LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

A genome-wide association study of susceptibility to upper urinary tract infections.

BACKGROUND: Our goal was to identify genetic and modifiable risk factors for upper urinary tract infections (UTIs). METHODS: We used data from UK Biobank, The Trøndelag Health Study (HUNT), and Michigan Genomics Initiative (MGI) to conduct genome-wide association studies (GWASs) and sex-stratified analyses on upper UTI. Mendelian randomization (MR) analyses were conducted to examine potential causal relationships between cardiometabolic risk factors and upper UTIs. RESULTS: One genome-wide significant (P ≤ 5E-08) locus was associated with the susceptibility to upper UTI, located near TSN in the female-only analysis. Additionally, we identified suggestive (P ≤ 5E-06) loci near DNAI3 for the females, SCAMP1-AS1 for the males, and near TSN, LINC00603, and HLA-DQA2 for both sexes. In MR analyses, higher genetically predicted lifetime smoking scores were associated with an increased risk of developing upper UTI for females and both sexes (OR of 4.84, P = 4.50E-06 and OR of 2.79, P = 3.02E-05, respectively). CONCLUSIONS: We found that genetic variants near TSN was associated with the risk of upper UTIs among females. In addition, we found several genetic loci with suggestive associations with the risk of upper UTIs. Finally, MR analyses found smoking to be a potential causal risk factor for upper UTIs.

Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Despite overlap between genetic risk loci for ALL and hematologic traits, the etiological relevance of dysregulated blood-cell homeostasis remains unclear. We investigated this question in a genome-wide association study (GWAS) of childhood ALL (2,666 affected individuals, 60,272 control individuals) and a multi-trait GWAS of nine blood-cell indices in the UK Biobank. We identified 3,000 blood-cell-trait-associated (p < 5.0 × 10-8) variants, explaining 4.0% to 23.9% of trait variation and including 115 loci associated with blood-cell ratios (LMR, lymphocyte-to-monocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio). ALL susceptibility was genetically correlated with lymphocyte counts (rg = 0.088, p = 4.0 × 10-4) and PLR (rg = -0.072, p = 0.0017). In Mendelian randomization analyses, genetically predicted increase in lymphocyte counts was associated with increased ALL risk (odds ratio [OR] = 1.16, p = 0.031) and strengthened after accounting for other cell types (OR = 1.43, p = 8.8 × 10-4). We observed positive associations with increasing LMR (OR = 1.22, p = 0.0017) and inverse effects for NLR (OR = 0.67, p = 3.1 × 10-4) and PLR (OR = 0.80, p = 0.002). Our study shows that a genetically induced shift toward higher lymphocyte counts, overall and in relation to monocytes, neutrophils, and platelets, confers an increased susceptibility to childhood ALL.

A mediation analysis framework based on variance component to remove genetic confounding effect.

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Comparison of multiple imputation and other methods for the analysis of imputed genotypes.

BACKGROUND: Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM). RESULTS: Our simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage. CONCLUSIONS: The unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF [Formula: see text] 0.001 and Rsq [Formula: see text] 0.3.

Systematically identifying genetic signatures including novel SNP-clusters, nonsense variants, frame-shift INDELs, and long STR expansions that potentially link to unknown phenotypes existing in dog breeds.

BACKGROUND: In light of previous studies that profiled breed-specific traits or used genome-wide association studies to refine loci associated with characteristic morphological features in dogs, the field has gained tremendous genetic insights for known dog traits observed among breeds. Here we aim to address the question from a reserve perspective: whether there are breed-specific genotypes that may underlie currently unknown phenotypes. This study provides a complete set of breed-specific genetic signatures (BSGS). Several novel BSGS with significant protein-altering effects were highlighted and validated. RESULTS: Using the next generation whole-genome sequencing technology coupled with unsupervised machine learning for pattern recognitions, we constructed and analyzed a high-resolution sequence map for 76 breeds of 412 dogs. Genomic structures including novel single nucleotide polymorphisms (SNPs), SNP clusters, insertions, deletions (INDELs) and short tandem repeats (STRs) were uncovered mutually exclusively among breeds. We also partially validated some novel nonsense variants by Sanger sequencing with additional dogs. Four novel nonsense BSGS were found in the Bernese Mountain Dog, Samoyed, Bull Terrier, and Basset Hound, respectively. Four INDELs resulting in either frame-shift or codon disruptions were found in the Norwich Terrier, Airedale Terrier, Chow Chow and Bernese Mountain Dog, respectively. A total of 15 genomic regions containing three types of BSGS (SNP-clusters, INDELs and STRs) were identified in the Akita, Alaskan Malamute, Chow Chow, Field Spaniel, Keeshond, Shetland Sheepdog and Sussex Spaniel, in which Keeshond and Sussex Spaniel each carried one amino-acid changing BSGS in such regions. CONCLUSION: Given the strong relationship between human and dog breed-specific traits, this study might be of considerable interest to researchers and all. Novel genetic signatures that can differentiate dog breeds were uncovered. Several functional genetic signatures might indicate potentially breed-specific unknown phenotypic traits or disease predispositions. These results open the door for further investigations. Importantly, the computational tools we developed can be applied to any dog breeds as well as other species. This study will stimulate new thinking, as the results of breed-specific genetic signatures may offer an overarching relevance of the animal models to human health and disease.

Discovery and Mediation Analysis of Cross-Phenotype Associations Between Asthma and Body Mass Index in 12q13.2.

Twin studies suggest that shared genetics contributes to the comorbidity of asthma and obesity, but candidate-gene studies provide limited evidence of pleiotropy. We conducted genome-wide association analyses of asthma and body mass index (BMI; weight (kg)/height (m)2)) among 305,945 White British subjects recruited into the UK Biobank in 2006-2010. We searched for overlapping signals and conducted mediation analyses on genome-wide-significant cross-phenotype associations, assessing moderation by sex and age at asthma diagnosis, and adjusting for confounders of the asthma-BMI relationship. We identified a genome-wide-significant cross-phenotype association at rs705708 (asthma odds ratio = 1.05, 95% confidence interval: 1.03, 1.07; P = 7.20 × 10-9; and BMI ß = -0.065, 95% confidence interval: -0.087, -0.042; P = 1.30 × 10-8). rs705708 resides on 12q13.2, which harbors 9 other asthma- and BMI-associated variants (all P < 5 × 10-5 for asthma; all but one P < 5 × 10-5 for BMI). Follow-up analyses of rs705708 show that most of the BMI association occurred independently of asthma, with consistent magnitude between men and women and persons with and without asthma, irrespective of age at diagnosis; the asthma association was stronger for childhood versus adult asthma; and both associations remained after confounder adjustment. This suggests that 12q13.2 displays pleiotropy for asthma and BMI. Upon further characterization, 12q13.2 might provide a target for interventions that simultaneously prevent or treat asthma and obesity.

A pooled genome-wide association study identifies pancreatic cancer susceptibility loci on chromosome 19p12 and 19p13.3 in the full-Jewish population.

Jews are estimated to be at increased risk of pancreatic cancer compared to non-Jews, but their observed 50-80% excess risk is not explained by known non-genetic or genetic risk factors. We conducted a GWAS in a case-control sample of American Jews, largely Ashkenazi, including 406 pancreatic cancer patients and 2332 controls, identified in the dbGaP, PanScan I/II, PanC4 and GERA data sets. We then examined resulting SNPs with P < 10-7 in an expanded sample set, of 539 full- or part-Jewish pancreatic cancer patients and 4117 full- or part-Jewish controls from the same data sets. Jewish ancestries were genetically determined using seeded FastPCA. Among the full Jews, a novel genome-wide significant association was detected on chromosome 19p12 (rs66562280, per-allele OR = 1.55, 95% CI = 1.33-1.81, P = 10-7.6). A suggestive relatively independent association was detected on chromosome 19p13.3 (rs2656937, OR = 1.53, 95% CI = 1.31-1.78, P = 10-7.0). Similar associations were seen for these SNPs among the full and part Jews combined. This is the first GWAS conducted for pancreatic cancer in the increased-risk Jewish population. The SNPs rs66562280 and rs2656937 are located in introns of ZNF100-like and ARRDC5, respectively, and are known to alter regulatory motifs of genes that play integral roles in pancreatic carcinogenesis.

Examining the effect of obesity-associated gene variants on breast cancer survivors in a randomized weight loss intervention.

PURPOSE: Our study examined whether common variants of obesity-associated genes FTO, MC4R, BDNF, and CREB1 moderated the effects of a lifestyle intervention on weight change among breast cancer survivors. METHODS: 151 breast cancer survivors with a body mass index ≥ 25 kg/m2 were randomly assigned to a 6-month weight loss intervention or usual care group. Genotyping of FTO rs9939609, MC4R rs6567160, BDNF rs11030104, CREB1 rs17203016 was performed. Linear mixed models were used including the main effects of genotype (assuming a dominant genetic model), treatment arm on weight and percent body fat changes, and genotype by treatment interaction variable. All statistical tests were evaluated against a Bonferroni-corrected alpha of 0.0125. RESULTS: Women in the intervention group achieved significantly greater weight loss than the usual care group (5.9% vs 0.4%, p < 0.001), regardless of genotype. Changes in weight and percent body fat did not differ significantly between carriers of the FTO rs9939609, MC4R rs6567160, BDNF rs11030104, and CREB1 rs17203016 risk alleles compared to non-carriers (p-interaction > 0.0125 for each single-nucleotide polymorphisms). CONCLUSIONS: Women who are genetically predisposed to obesity and recently diagnosed with breast cancer may achieve significant and clinically meaningful weight loss through healthy eating and exercise. CLINICAL TRIAL REGISTRATION: NCT02863887 (Date of Registration: August 11, 2016); NCT02110641 (Date of Registration: April 10, 2014).

Inherited genetic susceptibility to acute lymphoblastic leukemia in Down syndrome.

Children with Down syndrome (DS) have a 20-fold increased risk of acute lymphoblastic leukemia (ALL) and distinct somatic features, including CRLF2 rearrangement in ∼50% of cases; however, the role of inherited genetic variation in DS-ALL susceptibility is unknown. We report the first genome-wide association study of DS-ALL, comprising a meta-analysis of 4 independent studies, with 542 DS-ALL cases and 1192 DS controls. We identified 4 susceptibility loci at genome-wide significance: rs58923657 near IKZF1 (odds ratio [OR], 2.02; Pmeta = 5.32 × 10-15), rs3731249 in CDKN2A (OR, 3.63; Pmeta = 3.91 × 10-10), rs7090445 in ARID5B (OR, 1.60; Pmeta = 8.44 × 10-9), and rs3781093 in GATA3 (OR, 1.73; Pmeta = 2.89 × 10-8). We performed DS-ALL vs non-DS ALL case-case analyses, comparing risk allele frequencies at these and other established susceptibility loci (BMI1, PIP4K2A, and CEBPE) and found significant association with DS status for CDKN2A (OR, 1.58; Pmeta = 4.1 × 10-4). This association was maintained in separate regression models, both adjusting for and stratifying on CRLF2 overexpression and other molecular subgroups, indicating an increased penetrance of CDKN2A risk alleles in children with DS. Finally, we investigated functional significance of the IKZF1 risk locus, and demonstrated mapping to a B-cell super-enhancer, and risk allele association with decreased enhancer activity and differential protein binding. IKZF1 knockdown resulted in significantly higher proliferation in DS than non-DS lymphoblastoid cell lines. Our findings demonstrate a higher penetrance of the CDKN2A risk locus in DS and serve as a basis for further biological insights into DS-ALL etiology.

Proteinarium: Multi-sample protein-protein interaction analysis and visualization tool.

We posit the likely architecture of complex diseases is that subgroups of patients share variants in genes in specific networks which are sufficient to give rise to a shared phenotype. We developed Proteinarium, a multi-sample protein-protein interaction (PPI) tool, to identify clusters of patients with shared gene networks. Proteinarium converts user defined seed genes to protein symbols and maps them onto the STRING interactome. A PPI network is built for each sample using Dijkstra's algorithm. Pairwise similarity scores are calculated to compare the networks and cluster the samples. A layered graph of PPI networks for the samples in any cluster can be visualized. To test this newly developed analysis pipeline, we reanalyzed publicly available data sets, from which modest outcomes had previously been achieved. We found significant clusters of patients with unique genes which enhanced the findings in the original study.

Body mass index and risk of dying from a bloodstream infection: A Mendelian randomization study.

BACKGROUND: In observational studies of the general population, higher body mass index (BMI) has been associated with increased incidence of and mortality from bloodstream infection (BSI) and sepsis. On the other hand, higher BMI has been observed to be apparently protective among patients with infection and sepsis. We aimed to evaluate the causal association of BMI with risk of and mortality from BSI. METHODS AND FINDINGS: We used a population-based cohort in Norway followed from 1995 to 2017 (the Trøndelag Health Study [HUNT]), and carried out linear and nonlinear Mendelian randomization analyses. Among 55,908 participants, the mean age at enrollment was 48.3 years, 26,324 (47.1%) were men, and mean BMI was 26.3 kg/m2. During a median 21 years of follow-up, 2,547 (4.6%) participants experienced a BSI, and 451 (0.8%) died from BSI. Compared with a genetically predicted BMI of 25 kg/m2, a genetically predicted BMI of 30 kg/m2 was associated with a hazard ratio for BSI incidence of 1.78 (95% CI: 1.40 to 2.27; p < 0.001) and for BSI mortality of 2.56 (95% CI: 1.31 to 4.99; p = 0.006) in the general population, and a hazard ratio for BSI mortality of 2.34 (95% CI: 1.11 to 4.94; p = 0.025) in an inverse-probability-weighted analysis of patients with BSI. Limitations of this study include a risk of pleiotropic effects that may affect causal inference, and that only participants of European ancestry were considered. CONCLUSIONS: Supportive of a causal relationship, genetically predicted BMI was positively associated with BSI incidence and mortality in this cohort. Our findings contradict the "obesity paradox," where previous traditional epidemiological studies have found increased BMI to be apparently protective in terms of mortality for patients with BSI or sepsis.

MendelProb: probability and sample size calculations for Mendelian studies of exome and whole genome sequence data.

Motivation: For the design of genetic studies, it is necessary to perform power calculations. Although for Mendelian traits the power of detecting linkage for pedigree(s) can be determined, it is also of great interest to determine the probability of identifying multiple pedigrees or unrelated cases with variants in the same gene. For many diseases, due to extreme locus heterogeneity this probability can be small. If only one family is observed segregating a variant classified as likely pathogenic or of unknown significance, the gene cannot be implicated in disease etiology. The probability of identifying several disease families or cases is dependent on the gene-specific disease prevalence and the sample size. The observation of multiple disease families or cases with variants in the same gene as well as evidence of pathogenicity from other sources, e.g. in silico prediction, expression and functional studies, can aid in implicating a gene in disease etiology. MendelProb can determine the probability of detecting a minimum number of families or cases with variants in the same gene. It can also calculate the probability of detecting genes with variants in different data types, e.g. identifying a variant in at least one family that can establish linkage and more the two additional families regardless of their size. Additionally, for a specified probability MendelProb can determine the number of probands which need to be screened to detect a minimum number of individuals with variants within the same gene. Results: A single Mendelian disease family is not sufficient to implicate a gene in disease etiology. It is necessary to observe multiple families or cases with potentially pathogenic variants in the same gene. MendelProb, an R library, was developed to determine the probability of observing multiple families and cases with variants within a gene and to also establish the numbers of probands to screen to detect multiple observations of variants within a gene. Availability and implementation: https://github.com/statgenetics/mendelprob.

Risks of preterm birth among non-Hispanic black and non-Hispanic white women: Effect modification by maternal age.

BACKGROUND: Preterm birth (PTB) disproportionately affects African American compared with Caucasian women, although reasons for this disparity remain unclear. Some suggest that a differential effect of maternal age by race/ethnicity, especially at older maternal ages, may explain disparities. OBJECTIVE: To determine whether the relationship between maternal age and preterm birth varies by race/ethnicity among primiparae non-Hispanic blacks (NHB) and non-Hispanic whites (NHW). METHODS: A cross-sectional study of 367 081 singleton liveborn first births to NHB and NHW women in California from 2008 to 2012 was conducted. Rate ratios (RR) were estimated for PTB and its subtypes-spontaneous and clinician-initiated-after adjusting for confounders through Poisson regression. Universal age/race reference groups (NHW, 25-29 years) and race-specific reference groups (NHW or NHB, 25-29 years) were used for comparisons. RESULTS: Among all women, RR of PTB was highest at the extremes of age (<15 and ≥40 years). Among NHBs, the risk of PTB was higher than among NHWs at all maternal ages (adjusted RR of PTB 1.38-2.93 vs 0.98-2.38). However, using race-specific reference groups, the risk of PTB for NHB women (RR 0.91-1.88) vs NHW women (RR 0.98-2.39) was nearly identical at all maternal ages, with overlapping confidence intervals. Analyses did not demonstrate substantial divergence of risk with advancing maternal age. PTB, spontaneous PTB, and clinician-initiated PTB demonstrated similar risk patterns at younger but not older maternal ages, where risk of clinician-initiated PTB increased sharply for all women. CONCLUSIONS: Primiparae NHBs demonstrated increased risk of PTB, spontaneous PTB, and clinician-initiated PTB compared with NHWs at all maternal ages. However, RRs using race-specific reference groups converged across maternal ages, indicating a similar independent effect of maternal age on PTB by race/ethnicity. A differential effect of maternal age does not appear to explain disparities in preterm birth by race/ethnicity.

BMI1 enhancer polymorphism underlies chromosome 10p12.31 association with childhood acute lymphoblastic leukemia.

Genome-wide association studies of childhood acute lymphoblastic leukemia (ALL) have identified regions of association at PIP4K2A and upstream of BMI1 at chromosome 10p12.31-12.2. The contribution of both loci to ALL risk and underlying functional variants remain to be elucidated. We carried out single nucleotide polymorphism (SNP) imputation across chromosome 10p12.31-12.2 in Latino and non-Latino white ALL cases and controls from two independent California childhood leukemia studies, and additional Genetic Epidemiology Research on Aging study controls. Ethnicity-stratified association analyses were performed using logistic regression, with meta-analysis including 3,133 cases (1,949 Latino, 1,184 non-Latino white) and 12,135 controls (8,584 Latino, 3,551 non-Latino white). SNP associations were identified at both BMI1 and PIP4K2A. After adjusting for the lead PIP4K2A SNP, genome-wide significant associations remained at BMI1, and vice-versa (pmeta < 10-10 ), supporting independent effects. Lead SNPs differed by ethnicity at both peaks. We sought functional variants in tight linkage disequilibrium with both the lead Latino SNP among Admixed Americans and lead non-Latino white SNP among Europeans. This pinpointed rs11591377 (pmeta = 2.1 x 10-10 ) upstream of BMI1, residing within a hematopoietic stem cell enhancer of BMI1, and which showed significant preferential binding of the risk allele to MYBL2 (p = 1.73 x 10-5 ) and p300 (p = 1.55 x 10-3 ) transcription factors using binomial tests on ChIP-Seq data from a SNP heterozygote. At PIP4K2A, we identified rs4748812 (pmeta = 1.3 x 10-15 ), which alters a RUNX1 binding motif and demonstrated chromosomal looping to the PIP4K2A promoter. Fine-mapping chromosome 10p12 in a multi-ethnic ALL GWAS confirmed independent associations and identified putative functional variants upstream of BMI1 and at PIP4K2A.

Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy.

In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.

Identification of genetic variants associated with dengue or West Nile virus disease: a systematic review and meta-analysis.

BACKGROUND: Dengue and West Nile viruses are highly cross-reactive and have numerous parallels in geography, potential vector host (Aedes family of mosquitoes), and initial symptoms of infection. While the vast majority (> 80%) of both dengue and West Nile virus infections result in asymptomatic infections, a minority of individuals experience symptomatic infection and an even smaller proportion develop severe disease. The mechanisms by which these infections lead to severe disease in a subset of infected individuals is incompletely understood, but individual host differences including genetic factors and immune responses have been proposed. We sought to identify genetic risk factors that are associated with more severe disease outcomes for both viruses in order to shed light on possible shared mechanisms of resistance and potential therapeutic interventions. METHODS: We applied a search strategy using four major databases (Medline, PubMed, Embase, and Global Health) to find all known genetic associations identified to date with dengue or West Nile virus disease. Here we present a review of our findings and a meta-analysis of genetic variants identified. RESULTS: We found genetic variations that are significantly associated with infections of these viruses. In particular we found variation within the OAS1 (meta-OR = 0.83, 95% CI: 0.69-1.00) and CCR5 (meta-OR = 1.29, 95% CI: 1.08-1.53) genes is significantly associated with West Nile virus disease, while variation within MICB (meta-OR = 2.35, 95% CI: 1.68-3.29), PLCE1 (meta-OR = 0.55, 95% CI: 0.42-0.71), MBL2 (meta-OR = 1.54, 95% CI: 1.02-2.31), and IFN-γ (meta-OR = 2.48, 95% CI: 1.30-4.71), is associated with dengue disease. CONCLUSIONS: Despite substantial heterogeneity in populations studied, genes examined, and methodology, significant associations with genetic variants were found across studies within both diseases. These gene associations suggest a key role for immune mechanisms in susceptibility to severe disease. Further research is needed to elucidate the role of these genes in disease pathogenesis and may reveal additional genetic factors associated with disease severity.

Parental Age and Risk of Pediatric Cancer in the Offspring: A Population-Based Record-Linkage Study in California.

Linking birth records and cancer registry data from California, we conducted a population-based study with 23,419 cases and 87,593 matched controls born during 1978-2009 to investigate the relationship of parental age to risk of pediatric cancer. Compared with children born to mothers aged 20-24 years, those born to mothers in older age groups had a 13%-36% higher risk of pediatric cancer; the odds ratio for each 5-year increase in maternal age was 1.06 (95% confidence interval (CI): 1.04, 1.09). For cancer diagnosed in children in age groups 0-14 years and 15-19 years, the odds ratios for each 5-year increase in maternal age were 1.05 (95% CI: 1.02, 1.07) and 1.14 (95% CI: 1.09, 1.19), respectively. Having an older father also conferred an increased risk, with an odds ratio for each 5-year increase of 1.03 (95% CI: 1.02, 1.05) for cancer diagnosed at ages 0-19 years and 1.03 (95% CI: 1.02, 1.05) for cancer diagnosed at ages 0-14 years. While advancing maternal age increased risk of leukemia and central nervous system tumors, older paternal age was not associated with risk of either type. Both maternal and paternal older ages were associated with risk of lymphoma. In this large, population-based record-linkage study, advancing parental age, especially advancing maternal age, was associated with higher pediatric cancer risk, with variations across types of cancer.