Individual Single Nucleotide Polymorphisms Research Articles

Insilico Test of Functional Role of rs8068318 Polymorphism of Arterial Hypertension-Associated TBX2 Candidate Gene

Arterial hypertension is the most common disease, and cardiovascular pathology has for a long time topped the list of causes of global mortality. Despite the large number of scientific works devoted to this problem, the etiology of essential arterial hypertension is unknown. However, in recent years, genetic factors have become highly important, namely, the contribution of individual single nucleotide polymorphisms of various genes encoding single biological pathways that are involved in the development of hypertension. In the course of this study, the functional role of rs8068318 polymorphism of the TBX2 candidate gene associated with the development of arterial hypertension was studied. The selection of the polymorphic locus was based on the data of the catalog of genome-wide association study (GWAS) of the National Human Genome Research Institute. The functional role was evaluated using online software: HaploReg (v4.1), GTExportal, and PolyPhen-2. The already conducted 68 genome-wide studies detected 382 single nucleotide hypertension-associated polymorphisms. The polymorphic locus - rs8068318 - of the TBX2 candidate gene is associated with the development of hypertension in 4 full-genomic studies, two of which examined its involvement in the development of hypertension in the European population. It was established that this SNP is in the area of hypersensitivity to DNase, the region of regulatory DNA motifs to 4 transcription factors; in the field of histones marking promoters and enhancers in 8 tissues; significantly associated with the expression of the TBX2-AS1 gene and the TBX2 gene in various organs and tissues. The polymorphic locus rs8068318 was found to be in disequilibrium in linkage (r2≥0.8) with 15 SNPs, which have significant epigenetic effects. Thus, the polymorphic locus rs8068318 of the TBX2 candidate gene, which is GWAS-significant for AH, and the 15SNPs linked to it have a significant functional role in the body.

1152 Genetic Risk Of Alzheimer’S Disease Is Linked To Short Sleep Duration

Abstract Introduction Sleep disturbances including short sleep duration are common in older adults, especially in those with Alzheimer’s disease (AD). However, it is unclear to what extent sleep duration is a manifestation of AD disease process. We examined whether genetic variants related to AD influence sleep duration in middle-aged and older adults and estimated the causal effects of AD on sleep duration using a mendelian randomization (MR) analysis. Methods We examined 406,687 UK Biobank participants with Caucasian genetic ancestry who self-reported sleep duration at baseline (2006-2010). Sleep duration was assessed by asking: “About how many hours sleep do you get in every 24 hours? (please include naps).” A genetic risk score for AD (AD-GRS) was calculated as a weighted sum of 23 previously identified AD-related single nucleotide polymorphisms in individuals of European ancestry. We evaluated whether AD-GRS predicted sleep duration using linear regression, adjusting for age, sex and principle components for genetic ancestry. We also stratified the analysis by age at baseline (≤55y or >55y) and conducted a MR analysis to estimate the effect of AD (ICD-9/10 codes for AD/dementia diagnosis) on sleep duration. Results The participants (aged 56.91±8.00y) had an average sleep duration of 7.2 (Standard deviation [SD]=1.1) hours and AD-GRS of 0.11 (SD=0.40) (range: -1.15~1.85). Higher AD-GRS score predicted shorter sleep duration (b= -0.013, 95%CI:-0.022,-0.005), mainly among those aged over 55y (b= -0.023, 95%CI:-0.034,-0.012) and not in those 55y or younger (b= 0.006, 95%CI:-0.012,0.013); p for interaction by age=0.02. MR analysis using AD-GRS as an instrumental variable suggested that AD was associated with 1.76 hrs (b=-1.76, -2.62~ -0.90) shorter sleep duration in those aged >55y. Conclusion Using a novel analytical approach, we found that higher genetic risk for AD predicted shorter sleep duration among older adults. This suggests shared genetic pathways; the biologic processes that lead to AD may also affect sleep duration. Support Dr. Leng received support from the National Institute on Aging (NIA) 1K99AG056598, and from GBHI, Alzheimer’s Association, and Alzheimer’s Society (GBHI ALZ UK-19-591141).

Assessment of a causal relationship between body mass index and atopic dermatitis

Assessment of a causal relationship between body mass index and atopic dermatitis

The identification of gene ontologies and candidate genes for digital dermatitis in beef cattle from a genome-wide association study

Bovine Digital Dermatitis (DD) is an infectious disease causing severe lameness in cattle. The aim of this study was to perform a Genome Wide Association Study (GWAS) and a Gene-Set Enrichment Analysis (GSEA) to identify candidate genes, instead of an individual Single Nucleotide Polymorphism (SNP),

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

BackgroundRecent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.MethodsWe performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls.ResultsThe gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene sets for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated by variant-level association test in the independent sample.ConclusionsThis comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

A structural brain network of genetic vulnerability to psychiatric illness

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome–connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting “vulnerability network” is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.

Prothrombotic genotypes and risk of major bleeding in patients with incident venous thromboembolism

BackgroundGenotypes associated with venous thromboembolism (VTE) may protect against bleeding due to a hypercoagulable state. Whether the risk of major bleeding is reduced in parallel with an increasing number of prothrombotic genotypes during anticoagulant treatment in VTE remains unknown. ObjectivesTo investigate the association between multiple prothrombotic genotypes and risk of major bleeding in patients with VTE. MethodsPatients with incident VTE (n = 676) derived from the Tromsø Study were genotyped for rs6025 (F5), rs1799963 (F2), rs8176719 (ABO), rs2066865 (FGG) and rs2036914 (F11) single nucleotide polymorphisms (SNPs). Major bleeding events were recorded during the first year after VTE according to the International Society on Thrombosis and Haemostasis criteria. Cox-regression was used to calculate hazard ratios with 95% confidence intervals (CIs) for major bleeding adjusted for age, sex and duration of anticoagulation according to individual prothrombotic SNPs and categories of risk alleles (5-SNP score; 0–1, 2, 3 and ≥4). ResultsIn total, 50 patients experienced major bleeding (incidence rate: 9.5/100 person-years, 95% CI 7.2–12.5). The individual SNPs and number of risk alleles were not associated with major bleeding risk. The hazard ratios for major bleeding per category increase of genetic risk score were 1.0 (95% CI 0.8–1.3) for the total study population and 1.1 (95% CI 0.8–1.5) when patients with active cancer were excluded. Analyses restricted to the first 3 months after VTE yielded similar results. ConclusionOur findings suggest that an increasing number of prothrombotic risk alleles is not protective against major bleeding in VTE patients during anticoagulation.

Genetic variants of epidermal growth factor receptor (EGFR) and their association with colorectal cancer risk in North Indian population

BackgroundMost of the cancer types contain single nucleotide polymorphisms (SNPs) that are associated with a large number of genes. These SNPs are located in gene promoters, exons, introns as well as untranslated regions (UTRs) and influence gene expression by diverse mechanisms. These mechanisms depend on the role of the genetic elements in which the individual SNPs are located. AimWe aimed to examine the possible association of EGFR rs11543848 and rs2227984 gene polymorphism and colorectal cancer in North Indian population.Subjects and methods: Peripheral blood samples were collected and genomic DNA was extracted by phenol chloroform Isoamyl method. Presence or absence of polymorphism was analyzed by PCR-RFLP method and direct sequencing. ResultsThe frequency of G allele at rs11543848 was found to be higher in cases than in controls. This suggests that individual with either homozygous G or heterozygous G allele (GA + GG) at rs11543848 SNP site were at a higher risk for developing colorectal cancer (P = 0.0358) while at rs2227984, variant genotype AA (19.3%) and TA (51.7%) were more frequent in patients than in healthy controls (AA-13%, TA-46.7%) signifying the association of this SNP with colorectal cancer patients of North India. ConclusionExcellent correlation of rs11543848 was found with the clinicopathological factors and haplotypes suggested that rs11543848 might be a good prognostic factor for genetic susceptibility to CRC in North Indian population.

Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.

Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. Supplementary data are available at Bioinformatics online.

A Replication Study Identified Seven SNPs Associated with Quantitative Traits of Type 2 Diabetes among Chinese Population in A Cross-Sectional Study.

Genome-wide association studies (GWAS) have identified common variants for quantitative traits (insulin resistance and impaired insulin release) of type 2 diabetes (T2D) across different ethnics including China, but results were inconsistent. The study included 1654 subjects who were selected from the 2010–2012 China National Nutrition and Health Surveillance (CNNHS). Insulin resistance and impaired insulin release were assessed by homeostasis model assessment (HOMA). The study included 64 diabetes-related single nucleotide polymorphisms (SNPs), which were done using Mass ARRAY. A logistic regression model was employed to explore the associations of SNPs with insulin resistance and impaired insulin release by correcting for the confounders. The 5q11.2-rs4432842, RASGRP1-rs7403531, and SEC16B-rs574367 increased the risk of insulin resistance with OR = 1.23 (95% CI: 1.04–1.45, OR = 1.35 (95% CI: 1.13–1.62), OR = 1.34 (95% CI: 1.07–1.67), respectively, while MAEA-rs6815464 decreased the risk of insulin resistance (OR = 0.84, 95% CI: 0.71–1.00). CENTD2-rs1552224, TSPAN8-rs7961581 and ANK1-rs516946 was associated with increased risk of impaired insulin release with OR = 1.47 (95% CI: 1.09–1.99), OR = 1.25 (95% CI: 1.03–1.51), OR = 1.39 (95% CI: 1.07–1.81), respectively. Our findings would provide insight into the pathogenesis of individual SNPs and T2D.

Polygenic Mendelian Randomization.

Many exposures considered in Mendelian randomization (MR) studies are polygenic in that they are influenced by thousands of genetic variants. By using many single-nucleotide polymorphisms (SNPs) as instrumental variables, more variation in the exposure is explained, increasing the precision of MR. Furthermore, methods can be designed that relax the assumptions of MR, especially concerning direct pleiotropic effects on the outcome. This article reviews the concepts and assumptions underlying the commonly used polygenic MR methods. Using a polygenic score as an instrument is equivalent to a weighted mean of individual SNP results, and the other fundamental averages, median and mode, may also be used to estimate causal effects. Outlier detection is useful for identifying pleiotropic SNPs to be excluded from analysis. Bayesian approaches are available to incorporate prior beliefs about pleiotropy. These methods each entail different assumptions, and together provide a set of sensitivity analyses to help triangulate evidence about causality.

Genetic predictors to acupuncture response for hot flashes: an exploratory study of breast cancer survivors.

Because hot flashes are a common symptom experienced by women with breast cancer, we sought to explore genetic predictors associated with response to acupuncture for the treatment of hot flashes. Using data from our completed randomized controlled trial (Clinicaltrials.gov identifier: NCT01005108) on hot flashes among breast cancer survivors who provided biomarker collection (N = 108), we extracted and assayed DNA for single nucleotide polymorphisms in genes involved in neurotransmission, thermoregulation, and inflammation (ADORA1, COMT, TCL1A, and TRPV1). For our primary outcome we classified individuals with a 50% or more reduction in their hot flash composite score at the end of treatment as responders. We used Fisher exact test to identify individual and combined single nucleotide polymorphisms associated with treatment response. Among women (N = 57) who received acupuncture treatment (electro or sham), we found that women who were carriers of at least one of these six genotypes (ADORA1 rs41264025-GA or rs16851029-GG or rs12744240-GT, COMT rs6269-GA, TCL1A rs2369049-GG, and TRPV1 rs8065080-TT) were more likely to respond to acupuncture for hot flashes than noncarriers (70.3% vs 37.5%, P = 0.035). These six genotypes were not associated with response in women (N = 51) who received pharmacological hot flash treatment (gabapentin or placebo pill; 37.5% vs 37.5%, P = 1.0). In this exploratory, proof of concept study, we identified six genotypes that may predict response to acupuncture for hot flashes in breast cancer survivors. If confirmed by future studies, these findings may inform the development of personalized acupuncture for managing hot flashes.

Interaction Between Prematurity and the MAOA Gene on Mental Development in Children: A Longitudinal View.

This study aimed to examine the association of dopamine-related genes with mental and motor development and the gene-environment interaction in preterm and term children. A total of 201 preterm and 111 term children were examined for their development at 6, 12, 18, 24, and 36 months and were genotyped for 15 single-nucleotide polymorphisms (SNPs) in dopamine-related genes (DRD2, DRD3, DAT1, COMT, and MAOA). An independent sample of 256 preterm children was used for replication. Since the developmental age trends of preterm children differed from those of term children, the analyses were stratified by prematurity. Among the 8 SNPs on the MAOA gene examined in the whole learning sample, the results of linkage disequilibrium analysis indicated that they were located in one block (all D′ > 0.9), and rs2239448 was chosen as the tag (r2 > 0.85). In the analysis of individual SNPs in each dopamine-related gene, the tag SNP (rs2239448) in MAOA remained significantly associated with the mental scores of preterm children for the interaction with age trend (p < 0.0001; largest effect size of 0.65 at 24 months) after Bonferroni correction for multiple testing. Similar findings for rs2239448 were replicated in the independent sample (p = 0.026). However, none of the SNPs were associated with the motor scores of preterm children, and none were related to the mental or motor scores of term children. The genetic variants of the MAOA gene exert influence on mental development throughout early childhood for preterm, but not term, children.

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium.

Genomic prediction is an effective way to estimate the genomic breeding values from genetic information based on statistical methods such as best linear unbiased prediction (BLUP). The used of haplotype, clusters of linked single nucleotide polymorphism (SNP) as markers instead of individual SNPs can improve the accuracy of genomic prediction. Since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with a cluster of markers is higher compared to an individual marker. To make haplotypes efficient in genomic prediction, finding optimal ways to define haplotypes is essential. In this study, 770K or 50K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 3,498 cattle. Using SNP chip data, haplotype was defined in three different ways based on 1) the number of SNPs included, 2) length of haplotypes (bp), and 3) agglomerative hierarchical clustering based on LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; 5, 10, 20 or 50 SNPs on average per haplotype. A linear mixed model using haplotype to calculated the covariance matrix was applied for testing the prediction accuracy of each haplotype size. Also, conventional SNP-based linear mixed model was tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight (CWT), eye muscle area (EMA) and backfat thickness (BFT) were used as the phenotypes. This study reveals that using haplotypes generally showed increased accuracy compared to conventional SNP-based model for CWT and EMA, but found to be small or no increase in accuracy for BFT. LD clustering-based haplotypes specifically the five SNPs size showed the highest prediction accuracy for CWT and EMA. Meanwhile, the highest accuracy was obtained when length-based haplotypes with five SNPs were used for BFT. The maximum gain in accuracy was 1.3% from cross-validation and 4.6% from forward validation for EMA, suggesting that genomic prediction accuracy can be increased by using haplotypes. However, the improvement from using haplotypes may depend on the trait of interest. In addition, when the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles, thereby reducing computational costs. Therefore, finding optimal ways to define haplotypes and using the haplotype alleles as markers can improve the accuracy of genomic prediction.

Long Leukocyte Telomere Length Is Associated with Increased Risks of Soft Tissue Sarcoma: A Mendelian Randomization Study

Background: Leukocyte telomere length (LTL) has been associated with the risks of several cancers in observational studies. Mendelian randomization (MR) studies, using genetic variants as instrumental variables, have also shown associations of genetically predicted LTL with cancer risks. In this study, we performed the first MR analysis on soft tissue sarcoma (STS) to investigate the causal relationship between LTL and the risk of STS. Methods: Genotypes from eleven LTL-associated single nucleotide polymorphisms (SNPs) in 821 STS cases and 851 cancer-free controls were aggregated into a weighted genetic risk score (GRS) to predict LTL. Multivariate logistic regression was used to assess the association of STS risk with individual SNPs and aggregated GRS. Results: Four SNPs displayed evidence for an individual association between long LTL-conferring allele and increased STS risk: rs7675998 (odds ratio (OR) = 1.21, 95% confidence interval (CI) = 1.02–1.43), rs9420907 (OR = 1.31, 95% CI = 1.08–1.59), rs8105767 (OR = 1.18, 95% CI = 1.02–1.37), and rs412658 (OR = 1.18, 95% CI = 1.02–1.36). Moreover, longer genetically predicted LTL, calculated as GRS, was strongly associated with an increased risk of STS (OR = 1.44, 95% CI = 1.18–1.75, p < 0.001), and there was a significant dose-response association (p for trend <0.001 in tertile and quartile analyses). The association of longer LTL with higher STS risk was more evident in women than in men. In stratified analyses by major STS subtypes, longer LTL was significantly associated with higher risks of leiomyosarcoma and gastrointestinal stromal tumors. Conclusions: Longer LTL is associated with increased risks of STS.

The potential role of clock genes in children attention-deficit/hyperactivity disorder

BackgroundAttention deficit/ hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder and is thought to be associated with circadian system. MethodsWe performed a pathway-based study to test individual single nucleotide polymorphisms (SNPs) and the overall evidence of genetic polymorphisms involved in the circadian pathway in association with children ADHD susceptibility among a Chinese population. A community-based case–control study was conducted among Chinese children, and 168 ADHD patients and 233 controls were recruited using a combination diagnosis based on the diagnostic and statistical manual of mental disorders iv (DSM-IV) ADHD rating scale, Swanson, Nolan, and Pelham rating scale (SNAP-IV) rating scale, and semi-structured clinical interview. ResultsThe results of single-loci analyses identified that PER1 rs2518023 and ARNTL2 rs2306074 were nominally association with ADHD susceptibility (P < 0.05). Next, we applied multifactor dimensionality reduction (MDR), and classification and regression tree (CART) analyses to explore high-order gene–gene interactions among the functional SNPs to ADHD risks. The results indicated that interactions among the PER1 rs2518023, ARNTL2 rs2306074 and NR1D1 rs939347 were associated with the risk of ADHD in children. Individuals carrying the combination genotypes of the PER1 rs2518023 GG or GT, ARNTL2 rs2306074 TC or TT and NR1D1 rs939347 GA or AA displayed a significantly higher risk for ADHD than who carry the PER1 rs2518023 TT and CRY2 rs2292910 CA/CC genotypes (adjusted OR = 4.37, 95% CI = 2.16–8.85, P < 0.001). ConclusionsThese findings revealed the importance of genetic variations related to the circadian clock system to the susceptibility of children ADHD.

Clinical and Genetic Risk Prediction of Cognitive Impairment After Blood or Marrow Transplantation for Hematologic Malignancy

Using a candidate gene approach, we tested the hypothesis that individual single nucleotide polymorphisms (SNPs) and gene-level variants are associated with cognitive impairment in patients with hematologic malignancies treated with blood or marrow transplantation (BMT) and that inclusion of these SNPs improves risk prediction beyond that offered by clinical and demographic characteristics. In the discovery cohort, BMT recipients underwent a standardized battery of neuropsychological tests pre-BMT and at 6 months, 1 year, 2 years, and 3 years post-BMT. Associations between 68 candidate genes and cognitive impairment were assessed using generalized estimating equation models. Elastic-Net regression was used to build Base (sociodemographic), Clinical, and Combined (Base plus Clinical plus genetic) risk prediction models of post-BMT impairment. An independent nonoverlapping cohort from the BMT Survivor Study with self-report of learning/memory problems (as identified by their health care provider) was used for model replication. The discovery cohort included 277 participants (58.5% males; 68.6% non-Hispanic whites; and 46.6% allogeneic BMT recipients). Adjusting for BMT type, age at BMT, sex, race/ethnicity, and cognitive reserve, SNPs in the blood-brain barrier, telomere homeostasis, and DNA repair genes were significantly associated with cognitive impairment. Compared with the Clinical Model, the Combined Model had higher predictive power in both the discovery cohort (mean area under the receiver operating characteristic curve [AUC], 0.89; 95% CI, 0.85 to 0.93 v 0.77; 95% CI, 0.71 to 0.83; P = 1.24 × 10-9) and the replication cohort (AUC, 0.71; 95% CI, 0.66 to 0.76 v 0.63; 95% CI, 0.57 to 0.68; P = .004). Inclusion of candidate genetic variants enhanced the prediction of risk of post-BMT cognitive impairment beyond that offered by demographic/clinical characteristics and represents a step toward a personalized approach to managing patients at high risk for cognitive impairment after BMT.

Association between genetic risk scores and risk of narcolepsy: a case-control study.

Genome-wide association studies (GWASs) have identified a large number of single-nucleotide polymorphisms (SNPs) associated with narcolepsy. However, the sum impact of these SNPs on defining the genomic risk of narcolepsy remains unknown. In the present study, we investigated the associations between genetic risk scores (GRSs) and narcolepsy along with their predictive power. A case-control study consisting of 903 narcolepsy patients and 1,981 healthy control subjects was performed. Thirty-two SNPs previously reported to confer susceptibility to narcolepsy were assessed for their association with narcolepsy risk. Subsequently, we constructed four GRS groups comprising reported narcolepsy susceptibility SNPs located in different genomic regions, and tested their association with narcolepsy risk using a regression model. Receiver operating characteristic (ROC) curves were used to examine the discriminatory power of the GRSs for predicting narcolepsy. Nine individual SNPs were significantly associated with narcolepsy after Bonferroni correction. All four GRSs were strongly associated with narcolepsy risk even when GRSs were constructed using SNPs located outside the previously implicated human leukocyte antigen (HLA) region on chromosome 6. The odds ratio (OR) for narcolepsy risk increased with the number of genetic loci implicated, ranging from an OR of 2.016 (95% CI, 1.657-2.456) to an OR of 4.298 (95% CI, 3.378-5.481). GRS4, constructed using the narcolepsy-associated SNPs identified in the Chinese population, was most closely associated with narcolepsy risk. The results suggest that the GRS method for combining common genetic variations can significantly associate GRS scores with narcolepsy risk and may facilitate narcolepsy risk stratification for prevention trials, both for HLA-DQB1*06:02-positive and -negative individuals.

Myocardial infarction, prothrombotic genotypes, and venous thrombosis risk: The Tromsø Study

BackgroundThe risk of venous thromboembolism (VTE) is increased after a myocardial infarction (MI). Some prothrombotic genotypes associated with VTE have also been associated with risk of MI. Whether prothrombotic single‐nucleotide polymorphisms (SNPs) further increase the risk of VTE in MI patients is scarcely investigated. AimTo study the combined effect of MI and prothrombotic SNPs on the risk of VTE. MethodsCases with incident VTE (n = 641) and a randomly sampled subcohort weighted for age (n = 1761) were identified from the 4 to 6 surveys of the Tromsø Study (1994‐2012). DNA was genotyped for rs8176719 (ABO), rs6025 (F5), rs1799963 (F2), rs2066865 (FGG), and rs2036914 (F11). Hazard ratios (HRs) for VTE with 95% confidence intervals (CIs) were estimated by categories of risk alleles and MI status. ResultsPatients with MI had a 1.4‐fold increased risk of VTE, and adjustments for the 5 SNPs, either alone or in combination, did not affect this relationship (adjusted HR, 1.52; 95% CI, 1.12‐2.07). In subjects without MI, an increased risk of VTE was observed for each of the individual SNPs (≥1 vs. 0 risk alleles), and the risk increased linearly with increasing number of risk alleles in the 5‐SNP score. The combination of MI and prothrombotic genotypes, either as individual SNPs or in the 5‐SNP score, did not result in an excess risk of VTE. ConclusionThe relationship between MI and VTE was not explained by these 5 prothrombotic genotypes. Prothrombotic genotypes did not yield an excess risk of VTE in patients with MI.

Association Between Polymorphisms in DNA Damage Repair Genes and Radiation Therapy–Induced Early Adverse Skin Reactions in a Breast Cancer Population: A Polygenic Risk Score Approach

Genetic variations in DNA damage repair (DDR) genes may influence radiation therapy (RT)-induced acute normal tissue toxicity in patients with breast cancer. Identifying an individual or multiple single-nucleotide polymorphisms (SNPs) associated with RT-induced early adverse skin reactions (EASR) is critical for precision medicine in radiation oncology. At the completion of RT, EASR was assessed using the Oncology Nursing Society scale (0-6) in 416 patients with breast cancer, and Oncology Nursing Society score ≥4 was considered RT-induced EASR. PLINK set-based tests and subsequent individual SNP association analyses were conducted to identify genes and SNPs associated with EASR among the 53 DDR genes and 1968 SNPs. A weighted polygenic risk score (PRS) model was constructed to ascertain the association between the joint effect of risk alleles and EASR. The study population consisted of 264 Hispanic whites, 86 blacks or African Americans, 55 non-Hispanic whites, and 11 others. A total of 115 patients (27.6%) developed EASR. Five genes (ATM, CHEK1, ERCC2, RAD51C, and TGFB1) were significantly associated with RT-induced EASR. Nine SNPs within these 5 genes were further identified: ATM rs61915066, CHEK1 rs11220184, RAD51C rs302877, rs405684, TBFB1 rs4803455, rs2241714, and ERCC2 rs60152947, rs10404465, rs1799786. In a multivariable-adjusted PRS model, patients in a higher quartile of PRS were more likely to develop EASR compared with patients in the lowest quartile (ORq2 vs.q1 = 1.94, 95% CI, 0.86-4.39; ORq3 vs.q1 = 3.46, 95% CI, 1.57-7.63; ORq4 vs.q1 = 8.64, 95% CI, 3.92-19.02; and Ptrend < .0001). We newly identified the associations between 9 SNPs in ATM, CHEK1, RAD51C, TGFB1, and ERCC2 and RT-induced EASR. PRS modeling showed its potential in identifying populations at risk. Multiple SNPs in DDR genes may jointly contribute to interindividual variation in RT-induced EASR. Validation in an independent external cohort is required to determine the clinical significance of these predictive biomarkers.