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While public health surveillance has historically focused on monitoring clinical cases and consequences of infection (e.g., case reports and hospitalizations) [1], technological advances in genomic sequencing rooted in the Human Genome Project and other large-scale investments in human genetics and genomic research and technologies now allow the unprecedented opportunity for pathogen surveillance down to base pair patterns of variation. Historically, genomic studies in the US have been financially and scientifically well supported by several agencies including the US Department of Energy, National Institutes of Health (NIH), and private industry. Nearly absent from the US genomic-centric efforts is explicit collaboration with and investment by the primary public health agency, the Centers for Disease Control and Prevention (CDC). The CDC, part of the US Department of Health and Human Services (HHS), typically leads disease surveillance and works in conjunction with other HHS agencies, such as the Indian Health Services, as well as public health agencies organized at the state level.

The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.

When applied in large scale to electronic medical record data, the PheWAS approach replicates GWAS associations and reveals potentially new pleiotropic associations. Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10 −6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort ( n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.

A better understanding of genetic architecture will help enhance precision medicine and clinical care. Towards this end, we investigate sex-stratified analyses for several traits in the Hybrid Mouse Diversity Panel (HMDP) and UK Biobank to assess trait polygenicity and identify contributing loci. By comparing allelic effect directions in males and females, we hypothesize that non-associated loci should show random effect directions across sexes. Instead, we observe strong concordance in effect direction, even among alleles lacking nominal statistical significance. Our findings suggest hundreds of loci influence each mouse trait and thousands affect each human trait, including traits with no significant loci under conventional approaches. We also detect patterns consistent with spurious widespread epistasis. These results highlight the value of sex-stratified analyses in uncovering novel loci, suggest a method for identifying biologically relevant associations beyond statistical thresholds, and caution that pervasive main effects may produce misleading epistatic signals. Here the authors use sex-stratified genetic analyses in mice and humans to uncover hidden trait influences, revealing many undetected loci and cautioning against misleading genetic interactions from widespread effects.

Estimating heritability has been fundamental in understanding the genetic contributions to complex disorders like late-onset Alzheimer’s disease (LOAD) and provides a rationale for identifying genetic factors associated with disease susceptibility. While numerous studies have established substantial genetic contribution for LOAD, the interpretation of heritability estimates remains challenging. These challenges are further complicated by the binary nature of LOAD status, where estimation and interpretation require additional considerations. Through a systematic review, we identified LOAD heritability estimates from 6 twin studies and 17 genome-wide association studies, all conducted in populations of European ancestry. We demonstrate that these heritability estimates for LOAD vary considerably. The variation reflects not only differences in study design and methodological approaches but also the underlying study population characteristics. Our findings indicate that commonly cited heritability estimates, often treated as universal values, should be interpreted within specific population contexts and methodological frameworks.

Pain is a common and complex non-motor symptom in people with Parkinson’s disease (PWP). Little is known about the genetic drivers of pain in PWP, and progress in its study has been challenging. Here, we conducted two genome-wide association studies (GWAS) to identify genetic variants associated with pain experienced during the earliest stages of Parkinson’s disease. The study population consisted of 4,159 PWP of European ancestry who were mapped to five previously-described, longitudinal pain trajectories. In the first GWAS, the extreme pain trajectories (highest burden versus no significant pain over time) were compared, and in the second GWAS, a multinomial approach was undertaken. While no variant reached genome-wide significance, we identified promising associations, such as rs117108018 (OR GWAS−Extreme =8.96, p GWAS−Extreme =2.5 × 10 − 7 ), a brain/nerve eQTL for L3MBTL3 and EPB41L2 , and rs61881484 (p GWAS−Multinomial =2 × 10 − 7 ), which intersects a transcription factor peak targeting CREB1 , critical in sensory neuron synaptic plasticity and neuropathic pain regulation. Gene-based tests implicated CTNNB1 (p GWAS−Extreme =3.2 × 10 − 5 ), KLK7 (p GWAS−Extreme =7 × 10 − 5 ), and SLITRK3 (p GWAS−Multinomial =3.2 × 10 − 5 ), which have been associated with neurodevelopment. At the pathway-level, there was an enrichment for genes involved in neurotransmitter regulation and opioid dependence. This study implicates neuropathic pain mechanisms as prominent drivers of elevated pain in PWP, suggests potential therapeutic genetic targets for further research.

Hypertension is more prevalent in African Americans (AA) than other ethnic groups. Genome-wide association studies (GWAS) have identified loci associated with hypertension and other cardio-metabolic traits like type 2 diabetes, coronary artery disease, and body mass index (BMI), however the AA population is underrepresented in these studies. In this study, we examined a large AA cohort for the generalizability of 14 Metabochip array SNPs with previously reported European hypertension associations. To evaluate associations, we analyzed genotype data of 14 SNPs for their associations with a diagnosis of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in a case-control study of an AA population (N = 9,534). We also performed an age-stratified analysis (>30, 30≥59 and ≥60 years) following the hypertension definition described by the 8th Joint National Committee (JNC). Associations were adjusted for BMI, age, age2, sex, clinical confounders, and genetic ancestry using multivariable regression models to estimate odds ratios (ORs) and beta-coefficients. Analyses stratified by sex were also conducted. Meta-analyses (including both BioVU and COGENT-BP cohorts) were performed using a random-effects model. We found rs880315 to be associated with systolic hypertension (SBP≥140 mmHg) in the entire cohort (OR = 1.14, p = 0.003) and within women only (OR = 1.16, p = 0.012). Variant rs17080093 associated with lower SBP and DBP (β = -2.99, p = 0.0352 and - β = 1.69, p = 0.0184) among younger individuals, particularly in younger women (β = -3.92, p = 0.0025 and β = -1.87, p = 0.0241 for SBP and DBP respectively). SNP rs1530440 associated with higher SBP and DBP measurements (younger individuals β = 4.1, p = 0.039 and β = 2.5, p = 0.043 for SBP and DBP; (younger women β = 4.5, p = 0.025 and β = 2.9, p = 0.028 for SBP and DBP), and hypertension risk in older women (OR = 1.4, p = 0.050). rs16948048 increases hypertension risk in younger individuals (OR = 1.31, p = 0.011). Among mid-age women rs880315 associated with higher risk of hypertension (OR = 1.20, p = 0.027). rs1361831 associated with DBP (β = -1.96, p = 0.02) among individuals older than 60 years. rs3096277 increases hypertension risk among older individuals (OR = 1.26 p = 0.0015), however, this variant also reduces SBP among younger women (β = -2.63, p = 0.0102). These findings suggest that European-descent and AA populations share genetic loci that contribute to blood pressure traits and hypertension. However, the OR and beta-coefficient estimates differ, and some are age-dependent. Additional genetic studies of hypertension in AA are warranted to identify new loci associated with hypertension and blood pressure traits in this population.

Advances in genotyping and sequencing technologies, coupled with the development of sophisticated statistical methods, have afforded investigators novel opportunities to define the role of sequence variation in the development of common human diseases. At the forefront of these investigations is the use of dense maps of single-nucleotide polymorphisms (SNPs) and the haplotypes derived from these polymorphisms. Here we review basic concepts of high-density genetic maps of SNPs and haplotypes and how they are typically generated and used in human genetic research. We also provide useful examples and tools available for researchers interested in incorporating haplotypes into their studies. Finally, we discuss the latest concepts for the analysis of haplotypes related to human disease, including haplotype blocks, the International HapMap Project, and the future directions of these resources.

Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip ( n  = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRS LDL-C was nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (n rs495828 = 53 and n rs505922 = 59); strongest association with venous embolism, odds ratio (OR rs495828 1.33 (p = 1.32 x 10 −199 ), and thrombosis OR rs505922 1.33, p = 2.2 x10 -265 . Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10 −191 ; CRHR1 ( rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10 −12 . The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10 -23 , lupus OR 0.84, p = 3.97 x 10 −06 . PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10 −13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.