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Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative ‘genome graph’ can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual’s infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.

Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.

Induction of nucleotide-binding oligomerization domain 2 (NOD2) and downstream receptor-interacting serine/threonine-protein kinase 2 (RIPK2) by human cytomegalovirus (HCMV) is known to up-regulate antiviral responses and suppress virus replication. We investigated the role of nucleotide-binding oligomerization domain 1 (NOD1), which also signals through RIPK2, in HCMV control. NOD1 activation by Tri-DAP (NOD1 agonist) suppressed HCMV and induced IFN-β. Mouse CMV was also inhibited through NOD1 activation. NOD1 knockdown (KD) or inhibition of its activity with small molecule ML130 enhanced HCMV replication in vitro. NOD1 mutations displayed differential effects on HCMV replication and antiviral responses. In cells overexpressing the E56K mutation in the caspase activation and recruitment domain, virus replication was enhanced, but in cells overexpressing the E266K mutation in the nucleotide-binding domain or the wild-type NOD1, HCMV was inhibited, changes that correlated with IFN-β expression. The interaction of NOD1 and RIPK2 determined the outcome of virus replication, as evidenced by enhanced virus growth in NOD1 E56K mutant cells (which failed to interact with RIPK2). NOD1 activities were executed through IFN-β, given that IFN-β KD reduced the inhibitory effect of Tri-DAP on HCMV. Signaling through NOD1 resulting in HCMV suppression was IKKα-dependent and correlated with nuclear translocation and phosphorylation of IRF3. Finally, NOD1 polymorphisms were significantly associated with the risk of HCMV infection in women who were infected with HCMV during participation in a glycoprotein B vaccine trial. Collectively, our data indicate a role for NOD1 in HCMV control via RIPK2- IKKα-IRF3 and suggest that its polymorphisms predict the risk of infection.

Gene–environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.Despite their impact on human complex traits and diseases, gene–environment interactions (G × E) remain challenging to assess statistically. The authors review considerations for the conceptualization, methodology, interpretation and reporting of G × E studies, and provide recommendations on how to avoid common pitfalls.

Undernutrition and diarrheal disease are leading causes of global childhood morbidity and mortality. Undernutrition can present as a cause or consequence of diarrheal diseases, leading us to hypothesize these phenotypes share a common genetic basis. Our identification of cAMP responsive element modulator ( CREM ) as a transcriptional regulator that influences susceptibility to both undernutrition and diarrheal disease in children growing up in an impoverished Bangladeshi community advances our understanding of the interaction of two major causes of childhood illness and offers the potential of therapy targeted to the cAMP-regulated transcription factor, CREM .

Background Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. Results We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance ( P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance ( P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. Conclusions Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5–3.1% for an array of one million sites and 0.7–7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

Background Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. Results We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n  = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal ( p  < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci ( HFE , KIT , HBS1L/MYB , CITED2/FILNC1 , ABO , HBA1/2 , and PLIN4/5 ). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. Conclusion This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio Me biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1 , with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease. Diseases often run in families. These disease are frequently linked to changes in DNA that are passed down through generations. Close family members may share these disease-causing mutations; so may distant relatives who inherited the same mutation from a common ancestor long ago. Geneticists use a method called linkage mapping to trace a disease found in multiple members of a family over generations to genetic changes in a shared ancestor. This allows scientists to pinpoint the exact place in the genome the disease-causing mutation occurred. Using computer algorithms, scientists can apply the same technique to identify mutations that distant relatives inherited from a common ancestor. Belbin et al. used this computational technique to identify a mutation that may cause unusually short stature or bone and joint problems in up to 2% of people of Puerto Rican descent. In the experiments, the genomes of about 32,000 New Yorkers who have volunteered to participate in the BioMe Biobank and their health records were used to search for genetic changes linked to extremely short stature. The search revealed that people who inherited two copies of this mutation from their parents were likely to be extremely short or to have bone and joint problems. People who inherited one copy had an increased likelihood of joint or bone problems. This mutation affects a gene responsible for making a form of protein called collagen that is important for bone growth. The analysis suggests the mutation first arose in a Native American ancestor living in Puerto Rico around the time that European colonization began. The mutation had previously been linked to a disorder called Steel syndrome that was thought to be rare. Belbin et al. showed this condition is actually fairly common in people whose ancestors recently came from Puerto Rico, but may often go undiagnosed by their physicians. The experiments emphasize the importance of including diverse populations in genetic studies, as studies of people of predominantly European descent would likely have missed the link between this disease and mutation.

Children in low-to-middle-income countries often suffer from multiple enteric infections in their first few years of life, many of which have the potential for long-lasting effects. These children are already likely to be malnourished and underweight, and chronic intestinal disturbances exacerbate these conditions. Diarrhea is responsible for the deaths of more than 500,000 children each year, many of whom reside in low-to-middle-income countries (LMICs). Additionally, children with multiple diarrheal infections early in life have increased growth stunting and malnutrition and decreased vaccine efficacy. Two bacteria that contribute to the burden of diarrhea are Campylobacter jejuni and Campylobacter coli , both are endemic in Bangladesh. However, not all children that are exposed to these pathogens, including Campylobacter , will experience diarrhea. We hypothesized that host genetics may influence susceptibility to Campylobacter infections and performed a genome-wide association study in 534 children from two independent birth cohorts in Dhaka, Bangladesh. Infants were monitored for diarrhea for the first 2 years of life and only defined as controls if all diarrheal samples in the first year were negative for Campylobacter jejuni / C. coli . Each cohort was analyzed separately under an additive model and adjusted for length-for-age z-scores at birth and 12 months, sex, water treatment, and ancestry. In a fixed effect inverse-variance weighted meta-analysis of these two cohorts, we identified a genome-wide significant region on chromosome 8 in intron 4 of the rho guanine nucleotide exchange factor 10 gene ( ARHGEF10 ). Individuals with the G allele (rs13281104) had a 2-fold lower risk of having a Campylobacter -associated diarrheal episode than individuals with the A allele (OR 0.41, 95% CI 0.29 to 0.58, P = 3.6 × 10 −7 ). This SNP is associated with decreased expression of the neighboring gene, CLN8 , which may be involved in the transport of the cytolethal distending toxin produced by Campylobacter . IMPORTANCE Children in low-to-middle-income countries often suffer from multiple enteric infections in their first few years of life, many of which have the potential for long-lasting effects. These children are already likely to be malnourished and underweight, and chronic intestinal disturbances exacerbate these conditions. Despite public health interventions aimed at improving water, sanitation, and hygiene, enteric infections are still a leading cause of death for children under five. Previous work has included transmission dynamics, pathogen characteristics, and evaluation of interventions. Here, we examined the role of host genetic variation in susceptibility to diarrhea-associated Campylobacter infection. In our meta-analysis of two independent birth cohorts from Dhaka, Bangladesh, we found that children carrying a specific genetic variant (rs13281104, in an intron of ARHGEF10 ) were half as likely to have a diarrhea-associated Campylobacter infection in their first year of life. This protective effect may be achieved by decreasing gene expression and thereby impacting host-pathogen interactions and host immune response.