Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study.

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.

A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping.

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54,542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene-trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta-analysis consisting of up to 35,900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine-mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine-mapping.

Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction.

BACKGROUND: Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine. Unfortunately, when it comes to understanding why machine learning models are predictive, applied research continues to rely on 'out of bag' (OOB) variable importance metrics (VIMPs) that are known to have considerable shortcomings within the statistics community. After explaining the limitations of OOB VIMPs - including bias towards correlated features and limited interpretability - we describe a modern approach called 'knockoff VIMPs' and explain its advantages. METHODS: We first evaluate current VIMP practices through an in-depth literature review of 50 recent random forest manuscripts. Next, we recommend organized and interpretable strategies for analysis with knockoff VIMPs, including computing them for groups of features and considering multiple model performance metrics. To demonstrate methods, we develop a random forest to predict 5-year incident stroke in the Sleep Heart Health Study and compare results based on OOB and knockoff VIMPs. RESULTS: Nearly all papers in the literature review contained substantial limitations in their use of VIMPs. In our demonstration, using OOB VIMPs for individual variables suggested two highly correlated lung function variables (forced expiratory volume, forced vital capacity) as the best predictors of incident stroke, followed by age and height. Using an organized analytic approach that considered knockoff VIMPs of both groups of features and individual features, the largest contributions to model sensitivity were medications (especially cardiovascular) and measured medical risk factors, while the largest contributions to model specificity were age, diastolic blood pressure, self-reported medical risk factors, polysomnography features, and pack-years of smoking. Thus, we reach very different conclusions about stroke risk factors using OOB VIMPs versus knockoff VIMPs. CONCLUSIONS: The near-ubiquitous reliance on OOB VIMPs may provide misleading results for researchers who use such methods to guide their research. Given the rapid pace of scientific inquiry using machine learning, it is essential to bring modern knockoff VIMPs that are interpretable and unbiased into widespread applied practice to steer researchers using random forest machine learning toward more meaningful results.

Reasons for participation in a child development study: Are cases with developmental diagnoses different from controls?

BACKGROUND: Current knowledge about parental reasons for allowing child participation in research comes mainly from clinical trials. Fewer data exist on parents' motivations to enrol children in observational studies. OBJECTIVES: Describe reasons parents of preschoolers gave for participating in the Study to Explore Early Development (SEED), a US multi-site study of autism spectrum disorder (ASD) and other developmental delays or disorders (DD), and explore reasons given by child diagnostic and behavioural characteristics at enrolment. METHODS: We included families of children, age 2-5 years, participating in SEED (n = 5696) during 2007-2016. We assigned children to groups based on characteristics at enrolment: previously diagnosed ASD; suspected ASD; non-ASD DD; and population controls (POP). During a study interview, we asked parents their reasons for participating. Two coders independently coded responses and resolved discrepancies via consensus. We fit binary mixed-effects models to evaluate associations of each reason with group and demographics, using POP as reference. RESULTS: Participants gave 1-5 reasons for participation (mean = 1.7, SD = 0.7). Altruism (48.3%), ASD research interest (47.4%) and perceived personal benefit (26.9%) were most common. Two novel reasons were knowing someone outside the household with the study conditions (peripheral relationship; 14.1%) and desire to contribute to a specified result (1.4%). Odds of reporting interest in ASD research were higher among diagnosed ASD participants (odds ratio [OR] 2.89, 95% confidence interval [CI] 2.49-3.35). Perceived personal benefit had higher odds among diagnosed (OR 1.92, 95% CI 1.61-2.29) or suspected ASD (OR 3.67, 95% CI 2.99-4.50) and non-ASD DD (OR 1.80, 95% CI 1.50-2.16) participants. Peripheral relationship with ASD/DD had lower odds among all case groups. CONCLUSIONS: We identified meaningful differences between groups in parent-reported reasons for participation. Differences demonstrate an opportunity for future studies to tailor recruitment materials and increase the perceived benefit for specific prospective participants.

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

Midlife vascular risk factors and midlife cognitive status in relation to prevalence of mild cognitive impairment and dementia in later life: The Atherosclerosis Risk in Communities Study.

INTRODUCTION: The interplay between midlife vascular risk factors and midlife cognitive function with later life mild cognitive impairment (MCI) and dementia (DEM) is not well understood. METHODS: In the Atherosclerosis Risk in Communities Study, cardiovascular risk factors and cognition were assessed in midlife, ages 45-64 years. In 2011-2013, 20-25 years later, all consenting Atherosclerosis Risk in Communities participants underwent a cognitive and neurological evaluation and were given adjudicated diagnoses of cognitively normal, MCI, or DEM. RESULTS: In 5995 participants with complete covariate data, midlife diabetes, hypertension, obesity, and hypercholesterolemia were associated with late-life MCI and DEM. Low midlife cognition function was also associated with greater likelihood of late-life MCI or DEM. Both midlife vascular risk factors and midlife cognitive function remained associated with later life MCI or DEM when both were in the model. DISCUSSION: Later life MCI and DEM were independently associated with midlife vascular risk factors and midlife cognition.

Effect of daily discrimination on naturalistic sleep health features in young adults.

OBJECTIVE: Racial inequities in sleep health are well documented and may be partially attributable to discrimination experiences. However, the effects of acute discrimination experiences on same-night sleep health are understudied. We quantified naturalistic discrimination experiences captured using ecological momentary assessment (EMA) and examined whether reporting discrimination on a given day predicted sleep health that night. METHOD: Participants completed baseline assessments and a 17-day EMA protocol, with text prompts delivered four times daily to collect discrimination experiences. Seven different daily sleep characteristics were ascertained each morning. Discrimination reasons (e.g., because of my racial identity) were reported by participants and categorized into any, racial, or nonracial discrimination. Outcomes included the seven sleep diary characteristics. We fit generalized linear mixed effects models for each sleep outcome and discrimination category, controlling for key covariates. RESULTS: The analytic sample included 116 self-identified Black and White individuals (48% Black, 71% assigned female at birth, average age = 24.5 years). Among Black participants, race-based discrimination was associated with a 0.5-hr reduction in total sleep time (TST). Among White individuals, nonracial discrimination was associated with a 0.6-hr reduction in TST, an earlier sleep offset, and reduced sleep efficiency (partly attributable to more nighttime awakenings). CONCLUSIONS: Young adults may sleep worse on nights after experiencing discrimination, and different types of discrimination affect different sleep outcomes for Black and White individuals. Future studies may consider developing treatments that account for different sleep vulnerabilities for people experiencing discrimination on a given day. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Race and sex differences in the longitudinal changes in multidimensional self-reported sleep health characteristics in aging older adults.

OBJECTIVES: We examined within-individual changes in self-reported sleep health as community-dwelling older adults age as well as potential differences in these changes by self-reported sex and racial identity. METHODS: Participants were from the United States and enrolled in the Rush Memory and Aging Project, Minority Aging Research Study, or Religious Orders Study (N = 3539, 20% Black, 75% female, mean 78years [range 65-103]), and they received annual, in-person clinical evaluations (median 5 visits [range 1-27]). A sleep health composite score measured the number of poor sleep characteristics among satisfaction, daytime sleepiness, efficiency, and duration. Mixed effects models estimated associations of age, race, sex, and their interactions on the composite and individual sleep measures, accounting for key confounders. RESULTS: As they aged, Black participants shifted from reporting two poor sleep characteristics to one poor sleep characteristic, while White participants shifted from one poor characteristic to two. Regardless of age, sex, and race, participants reported that they "often" felt satisfied with their sleep and "sometimes" had trouble staying asleep. Females over age 85 and males of all ages reported the most daytime sleepiness, and older White participants (>age 90) reported the most difficulty falling asleep. CONCLUSIONS: Although self-reported sleep characteristics were typically stable across age, identifying race and sex differences in self-reported sleep health can help guide future research to understand the mechanisms that underlie these differences.

Proximity to Swine Farming Operations as a Risk Factor for Eosinophilic Esophagitis.

We aimed to determine whether residential proximity to permitted swine facilities was associated with an increased risk of eosinophilic esophagitis (EoE) by conducting a case-control study using 2 complementary data sources: 1 from a tertiary care center (n = 401 cases and 1805 controls) and 1 from a large pathology group (n = 904 cases and 4074 controls). Addresses of the subjects and swine facilities were geocoded, and adjusted odds of EoE relative to proximity to and density of swine facilities were calculated. We observed a positive association between proximity to a permitted swine facility (<1 mile) and odds of EoE (adjusted odds ratio R, 2.56; 95% CI, 1.33-4.95) in the tertiary center data; density of farms (>10 farms/census tract) was also positively associated (adjusted odds ratio, 2.76; 95% CI, 1.30-5.84). However, this association was not observed in the pathology database. Though proximity to and density of swine operations were associated with EoE, associations were sensitive to the database used.

Examining demographic and psychosocial factors related to self-weighing behavior during pregnancy and postpartum periods.

Black childbearing individuals in the US experience a higher risk of postpartum weight retention (PPWR) compared to their White counterparts. Given that PPWR is related to adverse health outcomes, it is important to investigate predictors of weight-related health behaviors, such as self-weighing (i.e., using a scale at home). Regular self-weighing is an evidence-based weight management strategy, but there is minimal insight into sociodemographic factors related to frequency. The Postpartum Mothers Mobile Study (PMOMS) facilitated longitudinal ambulatory weight assessments to investigate racial inequities in PPWR. Our objective for the present study was to describe self-weighing behavior during and after pregnancy in the PMOMS cohort, as well as related demographic and psychosocial factors. Applying tree modeling and multiple regression, we examined self-weighing during and after pregnancy. Participants (N = 236) were 30.2 years old on average (SD = 4.7), with the majority being college-educated (53.8%, n = 127), earning at least $30,000 annually (61.4%, n = 145), and self-identifying as non-Hispanic White (NHW; 68.2%, n = 161). Adherence to regular self-weighing (at least once weekly) was highest among participants during pregnancy, with a considerable decline after giving birth. Low-income Black participants (earning < $30,000) were significantly less likely to reach a completion rate of ≥ 80% during pregnancy (AOR = 0.10) or the postpartum period (AOR = 0.16), compared to NHW participants earning at least $30,000 annually. Increases in perceived stress were associated with decreased odds of sustained self-weighing after delivery (AOR = 0.79). Future research should consider behavioral differences across demographic intersections, such as race and socioeconomic status, and the impact on efficacy of self-weighing.

A Distinct Three-Factor Structure of Restricted and Repetitive Behaviors in an Epidemiologically Sound Sample of Preschool-Age Children with Autism Spectrum Disorder.

Prior studies investigating restricted and repetitive behavior (RRB) subtypes within autism spectrum disorder (ASD) have found varied factor structures for symptom groupings, in part, due to variation in symptom measurement and broad sample age ranges. This study examined RRBs among 827 preschool-age children, ages 35 to 71 months, through an exploratory factor analysis of RRB items from the Autism Diagnostic Interview-Revised (ADI-R) collected through the Study to Explore Early Development. The factor structures of RRBs among children with confirmed ASD versus those with non-autism developmental concerns were qualitatively compared. Correlations between RRB factors and participant characteristics were examined in the ASD group. Three conceptually well-defined factors characterized as repetitive sensorimotor behaviors (RSMB), insistence on sameness (IS), and a novel stereotyped speech (SPEECH) factor emerged for the ASD group only. Distinct factors were supported by different clinical correlates. Findings have implications for improving differential diagnosis and understanding of ASD symptomatology in this age range.

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations.

BACKGROUND: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. METHODS: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. RESULTS: Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. CONCLUSIONS: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.