SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

Skin depletion of Kif3a resembles the pediatric atopic dermatitis transcriptome profile.

Skin deficiency of kinesin family member 3A causes disrupted skin barrier function and promotes development of atopic dermatitis (AD). It is not known how well Kif3aK14∆/∆ mice approximate the human AD transcriptome. To determine the skin transcriptomic profile of Kif3aK14∆/∆ mice and compare it with other murine AD models and human AD, we performed RNA-seq of full-thickness skin and epidermis from 3- and 8-week-old Kif3aK14∆/∆ mice and compared the differentially expressed genes (DEGs) with transcriptomic datasets from mite-induced NC/Nga, flaky tail (Tmem79ma/ma Flgft/ft), and filaggrin-mutant (Flgft/ft) mice, as well as human AD transcriptome datasets including meta-analysis derived atopic dermatitis [MADAD] and the pediatric atopic dermatitis [PAD]. We then interrogated the Kif3aK14∆/∆ skin DEGs using the LINCS-L1000 database to identify potential novel drug targets for AD treatment. We identified 471 and 901 DEGs at 3 and 8 weeks of age, respectively, in the absence of Kif3a. Kif3aK14∆/∆ mice had 3.5-4.5 times more DEGs that overlapped with human AD DEGs compared to the flaky tail and Flgft/ft mice. Further, 55%, 85% and 75% of 8-week Kif3aK14∆/∆ DEGs overlapped with the MADAD and PAD non-lesional and lesional gene lists, respectively. Kif3aK14∆/∆ mice spontaneously develop a human AD-like gene signature, which better represents pediatric non-lesional skin compared to other mouse models including flaky tail, Flgft/ft and NC/Nga. Thus, Kif3aK14∆/∆ mice may model pediatric skin that is a precursor to the development of lesions and inflammation, and hence may be a useful model to study AD pathogenesis.

A roadmap for multi-omics data integration using deep learning.

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Racial differences in length of stay and readmission for asthma in the all of us research program.

BACKGROUND: This study addresses the limited research on racial disparities in asthma hospitalization outcomes, specifically length of stay (LOS) and readmission, across the U.S. METHODS: We analyzed in-patient and emergency department visits from the All of Us Research Program, identifying various risk factors (demographic, comorbid, temporal, and place-based) associated with asthma LOS and 30-day readmission using Bayesian mixed-effects models. RESULTS: Of 17,233 patients (48.0% White, 30.7% Black, 19.7% Hispanic/Latino, 1.3% Asian, and 0.3% Middle Eastern and North African) with 82,188 asthma visits, Black participants had 20% shorter LOS and 12% higher odds of readmission, compared to White participants in multivariate analyses. Public-insured patients had 14% longer LOS and 39% higher readmission odds than commercially insured patients. Weekend admissions resulted in a 12% shorter LOS but 10% higher readmission odds. Asthmatics with chronic diseases had a longer LOS (range: 6-39%) and higher readmission odds (range: 9-32%) except for those with allergic rhinitis, who had a 23% shorter LOS. CONCLUSIONS: A comprehensive understanding of the factors influencing asthma hospitalization, in conjunction with diverse datasets and clinical-community partnerships, can help physicians and policymakers to systematically address racial disparities, healthcare utilization and equitable outcomes in asthma care.

Genome-wide admixture and association analysis identifies African ancestry-specific risk loci of eosinophilic esophagitis in African Americans.

BACKGROUND: Eosinophilic esophagitis (EoE), a chronic allergic inflammatory disease, is linked to multiple genetic risk factors, but studies have focused on populations of European ancestry. Few studies have assessed Black or African American (AA) populations for loci involved in EoE susceptibility. OBJECTIVE: We performed admixture mapping (AM) and genome-wide association study (GWAS) of EoE using participants from AA populations. METHODS: We conducted AM and GWAS of EoE using 137 EoE cases and 1465 healthy controls from the AA population. Samples were genotyped using molecular evolutionary genetics analysis (MEGA). Genotype imputation was carried out with the Consortium on Asthma Among African-Ancestry Populations in the Americas (CAAPA) reference panel using the Michigan Imputation Server. Global and local ancestry inference was carried out, followed by fine mapping and RNA sequencing. After quality control filtering, over 6,000,000 variants were tested by logistic regression adjusted for sex, age, and global ancestry. RESULTS: The global African ancestry proportion was found to be significantly lower among cases than controls (0.751 vs 0.786, P = .012). Case-only AM identified 3 significant loci (9p13.3, 12q24.22-23, and 15q11.2) associated with EoE, of which 12q24.22-23 and 9p13.3 were further replicated in the case-control analysis, with associations observed with African ancestry. Fine mapping and multiomic functional annotations prioritized the variants rs11068264 (FBXW8) and rs7307331 (VSIG10) at 12q24.23 and rs2297879 (ARHGEF39) at 9p13.3. GWAS identified 1 genome-wide significant locus at chromosome 1p22.3 (rs17131726, DDAH1) and 10 other suggestive loci. Most GWAS variants were low-frequency African ancestry-specific variants. RNA sequencing revealed that esophageal DDAH1 and VSIG10 were downregulated and ARHGEF39 upregulated among EoE cases. CONCLUSIONS: GWAS and AM for EoE in AA revealed that African ancestry-specific genetic susceptibility loci exist at 1p22.3, 9p13.3, and 12q24.23, providing evidence of ancestry-specific inheritance of EoE. More independent genetic studies of different ancestries for EoE are needed.

Airway transcriptome networks identify susceptibility to frequent asthma exacerbations in children.

BACKGROUND: Frequent asthma exacerbators, defined as those experiencing more than 1 hospitalization in a year for an asthma exacerbation, represent an important subgroup of individuals with asthma. However, this group remains poorly defined and understudied in children. OBJECTIVE: Our aim was to determine the molecular mechanisms underlying asthma pathogenesis and exacerbation frequency. METHODS: We performed RNA sequencing of upper airway cells from both frequent and nonfrequent exacerbators enrolled in the Ohio Pediatric Asthma Repository. RESULTS: Through molecular network analysis, we found that nonfrequent exacerbators display an increase in modules enriched for immune system processes, including type 2 inflammation and response to infection. In contrast, frequent exacerbators showed expression of modules enriched for nervous system processes, such as synaptic formation and axonal outgrowth. CONCLUSION: These data suggest that the upper airway of frequent exacerbators undergoes peripheral nervous system remodeling, representing a novel mechanism underlying pediatric asthma exacerbation.

Understanding racial disparities in childhood asthma using individual- and neighborhood-level risk factors.

BACKGROUND: Racial disparities in childhood asthma outcomes result from a complex interplay of individual- and neighborhood-level factors. OBJECTIVES: We sought to examine racial disparities in asthma-related emergency department (ED) visits between African American (AA) and European American (EA) children. METHODS: This is a retrospective study of patients younger than 18 years who visited the ED at Cincinnati Children's for asthma from 2009 to 2018. The outcome was number of ED visits during a year. We assessed 11 social, economic, and environmental variables. Mediation and mixed-effects analyses were used to assess relationships between race, mediators, and number of ED visits. RESULTS: A total of 31,114 children (46.1% AA, 53.9% EA) had 186,779 asthma-related ED visits. AA children had more visits per year than EA children (2.23 vs 2.15; P < .001). Medicaid insurance was associated with a 7% increase in rate of ED visits compared with commercial insurance (1.07; 95% CI, 1.03-1.1). Neighborhood socioeconomic deprivation was associated with an increased rate of ED visits in AA but not in EA children. Area-level particulate matter with diameter less than 2.5 µm, pollen, and outdoor mold were associated with an increased rate of ED visits for both AA and EA children (all P < .001). Associations between race and number of ED visits were mediated by insurance, area-level deprivation, particulate matter with diameter less than 2.5 µm, and outdoor mold (all P < .001), altogether accounting for 55% of the effect of race on ED visits. Race was not associated with number of ED visits (P = .796) after accounting for mediators. CONCLUSIONS: Racial disparities in asthma-related ED visits are mediated by social, economic, and environmental factors, which may be amenable to interventions aimed at improving outcomes and eliminating inequities.

Multiancestral polygenic risk score for pediatric asthma.

BACKGROUND: Asthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. A polygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait. OBJECTIVES: This study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries. METHODS: This study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. A phenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes. RESULTS: The multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, PFDR = 3.71 × 10-65) and related phenotypes. CONCLUSIONS: A multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma.

Comorbidities in childhood-onset and adult-onset asthma.

BACKGROUND: Age of asthma onset has emerged as an important determinant of asthma phenotypes; however, the comorbidities that predominate in either childhood- or adult-onset asthma are not known. OBJECTIVE: To identify comorbidities associated with adult-onset asthma vs childhood-onset asthma and with age of asthma diagnosis. METHODS: We analyzed data on 27,437 adult participants in the National Health and Nutrition Examination Surveys conducted from 2001 to 2018. Logistic regression adjusted for covariates was used to identify comorbidities associated with the asthma phenotypes and age of asthma diagnosis. RESULTS: Approximately 12.6% of participants were ever diagnosed with asthma; the prevalence of childhood-onset (before 18 years old) and adult-onset (≥ 18 years old) current asthma was 2.7% and 5.5%, respectively. After adjustment for covariates including age, adult-onset asthma was associated with higher odds of obesity (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.09-1.96), hypercholesterolemia (OR, 1.67; 95% CI, 1.08-2.56), borderline high serum triglycerides (OR, 1.78; 95% CI, 1.17-2.71), and osteoarthritis (OR, 1.52; 95% CI, 1.04-2.20) than was childhood-onset asthma. Older age of asthma diagnosis (per 5-year increase) was also associated with higher odds of diabetes (OR, 1.04; 95% CI, 1.00-1.07) and hypertension (OR, 1.05; 95% CI, 1.02-1.07), whereas younger age of asthma diagnosis was associated with higher odds of chronic obstructive pulmonary disease (OR, 1.12; 95% CI, 1.04-1.19). CONCLUSION: Age- and covariates-adjusted prevalence of obesity, dyslipidemia, arthritis, diabetes, and hypertension is higher in adult-onset asthma than in childhood-onset asthma, and with older age of asthma diagnosis. Conversely, the prevalence of chronic obstructive pulmonary disease increases with younger age of asthma diagnosis.

Unraveling racial disparities in asthma emergency department visits using electronic healthcare records and machine learning.

OBJECTIVE: Hospital emergency department (ED) visits by asthmatics differ based on race and season. The objectives of this study were to investigate season- and race-specific disparities for asthma risk, and to identify environmental exposure variables associated with ED visits among more than 42,000 individuals of African American (AA) and European American (EA) descent identified through electronic health records (EHRs). METHODS: We examined data from 42,375 individuals (AAs = 14,491, EAs = 27,884) identified in EHRs. We considered associated demographic (race, age, gender, insurance), clinical (smoking status, ED visits, FEV1%), and environmental exposures data (mold, pollen, and pollutants). Machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and decision tree (DT) were used to build and identify race- and -season-specific predictive models for asthma ED visits. RESULTS: Significant differences in ED visits and FEV1% among AAs and EAs were identified. ED visits by AAs was 32.0% higher than EAs and AAs had 6.4% lower FEV1% value than EAs. XGB model was used to accurately classify asthma patients visiting ED into AAs and EAs. Pollen factor and pollution (PM2.5, PM10) were the key variables for asthma in AAs and EAs, respectively. Age and cigarette smoking increase asthma risk independent of seasons. CONCLUSIONS: In this study, we observed racial and season-specific disparities between AAs and EAs asthmatics for ED visit and FEV1% severity, suggesting the need to address asthma disparities through key predictors including socio-economic status, particulate matter, and mold.

Genetic ancestry differences in pediatric asthma readmission are mediated by socioenvironmental factors.

BACKGROUND: Social and financial hardships, combined with disease managment and environmental factors explain approximately 80% of the observed disparity in asthma-related readmissions between Black and White children. OBJECTIVE: We sought to determine whether asthma-related readmissions differed by degree of African ancestry and the extent to which such an association would also be explained by socioenvironmental risk factors. METHODS: This study used data from a prospective cohort study of 695 Black and White children aged 1 to 16 years with an asthma-related admission. The primary outcome was a similar readmission within 12 months. Each subject's African ancestry was determined by single nucleotide polymorphisms on a continuous scale ranging from 0 to 1 (0 = no African ancestry; 1 = 100% African ancestry). We also assessed 37 social, environmental, and clinical variables that we clustered into 6 domains (for example, hardship, disease management). Survival and mediation analyses were conducted. RESULTS: A total of 134 children (19.3%) were readmitted within 12 months. Higher African ancestry was associated with asthma readmission (odds ratio 1.11, 95% confidence interval 1.05-1.18 for every 10% increase in African ancestry) with adjustment for age and gender. The association between African ancestry and readmission was mediated by hardship (sß = 3.42, P < .001) and disease management (sß = 0.046, P = .001), accounting for >50% of African ancestry's effect on readmission. African ancestry was no longer significantly associated with readmission (sß = 0.035, P = .388) after accounting for these mediators. CONCLUSIONS: African ancestry was strongly associated with readmission, and the association was mediated by family hardship and disease management. These results are consistent with the notion that asthma-related racial disparities are driven by factors like structural racism and social adversity.

Genome-wide analysis revealed sex-specific gene expression in asthmatics.

Global gene-expression analysis has shown remarkable difference between males and females in response to exposure to many diseases. Nevertheless, gene expression studies in asthmatics have so far focused on sex-combined analysis, ignoring inherent variabilities between the sexes, which potentially drive disparities in asthma prevalence. The objectives of this study were to identify (1) sex-specific differentially expressed genes (DEGs), (2) genes that show sex-interaction effects and (3) sex-specific pathways and networks enriched in asthma risk. We analyzed 711 males and 689 females and more than 2.8 million transcripts covering 20 000 genes leveraged from five different tissues and cell types (i.e. epithelial, blood, induced sputum, T cells and lymphoblastoids). Using tissue-specific meta-analysis, we identified 439 male- and 297 female-specific DEGs in all cell types, with 32 genes in common. By linking DEGs to the genome-wide association study (GWAS) catalog and the lung and blood eQTL annotation data from GTEx, we identified four male-specific genes (FBXL7, ITPR3 and RAD51B from epithelial tissue and ALOX15 from blood) and one female-specific gene (HLA-DQA1 from epithelial tissue) that are disregulated during asthma. The hypoxia-inducible factor 1 signaling pathway was enriched only in males, and IL-17 and chemokine signaling pathways were enriched in females. The cytokine-cytokine signaling pathway was enriched in both sexes. The presence of sex-specific genes and pathways demonstrates that sex-combined analysis does not identify genes preferentially expressed in each sex in response to diseases. Linking DEG and molecular eQTLs to GWAS catalog represents an important avenue for identifying biologically and clinically relevant genes.

The social, economic, political, and genetic value of race and ethnicity in 2020.

Disparities across racial and ethnic groups are present for a range of health outcomes. In this opinion piece, we consider the origin of racial and ethnic groupings, a history that highlights the sociopolitical nature of these terms. Indeed, the terms race and ethnicity exist purely as social constructs and must not be used interchangeably with genetic ancestry. There is no scientific evidence that the groups we traditionally call "races/ethnicities" have distinct, unifying biological or genetic basis. Such a focus runs the risk of compounding equity gaps and perpetuating erroneous conclusions. That said, we suggest that the terms race and ethnicity continue to have purpose as lenses through which to quantify and then close racial and ethnic disparities. Understanding the root cause of such health disparities-namely, longstanding racism and ethnocentrism-could promote interventions and policies poised to equitably improve population health.

Correction to: The social, economic, political, and genetic value of race and ethnicity in 2020.

An amendment to this paper has been published and can be accessed via the original article.

Joint variable selection and network modeling for detecting eQTLs.

In this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL - Multivariate Spike and Slab Lasso, SSUR - Sparse Seemingly Unrelated Bayesian Regression, and OBFBF - Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).

Environmental Determinants of Coronavirus Disease 2019 (COVID-19).

PURPOSE FOR REVIEW: Since the coronavirus SARS-CoV-2 outbreak in China in late 2019 turned into a global pandemic, numerous studies have reported associations between environmental factors, such as weather conditions and a range of air pollutants (particulate matter, nitrogen dioxide, ozone, etc.) and the first wave of COVID-19 cases. This review aims to offer a critical assessment of the role of environmental exposure risk factors on SARS-CoV-2 infections and COVID-19 disease severity. RECENT FINDINGS: In this review, we provide a critical assessment of COVID-19 risk factors, identify gaps in our knowledge (e.g., indoor air pollution), and discuss methodological challenges of association and causation and the impact lockdowns had on air quality. In addition, we will draw attention to ethnic and socioeconomic factors driving viral transmission related to COVID-19. The complex role angiotensin-converting enzyme 2 (ACE2) plays in COVID-19 and future promising avenues of research are discussed. To demonstrate causality, we stress the need for future epidemiologic studies integrating personal air pollution exposures, detailed clinical COVID-19 data, and a range of socioeconomic factors, as well as in vitro and in vivo mechanistic studies.

Air pollution and the pandemic: Long-term PM2.5 exposure and disease severity in COVID-19 patients.

BACKGROUND AND OBJECTIVE: Ecological studies have suggested an association between exposure to particulate matter ≤2.5 µm (PM2.5 ) and coronavirus disease 2019 (COVID-19) severity. However, these findings are yet to be validated in individual-level studies. We aimed to determine the association of long-term PM2.5 exposure with hospitalization among individual patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We estimated the 10-year (2009-2018) PM2.5 exposure at the residential zip code of COVID-19 patients diagnosed at the University of Cincinnati healthcare system between 13 March 2020 and 30 September 2020. Logistic regression was used to determine the odds ratio (OR) and 95% CI for COVID-19 hospitalizations associated with PM2.5 , adjusting for socioeconomic characteristics and comorbidities. RESULTS: Among the 14,783 COVID-19 patients included in our study, 13.6% were hospitalized; the geometric mean (SD) PM2.5 was 10.48 (1.12) µg/m3 . In adjusted analysis, 1 µg/m3 increase in 10-year annual average PM2.5 was associated with 18% higher hospitalization (OR: 1.18, 95% CI: 1.11-1.26). Likewise, 1 µg/m3 increase in PM2.5 estimated for the year 2018 was associated with 14% higher hospitalization (OR: 1.14, 95% CI: 1.08-1.21). CONCLUSION: Long-term PM2.5 exposure is associated with increased hospitalization in COVID-19. Therefore, more stringent COVID-19 prevention measures may be needed in areas with higher PM2.5 exposure to reduce the disease morbidity and healthcare burden.

MI-MAAP: marker informativeness for multi-ancestry admixed populations.

BACKGROUND: Admixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection. RESULTS: To address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data. CONCLUSIONS: MI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

PAMAM: Power analysis in multiancestry admixture mapping.

Admixed populations arise when two or more previously isolated populations interbreed. Admixture mapping (AM) methods are used for tracing the ancestral origin of disease-susceptibility genetic loci in the admixed population such as African American and Latinos. AM is different from genome-wide association studies in that ancestry rather than genotypes are tracked in the association process. The power and sample size of AM primarily depend on proportion of admixture and differences in the risk allele frequencies among the ancestral populations. Ensuring sufficient power to detect the effect of ancestry on disease susceptibility is critical for interpretability and reliability of studies using AM approach. However, there is no power and sample size analysis tool existing for AM studies in admixed population. In this study, we developed power analysis of multiancestry AM (PAMAM) to estimate power and sample size for two-way and three-way population admixtures. PAMAM is the first web-based bioinformatics tool developed to calculate power and sample size in admixed population under a variety of genetic and disease phenotype models. It is a valuable resource for investigators to design a cost-efficient study and develop grant application to pursue AM studies. PAMAM is built on JavaScript back-end with HTML front-end. It is accessible through any modern web browser such as Firefox, Internet Explorer, and Google Chrome regardless of operating system. It is a user-friendly tool containing links for support information including user manual and examples, and freely available at https://research.cchmc.org/mershalab/PAMAM/login.html.

Correction to: Comprehensive functional annotation of susceptibility variants associated with asthma.

In the original article published, the "p" value in the Fig. 5 legend is incorrectly presented as *p < 0.50. The correct p value is *p < 0.050.