Genetic architecture of plasma Alzheimer disease biomarkers.

Genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) Alzheimer's Disease (AD) biomarker levels have identified novel genes implicated in disease risk, onset and progression. However, lumbar punctures have limited availability and may be perceived as invasive. Blood collection is readily available and well accepted, but it is not clear whether plasma biomarkers will be informative for genetic studies. Here we perform genetic analyses on concentrations of plasma amyloid-ß peptides Aß40 (n = 1,467) and Aß42 (n = 1,484), Aß42/40 (n = 1467) total tau (n = 504), tau phosphorylated (p-tau181; n = 1079) and neurofilament light (NfL; n = 2,058). GWAS and gene-based analysis was used to identify single variant and genes associated with plasma levels. Finally, polygenic risk score and summary statistics were used to investigate overlapping genetic architecture between plasma biomarkers, CSF biomarkers and AD risk. We found a total of six genome-wide significant signals. APOE was associated with plasma Aß42, Aß42/40, tau, p-tau181 and NfL. We proposed 10 candidate functional genes on the basis of 12 single nucleotide polymorphism-biomarker pairs and brain differential gene expression analysis. We found a significant genetic overlap between CSF and plasma biomarkers. We also demonstrate that it is possible to improve the specificity and sensitivity of these biomarkers, when genetic variants regulating protein levels are included in the model. This current study using plasma biomarker levels as quantitative traits can be critical to identification of novel genes that impact AD and more accurate interpretation of plasma biomarker levels.

Gene-lifestyle interactions in the genomics of human complex traits.

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

Deriving stratified effects from joint models investigating gene-environment interactions.

BACKGROUND: Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. RESULTS: In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the "joint" model. We performed simulation studies to assess our estimators' accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators' retained accuracy after filtering SNPs by sample size to mitigate potential bias. CONCLUSIONS: These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.

Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids.

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.

Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions.

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.

Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries.

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure.

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10-8) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10-8). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).

Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale.

BACKGROUND: Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They demonstrated the power of collaborative research through meta-analysis of study-specific results. METHODS AND RESULTS: The Gene-Lifestyle Interactions Working Group was formed to facilitate the first large, concerted, multiancestry study to systematically evaluate gene-lifestyle interactions. In stage 1, genome-wide interaction analysis is performed in 53 cohorts with a total of 149 684 individuals from multiple ancestries. In stage 2 involving an additional 71 cohorts with 460 791 individuals from multiple ancestries, focused analysis is performed for a subset of the most promising variants from stage 1. In all, the study involves up to 610 475 individuals. Current focus is on cardiovascular traits including blood pressure and lipids, and lifestyle factors including smoking, alcohol, education (as a surrogate for socioeconomic status), physical activity, psychosocial variables, and sleep. The total sample sizes vary among projects because of missing data. Large-scale gene-lifestyle or more generally gene-environment interaction (G×E) meta-analysis studies can be cumbersome and challenging. This article describes the design and some of the approaches pursued in the interaction projects. CONCLUSIONS: The Gene-Lifestyle Interactions Working Group provides an excellent framework for understanding the lifestyle context of genetic effects and to identify novel trait loci through analysis of interactions. An important and novel feature of our study is that the gene-lifestyle interaction (G×E) results may improve our knowledge about the underlying mechanisms for novel and already known trait loci.

Rare coding variants associated with blood pressure variation in 15 914 individuals of African ancestry.

OBJECTIVES: Hypertension is a major risk factor for all cardiovascular diseases, especially among African Americans. This study focuses on identifying specific blood pressure (BP) genes using 15 914 individuals of African ancestry from eight cohorts (Africa America Diabetes Mellitus, Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in young Adults, Genetics Network, Genetic Epidemiology Network of Arteriopathy, Howard University Family Study, Hypertension Genetic Epidemiology Network, and Loyola University Chicago Cohort) to further genetic findings in this population which has generally been underrepresented in BP studies. METHODS: We genotyped and performed various single variant and gene-based exome-wide analyses on 15 914 individuals on the Illumina HumanExome Beadchip v1.0 or v1.1 to test association with SBP and DBP long-term average residuals that were adjusted for age, age-squared, sex, and BMI. RESULTS: We identified rare variants affecting SBP and DBP in 10 genes: AFF1, GAPDHS, SLC28A3, COL6A1, CRYBA2, KRBA1, SEL1L3, YOD1, CCDC13, and QSOX1. Prior experimental evidence for six of these 10 candidate genes supports their involvement in cardiovascular mechanisms, corroborating their potential roles in BP regulation. CONCLUSION: Although our results require replication or validation due to their low numbers of carriers, and an ethnicity-specific genotyping array may be more informative, this study, which has identified several candidate genes in this population most susceptible to hypertension, presents one of the largest African-ancestry BP studies to date and the largest including analysis of rare variants.

Gene-smoking interactions identify several novel blood pressure loci in the Framingham Heart Study.

BACKGROUND: Cardiovascular diseases are among the most significant health problems in the United States. Blood pressure (BP) variability has a genetic component, and most of the genetic variance remains to be identified. One promising strategy for gene discovery is genome-wide analysis of interactions between single nucleotide polymorphisms (SNPs) and environmental factors related to cardiovascular diseases. METHODS: We investigated SNP-smoking interaction effects on BP in genome-wide data in 6,889 participants from the Framingham Heart Study. We performed the standard 1 degree of freedom (df) test of the interaction effect and the joint 2 df test of main and interaction effects. Three smoking measures were used: cigarettes per day (CPD), pack years of smoking, and smoking status. RESULTS: We identified 7 significant and 21 suggestive BP loci. Identified through the joint 2 df test, significant SBP loci include: rs12149862 (P = 3.65×10(-9)) in CYB5B, rs2268365 (P = 4.85×10(-8)) in LRP2, rs133980 (P = 1.71×10(-8) with CPD and P = 1.07×10(-8) with pack-years) near MN1, and rs12634933 (P = 4.05×10(-8)) in MECOM. Through 1 df interaction analysis, 1 suggestive SBP locus at SNP rs8010717 near NRXN3 was identified using all 3 smoking measures (P = 3.27×10(-7) with CPD, P = 1.03×10(-7) with pack-years, and P = 1.19×10(-7) with smoking status). CONCLUSIONS: Several of these BP loci are biologically plausible, providing physiological connection to BP regulation. Our study demonstrates that SNP-smoking interactions can enhance gene discovery and provide insight into novel pathways and mechanisms regulating BP.

Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects.

Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their linkage disequilibrium blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low-frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still under way, we expect that later versions will provide better imputation performance.

Comparison of marker types and map assumptions using Markov chain Monte Carlo-based linkage analysis of COGA data.

We performed multipoint linkage analysis of the electrophysiological trait ECB21 on chromosome 4 in the full pedigrees provided by the Collaborative Study on the Genetics of Alcoholism (COGA). Three Markov chain Monte Carlo (MCMC)-based approaches were applied to the provided and re-estimated genetic maps and to five different marker panels consisting of microsatellite (STRP) and/or SNP markers at various densities. We found evidence of linkage near the GABRB1 STRP using all methods, maps, and marker panels. Difficulties encountered with SNP panels included convergence problems and demanding computations.