Genome-wide association studies and beyond

Analysis of randomly mutated AlSRKb genes reveals that most loss-of-function mutations cause defects in plasma membrane localization

Only very limited information is available on why some nonsynonymous variants severely alter gene function while others have no effect. To identify the characteristic features of mutations that strongly influence gene function, this study focused on SRK which encodes a highly polymorphic receptor kinase expressed in stigma papillary cells that underlies a female determinant of self-incompatibility in Brassicaceae. A set of 300 Arabidopsis thaliana transformants expressing mutated SRKb from A. lyrata was constructed using error-prone PCR and the genotype and self-incompatibility phenotype of each transformant were determined. Almost all the transformants showing the self-incompatibility defect contained mutations in AlSRKb that altered localization to the plasma membrane. The observed mutations occurred in amino acid residues that were highly conserved across S haplotypes and whose predicted locations were in the interior of the protein. Our findings suggested that mutations causing the self-incompatibility defect were more likely to result from changes to AlSRKb biosynthesis than from loss of AlSRKb function. In addition, we examined whether the RandomForest and Extreme Gradient Boosting methods could predict the self-incompatibility phenotypes of SRK mutants.

From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches

Osteoporosis (OP) is a prevalent skeletal disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The advancements in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have provided significant insights into the molecular mechanisms driving OP. These technologies offer critical perspectives on genetic predispositions, gene expression regulation, protein signatures, and metabolic alterations, enabling the identification of novel biomarkers for diagnosis and therapeutic targets. This review underscores the potential of these multi-omics approaches to bridge the gap between basic research and clinical applications, paving the way for precision medicine in OP management. By integrating these technologies, researchers can contribute to improved diagnostics, preventative strategies, and treatments for patients suffering from OP and related conditions.

Impact of Epigenetics, Diet, and Nutrition-Related Pathologies on Wound Healing

Chronic wounds pose a significant challenge to healthcare. Stemming from impaired wound healing, the consequences can be severe, ranging from amputation to mortality. This comprehensive review explores the multifaceted impact of chronic wounds in medicine and the roles that diet and nutritional pathologies play in the wound-healing process. It has been well established that an adequate diet is crucial to proper wound healing. Nutrients such as vitamin D, zinc, and amino acids play significant roles in cellular regeneration, immune functioning, and collagen synthesis and processing. Additionally, this review discusses how patients with chronic conditions like diabetes, obesity, and nutritional deficiencies result in the formation of chronic wounds. By integrating current research findings, this review highlights the significant impact of the genetic make-up of an individual on the risk of developing chronic wounds and the necessity for adequate personalized dietary interventions. Addressing the nutritional needs of individuals, especially those with chronic conditions, is essential for improving wound outcomes and overall patient care. With new developments in the field of genomics, there are unprecedented opportunities to develop targeted interventions that can precisely address the unique metabolic needs of individuals suffering from chronic wounds, thereby enhancing treatment effectiveness and patient outcomes.

Genome-Wide Association Study for Milk Somatic Cell Score in Holstein Friesian Cows in Slovenia

Mastitis is a serious challenge for the dairy industry, leading to economic losses and affecting milk quality. The aim of this study is to identify genetic factors associated with mastitis resistance by conducting a genome-wide association study (GWAS) for the somatic cell score (SCS). Phenotypic records of 350 Holstein Friesian cows were obtained from the Slovenian Cattle Recording Scheme Database and consisted of around 1500 lactation data from 2012 to 2023 collected on a single farm in Slovenia. Corresponding genotypic data were also retrieved from the same database and genotyped using the Illumina BovineSNP50 BeadChip (Illumina, Inc., San Diego, CA, USA). For the association study, three SCS parameters were considered, including lactation mean somatic cell score (LM_SCS), maximum SCS value (SCSMAX), and top three mean value of SCS (TOP3). After performing a GWAS using FarmCPU and BLINK models, five significant SNPs associated with the TOP3 trait were found on BTA 14, 15, 22, and 29. The identified SNP markers were closely linked to six known candidate genes (DNASE1L3, SLC36A4, ARMC1, PDE7A, MMP13, CD44). These results indicate potential genetic markers associated with SCS in the Slovenian Holstein Friesian population.

Overnutrition and Lipotoxicity: Impaired Efferocytosis and Chronic Inflammation as Precursors to Multifaceted Disease Pathogenesis

Overnutrition, driven by the consumption of high-fat, high-sugar diets, has reached epidemic proportions and poses a significant global health challenge. Prolonged overnutrition leads to the deposition of excessive lipids in adipose and non-adipose tissues, a condition known as lipotoxicity. The intricate interplay between overnutrition-induced lipotoxicity and the immune system plays a pivotal role in the pathogenesis of various diseases. This review aims to elucidate the consequences of impaired efferocytosis, caused by lipotoxicity-poisoned macrophages, leading to chronic inflammation and the subsequent development of severe infectious diseases, autoimmunity, and cancer, as well as chronic pulmonary and cardiovascular diseases. Chronic overnutrition promotes adipose tissue expansion which induces cellular stress and inflammatory responses, contributing to insulin resistance, dyslipidemia, and metabolic syndrome. Moreover, sustained exposure to lipotoxicity impairs the efferocytic capacity of macrophages, compromising their ability to efficiently engulf and remove dead cells. The unresolved chronic inflammation perpetuates a pro-inflammatory microenvironment, exacerbating tissue damage and promoting the development of various diseases. The interaction between overnutrition, lipotoxicity, and impaired efferocytosis highlights a critical pathway through which chronic inflammation emerges, facilitating the development of severe infectious diseases, autoimmunity, cancer, and chronic pulmonary and cardiovascular diseases. Understanding these intricate connections sheds light on potential therapeutic avenues to mitigate the detrimental effects of overnutrition and lipotoxicity on immune function and tissue homeostasis, thereby paving the way for novel interventions aimed at reducing the burden of these multifaceted diseases on global health.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

Schizophrenia in the genetic era: a review from development history, clinical features and genomic research approaches to insights of susceptibility genes

Schizophrenia is a devastating neuropsychiatric disorder affecting 1% of the world population and ranks as one of the disorders providing the most severe burden for society. Schizophrenia etiology remains obscure involving multi-risk factors, such as genetic, environmental, nutritional, and developmental factors. Complex interactions of genetic and environmental factors have been implicated in the etiology of schizophrenia. This review provides an overview of the historical origins, pathophysiological mechanisms, diagnosis, clinical symptoms and corresponding treatment of schizophrenia. In addition, as schizophrenia is a polygenic, genetic disorder caused by the combined action of multiple micro-effective genes, we further detail several approaches, such as candidate gene association study (CGAS) and genome-wide association study (GWAS), which are commonly used in schizophrenia genomics studies. A number of GWASs about schizophrenia have been performed with the hope to identify novel, consistent and influential risk genetic factors. Finally, some schizophrenia susceptibility genes have been identified and reported in recent years and their biological functions are also listed. This review may serve as a summary of past research on schizophrenia genomics and susceptibility genes (NRG1, DISC1, RELN, BDNF, MSI2), which may point the way to future schizophrenia genetics research. In addition, depending on the above discovery of susceptibility genes and their exact function, the development and application of antipsychotic drugs will be promoted in the future.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

Background

Anthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined.

Objectives

This study examined gene-level associations with cardiomyopathy among cancer survivors using whole-exome sequencing data.

Methods

For discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children's Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10-6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively.

Results

Median age at cancer diagnosis for discovery cases and control subjects was 6 years and 8 years, respectively. Gene-level association for P2RX7 (OR: 0.10; 95% CI: 0.04-0.27; P = 2.19 × 10-6) was successfully replicated (HR: 0.65; 95% CI: 0.47-0.90; P = 0.009) in the CCSS cohort. Additional signals were identified on TNIK, LRRK2, MEFV, NOBOX, and FBN3. Individual SNPs across all discovery genes, except FBN3, were replicated.

Conclusions

In our study, SNP sets having 1 or no copies of P2RX7 variant alleles were associated with reduced risk of cardiomyopathy, presenting a potential therapeutic target to mitigate cardiac outcomes in cancer survivors.

New insights into the genetic predisposition of brucellosis and its effect on the gut and vaginal microbiota in goats

Goats contribute significantly to the global food security and industry. They constitute a main supplier of meat and milk for large proportions of people in Egypt and worldwide. Brucellosis is a zoonotic infectious disease that causes a significant economic loss in animal production. A case-control genome-wide association analysis (GWAS) was conducted using the infectious status of the animal as a phenotype. The does that showed abortion during the last third period of pregnancy and which were positive to both rose bengal plate and serum tube agglutination tests, were considered as cases. Otherwise, they were considered as controls. All animals were genotyped using the Illumina 65KSNP BeadChip. Additionally, the diversity and composition of vaginal and fecal microbiota in cases and controls were investigated using PCR-amplicone sequencing of the V4 region of 16S rDNA. After applying quality control criteria, 35,818 markers and 66 does were available for the GWAS test. The GWAS revealed a significantly associated SNP (P = 5.01 × 10-7) located on Caprine chromosome 15 at 29 megabases. Four other markers surpassed the proposed threshold (P = 2.5 × 10-5). Additionally, fourteen genomic regions accounted for more than 0.1% of the variance explained by all genome windows. Corresponding markers were located within or in close vicinity to several candidate genes, such as ARRB1, RELT, ATG16L2, IGSF21, UBR4, ULK1, DCN, MAPB1, NAIP, CD26, IFIH1, NDFIP2, DOK4, MAF, IL2RB, USP18, ARID5A, ZAP70, CNTN5, PIK3AP1, DNTT, BLNK, and NHLRC3. These genes play important roles in the regulation of immune responses to the infections through several biological pathways. Similar vaginal bacterial community was observed in both cases and controls while the fecal bacterial composition and diversity differed between the groups (P < 0.05). Faeces from the control does showed a higher relative abundance of the phylum Bacteroidota compared to cases (P < 0.05), while the latter showed more Firmicutes, Spirochaetota, Planctomycetota, and Proteobacteria. On the genus level, the control does exhibited higher abundances of Rikenellaceae RC9 gut group and Christensenellaceae R-7 group (P < 0.05), while the infected does revealed higher Bacteroides, Alistipes, and Prevotellaceae UCG-003 (P < 0.05). This information increases our understanding of the genetics of the susceptibility to Brucella in goats and may be useful in breeding programs and selection schemes that aim at controlling the disease in livestock.

Impact of Genetic Variations on Thromboembolic Risk in Saudis with Sickle Cell Disease

BACKGROUND

Sickle cell disease (SCD) is a Mendelian disease characterized by multigenic phenotypes. Previous reports indicated a higher rate of thromboembolic events (TEEs) in SCD patients. A number of candidate polymorphisms in certain genes (e.g., FVL, PRT, and MTHFR) were previously reported as risk factors for TEEs in different clinical conditions. This study aimed to genotype these genes and other loci predicted to underlie TEEs in SCD patients.

METHODOLOGY

A multi-center genome-wide association study (GWAS) involving Saudi SCD adult patients with a history of TEEs (n = 65) and control patients without TEE history (n = 285) was performed. Genotyping used the 10× Affymetrix Axiom array, which includes 683,030 markers. Fisher's exact test was used to generate p-values of TEE associations with each single-nucleotide polymorphism (SNP). The haplotype analysis software tool version 1.05, designed by the University of Göttingen, Germany, was used to identify the common inherited haplotypes.

RESULTS

No association was identified between the targeted single-nucleotide polymorphism rs1801133 in MTHFR and TEEs in SCD (p = 0.79). The allele frequency of rs6025 in FVL and rs1799963 in PRT in our cohort was extremely low (<0.01); thus, both variants were excluded from the analysis as no meaningful comparison was possible. In contrast, the GWAS analysis showed novel genome-wide associations (p < 5 × 10-8) with seven signals; five of them were located on Chr 11 (rs35390334, rs331532, rs317777, rs147062602, and rs372091), one SNP on Chr 20 (rs139341092), and another on Chr 9 (rs76076035). The other 34 SNPs located on known genes were also detected at a signal threshold of p < 5 × 10-6. Seven of the identified variants are located in olfactory receptor family 51 genes (OR51B5, OR51V1, OR51A1P, and OR51E2), and five variants were related to family 52 genes (OR52A5, OR52K1, OR52K2, and OR52T1P). The previously reported association between rs5006884-A in OR51B5 and fetal hemoglobin (HbF) levels was confirmed in our study, which showed significantly lower levels of HbF (p = 0.002) and less allele frequency (p = 0.003) in the TEE cases than in the controls. The assessment of the haplotype inheritance pattern involved the top ten significant markers with no LD (rs353988334, rs317777, rs14788626882, rs49188823, rs139349992, rs76076035, rs73395847, rs1368823, rs8888834548, and rs1455957). A haplotype analysis revealed significant associations between two haplotypes (a risk, TT-AA-del-AA-ins-CT-TT-CC-CC-AA, and a reverse protective, CC-GG-ins-GG-del-TT-CC-TT-GG-GG) and TEEs in SCD (p = 0.024, OR = 6.16, CI = 1.34-28.24, and p = 0.019, OR = 0.33, CI = 0.13-0.85, respectively).

CONCLUSIONS

Seven markers showed novel genome-wide associations; two of them were exonic variants (rs317777 in OLFM5P and rs147062602 in OR51B5), and less significant associations (p < 5 × 10-6) were identified for 34 other variants in known genes with TEEs in SCD. Moreover, two 10-SNP common haplotypes were determined with contradictory effects. Further replication of these findings is needed.

Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.

Genomic medicine on the frontier of precision medicine

Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.

Genomic and neuroimaging approaches to bipolar disorder

BACKGROUND

To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder.

AIMS

To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies.

METHOD

We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings.

RESULTS

ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder.

CONCLUSIONS

The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder.

Mitochondria Dysfunction in Frontotemporal Dementia/Amyotrophic Lateral Sclerosis: Lessons From Drosophila Models

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are neurodegenerative disorders characterized by declining motor and cognitive functions. Even though these diseases present with distinct sets of symptoms, FTD and ALS are two extremes of the same disease spectrum, as they show considerable overlap in genetic, clinical and neuropathological features. Among these overlapping features, mitochondrial dysfunction is associated with both FTD and ALS. Recent studies have shown that cells derived from patients' induced pluripotent stem cells (iPSC)s display mitochondrial abnormalities, and similar abnormalities have been observed in a number of animal disease models. Drosophila models have been widely used to study FTD and ALS because of their rapid generation time and extensive set of genetic tools. A wide array of fly models have been developed to elucidate the molecular mechanisms of toxicity for mutations associated with FTD/ALS. Fly models have been often instrumental in understanding the role of disease associated mutations in mitochondria biology. In this review, we discuss how mutations associated with FTD/ALS disrupt mitochondrial function, and we review how the use of Drosophila models has been pivotal to our current knowledge in this field.

A Core Omnigenic Non-coding Trait Governing Dex-Induced Osteoporotic Effects Identified Without DEXA

Iatrogenic glucocorticoid (GC)-induced osteoporosis (GIO) is an idiosyncratic form of secondary osteoporosis. Genetic predisposition among individuals may give rise to variant degree of phenotypic changes but there has yet been a documented unified pathway to explain the idiosyncrasy. In this study, we argue that the susceptibility to epigenetic changes governing molecular cross talks along the BMP and PI3K/Akt pathway may underline how genetic background dictate GC-induced bone loss. Concordantly, osteoblasts from BALB/c or C57BL/6 neonatal mice were treated with dexamethasone for transcriptome profiling. Furthermore, we also confirmed that GC-pre-conditioned mesenchymal stem cells (MSCs) would give rise to defective osteogenesis by instigating epigenetic changes which affected the accessibility of enhancer marks. In line with these epigenetic changes, we propose that GC modulates a key regulatory network involving the scavenger receptor Cd36 in osteoblasts pre-conditioning pharmacological idiosyncrasy in GIO.

Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition

As individuals seek increasingly individualised nutrition and lifestyle guidance, numerous apps and nutrition programmes have emerged. However, complex individual variations in dietary behaviours, genotypes, gene expression and composition of the microbiome are increasingly recognised. Advances in digital tools and artificial intelligence can help individuals more easily track nutrient intakes and identify nutritional gaps. However, the influence of these nutrients on health outcomes can vary widely among individuals depending upon life stage, genetics and microbial composition. For example, folate may elicit favourable epigenetic effects on brain development during a critical developmental time window of pregnancy. Genes affecting vitamin B12 metabolism may lead to cardiometabolic traits that play an essential role in the context of obesity. Finally, an individual's gut microbial composition can determine their response to dietary fibre interventions during weight loss. These recent advances in understanding can lead to a more complete and integrated approach to promoting optimal health through personalised nutrition, in clinical practice settings and for individuals in their daily lives. The purpose of this review is to summarise presentations made during the DSM Science and Technology Award Symposium at the 13th European Nutrition Conference, which focused on personalised nutrition and novel technologies for health in the modern world.

Genetic Polymorphisms in Pharmaceuticals and Chemotherapy

The study of genetic polymorphisms has significantly advanced the field of personalized medicine. Polymorphism of genes influence the efficacy of drugs used for treating medical conditions such as depression, cardiac diseases, thromboembolic disorders, oncological diseases, etc. The study of genetic polymorphism is beneficial for drug safety as well as for assessing therapeutic outcomes. Understanding and detecting genetic polymorphisms early on in patients can be useful in selecting the correct chemotherapeutic agent and appropriate dosage for a patient. Knowing the genetic profile of a patient and the interindividual response to various drugs significantly influences the proper selection of medication - a key step towards personalized medicine. Polymorphisms also make patients susceptible to certain cancers and identification of these polymorphisms early can be useful for a personalized treatment plan. The Genome-Wide Association Studies (GWAS) project where millions of genetic variants in the genomes of many individuals are studied to identify connections between what is present on the gene and the phenotype of the patient has enhanced the prospect of personalized medicine. GWAS has been used to identify hundreds of diseases associated to genetic polymorphisms. Individual pharmacokinetic profiles of patients to drugs enable the development of early surveillance protocols to prophylactically prevent patients from having adverse reactions. Furthermore, patient-derived cellular organoids are another advancement that allows researchers to screen for polymorphisms of the patient for adverse reactions from chemotherapy and will allow for the development of new medications that are specific to the profile of the patient’s tumor. These advances have led to significant progress towards personalized medicine. The functional consequences of genetic polymorphism on cancer drugs and treatment are studied here.

Genetic variants of SDCCAG8 and MAGI2 in mitosis-related pathway genes are independent predictors of cutaneous melanoma-specific survival

Mitosis is a prognostic factor for cutaneous melanoma (CM), but accurate mitosis detection in CM tissues is difficult. Therefore, the 8th Edition of the American Joint Committee on Cancer staging system has removed the mitotic rate as a category criterion of the tumor T-category, based on the evidence that the mitotic rate was not an independent prognostic factor for melanoma survival. As single-nucleotide polymorphisms (SNPs) have been shown to be potential predictors for cutaneous melanoma-specific survival (CMSS), we investigated the potential prognostic value of SNPs in mitosis-related pathway genes in CMSS by analyzing their associations with outcomes of 850 CM patients from The University of Texas MD Anderson Cancer Center in a discovery dataset and validated the findings in another dataset of 409 CM patients from the Harvard University Nurses' Health Study and Health Professionals Follow-up Study. In both datasets, we identified two SNPs (SDCCAG8 rs10803138 G>A and MAGI2 rs3807694 C>T) as independent prognostic factors for CMSS, with adjusted allelic hazards ratios of 1.49 (95% confidence interval = 1.17-1.90, P = .001) and 1.45 (1.13-1.86, P = .003), respectively. Furthermore, their combined unfavorable alleles also predicted a poor survival in both discovery and validation datasets in a dose-response manner (Ptrend  = .0006 and .0001, respectively). Additional functional analysis revealed that both SDCCAG8 rs10803138 A and MAGI2 rs3807694 T alleles were associated with elevated mRNA expression levels in normal tissues. Therefore, these findings suggest that SDCCAG8 rs10803138 G>A and MAGI2 rs3807694 C>T are independent prognostic biomarkers for CMSS, possibly by regulating the mRNA expression of the corresponding genes involved in mitosis.

How are social determinants of health integrated into epigenetic research? A systematic review

OBJECTIVE

We systematically review the literature on social epigenetics, examining how empirical research to date has conceptualized and operationalized social determinants of health (SDOH).

METHODS

Using comprehensive search procedures, we identified studies that consider the impact of SDOH on DNA methylation (DNAm), the most common measure of epigenetic change in research on human adult populations. We analyzed the studies to determine: 1) which populations and environments have been investigated in the literature; 2) how SDOH are defined and operationalized; 3) which SDOH have been linked to DNAm; and 4) what lessons from the SDOH literature can be better integrated into future studies exploring the social determinants of health and epigenetic outcomes.

RESULTS

We identified 67 studies, with 39 to 8397 participants. The SDOH most commonly considered were early life socioeconomic exposures and early life trauma or mental health. Our review highlights four broad challenges: a) high dependence on convenience sampling, b) limited racial/ethnic, and geographic diversity in sampling frames, c) overreliance on individual sociodemographic characteristics as proxies for broader stratification processes, and d) a focus on downstream social determinants of health and individualized experiences with social stressors.

CONCLUSIONS

Future social epigenetics research should prioritize larger, more diverse and representative population-based samples and employ the SDOH framework to better inform the conceptualization of research questions and interpretation of findings. In particular, the simplified depiction of race/ethnicity, gender, and socioeconomic status as individual-level characteristics should be updated with an explicit acknowledgement that these characteristics are more accurately interpreted as cues used by society to differentiate subpopulations. Social epigenetics research can then more clearly elucidate the biological consequences of these social exposures for patterns of gene expression, subsequent disease etiology, and health inequities.

The genetic architecture of larval aggregation behavior in Drosophila

Many insect species exhibit basal social behaviors such as aggregation, which play important roles in their feeding and mating ecologies. However, the evolutionary, genetic, and physiological mechanisms that regulate insect aggregation remain unknown for most species. Here, we used natural populations of Drosophila melanogaster to identify the genetic architecture that drives larval aggregation feeding behavior. By using quantitative and reverse genetic approaches, we have identified a complex neurogenetic network that plays a role in regulating the decision of larvae to feed in either solitude or as a group. Results from single gene, RNAi-knockdown experiments show that several of the identified genes represent key nodes in the genetic network that determines the level of aggregation while feeding. Furthermore, we show that a single non-coding variant in the gene CG14205, a putative acyltransferase, is associated with both decreased mRNA expression and increased aggregate formation, which suggests that it has a specific role in inhibiting aggregation behavior. Our results identify, for the first time, the genetic components which interact to regulate naturally occurring levels of aggregation in D. melanogaster larvae.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Novel genetic variants of PIP5K1C and MVB12B of the endosome-related pathway predict cutaneous melanoma-specific survival

Endosomes regulate cell polarity, adhesion, signaling, immunity, and tumor progression, which may influence cancer outcomes. Here we evaluated associations between 36,068 genetic variants of 228 endosome-related pathway genes and cutaneous melanoma disease-specific survival (CMSS) using genotyping data from two previously published genome-wide association studies. The discovery dataset included 858 CM patients with 95 deaths from The University of Texas MD Anderson Cancer Center, and the replication dataset included 409 CM patients with 48 deaths from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). In multivariate Cox proportional hazards regression analysis, we found that two novel SNPs (PIP5K1C rs11666894 A>C and MVB12B rs12376285 C>T) predicted CMSS, with adjusted hazards ratios of 1.47 (95% confidence interval = 1.15-1.89 and P = 0.002) and 1.73 (1.30-2.31 and 0.0002), respectively. Combined analysis of risk genotypes of these two SNPs revealed a dose-dependent decrease in CMSS associated with an increased number of risk genotypes (P _trend = 0.0002). Subsequent expression quantitative trait loci (eQTL) analysis revealed that PIP5K1C rs11666894 was associated with mRNA expression levels in lymphoblastoid cell lines from 373 European descendants (P<0.0001) and that MVB12B rs12376285 was associated with mRNA expression levels in cultured fibroblasts from 605 European-Americans (P<0.0001). Our findings suggest that novel genetic variants of PIP5K1C and MVB12B in the endosome-related pathway genes may be promising prognostic biomarkers for CMSS, but these results need to be validated in future larger studies.

The Biological Significance of Multi-copy Regions and Their Impact on Variant Discovery

Identification of genetic variants via high-throughput sequencing (HTS) technologies has been essential for both fundamental and clinical studies. However, to what extent the genome sequence composition affects variant calling remains unclear. In this study, we identified 63,897 multi-copy sequences (MCSs) with a minimum length of 300 bp, each of which occurs at least twice in the human genome. The 151,749 genomic loci (multi-copy regions, or MCRs) harboring these MCSs account for 1.98% of the genome and are distributed unevenly across chromosomes. MCRs containing the same MCS tend to be located on the same chromosome. Gene Ontology (GO) analyses revealed that 3800 genes whose UTRs or exons overlap with MCRs are enriched for Golgi-related cellular component terms and various enzymatic activities in the GO biological function category. MCRs are also enriched for loci that are sensitive to neocarzinostatin-induced double-strand breaks. Moreover, genetic variants discovered by genome-wide association studies and recorded in dbSNP are significantly underrepresented in MCRs. Using simulated HTS datasets, we show that false variant discovery rates are significantly higher in MCRs than in other genomic regions. These results suggest that extra caution must be taken when identifying genetic variants in the MCRs via HTS technologies.

Novel Variants of ELP2 and PIAS1 in the Interferon Gamma Signaling Pathway Are Associated with Non-Small Cell Lung Cancer Survival

BACKGROUND

IFNγ is a pleiotropic cytokine that plays critical immunomodulatory roles in intercellular communication in innate and adaptive immune responses. Despite recognition of IFNγ signaling effects on host defense against viral infection and its utility in immunotherapy and tumor progression, the roles of genetic variants of the IFNγ signaling pathway genes in survival of patients with cancer remain unknown.

METHODS

We used a discovery genotyping dataset from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 1,185) and a replication genotyping dataset from the Harvard Lung Cancer Susceptibility Study (n = 984) to evaluate associations between 14,553 genetic variants in 150 IFNγ pathway genes and survival of non-small cell lung cancer (NSCLC).

RESULTS

The combined analysis identified two independent potentially functional SNPs, ELP2 rs7242481G>A and PIAS1 rs1049493T>C, to be significantly associated with NSCLC survival, with a combined HR of 0.85 (95% confidence interval, 0.78-0.92; P < 0.0001) and 0.87 (0.81-0.93; P < 0.0001), respectively. Expression quantitative trait loci analyses showed that the survival-associated ELP2 rs7242481A allele was significantly associated with increased mRNA expression levels of elongator acetyltransferase complex subunit 2 (ELP2) in 373 lymphoblastoid cell lines and 369 whole-blood samples. The PIAS1 rs1049493C allele was significantly associated with decreased mRNA expression levels of PIAS1 in 383 normal lung tissues and 369 whole-blood samples.

CONCLUSIONS

Genetic variants of IFNγ signaling genes are potential prognostic markers for NSCLC survival, likely through modulating the expression of key genes involved in host immune response.

IMPACT

Once validated, these variants could be useful predictors of NSCLC survival.

The how and why of lncRNA function: An innate immune perspective

Next-generation sequencing has provided a more complete picture of the composition of the human transcriptome indicating that much of the "blueprint" is a vastness of poorly understood non-protein-coding transcripts. This includes a newly identified class of genes called long noncoding RNAs (lncRNAs). The lack of sequence conservation for lncRNAs across species meant that their biological importance was initially met with some skepticism. LncRNAs mediate their functions through interactions with proteins, RNA, DNA, or a combination of these. Their functions can often be dictated by their localization, sequence, and/or secondary structure. Here we provide a review of the approaches typically adopted to study the complexity of these genes with an emphasis on recent discoveries within the innate immune field. Finally, we discuss the challenges, as well as the emergence of new technologies that will continue to move this field forward and provide greater insight into the biological importance of this class of genes. This article is part of a Special Issue entitled: ncRNA in control of gene expression edited by Kotb Abdelmohsen.

"H" for Heterogeneity in the Algorithm for Type 2 Diabetes Management

PURPOSE OF REVIEW

Genetic, socioeconomic and clinical features vary considerably among individuals with type 2 diabetes (T2D) influencing disease development, progression and response to therapy. Although a patient-centred approach to pharmacologic therapy of T2D is widely recommended, patients are often treated similarly, irrespective of the differences that may affect therapeutic response. Addressing the heterogeneity of T2D is a major task of diabetes research to lower the high rate of treatment failure as well as to reduce the risk of long-term complications.

RECENT FINDINGS

A pathophysiology-based clustering system seems the most promising to help in the stratification of diabetes in terms of complication risk and response to treatment. This urges for clinical studies looking at novel biomarkers related to the different metabolic pathways of T2D and able to inform about the therapeutic cluster of each patient. Here, we review the main settings of diabetes heterogeneity, to what extent it has been already addressed and the current gaps in knowledge towards a personalized therapeutic approach that considers the distinctive features of each patient.

Population genetic simulation study of power in association testing across genetic architectures and study designs

While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

Generating High Density, Low Cost Genotype Data in Soybean [Glycine max (L.) Merr.]

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK's Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean.

Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?

BACKGROUND

Genome-wide association studies (GWAS) have identified thousands of susceptibility variants, although most have been associated with small individual risk estimates that offer little predictive value. However, combining multiple variants into polygenic risk scores (PRS) may be more informative. Multiple studies have developed PRS composed of GWAS-identified variants for cutaneous cancers. This review highlights data from these studies.

OBJECTIVES

To review published GWAS and PRS studies for melanoma, cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), and discuss their potential clinical utility.

METHODS

We searched PubMed and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue to identify relevant studies.

RESULTS

Results from 21 GWAS (11 melanoma, 3 cSCC, 7 BCC) and 11 PRS studies are summarized. Six loci in pigmentation genes overlap between these three cancers (ASIP/RALY, IRF4, MC1R, OCA2, SLC45A2 and TYR). Additional loci overlap for cSCC/BCC and BCC/melanoma, but no other loci are shared between cSCC and melanoma. PRS for melanoma show roughly two-to-threefold increases in risk and modest improvements in risk prediction (2-7% increases). PRS are associated with twofold and threefold increases in risk of cSCC and BCC, respectively, with small improvements (2% increase) in predictive ability.

CONCLUSIONS

Existing data indicate that PRS may offer small, but potentially meaningful, improvements to risk prediction. Additional research is needed to clarify the potential utility of PRS in cutaneous carcinomas. Clinical translation will require well-powered validation studies incorporating known risk factors to evaluate PRS as tools for screening. What's already known about this topic? Over 50 susceptibility loci for melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) have been identified in genome-wide association studies (GWAS). Polygenic risk scores (PRS) using variants identified from GWAS have also been developed for melanoma, BCC and cSCC, and investigated with respect to clinical risk prediction. What does this study add? This review provides an overview of GWAS findings and the potential clinical utility of PRS for melanoma, BCC and cSCC.

Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

Background

Metabolic syndrome (MetS), defined as a cluster of metabolic risk factors including dyslipidemia, insulin-resistance, and elevated blood pressure, has been known as partly heritable. MetS effects the lives of many people worldwide, yet females have been reported to be more vulnerable to this cluster of risks.

Methods

To elucidate genetic variants underlying MetS specifically in females, we performed a genome-wide association study (GWAS) for MetS as well as its component traits in a total of 9932 Korean female subjects (including 2276 MetS cases and 1692 controls). To facilitate the prediction of MetS in females, we calculated a genetic risk score (GRS) combining 14 SNPs detected in our GWA analyses specific for MetS.

Results

GWA analyses identified 14 moderate signals (P_meta < 5X10^− 5) specific to females for MetS. In addition, two genome-wide significant female-specific associations (P_meta < 5X10^− 8) were detected for rs455489 in DSCAM for fasting plasma glucose (FPG) and for rs7115583 in SIK3 for high-density lipoprotein cholesterol (HDLC). Logistic regression analyses (adjusted for area and age) between the GRS and MetS in females indicated that the GRS was associated with increased prevalence of MetS in females (P = 5.28 × 10^− 14), but not in males (P = 3.27 × 10^− 1). Furthermore, in the MetS prediction models using GRS, the area under the curve (AUC) of the receiver operating characteristics (ROC) curve was higher in females (AUC = 0.85) than in males (AUC = 0.57).

Conclusion

This study highlights new female-specific genetic variants associated with MetS and its component traits and suggests that the GRS of MetS variants is a likely useful predictor of MetS in females.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0830-y) contains supplementary material, which is available to authorized users.

APOL1-Associated Kidney Disease in Brazil

Introduction

Coding variants in apolipoprotein L-1 (APOL1) are associated with an increased risk of end-stage kidney disease (ESRD) in African American individuals under a recessive model of inheritance. The effect of the APOL1 risk alleles on kidney disease has been observed in studies in African American and African populations. Despite the 130 million individuals of recent African ancestry in South America, the impact of APOL1 has not been explored.

Methods

In this case-control study, we tested APOL1 genotype in 106 Brazilian HD (hemodialysis) patients with African ancestry and compared risk allele frequency with 106 healthy first-degree relatives. The association of risk alleles and ESRD was calculated with a linear mixed model and was adjusted for relatedness and additional confounders. In a broader survey, the age of dialysis initiation and APOL1 variants were analyzed in 274 HD patients.

Results

Two APOL1 risk alleles were 10 times more common in patients with ESRD than in controls (9.4% vs. 0.9%; odds ratio [OR]: 10.95, SE = 1.49, P = 0.0017). Carriers of 2 risk alleles initiated dialysis 12 years earlier than patients with zero risk alleles.

Conclusion

The APOL1 risk variants were less frequent in dialysis patients of African ancestry in Brazil than in the United States. Nonetheless, carriers of 2 risk variants had 10-fold higher odds of ESRD. Age of dialysis initiation was markedly lower in 2-risk allele carriers, suggesting a more aggressive disease phenotype. The Brazilian population represents an opportunity to identify different sets of genetic modifiers or environmental triggers that might be present in more extensively studied populations.

GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations

PURPOSE OF REVIEW

Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.

RECENT FINDINGS

Several common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.

SUMMARY

GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.

Insights into respiratory disease through bioinformatics

Respiratory diseases such as asthma, chronic obstructive pulmonary disease and lung cancer represent a critical area for medical research as millions of people are affected globally. The development of new strategies for treatment and/or prevention, and the identification of biomarkers for patient stratification and early detection of disease inception are essential to reducing the impact of lung diseases. The successful translation of research into clinical practice requires a detailed understanding of the underlying biology. In this regard, the advent of next-generation sequencing and mass spectrometry has led to the generation of an unprecedented amount of data spanning multiple layers of biological regulation (genome, epigenome, transcriptome, proteome, metabolome and microbiome). Dealing with this wealth of data requires sophisticated bioinformatics and statistical tools. Here, we review the basic concepts in bioinformatics and genomic data analysis and illustrate the application of these tools to further our understanding of lung diseases. We also highlight the potential for data integration of multi-omic profiles and computational drug repurposing to define disease subphenotypes and match them to targeted therapies, paving the way for personalized medicine.

Gene editing in the context of an increasingly complex genome

The reporting of the first draft of the human genome in 2000 brought with it much hope for the future in what was felt as a paradigm shift toward improved health outcomes. Indeed, we have now mapped the majority of variation across human populations with landmark projects such as 1000 Genomes; in cancer, we have catalogued mutations across the primary carcinomas; whilst, for other diseases, we have identified the genetic variants with strongest association. Despite this, we are still awaiting the genetic revolution in healthcare to materialise and translate itself into the health benefits for which we had hoped. A major problem we face relates to our underestimation of the complexity of the genome, and that of biological mechanisms, generally. Fixation on DNA sequence alone and a 'rigid' mode of thinking about the genome has meant that the folding and structure of the DNA molecule -and how these relate to regulation- have been underappreciated. Projects like ENCODE have additionally taught us that regulation at the level of RNA is just as important as that at the spatiotemporal level of chromatin.In this review, we chart the course of the major advances in the biomedical sciences in the era pre- and post the release of the first draft sequence of the human genome, taking a focus on technology and how its development has influenced these. We additionally focus on gene editing via CRISPR/Cas9 as a key technique, in particular its use in the context of complex biological mechanisms. Our aim is to shift the mode of thinking about the genome to that which encompasses a greater appreciation of the folding of the DNA molecule, DNA- RNA/protein interactions, and how these regulate expression and elaborate disease mechanisms.Through the composition of our work, we recognise that technological improvement is conducive to a greater understanding of biological processes and life within the cell. We believe we now have the technology at our disposal that permits a better understanding of disease mechanisms, achievable through integrative data analyses. Finally, only with greater understanding of disease mechanisms can techniques such as gene editing be faithfully conducted.

Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.

Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease

Genetic variation in cis-regulatory elements related to splicing machinery and splicing regulatory elements (SREs) results in exon skipping and undesired protein products. We developed a splicing decision model to identify actionable loci among common SNPs for gene regulation. The splicing decision model identified SNPs affecting exon skipping by analyzing sequence-driven alternative splicing (AS) models and by scanning the genome for the regions with putative SRE motifs. We used non-Hispanic Caucasians with neuroimaging, and fluid biomarkers for Alzheimer's disease (AD) and identified 17,088 common exonic SNPs affecting exon skipping. GWAS identified one SNP (rs1140317) in HLA-DQB1 as significantly associated with entorhinal cortical thickness, AD neuroimaging biomarker, after controlling for multiple testing. Further analysis revealed that rs1140317 was significantly associated with brain amyloid-f deposition (PET and CSF). HLA-DQB1 is an essential immune gene and may regulate AS, thereby contributing to AD pathology. SRE may hold potential as novel therapeutic targets for AD.

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis

Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Results: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Conclusions: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics.

Regulatory variants in cancer-related pathway genes predict survival of patients with surgically resected non-small cell lung cancer

BACKGROUND

We conducted this study to identify genetic variants in cancer-related pathway genes which can predict prognosis of NSCLC patients after surgery, using a comprehensive list of regulatory single nucleotide polymorphisms (SNPs) prioritized by RegulomeDB.

METHOD

A total of 509 potentially functional SNPs in cancer-related pathway genes selected from RegulomeDB were evaluated. These SNPs were analyzed in a discovery set (n=354), and a replication study was performed in an independent set (n=772). The association of the SNPs with overall survival (OS) and disease-free survival (DFS) were analyzed.

RESULTS

In the discovery set, 76 SNPs were significantly associated with OS or DFS. Among the 76 SNPs, the association was consistently observed for 5 SNPs (ERCC1 rs2298881C>A, BRCA2 rs3092989G>A, NELFE rs440454C>T, PPP2R4 rs2541164G>A, and LTBP4 rs3786527G>A) in the validation set. In combined analysis, ERCC1 rs2298881C>A, BRCA2 rs3092989, NELFE rs440454C>T, and PPP2R4 rs2541164G>A were significantly associated with OS and DFS (adjusted HR ·aHR· for OS=1.46, 0.62, 078, and 0.76, respectively; P=0.003, 0.002, 0.007, and 0.003 respectively; and aHR for DFS=1.27, 0.69, 0.86, and 0.82, respectively; P=0.02, 0.002, 0.03, and 0.008, respectively). The LTBP4 rs3786527G>A was significantly associated with better OS (aHR=0.75; P=0.003).

CONCLUSION

Our results suggest that five SNPs in the cancer-related pathway genes may be useful for the prediction of the prognosis in patients with surgically resected NSCLC.

The complex genetics of gait speed: genome-wide meta-analysis approach

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.

Genetic Programming of Hypertension

The heritability of hypertension (HTN) is widely recognized and as a result, extensive studies ranging from genetic linkage analyses to genome-wide association studies are actively ongoing to elucidate the etiology of both monogenic and polygenic forms of HTN. Due to the complex nature of essential HTN, however, single genes affecting blood pressure (BP) variability remain difficult to isolate and identify and have rendered the development of single-gene targeted therapies challenging. The roles of other causative factors in modulating BP, such as gene-environment interactions and epigenetic factors, are increasingly being brought to the forefront. In this review, we discuss the various monogenic HTN syndromes and corresponding pathophysiologic mechanisms, the different methodologies employed in genetic studies of essential HTN, the mechanisms for epigenetic modulation of essential HTN, pharmacogenomics and HTN, and finally, recent advances in genetic studies of essential HTN in the pediatric population.

Polymorphisms in cancer-related pathway genes and lung cancer

We evaluated the associations between potentially functional variants in a comprehensive list of cancer-related genes and lung cancer in a Korean population.

A total of 1969 potentially functional single nucleotide polymorphisms (SNPs) of 1151 genes involved in carcinogenesis were evaluated using an Affymetrix custom-made GeneChip in 610 nonsmall cell lung cancer patients and 610 healthy controls. A replication study was conducted in an independent set of 490 cases and 486 controls. 68 SNPs were significantly associated with lung cancer in the discovery set and tested for replication.

Among the 68 SNPs, three SNPs (corepressor interacting with RBPJ 1 (CIR1) rs13009079T>C, ribonucleotide reductase M1 (RRM1) rs1465952T>C and solute carrier family 38, member 4 (SLC38A4) rs2429467C>T) consistantly showed significant associations with lung cancer in the replication study. In combined analysis, adjusted odds ratio for CIR1 rs13009079T>C, RRM1 rs1465952T>C and SLC38A4 rs2429467C>T were 0.69, 0.71 and 0.73, respectively (p=4×10−5, 0.01 and 0.001, respectively) under the dominant model. The relative mRNA expression level of CIR1 was significantly associated with rs13009079T>C genotypes in normal lung tissues (ptrend=0.03).

These results suggest that the three SNPs, particularly CIR1 rs13009079T>C, may play a role in the pathogenesis of lung cancer.

How far from the SNP may the causative genes be?

While GWAS identify many disease-associated SNPs, using them to decipher disease mechanisms is hindered by the difficulty in mapping SNPs to genes. Most SNPs are in non-coding regions and it is often hard to identify the genes they implicate. To explore how far the SNP may be from the affected genes we used a pathway-based approach. We found that affected genes are often up to 2 Mbps away from the associated SNP, and are not necessarily the closest genes to the SNP. Existing approaches for mapping SNPs to genes leave many SNPs unmapped to genes and reveal only 86 significant phenotype-pathway associations for all known GWAS hits combined. Using the pathway-based approach we propose here allows mapping of virtually all SNPs to genes and reveals 435 statistically significant phenotype-pathway associations. In search for mechanisms that may explain the relationships between SNPs and distant genes, we found that SNPs that are mapped to distant genes have significantly more large insertions/deletions around them than other SNPs, suggesting that these SNPs may sometimes be markers for large insertions/deletions that may affect large genomic regions.

A Quantitative Genomic Approach for Analysis of Fitness and Stress Related Traits in a Drosophila melanogaster Model Population

The ability of natural populations to withstand environmental stresses relies partly on their adaptive ability. In this study, we used a subset of the Drosophila Genetic Reference Panel, a population of inbred, genome-sequenced lines derived from a natural population of Drosophila melanogaster, to investigate whether this population harbors genetic variation for a set of stress resistance and life history traits. Using a genomic approach, we found substantial genetic variation for metabolic rate, heat stress resistance, expression of a major heat shock protein, and egg-to-adult viability investigated at a benign and a higher stressful temperature. This suggests that these traits will be able to evolve. In addition, we outline an approach to conduct pathway associations based on genomic linear models, which has potential to identify adaptive genes and pathways, and therefore can be a valuable tool in conservation genomics.

Strategies for Imputing and Analyzing Rare Variants in Association Studies

Rare genetic variants may be responsible for a significant amount of the uncharacterized genetic risk underlying many diseases. An efficient approach to characterizing the disease burden of rare variants may be to impute them into existing large datasets. It is well known that the ability to impute a rare variant is dependent both on the array choice and number of individuals in the reference panel carrying that variant, although it is still unclear exactly how well imputation will work for rare variants. Here, we review the additional challenges that arise when imputing rare variants, looking at studies that have been able to impute rare variants, methods behind merging reference panels, approaches for imputing rare variants, and methods for analyzing rare variants.