Mapping genes that predict treatment outcome in admixed populations

A Genetics-Focused Lens on Social Constructs in Pharmacy Education

OBJECTIVE

Incorporating diversity, equity, inclusion, and anti-racism principles into clinical and didactic education is essential because each influence cognitive and affective attitudes in pharmacy practice. Educators must learn from the past to enlighten the future. For example, race is a social construct, not a biological construct. However, it persistently acts as a surrogate for determining medical diagnoses and treatment.

FINDINGS

Precision medicine and pharmacogenomics can serve as a basis for deconstructing social constructs surrounding race and other social determinants of health.

SUMMARY

In this review, the authors highlight why using race in health education will lead to less-than-optimal clinical decisions and discuss best practices for incorporating diversity, equity, inclusion, and anti-racism into health education from a pharmacogenomic-based perspective.

Improving equity in kidney transplant allocation policies through a novel genetic metric: The Matched Donor Potential

The demand for donors' kidneys continues to increase amid a shortage of available donors. Managing policies to thoughtfully allocate this scarce resource is a complex process. Although human leukocyte antigen (HLA) matching has been shown to prolong graft survival, its relative contribution to allocation schemes is empirically compromised owing to competing priorities. We explored using a new metric, Matched Donor Potential (MDP), to facilitate improved HLA matching while promoting equity. We interrogated all active kidney waitlist patients (N = 164 427), their corresponding unacceptable antigen files, and all effective donors in the Scientific Registry of Transplant Recipients (January 1, 2016-December 31, 2017). Cause-specific hazard functions were evaluated to assess the potential impact of the MDP metric on deceased donor transplant access rates for all candidates. Access was affected by ethnicity, blood group type, and calculated Panel Reactive Antibody (cPRA). Importantly, we show that access to transplantation is influenced by the patient's own HLA makeup regardless of their ethnicity and by the HLA makeup of effective donors. The MDP metric demonstrates a high association with access to transplantation. Adjusting Cox models to include this new metric resulted in improved access to kidney transplantation for waitlist candidates of minority heritage while significantly promoting HLA matching. Thus, the MDP metric accounts for balanced, equitable organ allocation algorithms.

Collecting, Using, and Reporting Race and Ethnicity Information: Implications for Research in Nutrition Education, Practice, and Policy to Promote Health Equity

This report will describe approaches for collecting, analyzing, and reporting race and ethnicity information in nutrition education and behavior research, practice, and policy to advance health equity. Race and ethnicity information is used to describe study participants and compare nutrition and health-related outcomes. Depending on the study design, race and ethnicity categories are often defined by the research question or other standardized approaches. Participant self-reported data are more acceptable than researcher adjudicated identification data, which can add bias and/or error. Valid methods to collect, use, and report race and ethnicity information are foundational to publication quality, findings of value, contribution to the knowledge base, and health equity.

Racial Disparities in Triple Negative Breast Cancer: A Review of the Role of Biologic and Non-biologic Factors

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that lacks expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2). TNBC constitutes about 15–30 percent of all diagnosed invasive breast cancer cases in the United States. African-American (AA) women have high prevalence of TNBC with worse clinical outcomes than European-American (EA) women. The contributing factors underlying racial disparities have been divided into two major categories based on whether they are related to lifestyle (non-biologic) or unrelated to lifestyle (biologic). Our objective in the present review article was to understand the potential interactions by which these risk factors intersect to drive the initiation and development of the disparities resulting in the aggressive TNBC subtypes in AA women more likely than in EA women. To reach our goal, we conducted literature searches using MEDLINE/PubMed to identify relevant articles published from 2005 to 2019 addressing breast cancer disparities primarily among AA and EA women in the United States. We found that disparities in TNBC may be attributed to racial differences in biological factors, such as tumor heterogeneity, population genetics, somatic genomic mutations, and increased expression of genes in AA breast tumors which have direct link to breast cancer. In addition, a large number of non-biologic factors, including socioeconomic deprivation adversities associated with poverty, social stress, unsafe neighborhoods, lack of healthcare access and pattern of reproductive factors, can promote comorbid diseases such as obesity and diabetes which may adversely contribute to the aggression of TNBC biology in AA women. Further, the biological risk factors directly linked to TNBC in AA women may potentially interact with non-biologic factors to promote a higher prevalence of TNBC, more aggressive biology, and poor survival. The relative contributions of the biologic and non-biologic factors and their potential interactions is essential to our understanding of disproportionately high burden and poor survival rates of AA women with TNBC.

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/.

Gene-based analysis of genes related to neurotrophic pathway suggests association of BDNF and VEGFA with antidepressant treatment-response in depressed patients

It is well established that brain-derived neurotrophic factor (BDNF) signaling pathway plays a key role in the pathophysiology of major depressive disorder (MDD) and in therapeutic mechanisms of antidepressants. We aim to identify genetic vairiants related to MDD susceptibility and antidepressant therapeutic response by using gene-based association analysis with genes related to the neurotrophic pathway. The present study investigated the role of genetic variants in the 10 neurotrophic-related genes (BDNF, NGFR, NTRK2, MTOR, VEGFA, S100A10, SERPINE1, ARHGAP33, GSK3B, CREB1) in MDD susceptibility through a case-control (455 MDD patients and 2,998 healthy controls) study and in antidepressant efficacy (n = 455). Measures of antidepressant therapeutic efficacy were evaluated using the 21-item Hamilton Rating Scale for Depression. Our single-marker and gene-based analyses with ten genes related to the neurotrophic pathway identified 6 polymorphisms that reached a significant level (p-value < 5.0 × 10⁻³) in both meta- and mega-analyses in antidepressant therapeutic response. One polymorphism was mapped to BDNF and 5 other polymorphisms were mapped to VEGFA. For case-control association study, we found that all of these reported polymorphisms and genes did not reach a suggestive level. The present study supported a role of BDNF and VEGFA variants in MDD therapeutic response.

AdmixPower: Statistical Power and Sample Size Estimation for Mapping Genetic Loci in Admixed Populations

Admixed populations result from recent admixture of two or more ancestral populations with divergent allele frequencies. The genome of each admixed individual is a mosaic of haplotypes inherited from the ancestral populations. Despite the substantial work to assess power and sample size requirements for association mapping in genetically homogeneous populations of European ancestry, power and sample size estimation methods for mapping genes in genetically heterogeneous admixed populations such as African Americans are lacking. Admixture mapping is a method that traces the ancestral origin of disease-susceptibility genetic loci in the admixed population. We developed AdmixPower, a freely available tool set based on the open-source R software, to perform power and sample size analysis for genetically heterogeneous admixed populations considering continuous or dichotomous outcomes with a case-only or case-control study design. AdmixPower can be used to compute the sample size required to achieve investigator-specified statistical power under several key parameters including ancestry odds ratio, genotype risk ratio, parental risk ratio, an underlying genetic risk model, trait type, and admixture model (hybrid-isolation or continuous gene flow model). We demonstrate that differences in the key parameters in the admixed population results in substantial differences in the sample size required to achieve adequate power in admixture mapping studies. Our tool provides a resource for researchers to develop a strategy to minimize cost and maximize the success of identifying disease-susceptibility loci in an admixed population. R code used in the sample size and power analysis is freely available from https://research.cchmc.org/mershalab/Tools.html.

Ethnicity and ERG frequency in prostate cancer

Emerging observations emphasize a distinct biology of prostate cancer among men of different ethnicities and races, as demonstrated by remarkable differences in the frequency of ERG oncogenic activation, one of the most common and widely studied prostate cancer driver genes. Worldwide assessment of ERG alterations frequencies show consistent trends, with men of European ancestry having the highest rates of alteration and men of African or Asian ancestries having considerably lower alteration rates. However, data must be interpreted cautiously, owing to variations in assay platforms and specimen types, as well as ethnic and geographical classifications. Many opportunities and challenges remain in assessing cancer-associated molecular alterations at a global level, and these need to be addressed in order to realize the true potential of precision medicine for all cancer patients.

Perspective on the Genetic Response to Antiparasitics: A Review Article

BACKGROUND

Drugs' pharmacokinetics and pharmacodynamics can be affected by diverse genetic variations, within which simple nucleotide polymorphisms (SNPs) are the most common. Genetic variability is one of the factors that could explain questions like why a given drug does not have the desired effect or why do adverse drug reactions arise.

METHODS

In this retrospective observational study, literature search limits were set within PubMed database as well as the epidemiological bulletins published by the Mexican Ministry of Health, from Jan 1st 2001 to Mar 31st 2017 (16 years).

RESULTS

Metabolism of antiparasitic drugs and their interindividual responses are mainly modified by variations in cytochrome P450 enzymes. These enzymes show high frequencies of polymorphic variability thus affecting the expression of CYP2C, CYP2A, CYP2A6, CYP2D6, CYP2E6 and CYP2A6 isoforms. Research in this field opens the door to new personalized treatment approaches in medicine.

CONCLUSION

Clinical and pharmacological utility yield by applying pharmacogenetics to antiparasitic treatments is not intended as a mean to improve the prescription process, but to select or exclude patients that could present adverse drug reactions as well as to evaluate genetic alterations which result in a diversity of responses, ultimately seeking to provide a more effective and safe treatment; therefore choosing a proper dose for the appropriate patient and the optimal treatment duration. Furthermore, pharmacogenetics assists in the development of vaccines. In other words, the aim of this discipline is to find therapeutic targets allowing personalized treatments.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Pathway Analysis of Genome-wide Association Study in Childhood Leukemia among Hispanics

BACKGROUND

The incidence of acute lymphoblastic leukemia (ALL) is nearly 20% higher among Hispanics than non-Hispanic Whites. Previous studies have shown evidence for association between risk of ALL and variation within IKZF1, ARID5B, CEBPE, CDKN2A, GATA3, and BM1-PIP4K2A genes. However, variants identified only account for <10% of the genetic risk of ALL.

METHODS

We applied pathway-based analyses to genome-wide association study (GWAS) data from the California Childhood Leukemia Study to determine whether different biologic pathways were overrepresented in childhood ALL and major ALL subtypes. Furthermore, we applied causal inference and data reduction methods to prioritize candidate genes within each identified overrepresented pathway, while accounting for correlation among SNPs.

RESULTS

Pathway analysis results indicate that different ALL subtypes may involve distinct biologic mechanisms. Focal adhesion is a shared mechanism across the different disease subtypes. For ALL, the top five overrepresented Kyoto Encyclopedia of Genes and Genomes pathways include axon guidance, protein digestion and absorption, melanogenesis, leukocyte transendothelial migration, and focal adhesion (PFDR < 0.05). Notably, these pathways are connected to downstream MAPK or Wnt signaling pathways which have been linked to B-cell malignancies. Several candidate genes for ALL, such as COL6A6 and COL5A1, were identified through targeted maximum likelihood estimation.

CONCLUSIONS

This is the first study to show distinct biologic pathways are overrepresented in different ALL subtypes using pathway-based approaches, and identified potential gene candidates using causal inference methods.

IMPACT

The findings demonstrate that newly developed bioinformatics tools and causal inference methods can provide insights to furthering our understanding of the pathogenesis of leukemia. Cancer Epidemiol Biomarkers Prev; 25(5); 815-22. ©2016 AACR.

Mapping asthma-associated variants in admixed populations

Admixed populations arise when two or more previously isolated populations interbreed. Mapping asthma susceptibility loci in an admixed population using admixture mapping (AM) involves screening the genome of individuals of mixed ancestry for chromosomal regions that have a higher frequency of alleles from a parental population with higher asthma risk as compared with parental population with lower asthma risk. AM takes advantage of the admixture created in populations of mixed ancestry to identify genomic regions where an association exists between genetic ancestry and asthma (in contrast to between the genotype of the marker and asthma). The theory behind AM is that chromosomal segments of affected individuals contain a significantly higher-than-average proportion of alleles from the high-risk parental population and thus are more likely to harbor disease-associated loci. Criteria to evaluate the applicability of AM as a gene mapping approach include: (1) the prevalence of the disease differences in ancestral populations from which the admixed population was formed; (2) a measurable difference in disease-causing alleles between the parental populations; (3) reduced linkage disequilibrium (LD) between unlinked loci across chromosomes and strong LD between neighboring loci; (4) a set of markers with noticeable allele-frequency differences between parental populations that contributes to the admixed population (single nucleotide polymorphisms (SNPs) are the markers of choice because they are abundant, stable, relatively cheap to genotype, and informative with regard to the LD structure of chromosomal segments); and (5) there is an understanding of the extent of segmental chromosomal admixtures and their interactions with environmental factors. Although genome-wide association studies have contributed greatly to our understanding of the genetic components of asthma, the large and increasing degree of admixture in populations across the world create many challenges for further efforts to map disease-causing genes. This review, summarizes the historical context of admixed populations and AM, and considers current opportunities to use AM to map asthma genes. In addition, we provide an overview of the potential limitations and future directions of AM in biomedical research, including joint admixture and association mapping for asthma and asthma-related disorders.

Impact of Population Stratification on Family-Based Association in an Admixed Population

Population substructure is a well-known confounder in population-based case-control genetic studies, but its impact in family-based studies is unclear. We performed population substructure analysis using extended families of admixed population to evaluate power and Type I error in an association study framework. Our analysis shows that power was improved by 1.5% after principal components adjustment. Type I error was also reduced by 2.2% after adjusting for family substratification. The presence of population substructure was underscored by discriminant analysis, in which over 92% of individuals were correctly assigned to their actual family using only 100 principal components. This study demonstrates the importance of adjusting for population substructure in family-based studies of admixed populations.

Association of genetic variation in IKZF1, ARID5B, and CEBPE and surrogates for early-life infections with the risk of acute lymphoblastic leukemia in Hispanic children

BACKGROUND

Genome-wide association studies focusing on European-ancestry populations have identified ALL risk loci on IKZF1, ARID5B, and CEBPE. To capture the impacts of these genes on ALL risk in the California Hispanic population, we comprehensively assessed the variation within the genes and further assessed the joint effects between the genetic variation and surrogates for early-life infections (the presence of older siblings, daycare attendance, and ear infections).

METHODS

Genotypic data for 323 Hispanic ALL cases and 454 controls from the California Childhood Leukemia Study were generated using Illumina OmniExpress v1 platform. Logistic regression assuming a log-additive model estimated odds ratios (OR) associated with each SNP, adjusted for age, sex, and the first five principal components. In addition, we examined potential interactions between six ALL risk alleles and surrogates for early-life infections using logistic regression models that included an interaction term.

RESULTS

Significant associations between genotypes at IKZF1, ARID5B, and CEBPE and ALL risk were identified: rs7780012, OR 0.50, 95% confidence interval (CI) 0.35-0.71 (p = 0.004); rs7089424, OR 2.12, 95% CI 1.70-2.65 (p = 1.16 × 10(-9)); rs4982731, OR 1.69, 95% CI 1.37-2.08 (p = 2.35 × 10(-6)), respectively. Evidence for multiplicative interactions between genetic variants and surrogates for early-life infections with ALL risk was not observed.

CONCLUSIONS

Consistent with findings in non-Hispanic White population, our study showed that variants within IKZF1, ARID5B, and CEBPE were associated with increased ALL risk, and the effects for ARID5B and CEBPE were most prominent in the high-hyperdiploid ALL subtype in the California Hispanic population. Results implicate the ARID5B, CEBPE, and IKZF1 genes in the pathogenesis of childhood ALL.

Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities

This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person's physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using "ancestry" (or biogeographical ancestry) to describe actual genetic variation, "race" to describe health disparity in societies characterized by racial categories, and "ethnicity" to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals' biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals.

Dopamine transporter genotype and stimulant dose-response in youth with attention-deficit/hyperactivity disorder

OBJECTIVES

This study seeks to determine if variation in the dopamine transporter gene (SLC6A3/DAT1) moderates the dose-response effects of long-acting dexmethylphenidate (D-MPH) and mixed amphetamine salts (MAS) in children with attention-deficit/hyperactivity disorder (ADHD).

METHODS

Fifty-six children and adolescents (mean age=11.7±2.2) participated in a double-blind, two period crossover, dose-response study with a randomized placebo week in each 4 week drug period. Each period consisted of sequential week-long exposures to three dose levels (10, 20, 25-30 mg, depending upon weight) of D-MPH or MAS.

RESULTS

Doses of 10-20 mg of either D-MPH or MAS had little to no effect on hyperactivity-impulsivity and total ADHD symptom scores in subjects with the 9/9 genotype; this was in contrast to the dose-response curves of subjects with either the 10/10 or 10/9 genotype.

CONCLUSIONS

ADHD youth with the 9/9 genotype may require higher stimulant doses to achieve adequate symptom control.

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

BACKGROUND

Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL).

RESULTS

In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations.

CONCLUSIONS

In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

Secondary use of clinical data: the Vanderbilt approach

The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research. The Vanderbilt research data warehouse framework consists of identified and de-identified clinical data repositories, fee-for-service custom services, and tools built atop the data layer to assist researchers across the enterprise. Providing resources dedicated to research initiatives benefits not only the research community, but also clinicians, patients and institutional leadership. This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

Genetic variants in ARID5B and CEBPE are childhood ALL susceptibility loci in Hispanics

Recent genome-wide studies conducted in European Whites have identified novel susceptibility genes for childhood acute lymphoblastic leukemia (ALL). We sought to examine whether these loci are susceptibility genes among Hispanics, whose reported incidence of childhood ALL is the highest of all ethnic groups in California, and whether their effects differ between Hispanics and non-Hispanic Whites (NHWs). We genotyped 13 variants in these genes among 706 Hispanic (300 cases, 406 controls) and 594 NHW (225 cases, 369 controls) participants in a matched population-based case–control study in California. We found significant associations for the five studied ARID5B variants in both Hispanics (p values of 1.0 × 10⁻⁹ to 0.004) and NHWs (p values of 2.2 × 10⁻⁶ to 0.018). Risk estimates were in the same direction in both groups (ORs of 1.53–1.99 and 1.37–1.84, respectively) and strengthened when restricted to B-cell precursor high-hyperdiploid ALL (>50 chromosomes; ORs of 2.21–3.22 and 1.67–2.71, respectively). Similar results were observed for the single CEBPE variant. Hispanics and NHWs exhibited different susceptibility loci at CDKN2A. Although IKZF1 loci showed significant susceptibility effects among NHWs (p < 1 × 10⁻⁵), their effects among Hispanics were in the same direction but nonsignificant, despite similar minor allele frequencies. Future studies should examine whether the observed effects vary by environmental, immunological, or lifestyle factors.

Ancestry inference in complex admixtures via variable-length Markov chain linkage models

Inferring the ancestral origin of chromosomal segments in admixed individuals is key for genetic applications, ranging from analyzing population demographics and history, to mapping disease genes. Previous methods addressed ancestry inference by using either weak models of linkage disequilibrium, or large models that make explicit use of ancestral haplotypes. In this paper we introduce ALLOY, an efficient method that incorporates generalized, but highly expressive, linkage disequilibrium models. ALLOY applies a factorial hidden Markov model to capture the parallel process producing the maternal and paternal admixed haplotypes, and models the background linkage disequilibrium in the ancestral populations via an inhomogeneous variable-length Markov chain. We test ALLOY in a broad range of scenarios ranging from recent to ancient admixtures with up to four ancestral populations. We show that ALLOY outperforms the previous state of the art, and is robust to uncertainties in model parameters.

Ovarian cancer and DNA repair: DNA ligase IV as a potential key

Ovarian cancer (OC) is the sixth most common cancer and the seventh cause of death from cancer in women. The etiology and the ovarian carcinogenesis still need clarification although ovulation may be determinant due to its carcinogenic role in ovarian surface epithelium. The link between ovarian carcinogenesis and DNA repair is well established and it became clear that alterations in DNA damage response may affect the risk to develop OC. Polymorphisms are variations in the DNA sequence that exist in normal individuals of a population and are capable to change, among other mechanisms, the balance between DNA damage and cellular response. Consequently, genetic variability of the host has a great role in the development, progression and consequent prognosis of the oncologic patient as well as in treatment response. Standard treatment for OC patients is based on cytoreductive surgery, followed by chemotherapy with a platinum agent and a taxane. Although 80% of the patients respond to the first-line therapy, the development of resistance is common although the mechanisms underlying therapy failure remain mostly unknown. Because of their role in oncology, enzymes involved in the DNA repair pathways, like DNA Ligase IV (LIG4), became attractive study targets. It has been reported that variations in LIG4 activity can lead to a hyper-sensitivity to DNA damage, deregulation of repair and apoptosis mechanisms, affecting the susceptibility to cancer development and therapy response. To overcome resistance mechanisms, several investigations have been made and the strategy to target crucial molecular pathways, such as DNA repair, became one of the important areas in clinical oncology. This review aims to elucidate the link between DNA repair and OC, namely which concerns the role of LIG4 enzyme, and how genetic polymorphisms in LIG4 gene can modulate the activity of the enzyme and affect the ovarian carcinogenesis and treatment response. Moreover, we try to understand how LIG4 inhibition can be a potential contributor for the development of new cancer treatment strategies.

Individualized risk for statin-induced myopathy: current knowledge, emerging challenges and potential solutions

Skeletal muscle toxicity is the primary adverse effect of statins. In this review, we summarize current knowledge regarding the genetic and nongenetic determinants of risk for statin induced myopathy. Many genetic factors were initially identified through candidate gene association studies limited to pharmacokinetic (PK) targets. Through genome-wide association studies, it has become clear that SLCO1B1 is among the strongest PK predictors of myopathy risk. Genome-wide association studies have also expanded our understanding of pharmacodynamic candidate genes, including RYR2. It is anticipated that deep resequencing efforts will define new loci with rare variants that also contribute, and sophisticated computational approaches will be needed to characterize gene-gene and gene-environment interactions. Beyond environment, race is a critical covariate, and its influence is only partly explained by geographic differences in the frequency of known pharmacodynamic and PK variants. As such, admixture analyses will be essential for a full understanding of statin-induced myopathy.

AncestrySNPminer: a bioinformatics tool to retrieve and develop ancestry informative SNP panels

A wealth of genomic information is available in public and private databases. However, this information is underutilized for uncovering population specific and functionally relevant markers underlying complex human traits. Given the huge amount of SNP data available from the annotation of human genetic variation, data mining is a faster and cost effective approach for investigating the number of SNPs that are informative for ancestry. In this study, we present AncestrySNPminer, the first web-based bioinformatics tool specifically designed to retrieve Ancestry Informative Markers (AIMs) from genomic data sets and link these informative markers to genes and ontological annotation classes. The tool includes an automated and simple "scripting at the click of a button" functionality that enables researchers to perform various population genomics statistical analyses methods with user friendly querying and filtering of data sets across various populations through a single web interface. AncestrySNPminer can be freely accessed at https://research.cchmc.org/mershalab/AncestrySNPminer/login.php.

Matching on Race and Ethnicity in Case-Control Studies as a Means of Control for Population Stratification

Some investigators argue that controlling for self-reported race or ethnicity, either in statistical analysis or in study design, is sufficient to mitigate unwanted influence from population stratification. In this report, we evaluated the effectiveness of a study design involving matching on self-reported ethnicity and race in minimizing bias due to population stratification within an ethnically admixed population in California. We estimated individual genetic ancestry using structured association methods and a panel of ancestry informative markers, and observed no statistically significant difference in distribution of genetic ancestry between cases and controls (P=0.46). Stratification by Hispanic ethnicity showed similar results. We evaluated potential confounding by genetic ancestry after adjustment for race and ethnicity for 1260 candidate gene SNPs, and found no major impact (>10%) on risk estimates. In conclusion, we found no evidence of confounding of genetic risk estimates by population substructure using this matched design. Our study provides strong evidence supporting the race- and ethnicity-matched case-control study design as an effective approach to minimizing systematic bias due to differences in genetic ancestry between cases and controls.

Genotype-environment interactions and their translational implications

Organisms frequently encounter different environmental conditions. The physiological and behavioral responses to these conditions depend on the genetic make up of individuals. Genotype generally remains constant from one environment to another, although occasional spontaneous mutations may occur which cause it to change. However, when the same genotype is subjected to different environments, it can produce a wide range of phenotypes. These phenotypic variations are attributable to the effect of the environment on the expression and function of genes influencing the trait. Changes in the relative performance of genotypes across different environments are referred to as genotype-environment interactions (GEI). A general argument for research on the impact of GEI in common diseases is that it provides insights into disease processes at the population, individual and molecular levels. In humans, GEI is complicated by multiple factors including phenocopies, genocopies, epigenetics and imprinting. A better understanding of GEI is essential if patients are to make informed health choices guided by their genomic information. In this article, we clarify the role of the environment on phenotype, we describe how human population structure can obscure the resolution of GEI and we discuss how emerging biobanks across the globe can be coordinated to further our understanding of genotype-phenotype associations within the context of varying environment.