CYP1A1/2 haplotypes and lung cancer and assessment of confounding by population stratification

Prior studies of lung cancer and CYP1A1/2 in African-American and Latino populations have shown inconsistent results and have not yet investigated the haplotype block structure of CYP1A1/2 or addressed potential population stratification. To investigate haplotypes in the CYP1A1/2 region and lung cancer in African-Americans and Latinos, we conducted a case-control study (1998-2003). African-Americans (n = 535) and Latinos (n = 412) were frequency matched on age, sex, and self-reported race/ethnicity. We used a custom genotyping panel containing 50 single nucleotide polymorphisms in the CYP1A1/2 region and 184 ancestry informative markers selected to have large allele frequency differences between Africans, Europeans, and Amerindians. Latinos exhibited significant haplotype main effects in two blocks even after adjusting for admixture [odds ratio (OR), 2.02; 95% confidence interval (95% CI), 1.28-3.19 and OR, 0.55; 95% CI, 0.36-0.83], but no main effects were found among African-Americans. Adjustment for admixture revealed substantial confounding by population stratification among Latinos but not African-Americans. Among Latinos and African-Americans, interactions between smoking level and haplotypes were not statistically significant. Evidence of population stratification among Latinos underscores the importance of adjusting for admixture in lung cancer association studies, particularly in Latino populations. These results suggest that a variant occurring within the CYP1A2 region may be conferring an increased risk of lung cancer in Latinos.

Phased polyploid genomes provide deeper insight into the multiple origins of domesticated Saccharomyces cerevisiae beer yeasts

Yeasts, and in particular Saccharomyces cerevisiae, have been used for brewing beer for thousands of years. Population genomic surveys highlighted that beer yeasts are polyphyletic, with the emergence of different domesticated subpopulations characterized by high genetic diversity and ploidy level. However, the different origins of these subpopulations are still unclear as reconstruction of polyploid genomes is required. To gain better insight into the differential evolutionary trajectories, we sequenced the genomes of 35 Saccharomyces cerevisiae isolates coming from different beer-brewing clades, using a long-read sequencing strategy. By phasing the genomes and using a windowed approach, we identified three main beer subpopulations based on allelic content (European dominant, Asian dominant, and African beer). They were derived from different admixtures between populations and are characterized by distinctive genomic patterns. By comparing the fully phased genes, the most diverse in our dataset are enriched for functions relevant to the brewing environment such as carbon metabolism, oxidoreduction, and cell wall organization activity. Finally, independent domestication, evolution, and adaptation events across subpopulations were also highlighted by investigating specific genes previously linked to the brewing process. Altogether, our analysis based on phased polyploid genomes has led to new insight into the contrasting evolutionary history of beer isolates.

Extending Tests of Hardy-Weinberg Equilibrium to Structured Populations

Testing for Hardy-Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors, or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of "structural HWE." Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data.

Whole-Genome Sequencing Identifies Novel Functional Loci Associated with Lung Function in Puerto Rican Youth

Rationale: Puerto Ricans have the highest childhood asthma prevalence in the United States (23.6%); however, the etiology is uncertain.Objectives: In this study, we sought to uncover the genetic architecture of lung function in Puerto Rican youth with and without asthma who were recruited from the island (n = 836).Methods: We used admixture-mapping and whole-genome sequencing data to discover genomic regions associated with lung function. Functional roles of the prioritized candidate SNPs were examined with chromatin immunoprecipitation sequencing, RNA sequencing, and expression quantitative trait loci data.Measurements and Main Results: We discovered a genomic region at 1q32 that was significantly associated with a 0.12-L decrease in the lung volume of exhaled air (95% confidence interval, -0.17 to -0.07; P = 6.62 × 10-8) with each allele of African ancestry. Within this region, two SNPs were expression quantitative trait loci of TMEM9 in nasal airway epithelial cells and MROH3P in esophagus mucosa. The minor alleles of these SNPs were associated with significantly decreased lung function and decreased TMEM9 gene expression. Another admixture-mapping peak was observed on chromosome 5q35.1, indicating that each Native American ancestry allele was associated with a 0.15-L increase in lung function (95% confidence interval, 0.08-0.21; P = 5.03 × 10-6). The region-based association tests identified four suggestive windows that harbored candidate rare variants associated with lung function.Conclusions: We identified common and rare genetic variants that may play a critical role in lung function among Puerto Rican youth. We independently validated an inflammatory pathway that could potentially be used to develop more targeted treatments and interventions for patients with asthma.

Association between HLA Class II Alleles/Haplotypes and Genomic Ancestry in Brazilian Patients with Type 1 Diabetes: A Nationwide Exploratory Study

We aimed to identify HLA-DRB1, -DQA1, and -DQB1 alleles/haplotypes associated with European, African, or Native American genomic ancestry (GA) in admixed Brazilian patients with type 1 diabetes (T1D). This exploratory nationwide study enrolled 1599 participants. GA percentage was inferred using a panel of 46 ancestry informative marker-insertion/deletion. Receiver operating characteristic curve analysis (ROC) was applied to identify HLA class II alleles related to European, African, or Native American GA, and showed significant (p < 0.05) accuracy for identifying HLA risk alleles related to European GA: for DRB1*03:01, the area under the curve was (AUC) 0.533; for DRB1*04:01 AUC = 0.558, for DRB1*04:02 AUC = 0.545. A better accuracy for identifying African GA was observed for the risk allele DRB1*09:01AUC = 0.679 and for the protective alleles DRB1*03:02 AUC = 0.649, DRB1*11:02 AUC = 0.636, and DRB1*15:03 AUC = 0.690. Higher percentage of European GA was observed in patients with risk haplotypes (p < 0.05). African GA percentage was higher in patients with protective haplotypes (p < 0.05). Risk alleles and haplotypes were related to European GA and protective alleles/haplotypes to African GA. Future studies with other ancestry markers are warranted to fill the gap in knowledge regarding the genetic origin of T1D in highly admixed populations such as that found in Brazil.

Precision Medicine Needs to Think Outside the Box

Precision medicine offers a precious opportunity to change clinical practice and disrupt medicine’s reliance on crude racial, ethnic, or ancestral categories by focusing on an individual’s unique genetic, environmental, and lifestyle characteristics. However, precision medicine and the genomic studies that are its cornerstone have thus far failed to account for human diversity. This failure is made clearer when looking at individuals who encapsulate a mosaic of different genetic ancestries and do not fit neatly into existing population labels. This piece argues that precision medicine continues to rely on the same forms of crude categorization it seeks to unsettle. Until the scientific community creates inclusive solutions for individuals who fall outside or between our existing population labels, precision medicine will continue to fall short in its aims.

Polygenic risk scores and kidney traits in the Hispanic/Latino population: The Hispanic Community Health Study/Study of Latinos

Estimated glomerular filtration rate (eGFR) is used to evaluate kidney function and determine the presence of chronic kidney disease (CKD), a highly prevalent disease in the US^{1 , 2 , 3} that varies among subgroups of Hispanic/Latino individuals.^{4 , 5} The polygenic risk score (PRS) is a popular method that uses large genome-wide association studies (GWASs) to provide a strong estimate of disease risk.⁷ However, due to the limited availability of summary statistics from GWAS meta-analyses based on Hispanic/Latino populations, PRSs can only be computed using different ancestry GWASs. The performance of eGFR PRSs derived from other GWAS reference populations for Hispanic/Latino population has not been examined. We compared PRS constructions for eGFR prediction in Hispanic/Latino individuals using GWAS-significant variants, clumping and thresholding (C&T),⁸ and PRS-CS,²² as well as a combination of PRSs calculated with different reference GWAS meta-analyses from European and multi-ethnic studies in Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). All eGFR PRSs were highly associated with eGFR (p < 1E-20). Additionally, eGFR PRSs were significantly associated with lower risk of prevalent CKD at visit 1 or 2 and incident CKD at visit 2, with the combined PRSs having the best performance. These PRS findings were replicated in an additional dataset of Hispanic/Latino individuals using data from the Women's Health Initiative SNP Health Association Resource (WHI-SHARe).¹⁷.

Polymorphisms of xenobiotic-metabolizing and transporter genes, and the risk of gastric and colorectal cancer in an admixed population from the Brazilian Amazon

Colorectal (CRC) and gastric (GC) cancers are associated with increased morbidity and mortality. Single nucleotide polymorphisms (SNPs) of xenobiotic metabolism and transporter genes may play a role in the individual responses to exposure to substances implicated in susceptibility to cancer. The investigation of the genetic variation related to the activation and detoxification of xenobiotics may thus help to clarify the prevalence of neoplasms. We analyzed the role of 30 SNPs in xenobiotic-metabolizing and transporter genes in susceptibility to CRC and GC. The study included individuals diagnosed with CRC (n = 121) and GC (n = 95), and 141 controls (non-cancer patients) from the population of Belém, in the Brazilian Amazon. The results indicated an association between the polymorphisms rs2231142 (P = 0.013; OR = 3.01; 95% CI = 1.26-7.13), in the ABCG2 gene, and rs1801159 (P = 0.03; OR = 2.35; 95% CI = 1.14-5.05), in DPYD gene, with the risk of developing GC. The polymorphism rs17116806 of the DPYD gene was found to be associated with a lower risk of developing gastric (P≤0.0001; OR = 0.043; 95% CI = 0.015-0.12) or colorectal (P≤0.0001; OR = 0.076; 95% CI = 0.33-0.18) cancers, indicating that the same variant may play a similar role in different types of cancer tissue. Additionally, the carriers of the TT genotype of the polymorphism in the ABCB1 gene (rs1128503) presented a reduced probability of developing CRC (P = 0.0001; OR = 0.16; 95% CI = 0.06-0.41) as well as GC (P = 0.007; OR = 0.27; 95% CI = 0.1-0.7). Our findings indicate that polymorphisms in xenobiotic-metabolizing and transporter genes may modulate susceptibility to colorectal and gastric cancers.

Characterizing substructure via mixture modeling in large-scale genetic summary statistics

Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations. There is a need for methods to enable the harmonization of summary data where the underlying substructure is matched between datasets. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to enable the harmonization of genetic summary data by estimating and adjusting for substructure. In extensive simulations and application to public data, we show that Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and scans for potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse, publicly available summary data, resulting in improved and more equitable research.

Incorporating multiracial and multiethnic experiences into genetic counseling practice and research: A necessary opportunity

The conflation of race and genetic ancestry can have harmful consequences. Biological conceptualizations of race have long been used to justify inequities and distract from social structures that afford opportunities to some that are unjustly denied to others. Despite recent efforts within the scientific community to distinguish between the sociopolitical constructs of race and ethnicity and the biological constructs of genetic ancestry and genetic similarity, their conflation continues to influence genomic research and its translation into clinical care. One overlooked aspect of this problematic conflation is the extent to which discrete monoracial and monoethnic categorization systems persist and perpetuate unequal benefit-sharing in the clinical translation of genomic technologies. In genetic service delivery, reliance on discrete racial and ethnic categories undermines the clinical translation of genomic technologies for large segments of the global population. For multiracial and multiethnic individuals, who have complex identities that defy discrete categorization systems, the potential benefits of genomic discoveries are especially elusive. Scholars have recently begun to call for the inclusion of multiracial, multiethnic, and admixed individuals in race, ethnicity, and ancestry frameworks in genetics and genomics. However, little work has been done to explore and address the unique challenges and opportunities posed by multiracial/multiethnic individuals in genetic counseling specifically. We discuss how conceptualizing diversity along discrete racial and ethnic lines perpetuates inequitable patient care and limits efforts to increase inclusion and belonging within genetic counseling. Moreover, we argue that ongoing efforts to mitigate racial inequity must actively challenge the paradigm of monoracial and monoethnic categories to accomplish their goal.