Population Stratification in Genetic Association Studies

Secure discovery of genetic relatives across large-scale and distributed genomic data sets

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging owing to the burden of estimating kinship between all the pairs of individuals across data sets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing (LSH) approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us data sets. On a data set of 200,000 individuals split between two parties, SF-Relate detects 97% of third-degree or closer relatives within 15 h of runtime. Our work enables secure identification of relatives across large-scale genomic data sets.

The reciprocal associations between social deficits, social engagement, and inflammation: Longitudinal evidence comparing venous blood samples and dried blood spots and mapping the modifying role of phenotypic and genotypic depression

BACKGROUND

Social psychoneuroimmunology suggests an interplay between social deficits (loneliness and isolation) and chronic inflammation, but the direction of these relationships remains unclear. We estimated the reciprocal associations of social deficits and social engagement with levels of C-reactive protein (CRP), compared the consistency of the findings depending on the biological sampling method used, and examined the modifying role of phenotypic and genotypic depression.

METHODS

We used longitudinal nationally representative data from the US (Health and Retirement Study, 3 waves, 2006-16) and England (English Longitudinal Study of Ageing, 4 waves, 2004-18). Loneliness, social isolation, and social engagement were self-reported. CRP was measured using dried blood spots (US) and venous blood samples (England). Cross-lagged panel models were fitted and tested interactions with phenotypic depression (above-threshold depressive symptom scores) and genotypic depression (polygenic score for major depressive disorder).

RESULTS

We included 15,066 participants (mean age = 66.1 years, SD = 9.8) in the US and 10,290 (66.9 years, SD = 10.5) in England. We found reciprocal associations between loneliness and CRP using dried blood spots and venous blood samples. Higher CRP predicted higher subsequent loneliness and higher loneliness predicted elevated CRP. Both phenotypic and genotypic depression modified this reciprocal association. There were also reciprocal associations for social engagement in venous blood samples: higher CRP predicted lower social engagement and greater social engagement predicted lower subsequent CRP. Associations between social isolation and CRP were inconsistent and unidirectional.

CONCLUSIONS

Loneliness may increase chronic inflammation, whereas social engagement may reduce inflammation. As these relationships were reciprocal, there may be a loop between inflammation, loneliness, and social engagement. This loop was stronger in those with depression or at high genetic risk for major depressive disorder. This relationship for loneliness was present in both blood sampling methods despite contrasting methods of CRP measurement, indicating that the finding is not attributable to measurement bias in biomarkers.

Genome-wide association study for yield and quality of granulated cassava processed product

The starchy storage roots of cassava are commonly processed into a variety of products, including cassava granulated processed products (gari). The commercial value of cassava roots depends on the yield and quality of processed products, directly influencing the acceptance of new varieties by farmers, processors, and consumers. This study aims to estimate genetic advance through phenotypic selection and identify genomic regions associated and candidate genes linked with gari yield and quality. Higher single nucleotide polymorphism (SNP)-based heritability estimates compared to broad-sense heritability estimates were observed for most traits highlighting the influence of genetic factors on observed variation. Using genome-wide association analysis of 188 clones, genotyped using 53,150 genome-wide SNPs, nine SNPs located on seven chromosomes were significantly associated with peel loss, gari yield, color parameters for gari and eba, bulk density, swelling index, and textural properties of eba. Future research will focus on validating and understanding the functions of identified genes and their influence on gari yield and quality traits.

Associations between behavioral and self-reported impulsivity, brain structure, and genetic influences in middle childhood

Impulsivity undergoes a normative developmental trajectory from childhood to adulthood and is thought to be driven by maturation of brain structure. However, few large-scale studies have assessed associations between impulsivity, brain structure, and genetic susceptibility in children. In 9112 children ages 9-10 from the ABCD study, we explored relationships among impulsivity (UPPS-P impulsive behavior scale; delay discounting), brain structure (cortical thickness (CT), cortical volume (CV), and cortical area (CA)), and polygenic scores for externalizing behavior (PGSEXT). Both higher UPPS-P total scores and more severe delay-discounting had widespread, low-magnitude associations with smaller CA in frontal and temporal regions. No associations were seen between impulsivity and CV or CT. Additionally, higher PGSEXT was associated with both higher UPPS-P scores and with smaller CA and CV in frontal and temporal regions, but in non-overlapping cortical regions, underscoring the complex interplay between genetics and brain structure in influencing impulsivity. These findings indicate that, within large-scale population data, CA is significantly yet weakly associated with each of these impulsivity measures and with polygenic risk for externalizing behaviors, but in distinct brain regions. Future work should longitudinally assess these associations through adolescence, and examine associated functional outcomes, such as future substance use and psychopathology.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Genetic insight into a polygenic trait using a novel genome-wide association approach in a wild amphibian population

Body size variation is central in the evolution of life-history traits in amphibians, but the underlying genetic architecture of this complex trait is still largely unknown. Herein, we studied the genetic basis of body size and fecundity of the alternative morphotypes in a wild population of the Greek smooth newt (Lissotriton graecus). By combining a genome-wide association approach with linkage disequilibrium network analysis, we were able to identify clusters of highly correlated loci thus maximizing sequence data for downstream analysis. The putatively associated variants explained 12.8% to 44.5% of the total phenotypic variation in body size and were mapped to genes with functional roles in the regulation of gene expression and cell cycle processes. Our study is the first to provide insights into the genetic basis of complex traits in newts and provides a useful tool to identify loci potentially involved in fitness-related traits in small data sets from natural populations in non-model species.

Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis

PURPOSE

The prevalence of diabetes is increasing worldwide. The associations between the lipid profile and glycated hemoglobin (HbA1c), fasting glucose, and diabetes remain unclear, so we aimed to perform a cohort study and a two-sample Mendelian randomization (MR) study to investigate the causality between blood lipid profile and HbA1c, fasting glucose, and diabetes.

METHODS

A total of 25,171 participants from the Taiwan Biobank were enrolled. We applied a cohort study and an MR study to assess the association between blood lipid profile and HbA1c, fasting glucose, and diabetes. The summary statistics were obtained from the Asian Genetic Epidemiology Network (AGEN), and the estimates between the instrumental variables (IVs) and outcomes were calculated using the inverse-variance weighted (IVW) method. A series of sensitivity analyses were performed.

RESULTS

In the cohort study, high-density lipoprotein cholesterol (HDL-C) was negatively associated with HbA1c, fasting glucose, and diabetes, while the causal associations between HDL-C and HbA1c (βIVW = - 0.098, p = 0.003) and diabetes (βIVW = - 0.594, p < 0.001) were also observed. Furthermore, there was no pleiotropy effect in this study using the MR-Egger intercept test and MR-PRESSO global test.

CONCLUSIONS

Our results support the hypothesis that a genetically determined increase in HDL-C is causally related to a reduction in HbA1c and a lower risk of diabetes.

Genome-Wide Association Studies of 3 Distinct Recovery Phenotypes in Mild Ischemic Stroke

BACKGROUND AND OBJECTIVES

Stroke genetic research has made substantial progress in the past decade. Its recovery application, however, remains behind, in part due to its reliance on the modified Rankin Scale (mRS) score as a measure of poststroke outcome. The mRS does not map well to biological processes because numerous psychosocial factors drive much of what the mRS captures. Second, the mRS contains multiple disparate biological events into a single measure further limiting its use for biological discovery. This led us to investigate the effect of distinct stroke recovery phenotypes on genetic variation associations with Genome-Wide Association Studies (GWASs) by repurposing the NIH Stroke Scale (NIHSS) and its subscores.

METHODS

In the Vitamin Intervention for Stroke Prevention cohort, we estimated changes in cognition, motor, and global impairments over 2 years using specific measures. We included genotyped participants with a total NIHSS score greater than zero at randomization and excluded those with recurrent stroke during the trial. A GWAS linear mixed-effects model predicted score changes, with participant as a random effect, and included initial score, age, sex, treatment group, and the first 5 ancestry principal components.

RESULTS

In total, 1,270 participants (64% male) were included with a median NIHSS score of 2 (interquartile range [IQR] 1-3) and median age 68 (IQR 59-75) years. At randomization, 20% had cognitive deficits (NIHSS Cog-4 score >0) and 70% had ≥1 motor deficits (impairment score >1). At 2 years, these percentages improved to 7.2% with cognitive deficits and 30% with motor deficits. GWAS identified novel suggestive gene-impairment associations (p < 5e-6) for cognition (CAMK2D, EVX2, LINC0143, PTPRM, SGMS1, and SMAD2), motor (ACBD6, KDM4B, MARK4, PTPRS, ROBO1, and ROBO2), and global (MSR1 and ROBO2) impairments.

DISCUSSION

Defining domain-specific stroke recovery phenotypes and using longitudinal clinical trial designs can help detect novel genes associated with chronic recovery. These data support the use of granular endpoints to identify genetic associations related to stroke recovery.

GWAS reveals genomic associations with swine inflammation and necrosis syndrome

The recently identified swine inflammation and necrosis syndrome (SINS) occurs in high prevalence from newborn piglets to fattening pigs and resembles an important concern for animal welfare. The primary endogenous syndrome affects the tail, ears, teats, coronary bands, claws and heels. The basis of clinical inflammation and necrosis has been substantiated by histopathology, metabolomic and liver transcriptomic. Considerable variation in SINS scores is evident in offspring of different boars under the same husbandry conditions. The high complexity of metabolic alterations and the influence of the boar led to the hypothesis of a polygenic architecture of SINS. This should be investigated by a genome-wide association study. For this purpose, 27 sows were simultaneously inseminated with mixed semen from two extreme boars. The mixed semen always contained ejaculate from a Pietrain boar classified as extremely SINS susceptible and additionally either the ejaculate from a Pietrain boar classified as SINS stable or from a Duroc boar classified as SINS stable. The 234 piglets were phenotyped on day 3 of life, sampled and genetically assigned to the respective boar. The piglets showed the expected genetic differentiation with respect to SINS susceptibility. The suspected genetic complexity was confirmed both in the number and genome-wide distribution of 221 significantly associated SNPs, and led to 49 candidate genes. As the SNPs were almost exclusively located in noncoding regions, functional nucleotides have not yet been identified. The results suggest that the susceptibility of piglets to SINS depends not only on environmental conditions but also on genomic variation.

A Wolf in Sheep's Clothing: Idealisations and the aims of polygenic scores

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics.

Population stratification correction using Bayesian shrinkage priors for genetic association studies

INTRODUCTION

Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity.

MATERIALS AND METHODS

To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs.

RESULTS

Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses.

DISCUSSION

The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.

Strategies in Aggregation Tests for Rare Variants

Genome-wide association studies (GWAS) successfully identified numerous common variants involved in complex diseases, but only limited heritability was explained by these findings. Advances in high-throughput sequencing technology made it possible to assess the contribution of rare variants in common diseases. However, study of rare variants introduces challenges due to low frequency of rare variants. Well-established common variant methods were underpowered to identify the rare variants in GWAS. To address this challenge, several new methods have been developed to examine the role of rare variants in complex diseases. These approaches are based on testing the aggregate effect of multiple rare variants in a predefined genetic region. Provided here is an overview of statistical approaches and the protocols explaining step-by-step analysis of aggregations tests with the hands-on experience using R scripts in four categories: burden tests, adaptive burden tests, variance-component tests, and combined tests. Also explained are the concepts of rare variants, permutation tests, kernel methods, and genetic variant annotation. At the end we discuss relevant topics of bioinformatics tools for annotation, family-based design of rare-variant analysis, population stratification adjustment, and meta-analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Association of polygenic risk for bipolar disorder with grey matter structure and white matter integrity in youth

There is a gap in knowledge regarding the polygenic underpinnings of brain anomalies observed in youth bipolar disorder (BD). This study examined the association of a polygenic risk score for BD (BD-PRS) with grey matter structure and white matter integrity in youth with and without BD. 113 participants were included in the analyses, including 78 participants with both T1-weighted and diffusion-weighted MRI images, 32 participants with T1-weighted images only, and 3 participants with diffusion-weighted images only. BD-PRS was calculated using PRS-CS-auto and was based on independent adult genome-wide summary statistics. Vertex- and voxel-wise analyses examined the associations of BD-PRS with grey matter metrics (cortical volume [CV], cortical surface area [CSA], cortical thickness [CTh]) and fractional anisotropy [FA] in the combined sample, and separately in BD and HC. In the combined sample of participants with T1-weighted images (n = 110, 66 BD, 44 HC), higher BD-PRS was associated with smaller grey matter metrics in frontal and temporal regions. In within-group analyses, higher BD-PRS was associated with lower CTh of frontal, temporal, and fusiform gyrus in BD, and with lower CV and CSA of superior frontal gyrus in HC. In the combined sample of participants with diffusion-weighted images (n = 81, 49 BD, 32 HC), higher BD-PRS was associated with lower FA in widespread white matter regions. In summary, BD-PRS calculated based on adult genetic data was negatively associated with grey matter structure and FA in youth in regions implicated in BD, which may suggest neuroimaging markers of vulnerability to BD. Future longitudinal studies are needed to examine whether BD-PRS predicts neurodevelopmental changes in BD vs. HC and its interaction with course of illness and long-term medication use.

Sex differences in associations between APOE ε2 and longitudinal cognitive decline

INTRODUCTION

We examined whether sex modifies the association between APOE ε2 and cognitive decline in two independent samples.

METHODS

We used observational data from cognitively unimpaired non-Hispanic White (NHW) and non-Hispanic Black (NHB) adults. Linear mixed models examined interactive associations of APOE genotype (ε2 or ε4 carrier vs. ε3/ε3) and sex on cognitive decline in NHW and NHB participants separately.

RESULTS

In both Sample 1 (N = 9766) and Sample 2 (N = 915), sex modified the association between APOE ε2 and cognitive decline in NHW participants. Specifically, relative to APOE ε3/ε3, APOE ε2 protected against cognitive decline in men but not women. Among APOE ε2 carriers, men had slower decline than women. Among APOE ε3/ε3 carriers, cognitive trajectories did not differ between sexes. There were no sex-specific associations of APOE ε2 with cognition in NHB participants (N = 2010).

DISCUSSION

In NHW adults, APOE ε2 may protect men but not women against cognitive decline.

HIGHLIGHTS

We studied sex-specific apolipoprotein E (APOE) ε2 effects on cognitive decline. In non-Hispanic White (NHW) adults, APOE ε2 selectively protects men against decline. Among men, APOE ε2 was more protective than APOE ε3/ε3. In women, APOE ε2 was no more protective than APOE ε3/ε3. Among APOE ε2 carriers, men had slower decline than women. There were no sex-specific APOE ε2 effects in non-Hispanic Black (NHB) adults.

Cumulative Genetic Score and C9orf72 Repeat Status Independently Contribute to Amyotrophic Lateral Sclerosis Risk in 2 Case-Control Studies

Background and Objectives

Most patients with amyotrophic lateral sclerosis (ALS) lack a monogenic mutation. This study evaluates ALS cumulative genetic risk in an independent Michigan and Spanish replication cohort using polygenic scores.

Methods

Participant samples from University of Michigan were genotyped and assayed for the chromosome 9 open reading frame 72 hexanucleotide expansion. Final cohort size was 219 ALS and 223 healthy controls after genotyping and participant filtering. Polygenic scores excluding the C9 region were generated using an independent ALS genome-wide association study (20,806 cases, 59,804 controls). Adjusted logistic regression and receiver operating characteristic curves evaluated the association and classification between polygenic scores and ALS status, respectively. Population attributable fractions and pathway analyses were conducted. An independent Spanish study sample (548 cases, 2,756 controls) was used for replication.

Results

Polygenic scores constructed from 275 single-nucleotide variation (SNV) had the best model fit in the Michigan cohort. An SD increase in ALS polygenic score associated with 1.28 (95% CI 1.04-1.57) times higher odds of ALS with area under the curve of 0.663 vs a model without the ALS polygenic score (p value = 1 × 10-6). The population attributable fraction of the highest 20th percentile of ALS polygenic scores, relative to the lowest 80th percentile, was 4.1% of ALS cases. Genes annotated to this polygenic score enriched for important ALS pathomechanisms. Meta-analysis with the Spanish study, using a harmonized 132 single nucleotide variation polygenic score, yielded similar logistic regression findings (odds ratio: 1.13, 95% CI 1.04-1.23).

Discussion

ALS polygenic scores can account for cumulative genetic risk in populations and reflect disease-relevant pathways. If further validated, this polygenic score will inform future ALS risk models.

Enlightening the association between TNF-α -308 G > A and migraine: a meta-analysis with meta-regression and trial sequential analysis

BACKGROUND

Migraine is a complex neurological disorder that is characterized by a "lower threshold of neuronal hyperexcitability" with distinctive periodicity and complex vascular dysfunction. Genetic factors have impacted incredibly on the susceptibility of migraine and one such example is the TNF-α 308G > A.

AIM

Therefore, we aim to provide a glimpse of the association of the TNF-α 308G > A risk on the susceptibility of migraine.

METHOD

The pooled odds ratio with the associated 95% of confidence interval were calculated using different genetic models. Heterogeneity was accessed by using Cochran's Q Test and I² statistics and Begg's and Egger's tests were used for finding the publication bias, tests were two-sided, and a p-value of < 0.05 was considered statistically significant. The Trial Sequential Analysis with Meta-regression Analysis were also utilized to find out the sample size requirement for meta-analysis to avoid type I error and source of heterogeneity respectively.

RESULT

A total of 13 studies with cases: 7193 and controls: 23,091 were included and after using different genetic models, no overall association with migraine and its clinical subtype migraine with aura was observed (Allele model "OR: 1.28, 95% C.I. [0.96-1.69] and OR: 0.99,95% C.I. [0.69-1.42]) respectively. Interestingly, after sub-grouping using the "ethnicity criteria" in the migraine group, it was observed that the allelic genetic model and the dominant model were found to be significantly associated with the Asian ethnic group (OR: 1.79, 95% C.I. [1.13-2.84], and OR: 1.85, 95% C.I. [1.0927; 3.1580].

CONCLUSION

In conclusion, the present meta-analysis has provided evidence that 308G > A increases the risk of migraine only in the Asian population.

PSReliP: an integrated pipeline for analysis and visualization of population structure and relatedness based on genome-wide genetic variant data

BACKGROUND

Population structure and cryptic relatedness between individuals (samples) are two major factors affecting false positives in genome-wide association studies (GWAS). In addition, population stratification and genetic relatedness in genomic selection in animal and plant breeding can affect prediction accuracy. The methods commonly used for solving these problems are principal component analysis (to adjust for population stratification) and marker-based kinship estimates (to correct for the confounding effects of genetic relatedness). Currently, many tools and software are available that analyze genetic variation among individuals to determine population structure and genetic relationships. However, none of these tools or pipelines perform such analyses in a single workflow and visualize all the various results in a single interactive web application.

RESULTS

We developed PSReliP, a standalone, freely available pipeline for the analysis and visualization of population structure and relatedness between individuals in a user-specified genetic variant dataset. The analysis stage of PSReliP is responsible for executing all steps of data filtering and analysis and contains an ordered sequence of commands from PLINK, a whole-genome association analysis toolset, along with in-house shell scripts and Perl programs that support data pipelining. The visualization stage is provided by Shiny apps, an R-based interactive web application. In this study, we describe the characteristics and features of PSReliP and demonstrate how it can be applied to real genome-wide genetic variant data.

CONCLUSIONS

The PSReliP pipeline allows users to quickly analyze genetic variants such as single nucleotide polymorphisms and small insertions or deletions at the genome level to estimate population structure and cryptic relatedness using PLINK software and to visualize the analysis results in interactive tables, plots, and charts using Shiny technology. The analysis and assessment of population stratification and genetic relatedness can aid in choosing an appropriate approach for the statistical analysis of GWAS data and predictions in genomic selection. The various outputs from PLINK can be used for further downstream analysis. The code and manual for PSReliP are available at https://github.com/solelena/PSReliP .

Lipolysis and gestational diabetes mellitus onset: a case-cohort genome-wide association study in Chinese

BACKGROUND

Genetic knowledge of gestational diabetes mellitus (GDM) in Chinese women is quite limited. This study aimed to identify the risk factors and mechanism of GDM at the genetic level in a Chinese population.

METHODS

We conducted a genome-wide association study (GWAS) based on single nucleotide polymorphism (SNP) array genotyping (ASA-CHIA Bead chip, Illumina) and a case-cohort study design. Variants including SNPs, copy number variants (CNVs), and insertions-deletions (InDels) were called from genotyping data. A total of 2232 pregnant women were enrolled in their first/second trimester between February 2018 and December 2020 from Anqing Municipal Hospital in Anhui Province, China. The GWAS included 193 GDM patients and 819 subjects without a diabetes diagnosis, and risk ratios (RRs) and their 95% confidence intervals (CIs) were estimated by a regression-based method conditional on the population structure. The calling and quality control of genotyping data were performed following published guidelines. CNVs were merged into CNV regions (CNVR) to simplify analyses. To interpret the GWAS results, gene mapping and overexpression analyses (ORAs) were further performed to prioritize the candidate genes and related biological mechanisms.

RESULTS

We identified 14 CNVRs (false discovery rate corrected P values < 0.05) and two suggestively significant SNPs (P value < 0.00001) associated with GDM, and a total of 19 candidate genes were mapped. Ten genes were significantly enriched in gene sets related to lipase (triglyceride lipase and lipoprotein lipase) activity (LIPF, LIPK, LIPN, and LIPJ genes), oxidoreductase activity (TPH1 and TPH2 genes), and cellular components beta-catenin destruction complex (APC and GSK3B genes), Wnt signalosome (APC and GSK3B genes), and lateral element in the Gene Ontology resource (BRCA1 and SYCP2 genes) by two ORA methods (adjusted P values < 0.05).

CONCLUSIONS

Genes related to lipolysis, redox reaction, and proliferation of islet β-cells are associated with GDM in Chinese women. Energy metabolism, particularly lipolysis, may play an important role in GDM aetiology and pathology, which needs further molecular studies to verify.

Challenges in selecting admixture models and marker sets to infer genetic ancestry in a Brazilian admixed population

The inference of genetic ancestry plays an increasingly prominent role in clinical, population, and forensic genetics studies. Several genotyping strategies and analytical methodologies have been developed over the last few decades to assign individuals to specific biogeographic regions. However, despite these efforts, ancestry inference in populations with a recent history of admixture, such as those in Brazil, remains a challenge. In admixed populations, proportion and components of genetic ancestry vary on different levels: (i) between populations; (ii) between individuals of the same population, and (iii) throughout the individual's genome. The present study evaluated 1171 admixed Brazilian samples to compare the genetic ancestry inferred by tri-/tetra-hybrid admixture models and evaluated different marker sets from those with small numbers of ancestry informative markers panels (AIMs), to high-density SNPs (HDSNP) and whole-genome-sequence (WGS) data. Analyses revealed greater variation in the correlation coefficient of ancestry components within and between admixed populations, especially for minority ancestral components. We also observed positive correlation between the number of markers in the AIMs panel and HDSNP/WGS. Furthermore, the greater the number of markers, the more accurate the tri-/tetra-hybrid admixture models.

Quality Control Procedures for Genome-Wide Association Studies

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.

Does crime trigger genetic risk for type 2 diabetes in young adults? A G x E interaction study using national data

BACKGROUND

Living in neighborhoods perceived as disordered exacerbates genetic risk for type 2 diabetes (T2D) among older adults. It is unknown whether this gene-neighborhood interaction extends to younger adults. The present study aims to investigate whether crime, an objectively measured indicator of neighborhood disorder, triggers genetic risk for T2D among younger adults, and whether this hypothesized triggering occurs through exposure to obesity.

METHODS

Data were from the Wave I (2008) National Longitudinal Study of Adolescent to Adult Health. A standardized T2D polygenic score was created using 2014 GWAS meta-analysis results. Weighted mediation analyses using generalized structural equation models were conducted in a final sample of 7606 adults (age range: 25-34) to test the overall association of T2D polygenic scores with T2D, and the mediating path through obesity exposure in low, moderate, and high county crime-rate groups. Age, sex, ancestry, educational degree, household income, five genetic principal components, and county-level concentrated advantage and population density were adjusted.

RESULTS

The overall association between T2D polygenic score and T2D was not significant in low-crime areas (p = 0.453), marginally significant in moderate-crime areas (p = 0.064), and statistically significant in high-crime areas (p = 0.007). The mediating path through obesity was not significant in low or moderate crime areas (ps = 0.560 and 0.261, respectively), but was statistically significant in high-crime areas (p = 0.023). The indirect path through obesity accounted for 12% of the overall association in high-crime area.

CONCLUSION

A gene-crime interaction in T2D was observed among younger adults, and this association was partially explained by exposure to obesity.

Comparing mixed models and Random Forest association tests using naturalGWAS and a Striped Bass SNP dataset

In this study, we used the phenotype simulation package naturalGWAS to test the performance of Zhao's Random Forest method in comparison to an uncorrected Random Forest test, latent factor mixed models (LFMM), genome-wide efficient mixed models (GEMMA), and confounder adjusted linear regression (CATE). We created 400 sets of phenotypes, corresponding to five effect sizes and 2, 5, 15, or 30 causal loci, simulated from two empirical datasets containing SNPs from Striped Bass representing three and 13 populations. All association methods were evaluated for their ability to detect genotype-phenotype associations based on power, false discovery rates, and number of false positives. Genomic inflation was highest for uncorrected Random Forest and LFMM tests and lowest for Gemma and Zhao's Random Forest. All association tests had similar power to detect causal loci, and Zhao's Random Forest had the lowest false discovery rate in all scenarios. To measure the performance of association tests in small datasets with few loci surrounding a causal gene we also ran analyses again after removing causal loci from each dataset. All association tests were only able to find true positives, defined as loci located within 30k bp of a causal locus, in 3%-18% of simulations. In contrast, at least one false positive was found in 17%-44% of simulations. Zhao's Random Forest again identified the fewest false positives of all association tests studied. The ability to test the power of association tests for individual empirical datasets can be an extremely useful first step when designing a GWAS study.

Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: Experiences from the NHLBI TOPMed program

How race, ethnicity, and ancestry are used in genomic research has wide-ranging implications for how research is translated into clinical care and incorporated into public understanding. Correlation between race and genetic ancestry contributes to unresolved complexity for the scientific community, as illustrated by heterogeneous definitions and applications of these variables. Here, we offer commentary and recommendations on the use of race, ethnicity, and ancestry across the arc of genetic research, including data harmonization, analysis, and reporting. While informed by our experiences as researchers affiliated with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, these recommendations are applicable to basic and translational genomic research in diverse populations with genome-wide data. Moving forward, considerable collaborative effort will be required to ensure that race, ethnicity, and ancestry are described and used appropriately to generate scientific knowledge that yields broad and equitable benefit.

Exploration of genetic ancestry and socioeconomic status in the incidence of neuroblastoma: An ecological study

Although global differences in the incidence of neuroblastoma have been examined, the underlying mechanism has yet to be elucidated. Previous studies have suggested genetic ancestry and human development index (HDI) as contributing factors, but few studies have been conducted at the international level. Here, we aimed to examine whether the frequency of common genomic variation associated with neuroblastoma can affect its risk at the ecological level with consideration of the HDI. Minor allele frequencies (MAFs) for 22 single-nucleotide polymorphisms (SNPs) were abstracted from the Geography of Genetic Variants Browser. The number of incident neuroblastomas for each population was obtained from the Cancer Incidence in Five Continents series. Further, population pseudo-polygenic risk scores (pp-PRSs) were calculated as a sum of MAFs at the population level, each of which was weighted by effect sizes from prior studies. Negative binomial regression was used to estimate the incidence rate ratios (IRRs) and the 95% confidence intervals (CIs) to examine whether differences in MAFs across the population influence the risk of neuroblastoma, with and without adjustment for HDI and whether pp-PRSs can be a predictor of the risk of neuroblastoma. Overall, our results indicated that the neuroblastoma risk associated with variation in SNP frequency could not be differentiated from that of HDI at the ecological level. Additionally, pp-PRSs were not significantly associated with the risk of neuroblastoma (IRR: 0.99, 95% CI: 0.62-1.60). Further study using individual-level data is warranted to minimize the bias related to the use of population-level data in this study.

Inherent Nonlinear Distribution of High-Dimensional Genotypic Data Identified as a Possible Source of Confounding Factors in Population Structure Analysis

It has become routing work to detect and correct for population structure in genome-wide association analysis. A variety of methods have been proposed. Particularly, the methods based on spectral graph theory have shown superior performance. We discovered that the inherent nonlinear distribution of high-dimensional genotypic data was a possible source of confounding factors in population structure analysis, and was also the possible underlying reason that accounted for the superiority of these spectral-based methods. We verified this hypothesis by validating a variation of the Laplacian Eigen analysis: LAPMAP. The method could faithfully reveal the underlying population structures of HapMap II and III data sets. The inferred top eigenvectors together with minor eigenvectors were used to segregate samples by their ancestries. We found that the top 3 eigenvectors differentiated the 4 populations in phase II data set; the top 3 eigenvectors clustered the populations into 4 clusters, reflecting their continental origins. In phase III populations, 9 populations were well recognized. Next, we estimated admixture proportions for simulated individuals. The method showed comparable or better performance in capturing and correcting for modelled population structures. All experimental results showed that LAPMAP was robust, efficient and scalable to genome-wide association studies.

Association Between the LZTFL1 rs11385942 Polymorphism and COVID-19 Severity in Colombian Population

Genetic and non-genetic factors are responsible for the high interindividual variability in the response to SARS-CoV-2. Although numerous genetic polymorphisms have been identified as risk factors for severe COVID-19, these remain understudied in Latin-American populations. This study evaluated the association of non-genetic factors and three polymorphisms: ACE rs4646994, ACE2 rs2285666, and LZTFL1 rs11385942, with COVID severity and long-term symptoms by using a case-control design. The control group was composed of asymptomatic/mild cases (n = 61) recruited from a private laboratory, while the case group was composed of severe/critical patients (n = 63) hospitalized in the Hospital Universitario Mayor-Méderi, both institutions located in Bogotá, Colombia. Clinical follow up and exhaustive revision of medical records allowed us to assess non-genetic factors. Genotypification of the polymorphism of interest was performed by amplicon size analysis and Sanger sequencing. In agreement with previous reports, we found a statistically significant association between age, male sex, and comorbidities, such as hypertension and type 2 diabetes mellitus (T2DM), and worst outcomes. We identified the polymorphism LZTFL1 rs11385942 as an important risk factor for hospitalization (p < 0.01; OR = 5.73; 95% CI = 1.2-26.5, under the allelic test). Furthermore, long-term symptoms were common among the studied population and associated with disease severity. No association between the polymorphisms examined and long-term symptoms was found. Comparison of allelic frequencies with other populations revealed significant differences for the three polymorphisms investigated. Finally, we used the statistically significant genetic and non-genetic variables to develop a predictive logistic regression model, which was implemented in a Shiny web application. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC = 0.86; 95% confidence interval 0.79-0.93). These results suggest that LZTFL1 rs11385942 may be a potential biomarker for COVID-19 severity in addition to conventional non-genetic risk factors. A better understanding of the impact of these genetic risk factors may be useful to prioritize high-risk individuals and decrease the morbimortality caused by SARS-CoV2 and future pandemics.

Clarifying the causes of consistent and inconsistent findings in genetics

As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.

Genome-wide Association Study Reveals New Loci Associated With Pyrethroid Resistance in Aedes aegypti

Genome-wide association studies (GWAS) use genetic polymorphism across the genomes of individuals with distinct characteristics to identify genotype-phenotype associations. In mosquitoes, complex traits such as vector competence and insecticide resistance could benefit from GWAS. We used the Aedes aegypti 50k SNP chip to genotype populations with different levels of pyrethroid resistance from Northern Brazil. Pyrethroids are widely used worldwide to control mosquitoes and agricultural pests, and their intensive use led to the selection of resistance phenotypes in many insects including mosquitoes. For Ae. aegypti, resistance phenotypes are mainly associated with several mutations in the voltage-gated sodium channel, known as knockdown resistance (kdr). We phenotyped those populations with the WHO insecticide bioassay using deltamethrin impregnated papers, genotyped the kdr alleles using qPCR, and determined allele frequencies across the genome using the SNP chip. We identified single-nucleotide polymorphisms (SNPs) directly associated with resistance and one epistatic SNP pair. We also observed that the novel SNPs correlated with the known kdr genotypes, although on different chromosomes or not in close physical proximity to the voltage gated sodium channel gene. In addition, pairwise comparison of resistance and susceptible mosquitoes from each population revealed differentiated genomic regions not associated with pyrethroid resistance. These new bi-allelic markers can be used to genotype other populations along with kdr alleles to understand their worldwide distribution. The functional roles of the genes near the newly discovered SNPs require new studies to determine if they act synergistically with kdr alleles or reduce the fitness cost of maintaining resistant alleles.

IIb-RAD-sequencing coupled with random forest classification indicates regional population structuring and sex-specific differentiation in salmon lice (Lepeophtheirus salmonis)

The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between subpopulations must be studied to monitor louse species and enhance targeted control measures. We have used IIb-RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19,428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish the differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. We also uncovered SNP markers that could help determine the sex of the salmon louse. A large portion of the SNPs identified to be under directional selection was also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline.

A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed.

Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle

Improvement of carcass merit traits is a priority for the beef industry. Discovering DNA variants and genes associated with variation in these traits and understanding biological functions/processes underlying their associations are of paramount importance for more effective genetic improvement of carcass merit traits in beef cattle. This study integrates 10,488,742 imputed whole genome DNA variants, 31 plasma metabolites, and animal phenotypes to identify genes and biological functions/processes that are associated with carcass merit traits including hot carcass weight (HCW), rib eye area (REA), average backfat thickness (AFAT), lean meat yield (LMY), and carcass marbling score (CMAR) in a population of 493 crossbred beef cattle. Regression analyses were performed to identify plasma metabolites associated with the carcass merit traits, and the results showed that 4 (3-hydroxybutyric acid, acetic acid, citric acid, and choline), 6 (creatinine, L-glutamine, succinic acid, pyruvic acid, L-lactic acid, and 3-hydroxybutyric acid), 4 (fumaric acid, methanol, D-glucose, and glycerol), 2 (L-lactic acid and creatinine), and 5 (succinic acid, fumaric acid, lysine, glycine, and choline) plasma metabolites were significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using the 10,488,742 imputed SNPs, 103, 160, 83, 43, and 109 candidate genes were identified as significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 1 × 10^-5), respectively. By applying functional enrichment analyses for candidate genes of each trait, 26, 24, 26, 24, and 28 significant cellular and molecular functions were predicted for HCW, REA, AFAT, LMY, and CMAR, respectively. Among the five topmost significantly enriched biological functions for carcass merit traits, molecular transport and small molecule biochemistry were two top biological functions associated with all carcass merit traits. Lipid metabolism was the most significant biological function for LMY and CMAR and it was also the second and fourth highest biological function for REA and HCW, respectively. Candidate genes and enriched biological functions identified by the integrative analyses of metabolites with phenotypic traits and DNA variants could help interpret the results of previous genome-wide association studies for carcass merit traits. Our integrative study also revealed additional potential novel genes associated with these economically important traits. Therefore, our study improves understanding of the molecular and biological functions/processes that influence carcass merit traits, which could help develop strategies to enhance genomic prediction of carcass merit traits with incorporation of metabolomic data. Similarly, this information could guide management practices, such as nutritional interventions, with the purpose of boosting specific carcass merit traits.

Impact of Genetic Risk Score and Dietary Protein Intake on Vitamin D Status in Young Adults from Brazil

Given the relationship between vitamin D deficiency (VDD) and adverse outcomes of metabolic diseases, we investigated the interplay of dietary and genetic components on vitamin D levels and metabolic traits in young adults from Brazil. Genetic analysis, dietary intake, and anthropometric and biochemical measurements were performed in 187 healthy young adults (19−24 years). Genetic risk scores (GRS) from six genetic variants associated with vitamin D (vitamin D-GRS) and 10 genetic variants associated with metabolic disease (metabolic-GRS) were constructed. High vitamin D-GRS showed a significant association with low 25(OH)D concentrations (p = 0.001) and high metabolic-GRS showed a significant association with high fasting insulin concentrations (p = 0.045). A significant interaction was found between vitamin D-GRS and total protein intake (g/day) (adjusted for non-animal protein) on 25(OH)D (pinteraction = 0.006), where individuals consuming a high protein diet (≥73 g/d) and carrying >4 risk alleles for VDD had significantly lower 25(OH)D (p = 0.002) compared to individuals carrying ≤4 risk alleles. Even though our study did not support a link between metabolic-GRS and vitamin D status, our study has demonstrated a novel interaction, where participants with high vitamin D-GRS and consuming ≥73 g of protein/day had significantly lower 25(OH)D levels. Further research is necessary to evaluate the role of animal protein consumption on VDD in Brazilians.

Half a century later and we're back where we started: How the problem of locality turned in to the problem of portability

In the 1970s, Lewontin sparked a debate about a problem of locality, by making the case that any given heritability estimate is local to the original population and environment studied, and could not be generalized to other populations and environments. Nearly 50 years later, a new problem of portability has emerged: the predictive accuracy of polygenic scores diminishes when applied to populations whose characteristics are different from the original population sample. This paper briefly reviews the nature of each problem and analyzes their similarities and differences in three areas: 1) conceptual underpinnings, 2) causal explanations, and 3) practical, social, and political implications. Although conceptually and methodologically different from the problem of locality in important respects, the problem of portability facing contemporary genomics today should come as no surprise, as it is an inevitable outcome of the kinds of problematic inferences detailed by Lewontin nearly half a century ago.

A framework for research into continental ancestry groups of the UK Biobank

BACKGROUND

The UK Biobank is a large prospective cohort, based in the UK, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with "non-white British ancestry." While most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobank's "non-white British ancestry" samples. Here, we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset.

RESULTS

Reference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (N = 62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of "non-white British" ancestry, 50,685, 6653, 2782, and 2364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography.

CONCLUSIONS

Methods outlined here provide an avenue to leverage UK Biobank's deeply phenotyped data allowing researchers to maximize its potential in the study of health and disease in individuals of non-white British ancestry.

Statin treatment effectiveness and the SLCO1B1*5 reduced function genotype: long-term outcomes in women and men

Objective

To estimate the effect of rs4149056 (SLCO1B1*5) genotype (decreases statin transport) on cholesterol control and treatment duration in male and female primary care patients prescribed common statin medications.

Methods and Analysis

This study comprised 69 185 European‐ancestry UK Biobank cohort participants prescribed simvastatin or atorvastatin (aged 40‐79 years at first prescription, treatment duration 1 month to 29 years, mean 5.7 years). Principal outcomes were clinically high total cholesterol (>5 mmol/L) at baseline, plus treatment discontinuation.

Results

A total of 48.4% of 591 females homozygous for SLCO1B1*5 decreased function genotype had raised cholesterol vs 41.7% of those with functioning SLCO1B1 (odds ratio 1.31, 95% confidence interval [CI] 1.1‐1.55, P = .001). Fewer males had high cholesterol and the genotype effect was attenuated. In primary care prescribing, females homozygous for SLCO1B1*5 were more likely to stop receiving these statins (29.5%) than women with normal SLCO1B1 (25.7%) (hazard ratio [HR] 1.19, 95% CI 1.03‐1.37, P = .01), amounting to five discontinuations per 100 statin‐years in the SLCO1B1*5 group vs four in the normal SLCO1B1 function group. This remained significant after the first year of treatment (HR for discontinuing >1 year after first prescription 1.3, 95% CI 1.08‐1.56, P = .006). In men SLCO1B1*5 was only associated with treatment discontinuation in the first year.

Conclusions

In this large community sample of patients on commonly prescribed statins, the SLCO1B1*5 decreased function variant had much larger effects on cholesterol control and treatment duration in women than in men. Efforts to improve the effectiveness of statin therapy in women may need to include SLCO1B1*5 genotype‐guided statin selection.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Analysis of CYP2C19 genetic variants with ischaemic events in UK patients prescribed clopidogrel in primary care: a retrospective cohort study

OBJECTIVE

To determine whether CYP2C19 loss-of-function (LoF) alleles increase risk of ischaemic stroke and myocardial infarction (MI) in UK primary care patients prescribed clopidogrel.

DESIGN

Retrospective cohort analysis.

SETTING

Primary care practices in the UK from January 1999 to September 2017.

PARTICIPANTS

7483 European-ancestry adults from the UK Biobank study with genetic and linked primary care data, aged 36-79 years at time of first clopidogrel prescription.

INTERVENTIONS

Clopidogrel prescription in primary care, mean duration 2.6 years (range 2 months to 18 years).

MAIN OUTCOME MEASURE

Hospital inpatient-diagnosed ischaemic stroke, MI or angina while treated with clopidogrel.

RESULTS

28.7% of participants carried at least one CYP2C19 LoF variant. LoF carriers had higher rates of incident ischaemic stroke while treated with clopidogrel compared with those without the variants (8 per 1000 person-years vs 5.2 per 1000 person-years; HR 1.53, 95% CIs 1.04 to 2.26, p=0.031). LoF carriers also had increased risk of MI (HR 1.14, 95% CI 1.04 to 1.26, p=0.008). In combined analysis LoF carriers had increased risk of any ischaemic event (stroke or MI) (HR 1.17, 95% CI 1.06 to 1.29, p=0.002). Adjustment for aspirin coprescription produced similar estimates. In lifetables using observed incidence rates, 22.5% (95% CI 14.4% to 34.0%) of CYP2C19 LoF carriers on clopidogrel were projected to develop an ischaemic stroke by age 79 (oldest age in the study), compared with 15.4% (95% CI 11.4% to 20.5%) in non-carriers, that is, 7.1% excess stroke incidence in LoF carriers by age 79.

CONCLUSIONS

A substantial proportion of the UK population carry genetic variants that reduce metabolism of clopidogrel to its active form. In family practice patients on clopidogrel, CYP2C19 LoF variants are associated with substantially higher incidence of ischaemic events. Genotype-guided selection of antiplatelet medications may improve outcomes in patients carrying CYP2C19 genetic variants.

Mendelian Randomization Analysis as a Tool to Gain Insights into Causes of Diseases: A Primer

Many Mendelian randomization (MR) studies have been published recently, with inferences on the causal relationships between risk factors and diseases that have potential implications for clinical research. In nephrology, MR methods have been applied to investigate potential causal relationships of traditional risk factors, lifestyle factors, and biomarkers from omics technologies with kidney function or CKD. This primer summarizes the basic concepts of MR studies, highlighting methods used in recent applications, and emphasizes key elements in conducting and reporting of MR studies that are important for interpreting the results.

A systematic review of gene-by-intervention studies of alcohol and other substance use

Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.

The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the ‘genetically moderated treatment effect’ (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework ‘Triangulation WIthin a STudy’ (TWIST)’ in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.

Understanding how much a specific treatment’s effect is moderated by common genetic variation is an important public health question. If a person’s genetics means they will experience a much reduced treatment effect, as measured with respect to a particular health outcome, then they could be switched to an alternative therapy. When assessing the impact of such a switch at the population level, it is typical to only use data on those who are treated with the said drug. However, this analysis is compromised if genetic variants exist which moderate the treatment effect and affect the outcome through alternative pathways. In this paper we describe an extended analysis framework to estimating the ‘genetically moderated treatment effect’ (GMTE) that incorporates information on both treated and untreated individuals. With this larger set of information we show that four analysis approaches for estimating the GMTE are possible. Each one relies on a different set of assumptions to work correctly and provides estimates that are largely uncorrelated with one another. Our paper describes a decision framework for triangulating the findings from these four approaches in order to provide a more robust basis for decision making in public health.

Fast and efficient correction for population stratification in multi-locus genome-wide association studies

Reducing false discoveries caused by population stratification (PS) has always been a challenge in genome-wide association studies (GWAS). The current literature established several single marker approaches including genomic control (GC), EIGENSTRAT and generalized linear mixed model association test (GMMAT) and multi-marker methods such as LASSO mixed model (LASSOMM). However, the single-marker methods require prespecifying an arbitrary p value threshold in the selection process, likely resulting in suboptimal precision or recall. On the other hand, it appears that LASSOMM is extremely computationally intensive and may not suitable for large-scale GWAS. In this paper, we proposed a simple multi-marker approach (PCA-LASSO) combining principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). We utilize PCA to correct for the confounding effects of PS and LASSO with built-in cross-validation for a data-driven selection. Compared to the current single-marker approaches, the proposed PCA-LASSO provides optimal balance between precision and recall, and consequently superior F₁ scores. Similarly, compared to LASSOMM, PCA-LASSO markedly increases the precision while minimizing the loss of recall, and therefore improves the overall F₁ score in presence of PS. More importantly, PCA-LASSO drastically reduces the computational time by > 1000 times when compared to LASSOMM. We applied PCA-LASSO to a real dataset of Alzheimer's disease and successfully identified SNP rs429358 (Gene APOE4) which has been widely reported to be associated with the onset and elevated risk of Alzheimer's disease. In conclusion, PCA-LASSO is a simple, fast, but accurate approach for GWAS in presence of latent PS.

Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification

In bioinformatics, genome-wide association studies (GWAS) are used to detect associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits such as diseases. Significant differences in SNP counts between case and control groups can signal association between variants and phenotypic traits. Most traits are affected by multiple genetic locations. To detect these subtle associations, bioinformaticians need access to more heterogeneous data. Regulatory restrictions in cross-border health data exchange have created a surge in research on privacy-preserving solutions, including secure computing techniques. However, in studies of such scale, one must account for population stratification, as under- and over-representation of sub-populations can lead to spurious associations. We improve on the state of the art of privacy-preserving GWAS methods by showing how to adapt principal component analysis (PCA) with stratification control (EIGENSTRAT), FastPCA, EMMAX and the genomic control algorithm for secure computing. We implement these methods using secure computing techniques—secure multi-party computation (MPC) and trusted execution environments (TEE). Our algorithms are the most complex ones at this scale implemented with MPC. We present performance benchmarks and a security and feasibility trade-off discussion for both techniques.

Multi-Omics Approaches in Immunological Research

The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases.

Towards fine-scale population stratification modeling based on kernel principal component analysis and random forest

BACKGROUND

Population stratification modeling is essential in Genome-Wide Association Studies.

OBJECTIVE

In this paper, we aim to build a fine-scale population stratification model to efficiently infer individual genetic ancestry.

METHODS

Kernel Principal Component Analysis (PCA) and random forest are adopted to build the population stratification model, together with parameter optimization. We explore different PCA methods, including standard PCA and kernel PCA to extract relevant features from the genotype data that is transformed by vcf2geno, a pipeline from LASER software. These extracted features are fed into a random forest for ensemble learning. Parameter tuning is performed to jointly find the optimal number of principal components, kernel function for PCA and parameters of the random forest.

RESULTS

Experiments based on HGDP dataset show that kernel PCA with Sigmoid function and Gaussian function can achieve higher prediction accuracy than the standard PCA. Compared to standard PCA with the two principal components, the accuracy by using KPCA-Sigmoid with the optimal number of principal components can achieve around 100% and 200% improvement for East Asian and European populations, respectively.

CONCLUSION

With the optimal parameter configuration on both PCA and random forest, our proposed method can infer the individual genetic ancestry more accurately, given their variants.

Cultural Evolution of Genetic Heritability

Behavioral genetics and cultural evolution have both revolutionized our understanding of human behavior-largely independent of each other. Here we reconcile these two fields under a dual inheritance framework, offering a more nuanced understanding of the interaction between genes and culture. Going beyond typical analyses of gene-environment interactions, we describe the cultural dynamics that shape these interactions by shaping the environment and population structure. A cultural evolutionary approach can explain, for example, how factors such as rates of innovation and diffusion, density of cultural sub-groups, and tolerance for behavioral diversity impact heritability estimates, thus yielding predictions for different social contexts. Moreover, when cumulative culture functionally overlaps with genes, genetic effects become masked, unmasked, or even reversed, and the causal effects of an identified gene become confounded with features of the cultural environment. The manner of confounding is specific to a particular society at a particular time, but a WEIRD (Western, educated, industrialized, rich, democratic) sampling problem obscures this boundedness. Cultural evolutionary dynamics are typically missing from models of gene-to-phenotype causality, hindering generalizability of genetic effects across societies and across time. We lay out a reconciled framework and use it to predict the ways in which heritability should differ between societies, between socioeconomic levels and other groupings within some societies but not others, and over the life course. An integrated cultural evolutionary behavioral genetic approach cuts through the nature-nurture debate and helps resolve controversies in topics such as IQ.

Population genetic considerations for using biobanks as international resources in the pandemic era and beyond

The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.

Penalized linear mixed models for structured genetic data

Many genetic studies that aim to identify genetic variants associated with complex phenotypes are subject to unobserved confounding factors arising from environmental heterogeneity. This poses a challenge to detecting associations of interest and is known to induce spurious associations when left unaccounted for. Penalized linear mixed models (LMMs) are an attractive method to correct for unobserved confounding. These methods correct for varying levels of relatedness and population structure by modeling it as a random effect with a covariance structure estimated from observed genetic data. Despite an extensive literature on penalized regression and LMMs separately, the two are rarely discussed together. The aim of this review is to do so while examining the statistical properties of penalized LMMs in the genetic association setting. Specifically, the ability of penalized LMMs to accurately estimate genetic effects in the presence of environmental confounding has not been well studied. To clarify the important yet subtle distinction between population structure and environmental heterogeneity, we present a detailed review of relevant concepts and methods. In addition, we evaluate the performance of penalized LMMs and competing methods in terms of estimation and selection accuracy in the presence of a number of confounding structures.

Phenome-Wide Association Study With Focus on Oral Health Disparities and Individuals Who Did Not Have Cancer

The goal of this study was to test if oral health outcomes are associated with the same genetic markers in Black and White individuals who did not have cancer. From a total of 6,100 subjects from the Dental Registry and DNA Repository project, 1,042 individuals who self-identified as White and 266 as Black without a history of cancer were included in this analysis. Genotyping data from IRE1—rs196929, RHEB—rs2374261 and rs1109089, AXIN2—rs2240308 and rs11867417, and RPTOR—rs4396582, present in cell regulatory pathways, were analyzed. We ran separate analyses in self-reported Black and White groups to reduce possible confounding effects of population stratification. Internal diagnostic codes from our dental registry were converted into Phecodes in order to run the analysis using the PheWAS package, installed in R Studio software. Periodontitis was associated with RHEB in both Black and White patients, with the minor allele increasing the likelihood of developing periodontitis in the White group and yielding a protective effect in the Black individuals. The presence of ulcers and gingivitis were associated with RPTOR and AXIN2, respectively, in the White group, but an association was not detected for the Black group. On the other hand, phenotypes such as dental fracture, diseases of the tongue, attrition, erosion, abrasion, fordyce granules, and torus and exostosis were uniquely associated with the Black group. Periodontitis associated with RHEB in both Black and White patients, and associations found in Black individuals may be the result of social disparities that lead to higher levels of stress, and these observed differences require further study.