregulation in lung cancer (LC). We aimed to evaluate the role of C2CD4A/B variants in LC susceptibility.

METHODS

There were 710 cases with LC and 710 healthy controls enrolled in the study. The genotyping of twelve variants in C2CD4A/B was carried out by Agena MassARRAY system. Odds ratios (ORs) were calculated by logistic regression analysis to assess the relationship between these variants and LC predisposition.

RESULTS

Rs8037894 (OR = 0.81, p = 0.005), rs7172432 (OR = 0.83, p = 0.013), rs11856307 (OR = 0.86, p = 0.043), and rs1436953 (OR = 0.79, p = 0.002) were related to the reduced risk of LC. Besides, the relation of rs7172432 with LC risk in subjects aged > 60 years was observed. Rs4502156 conferred to the increased LC risk, while rs1436953 was associated with the lower susceptibility to LC among males. Rs731820, rs4502156, rs11071657, rs7172432, and rs11856307 contributed to the predisposition of LC among subjects with BMI > 24 kg/m², while rs7495253 was associated with an increased risk of LC in subjects with BMI ≤ 24 kg/m². The increased LC risk was found in rs4502156, while the protective risk effect of rs8037894, rs7172432, rs11856307, and rs1436953 on the occurrence of LC was observed in smokers and non-drinkers. Moreover, rs7495253 and rs7495931 had a higher risk of lymphatic metastasis. Rs1436953 was related to the reduced risk of lung adenocarcinoma, while rs4502156, rs8037894, rs7172432, rs11856307, and rs1436953 were related to the risk of small cell carcinoma.

CONCLUSIONS

Our results first display that C2CD4A/B polymorphisms served as protective factors for LC predisposition in a Chinese Han population. These findings could provide new biological insight into the understanding of C2CD4A/B genes on LC pathogenesis.

Visual Detection of ACE I/D Polymorphism Using T-ARMS-PCR Combined with a Lateral Flow Assay and Its Clinical Application

Insertions/deletions (indels) variations have been recognized as a promising marker for the development of various diseases. However, methods used for the genotyping of indels in studies were tedious, complicated, and required sophisticated or expensive instruments, as well as complex data analysis, which makes it difficult to meet the demand of point of care testing. Herein, we presented a fast and accurate biosensor (T-ARMS-PCR-LFA) by the combination of tetra-primer amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) and GoldMag lateral flow assay (LFA) for visual genotyping of ACE I/D polymorphism. ACE I/D can be distinguished by employing four primers in one PCR reaction, and genotyping results were presented by the visual inspection of colors on the nitrocellulose membrane of LFA strips within 5 min. And 50 of the human genomic DNA samples were used for the detection of ACE I/D to further validate the accuracy of the T-ARMS-PCR-LFA system. As a demonstration, we showed that ACE I/D could be genotyped using a low amount of DNA sample (25 ng) with an accuracy of 100%, without complicated operation steps and data analysis, which is better than that of the conventional method (agarose gel electrophoresis analysis after common PCR). In conclusion, the biosensor is highly applicable for genotyping specific large indel variants in clinical practices, which enables rapid clinical decision-making, improves the management of disease diagnosis, and facilitates personalized medicine.

HELLO: improved neural network architectures and methodologies for small variant calling

BACKGROUND

Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition.

RESULTS

Results from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller.

CONCLUSIONS

We believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello.

Rare deleterious germline variants and risk of lung cancer

Recent studies suggest that rare variants exhibit stronger effect sizes and might play a crucial role in the etiology of lung cancers (LC). Whole exome plus targeted sequencing of germline DNA was performed on 1045 LC cases and 885 controls in the discovery set. To unveil the inherited causal variants, we focused on rare and predicted deleterious variants and small indels enriched in cases or controls. Promising candidates were further validated in a series of 26,803 LCs and 555,107 controls. During discovery, we identified 25 rare deleterious variants associated with LC susceptibility, including 13 reported in ClinVar. Of the five validated candidates, we discovered two pathogenic variants in known LC susceptibility loci, ATM p.V2716A (Odds Ratio [OR] 19.55, 95%CI 5.04-75.6) and MPZL2 p.I24M frameshift deletion (OR 3.88, 95%CI 1.71-8.8); and three in novel LC susceptibility genes, POMC c.*28delT at 3' UTR (OR 4.33, 95%CI 2.03-9.24), STAU2 p.N364M frameshift deletion (OR 4.48, 95%CI 1.73-11.55), and MLNR p.Q334V frameshift deletion (OR 2.69, 95%CI 1.33-5.43). The potential cancer-promoting role of selected candidate genes and variants was further supported by endogenous DNA damage assays. Our analyses led to the identification of new rare deleterious variants with LC susceptibility. However, in-depth mechanistic studies are still needed to evaluate the pathogenic effects of these specific alleles.