Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort.

BACKGROUND: Pancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk. METHODS: The study subjects were randomly selected from the Korean Cancer Prevention Study-II and matched by age, sex, and blood collection point [pancreatic cancer incidence (n = 128) vs. control (n = 256)]. The baseline serum samples were analyzed by non-targeted metabolomics, and XGBoost was used to select significant metabolites related to pancreatic cancer incidence. Genomewide association study for the selected metabolites discovered valuable single nucleotide polymorphisms (SNPs). Moderation and mediation analysis were conducted to explore the variables related to pancreatic cancer risk. RESULTS: Eleven discriminant metabolites were selected by applying a cut-off of 4.0 in XGBoost. Five SNP presented significance in metabolite-GWAS (p ≤ 5 × 10-6) and logistic regression analysis. Among them, the pair metabolite of rs2370981, rs55870181, and rs72805402 displayed a different network pattern with clinical/biochemical indicators on comparison with allelic carrier and non-carrier. In addition, we demonstrated the indirect effect of rs59519100 on pancreatic cancer risk mediated by γ-glutamyl tyrosine, which affects the smoking status. The predictive ability for pancreatic cancer on the model using five SNPs and four pair metabolites with the conventional risk factors was the highest (AUC: 0.738 [0.661-0.815]). CONCLUSIONS: Signatures involving metabolites and SNPs discovered in the present research may be closely associated with the pathogenesis of pancreatic cancer and for use as predictive biomarkers allowing early pancreatic cancer diagnosis and therapy.

Complementary Experimental Methods in Genetics Open Up New Avenues of Research to Elucidate the Pathogenesis of Periodontitis.

A complex disease such as periodontitis is the sum of environmental and genetic effects. The personal genetic constitution interacts with the effects of internal and external risk factors like smoking, oral hygiene, malnutrition, emotional stress, and age. Accordingly, individuals who live in the same environmental context and share comparable lifestyle habits have different disease risks. Genetic research offers the identification of DNA sequence variants that have a causal role in disease etiology and allows the identification of disease relevant immune and metabolic pathways that contribute to disease susceptibility and pathogenesis in specific situations. Real advances have been made in genetic medical research in the last years. Starting from candidate gene association studies, new approaches were employed that have expanded the study design of genomewide association studies to genomewide meta-analyses and gene x environment interaction studies. Cost efficient whole-exome and whole-genome sequencing of patients with rare severe forms of periodontitis has the potential to identify genes and pathways with a direct role in the pathogenesis of common forms. In parallel, animal models were developed that use genetically highly diverse mouse lines to identify risk genes of human diseases. This chapter presents the main studies and the identified susceptibility genes that have clear statistical evidence. In addition, it describes pioneering studies that used advanced methods in experimental dental research, opening up new avenues of research. Although the knowledge of the genetic architecture of periodontitis is still in its infancy, genetic research is building the basis for future works with the potential to advance dental medicine in ways that will determine the various causes of periodontal diseases. This knowledge may eventually allow making predictions about disease risk for individual patients and leading to diagnosis and treatments that do not treat the symptoms but heal the disease.

A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data.

Although genomewide association studies (GWASs) have identified many genetic variants underlying complex traits, a large fraction of heritability still remains unexplained. Integrative analysis that incorporates additional information, such as expression quantitativetrait locus (eQTL) data into sequencing studies (denoted as transcriptomewide association study [TWAS]), can aid the discovery of trait-associated genetic variants. However, general TWAS methods only incorporate one eQTL-derived weight (e.g., cis-effect), and thus can suffer a substantial loss of power when the single estimated cis-effect is not predictive for the effect size of a genetic variant or when there are estimation errors in the estimated cis-effect, or if the data are not consistent with the model assumption. In this study, we propose an omnibus test (OT) which utilizes a Cauchy association test to integrate association evidence demonstrated by three different traditional tests (burden test, quadratic test, and adaptive test) using GWAS summary data with multiple eQTL-derived weights. The p value of the proposed test can be calculated analytically, and thus it is fast and efficient. We applied our proposed test to two schizophrenia (SCZ) GWAS summary data sets and two lipids trait (HDL) GWAS summary data sets. Compared with the three traditional tests, our proposed OT can identify more trait-associated genes.

Safe Linkage of Cohort and Population-Based Register Data in a Genomewide Association Study on Health Care Expenditure.

There are research questions whose answers require record linkage of multiple databases that may be characterized by limited options for full data sharing. For this purpose, the Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) consortium has supported the development of the ODISSEI Secure Supercomputer (OSSC) platform that allows researchers to link cohort data to data from Statistics Netherlands and run large-scale analyses in a high-performance computing (HPC) environment. Here, we report a successful record linkage genomewide association (GWA) study on expenditure for total health, mental health, primary and hospital care, and medication. Record linkage for genotype data from 16,726 participants from the Netherlands Twin Register (NTR) with data from Statistics Netherlands was accomplished in the secure OSSC platform, followed by gene-based tests and estimation of total and single nucleotide polymorphism (SNP)-based heritability. The total heritability of expenditure ranged between 29.4% (SE 0.8) and 37.5% (SE 0.8), but GWA analyses did not identify SNPs or genes that were genomewide significantly associated with health care expenditure. SNP-based heritability was between 0.0% (SE 3.5) and 5.4% (SE 4.0) and was different from zero for mental health care and primary care expenditure. We conclude that successfully linking genotype data to administrative health care expenditure data from Statistics Netherlands is feasible and demonstrates a series of analyses on health care expenditure. The OSSC platform offers secure possibilities for analyzing linked data in large scale and realizing sample sizes required for GWA studies, providing invaluable opportunities to answer many new research questions.

Genetic Susceptibility to Pneumonia: A GWAS Meta-Analysis Between the UK Biobank and FinnGen.

Pneumonia is a respiratory condition with complex etiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation. Here, we analyze pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9980 cases and 86,519 controls) and perform a genomewide association study meta-analysis. We use gene-based tests, colocalization, genetic correlation, latent causal variable (LCV) and polygenic prediction in an independent Australian sample (N = 5595) to draw insights into the etiology of pneumonia risk. We identify two independent loci on chromosome 15 (lead single-nucleotide polymorphisms rs2009746 and rs76474922) to be associated with pneumonia (p < 5e-8). Gene-based tests revealed 18 genes in chromosomes 15, 16 and 9, including IL127, PBX3, ApoB receptor (APOBR) and smoking related genes CHRNA3/5, statistically associated with pneumonia. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. LCV analysis suggests a strong genetic causal relationship with cardiovascular health phenotypes. Polygenic risk scores for pneumonia significantly predicted self-reported pneumonia in an independent sample, albeit with a small effect size (OR = 1.11 95% CI [1.04, 1.19], p < .05). Sensitivity analyses suggested the associations in chromosome 15 are mediated by smoking history, but the associations in chromosomes 16 and 9, and polygenic prediction were robust to adjustment for smoking. Altogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.

Genome-Wide Associations and Transcriptional Profiling Reveal ROS Regulation as One Underlying Mechanism of Sheath Blight Resistance in Rice.

Rice sheath blight, caused by the necrotrophic fungus Rhizoctonia solani Kühn, continues to be an important and challenging rice disease worldwide. Here, we used genome-wide association studies over a high-density rice array to facilitate the identification of potential novel genes and quantitative trait loci related to sheath blight resistance. We identified multiple regions that significantly associated with independent disease components in chromosomes 1, 4, and 11 under controlled condition. In particular, we investigated qLN1128, a quantitative trait locus enriched with defense-related genes that reduce disease lesions in a near-isogenic line. RNA profiling of the line carrying qLN1128 showed a number of differentially expressed genes related to the reactive oxygen species (ROS)-redox pathway. Histochemical staining revealed less ROS accumulation on the resistant line, suggesting efficient ROS deregulation that delays pathogen colonization. The detection of genomic regions controlling multiple mechanisms of resistance to sheath blight will provide tools to design effective breeding interventions in rice.

Gene Discovery Using Twins.

One of Nick's key early achievements at QIMR was to establish a twin study on melanoma risk factors. The Brisbane Twin Nevus Study (BTNS) had an initial focus on nevus (mole) count in adolescents but, reflecting Nick's broad interests, expanded in scope enormously over the decades. In the skin cancer arena, BTNS was essential to genetic discoveries in melanoma, eye color and pigmentation. Later studies amassed data on thousands of phenotypes, ranging from molecular phenotypes such as gene expression to studies where gene mapping findings in adolescents turned out to have translational potential in late-onset diseases. Nick's twin data have formed the basis for an enormous range of discoveries, with Nick and his colleagues continuing to capitalize on these data.

It's in the Bloody Genes!

Blood cell concentrations for most cell types are highly heritable. Data from Nick Martin's twin registry provided much of the data for the early heritability and linkage studies of blood cell related traits and have contributed significantly to more recent genomewide association studies that have successfully identified individual genetic loci.

Contributions of Nicholas Martin to Gambling Disorder Research.

Professor Nicholas G. Martin, from QIMR Berghofer Medical Research Institute in Brisbane, Australia, is a world leader in the effort to understand the genetic architecture underlying disordered gambling. This article pays tribute to Nick and his almost two decades of gambling research, highlighting his many strengths, ranging from the use of ingenious recruitment approaches, twin study methods, genomewide association studies, to facilitating international collaborations.

Genetics of Biochemical Phenotypes.

Biomarkers diagnose, predict or assess the risk of disease, and studies of the effects of genetic variation on biomarker phenotypes in the general population complement studies on patients diagnosed with disease. This paper traces the evolution of studies on biomarker genetics over the past 40 years through examples drawn from the work of Professor Martin and his colleagues.

A Brief History of the Collaboration between Dr Nathan Gillespie and Professor Nick Martin Including Personal Reflections.

This article describes Dr Nathan Gillespie's PhD training and supervision under Professor Nick Martin and their ongoing collaborations. Drs Gillespie and Martin have collaborated on numerous biometrical genetic analyses applied to cross-sectional and longitudinal twin data, combined molecular and phenotypic modeling, as well as genomewide meta-analyses of psychoactive substance use and misuse. Dr Gillespie remains an active collaborator with Professor Martin, including ongoing data collection, analysis and publications related to the Brisbane Longitudinal Twin Study.

Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts.

BACKGROUND: Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied. OBJECTIVES: To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale. METHODS: We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes. RESULTS: Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for α-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. CONCLUSIONS: We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

Identification of genetic risk factors of aggressive periodontitis using genomewide association studies in association with those of chronic periodontitis.

To identify the genetic risk factors for aggressive periodontitis (AgP), it is important to understand the progression and pathogenesis of AgP. The purpose of this review was to summarize the genetic risk factors for AgP identified through a case-control genomewide association study (GWAS) and replication study. The initial studies to identify novel AgP risk factors were potentially biased because they relied on previous studies. To overcome this kind of issue, an unbiased GWAS strategy was introduced to identify genetic risk factors for various diseases. Currently, three genes glycosyltransferase 6 domain containing 1 (GLT6D1), defensin α1 and α3 (DEFA1A3), and sialic acid-binding Ig-like lectin 5 (SIGLEC5) that reach the threshold for genomewide significance have been identified as genetic risk factors for AgP through a case-control GWAS.

Multiplex genomewide association analysis of breast milk fatty acid composition extends the phenotypic association and potential selection of FADS1 variants to arachidonic acid, a critical infant micronutrient.

BACKGROUND: Breast milk is the sole nutrition source during exclusive breastfeeding, and polyunsaturated fatty acids (FAs) are critical micronutrients in infant physical and cognitive development. There has been no prior genomewide association study of breast milk, hence our objective was to test for genetic association with breast milk FA composition. METHODS: We measured the fractional composition of 26 individual FAs in breast milk samples from three cohorts totalling 1142 Bangladeshi mothers whose infants were genotyped on the Illumina MEGA chip and replicated on a custom Affymetrix 30K SNP array (n=616). Maternal genotypes were imputed using IMPUTE. RESULTS: After running 33 separate FA fraction phenotypes, we found that SNPs known to be associated with serum FAs in the FADS1/2/3 region were also associated with breast milk FA composition (experiment-wise significance threshold 4.2×10-9). Hypothesis-neutral comparison of the 33 fractions showed that the most significant genetic association at the FADS1/2/3 locus was with fraction of arachidonic acid (AA) at SNP rs174556, with a very large per major allele effect size of 17% higher breast milk AA level. There was no evidence of independent association at FADS1/2/3 with any other FA or SNP after conditioning on AA and rs174556. We also found novel significant experiment-wise SNP associations with: polyunsaturated fatty acid (PUFA) 6/PUFA3 ratio (sorting nexin 29), eicosenoic (intergenic) and capric (component of oligomeric Golgi complex 3) acids; and six additional loci at genomewide significance (<5×10-8). CONCLUSIONS: AA is the primary FA in breast milk influenced by genetic variation at the FADS1/2/3 locus, extending the potential phenotypes under genetic selection to include breast milk composition, thereby possibly affecting infant growth or cognition. Breast milk FA composition is influenced by maternal genetics in addition to diet and body composition.

Unravelling the genetic loci for growth and carcass traits in Chinese Bamaxiang pigs based on a 1.4 million SNP array.

Bamaxiang pig is from Guangxi province in China, characterized by its small body size and two-end black coat colour. It is an important indigenous breed for local pork market and excellent animal model for biomedical research. In this study, we performed genomewide association studies (GWAS) on 43 growth and carcass traits in 315 purebred Bamaxiang pigs based on a 1.4 million SNP array. We observed considerable phenotypic variability in the growth and carcass traits in the Bamaxiang pigs. The corresponding SNP based heritability varied greatly across the 43 traits and ranged from 9.0% to 88%. Through a conditional GWAS, we identified 53 significant associations for 35 traits at p value threshold of 10-6 . Among which, 26 associations on chromosome 3, 7, 14 and X passed a genomewide significance threshold of 5 × 10-8 . The most remarkable loci were at around 30.6 Mb on chromosome 7, which had growth stage-dependent effects on body lengths and cannon circumferences and showed large effects on multiple carcass traits. We discussed HMGA1 NUDT3, EIF2AK1, TMEM132C and AFF2 that near the lead SNP of significant loci as plausible candidate genes for corresponding traits. We also showed that including phenotypic covariate in GWAS can help to reveal additional significant loci for the target traits. The results provide insight into the genetic architecture of growth and carcass traits in Bamaxiang pigs.

QTL mapping and GWAS reveal candidate genes controlling capsaicinoid content in Capsicum.

Capsaicinoids are unique compounds produced only in peppers (Capsicum spp.). Several studies using classical quantitative trait loci (QTLs) mapping and genomewide association studies (GWAS) have identified QTLs controlling capsaicinoid content in peppers; however, neither the QTLs common to each population nor the candidate genes underlying them have been identified due to the limitations of each approach used. Here, we performed QTL mapping and GWAS for capsaicinoid content in peppers using two recombinant inbred line (RIL) populations and one GWAS population. Whole-genome resequencing and genotyping by sequencing (GBS) were used to construct high-density single nucleotide polymorphism (SNP) maps. Five QTL regions on chromosomes 1, 2, 3, 4 and 10 were commonly identified in both RIL populations over multiple locations and years. Furthermore, a total of 109 610 SNPs derived from two GBS libraries were used to analyse the GWAS population consisting of 208 C. annuum-clade accessions. A total of 69 QTL regions were identified from the GWAS, 10 of which were co-located with the QTLs identified from the two biparental populations. Within these regions, we were able to identify five candidate genes known to be involved in capsaicinoid biosynthesis. Our results demonstrate that QTL mapping and GBS-GWAS represent a powerful combined approach for the identification of loci controlling complex traits.

Pleiotropic effects of regulatory variation in tan result in correlation of two pigmentation traits in Drosophila melanogaster.

Traits with a common genetic basis frequently display correlated phenotypic responses to selection or environmental conditions. In Drosophila melanogaster, pigmentation of the abdomen and a trident-shaped region on the thorax are genetically correlated. Here, we used a pooled replicated genomewide association approach (Pool-GWAS) to identify the genetic basis of variation in thoracic trident pigmentation in two Drosophila melanogaster populations. We confirmed the previously reported large effect of ebony and the association of the cosmopolitan inversion In(3R)Payne. For the first time, we identified tan as another major locus contributing to variation in trident pigmentation. Intriguingly, the regulatory variants of tan that were most strongly associated with female abdominal pigmentation also showed a strong association with trident pigmentation. We validated this common genetic basis in transgenic assays and found qualitatively similar effects on trident and abdominal pigmentation. Further work is required to determine whether this genetic correlation is favoured by natural selection or reflects a neutral by-product of a shared regulatory architecture.

Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana.

Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination (δ13 C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and δ13 C further suggests that this variation has consequences on water-use efficiency. Genome wide association analyses indicate a complex genetic architecture underlying not only variation in stomatal patterning but also to its covariation with carbon uptake parameters. Yet, we report two novel QTL affecting δ13 C independently of stomatal patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and covariation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favour the evolution of drought avoidance mechanisms over drought escape strategies.

Genomewide Association Study of Alcohol Dependence and Related Traits in a Thai Population.

BACKGROUND: Alcohol use (both quantity and dependence) is moderately heritable, and genomewide association studies (GWAS) have identified risk genes in European, African, and Asian populations. The most reproducibly identified risk genes affect alcohol metabolism. Well-known functional variants at the gene encoding alcohol dehydrogenase B and other alcohol dehydrogenases affect risk in European and African ancestry populations. Similarly, variants mapped to these same genes and a well-known null variant that maps to the gene that encodes aldehyde dehydrogenase 2 (ALDH2) also affect risk in various Asian populations. In this study, we completed the first GWAS for 3 traits related to alcohol use in a Thai population recruited initially for studies of methamphetamine dependence. METHODS: All subjects were evaluated with the Thai version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). A total of 1,045 subjects were available for analysis. Three traits were analyzed: flushing, maximum number of alcoholic beverages consumed in any lifetime 24-hour period ("MAXDRINKS"), and DSM-IV alcohol dependence criterion count. We also conducted a pleiotropy analysis with major depression, the only other psychiatric trait where summary statistics from a large-scale Asian-population GWAS are available. RESULTS: All 3 traits showed genomewide significant association with variants near ALDH2, with significance ranging from 2.01 × 10-14 (for flushing; lead single nucleotide polymorphism (SNP) PTPN11* rs143894582) to pmeta  = 5.80 × 10-10 (for alcohol dependence criterion count; lead SNP rs149212747). These lead SNPs flank rs671 and span a region of over a megabase, illustrating the need for prior biological information in identifying the actual effect SNP, rs671. We also identified significant pleiotropy between major depression and flushing. CONCLUSIONS: These results are consistent with prior findings in Asian populations and add new information regarding alcohol use-depression pleiotropy.

Genetics of Alcohol Use Disorder: A Role for Induced Pluripotent Stem Cells?

Alcohol use disorder (AUD) affects millions of people and costs nearly 250 billion dollars annually. Few effective FDA-approved treatments exist, and more are needed. AUDs have a strong heritability, but only a few genes have been identified with a large effect size on disease phenotype. Genomewide association studies (GWASs) have identified common variants with low effect sizes, most of which are in noncoding regions of the genome. Animal models frequently fail to recapitulate key molecular features of neuropsychiatric disease due to the polygenic nature of the disease, partial conservation of coding regions, and significant disparity in noncoding regions. By contrast, human induced pluripotent stem cells (hiPSCs) derived from patients provide a powerful platform for evaluating genes identified by GWAS and modeling complex interactions in the human genome. hiPSCs can be differentiated into a wide variety of human cells, including neurons, glia, and hepatic cells, which are compatible with numerous functional assays and genome editing techniques. In this review, we focus on current applications and future directions of patient hiPSC-derived central nervous system cells for modeling AUDs in addition to highlighting successful applications of hiPSCs in polygenic neuropsychiatric diseases.