Genetic background of patients from a university medical center in Manhattan: implications for personalized medicine

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Polygenic Risk for Type 2 Diabetes in African Americans

African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRSAA with other published scores. Results show that a 1-SD increase in the PRSAA was associated with 40-60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37-1.88; OR 1.40, 95% CI 1.16-1.70; and OR 1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population.

ARTICLE HIGHLIGHTS

Genomic ascertainment of primary central nervous system cancers in adolescents and young adults

Genomic ascertainment is the inversion of the traditional phenotype-first approach; with a "genome-first" approach, a cohort linked to electronic health records (EHR) undergoes germline sequencing (array, panel, exome, and genome) and deleterious variation of interest in a gene (or set of genes) are identified. Phenotype is then queried from the linked EHR and from call-back investigation and estimates of variant prevalence, disease penetrance, and phenotype can be determined. This should permit a better estimate of the full phenotypic spectrum, severity, and penetrance linked to a deleterious variant. For now, given the modest size, limited EHR, and age of participants in sequenced cohorts, genomic ascertainment approaches to investigate cancer in children and young adults will likely be restricted to descriptive studies and complement traditional phenotype-first work. Another issue is the ascertainment of the cohort itself: Participants need to survive long enough to enroll. Not accounting for this may lead to bias and incorrect estimates of variant prevalence. Adult-focused cohorts with EHR extending back into childhood, linked to cancer registries, and/or studies that permit recontact with participants may facilitate genomic ascertainment in pediatric cancer research. In summary, genomic ascertainment in pediatric primary brain cancer research remains largely untapped and merits further investigation.

Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression

AIMS

To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway.

METHODS

We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end-stage kidney disease within 5 years of follow-up.

RESULTS

In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut-offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low-, moderate- and high-risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0-19.6) and 3.7 (95% CI 2.0-6.8) in BioMe, and 5.4 (95% CI 2.5-11.9) and 2.3 (95% CI 1.4-3.9) in CANVAS, for high- versus low-risk and moderate- versus low-risk levels, respectively.

CONCLUSIONS

Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.

Genome-wide association study of prostate-specific antigen levels in 392,522 men identifies new loci and improves cross-ancestry prediction

We conducted a multi-ancestry genome-wide association study of prostate-specific antigen (PSA) levels in 296,754 men (211,342 European ancestry; 58,236 African ancestry; 23,546 Hispanic/Latino; 3,630 Asian ancestry; 96.5% of participants were from the Million Veteran Program). We identified 318 independent genome-wide significant (p≤5e-8) variants, 184 of which were novel. Most demonstrated evidence of replication in an independent cohort (n=95,768). Meta-analyzing discovery and replication (n=392,522) identified 447 variants, of which a further 111 were novel. Out-of-sample variance in PSA explained by our new polygenic risk score reached 16.9% (95% CI=16.1%-17.8%) in European ancestry, 9.5% (95% CI=7.0%-12.2%) in African ancestry, 18.6% (95% CI=15.8%-21.4%) in Hispanic/Latino, and 15.3% (95% CI=12.7%-18.1%) in Asian ancestry, and lower for higher age. Our study highlights how including proportionally more participants from underrepresented populations improves genetic prediction of PSA levels, with potential to personalize prostate cancer screening.

Cholesterol Contributes to Risk, Severity, and Machine Learning-Driven Diagnosis of Lyme Disease

BACKGROUND

Lyme disease is the most prevalent vector-borne disease in the US, yet its host factors are poorly understood and diagnostic tests are limited. We evaluated patients in a large health system to uncover cholesterol's role in the susceptibility, severity, and machine learning-based diagnosis of Lyme disease.

METHODS

A longitudinal health system cohort comprised 1 019 175 individuals with electronic health record data and 50 329 with linked genetic data. Associations of blood cholesterol level, cholesterol genetic scores comprising common genetic variants, and burden of rare loss-of-function (LoF) variants in cholesterol metabolism genes with Lyme disease were investigated. A portable machine learning model was constructed and tested to predict Lyme disease using routine lipid and clinical measurements.

RESULTS

There were 3832 cases of Lyme disease. Increasing cholesterol was associated with greater risk of Lyme disease and hypercholesterolemia was more prevalent in Lyme disease cases than in controls. Cholesterol genetic scores and rare LoF variants in CD36 and LDLR were associated with Lyme disease risk. Serological profiling of cases revealed parallel trajectories of rising cholesterol and immunoglobulin levels over the disease course, including marked increases in individuals with LoF variants and high cholesterol genetic scores. The machine learning model predicted Lyme disease solely using routine lipid panel, blood count, and metabolic measurements.

CONCLUSIONS

These results demonstrate the value of large-scale genetic and clinical data to reveal host factors underlying infectious disease biology, risk, and prognosis and the potential for their clinical translation to machine learning diagnostics that do not need specialized assays.

Evidence of Novel Susceptibility Variants for Prostate Cancer and a Multiancestry Polygenic Risk Score Associated with Aggressive Disease in Men of African Ancestry

BACKGROUND

Genetic factors play an important role in prostate cancer (PCa) susceptibility.

OBJECTIVE

To discover common genetic variants contributing to the risk of PCa in men of African ancestry.

DESIGN, SETTING, AND PARTICIPANTS

We conducted a meta-analysis of ten genome-wide association studies consisting of 19378 cases and 61620 controls of African ancestry.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Common genotyped and imputed variants were tested for their association with PCa risk. Novel susceptibility loci were identified and incorporated into a multiancestry polygenic risk score (PRS). The PRS was evaluated for associations with PCa risk and disease aggressiveness.

RESULTS AND LIMITATIONS

Nine novel susceptibility loci for PCa were identified, of which seven were only found or substantially more common in men of African ancestry, including an African-specific stop-gain variant in the prostate-specific gene anoctamin 7 (ANO7). A multiancestry PRS of 278 risk variants conferred strong associations with PCa risk in African ancestry studies (odds ratios [ORs] >3 and >5 for men in the top PRS decile and percentile, respectively). More importantly, compared with men in the 40-60% PRS category, men in the top PRS decile had a significantly higher risk of aggressive PCa (OR = 1.23, 95% confidence interval = 1.10-1.38, p = 4.4 × 10-4).

CONCLUSIONS

This study demonstrates the importance of large-scale genetic studies in men of African ancestry for a better understanding of PCa susceptibility in this high-risk population and suggests a potential clinical utility of PRS in differentiating between the risks of developing aggressive and nonaggressive disease in men of African ancestry.

PATIENT SUMMARY

In this large genetic study in men of African ancestry, we discovered nine novel prostate cancer (PCa) risk variants. We also showed that a multiancestry polygenic risk score was effective in stratifying PCa risk, and was able to differentiate risk of aggressive and nonaggressive disease.

A machine learning model identifies patients in need of autoimmune disease testing using electronic health records

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.

Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts

BACKGROUND

Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model.

METHODS

In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes-namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae.

FINDINGS

Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94-0·95; sensitivity of 0·94 [0·94-0·95] and specificity of 0·82 [0·81-0·83]) and 0·93 (0·92-0·93; sensitivity of 0·90 [0·89-0·90] and specificity of 0·88 [0·87-0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91-0·91; sensitivity of 0·84 [0·83-0·84] and specificity of 0·83 [0·82-0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0-1·0], 0·2% prevalence; decile 6: 11 [3·9-31], 3·1% prevalence; and decile 10: 56 [20-158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines.

INTERPRETATION

Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals.

FUNDING

National Institutes of Health.

Identification of genetic effects underlying type 2 diabetes in South Asian and European populations

South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.

Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

OBJECTIVE

Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.

RESEARCH DESIGN AND METHODS

Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).

RESULTS

Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.

CONCLUSIONS

Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Prognostic value of polygenic risk scores for adults with psychosis

Polygenic risk scores (PRS) summarize genetic liability to a disease at the individual level, and the aim is to use them as biomarkers of disease and poor outcomes in real-world clinical practice. To date, few studies have assessed the prognostic value of PRS relative to standards of care. Schizophrenia (SCZ), the archetypal psychotic illness, is an ideal test case for this because the predictive power of the SCZ PRS exceeds that of most other common diseases. Here, we analyzed clinical and genetic data from two multi-ethnic cohorts totaling 8,541 adults with SCZ and related psychotic disorders, to assess whether the SCZ PRS improves the prediction of poor outcomes relative to clinical features captured in a standard psychiatric interview. For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants.

Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease

AIM

Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers.

METHODS

This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years.

RESULTS

In 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min-1 [1.73 m]-2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (n = 460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (p < 0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRIevent for the high-risk group was 41% (p < 0.05).

CONCLUSIONS

KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.

Expanded carrier screening for recessively inherited disorders: economic burden and factors in decision-making when one individual in a couple is identified as a carrier

PURPOSE

When undergoing expanded carrier screening (ECS), couples are often screened sequentially to reduce need for a second individual's test. It is unknown how often partners of individuals found to be carriers complete the recommended testing with a sequential approach and what factors contribute to decision-making regarding partner testing. Additionally, the economic burden placed on individuals by ECS testing and its effect on partner testing has not been evaluated.

METHODS

In part 1, all individuals at a university-affiliated reproductive endocrinology and infertility practice identified to be carriers of a recessively inherited mutation using the Counsyl/Foresight ECS were included. Conditions were categorized by severity according to a previously described classification system. In part 2, all individuals who underwent ECS with a single test provider between September 1, 2013 and February 1, 2020 were contacted via email to complete a confidential and anonymized online survey.

RESULTS

In part 1, a total of 2061 patients were screened. 36.9% were carriers of one or more recessively inherited disorders. Twenty-seven percent of positively screened individuals did not have their partner screened. Carriers of a moderate condition had a trend towards a reduced odds for having their partner screened compared to a profound condition (OR 0.36, 95% CI 0.12-1.05, p = 0.06). Number of conditions was not predictive of subsequent partner screening (OR 0.95, 95% CI 0.72-1.25, p = 0.72). In part 2, the cost of ECS was not covered by insurance for 54.5% (103/189) and most paid over $300 out-of-pocket for testing (47.6%). The most common reason for not completing partner testing was that the results would not alter their course when seeking conception (33.3%). 73.5% of patients knew that the largest benefit of ECS comes from knowing a partner's results as well as their own.

CONCLUSIONS

Not all carriers of recessively inherited disorders choose to undergo partner screening. Patients found to be carrier of more debilitating genetic disorders may be more likely to screen their reproductive partners. For many, ECS testing is not covered by insurance, and this test may impose a significant economic burden. For some patients, the results of ECS would not change what they would do when seeking conception. Providers should evaluate whether a patient's ECS result would change their treatment course prior to testing.

Roadmap for Establishing Large-Scale Genomic Medicine Initiatives in Low- and Middle-Income Countries

In the post-genomic era, genomic medicine interventions as a key component of personalized medicine and tailored-made health care are greatly anticipated following recent scientific and technological advances. Indeed, large-scale sequencing efforts that explore human genomic variation have been initiated in several, mostly developed, countries across the globe, such as the United States, the United Kingdom, and a few others. Here, we highlight the successful implementation of large-scale national genomic initiatives, namely the Genome of Greece (GoGreece) and the DNA do Brasil (DNABr), aiming to emphasize the importance of implementing such initiatives in developing countries. Based on this experience, we also provide a roadmap for replicating these projects in other low-resource settings, thereby bringing genomic medicine in these countries closer to clinical fruition.

Expanded carrier screening for preconception reproductive risk assessment: Prevalence of carrier status in a Mexican population

OBJECTIVE

Genetic carrier screening has the potential to identify couples at risk of having a child affected with an autosomal recessive or X-linked disorder. However, the current prevalence of carrier status for these conditions in developing countries is not well defined. This study assesses the prevalence of carrier status of selected genetic conditions utilizing an expanded, pan-ethnic genetic carrier screening panel (ECS) in a large population of Mexican patients.

METHODS

Retrospective chart review of all patients tested with a single ECS panel at an international infertility center from 2012 to 2018 were included, and the prevalence of positive carrier status in a Mexican population was evaluated.

RESULTS

Eight hundred five individuals were analyzed with ECS testing for 283 genetic conditions. Three hundred fifty-two carriers (43.7%) were identified with 503 pathogenic variants in 145 different genes. Seventeen of the 391 participating couples (4.34%) were identified as being at-risk couples. The most prevalent alleles found were associated with alpha thalassemia, cystic fibrosis, GJB2 nonsyndromic hearing loss, biotinidase deficiency, and familial Mediterranean fever.

CONCLUSION

Based on the prevalence and severity of Mendelian disorders, we recommend that couples who wish to conceive regardless of their ethnicity background explore carrier screening and genetic counseling prior to reproductive medical treatment.

Association of the V122I Hereditary Transthyretin Amyloidosis Genetic Variant With Heart Failure Among Individuals of African or Hispanic/Latino Ancestry

Importance

Hereditary transthyretin (TTR) amyloid cardiomyopathy (hATTR-CM) due to the TTR V122I variant is an autosomal-dominant disorder that causes heart failure in elderly individuals of African ancestry. The clinical associations of carrying the variant, its effect in other African ancestry populations including Hispanic/Latino individuals, and the rates of achieving a clinical diagnosis in carriers are unknown.

Objective

To assess the association between the TTR V122I variant and heart failure and identify rates of hATTR-CM diagnosis among carriers with heart failure.

Design, Setting, and Participants

Cross-sectional analysis of carriers and noncarriers of TTR V122I of African ancestry aged 50 years or older enrolled in the Penn Medicine Biobank between 2008 and 2017 using electronic health record data from 1996 to 2017. Case-control study in participants of African and Hispanic/Latino ancestry with and without heart failure in the Mount Sinai BioMe Biobank enrolled between 2007 and 2015 using electronic health record data from 2007 to 2018.

Exposures

TTR V122I carrier status.

Main Outcomes and Measures

The primary outcome was prevalent heart failure. The rate of diagnosis with hATTR-CM among TTR V122I carriers with heart failure was measured.

Results

The cross-sectional cohort included 3724 individuals of African ancestry with a median age of 64 years (interquartile range, 57-71); 1755 (47%) were male, 2896 (78%) had a diagnosis of hypertension, and 753 (20%) had a history of myocardial infarction or coronary revascularization. There were 116 TTR V122I carriers (3.1%); 1121 participants (30%) had heart failure. The case-control study consisted of 2307 individuals of African ancestry and 3663 Hispanic/Latino individuals; the median age was 73 years (interquartile range, 68-80), 2271 (38%) were male, 4709 (79%) had a diagnosis of hypertension, and 1008 (17%) had a history of myocardial infarction or coronary revascularization. There were 1376 cases of heart failure. TTR V122I was associated with higher rates of heart failure (cross-sectional cohort: n = 51/116 TTR V122I carriers [44%], n = 1070/3608 noncarriers [30%], adjusted odds ratio, 1.7 [95% CI, 1.2-2.4], P = .006; case-control study: n = 36/1376 heart failure cases [2.6%], n = 82/4594 controls [1.8%], adjusted odds ratio, 1.8 [95% CI, 1.2-2.7], P = .008). Ten of 92 TTR V122I carriers with heart failure (11%) were diagnosed as having hATTR-CM; the median time from onset of symptoms to clinical diagnosis was 3 years.

Conclusions and Relevance

Among individuals of African or Hispanic/Latino ancestry enrolled in 2 academic medical center-based biobanks, the TTR V122I genetic variant was significantly associated with heart failure.

Genome-Wide Association Study of Heavy Smoking and Daily/Nondaily Smoking in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Introduction

Genetic variants associated with nicotine dependence have previously been identified, primarily in European-ancestry populations. No genome-wide association studies (GWAS) have been reported for smoking behaviors in Hispanics/Latinos in the United States and Latin America, who are of mixed ancestry with European, African, and American Indigenous components.

Methods

We examined genetic associations with smoking behaviors in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (N = 12 741 with smoking data, 5119 ever-smokers), using ~2.3 million genotyped variants imputed to the 1000 Genomes Project phase 3. Mixed logistic regression models accounted for population structure, sampling, relatedness, sex, and age.

Results

The known region of CHRNA5, which encodes the α5 cholinergic nicotinic receptor subunit, was associated with heavy smoking at genome-wide significance (p ≤ 5 × 10-8) in a comparison of 1929 ever-smokers reporting cigarettes per day (CPD) > 10 versus 3156 reporting CPD ≤ 10. The functional variant rs16969968 in CHRNA5 had a p value of 2.20 × 10-7 and odds ratio (OR) of 1.32 for the minor allele (A); its minor allele frequency was 0.22 overall and similar across Hispanic/Latino background groups (Central American = 0.17; South American = 0.19; Mexican = 0.18; Puerto Rican = 0.22; Cuban = 0.29; Dominican = 0.19). CHRNA4 on chromosome 20 attained p < 10-4, supporting prior findings in non-Hispanics. For nondaily smoking, which is prevalent in Hispanic/Latino smokers, compared to daily smoking, loci on chromosomes 2 and 4 achieved genome-wide significance; replication attempts were limited by small Hispanic/Latino sample sizes.

Conclusions

Associations of nicotinic receptor gene variants with smoking, first reported in non-Hispanic European-ancestry populations, generalized to Hispanics/Latinos despite different patterns of smoking behavior.

Implications

We conducted the first large-scale genome-wide association study (GWAS) of smoking behavior in a US Hispanic/Latino cohort, and the first GWAS of daily/nondaily smoking in any population. Results show that the region of the nicotinic receptor subunit gene CHRNA5, which in non-Hispanic European-ancestry smokers has been associated with heavy smoking as well as cessation and treatment efficacy, is also significantly associated with heavy smoking in this Hispanic/Latino cohort. The results are an important addition to understanding the impact of genetic variants in understudied Hispanic/Latino smokers.

Genome-wide analysis indicates association between heterozygote advantage and healthy aging in humans

Background

Genetic diversity is known to confer survival advantage in many species across the tree of life. Here, we hypothesize that such pattern applies to humans as well and could be a result of higher fitness in individuals with higher genomic heterozygosity.

Results

We use healthy aging as a proxy for better health and fitness, and observe greater heterozygosity in healthy-aged individuals. Specifically, we find that only common genetic variants show significantly higher excess of heterozygosity in the healthy-aged cohort. Lack of difference in heterozygosity for low-frequency variants or disease-associated variants excludes the possibility of compensation for deleterious recessive alleles as a mechanism. In addition, coding SNPs with the highest excess of heterozygosity in the healthy-aged cohort are enriched in genes involved in extracellular matrix and glycoproteins, a group of genes known to be under long-term balancing selection. We also find that individual heterozygosity rate is a significant predictor of electronic health record (EHR)-based estimates of 10-year survival probability in men but not in women, accounting for several factors including age and ethnicity.

Conclusions

Our results demonstrate that the genomic heterozygosity is associated with human healthspan, and that the relationship between higher heterozygosity and healthy aging could be explained by heterozygote advantage. Further characterization of this relationship will have important implications in aging-associated disease risk prediction.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0758-4) contains supplementary material, which is available to authorized users.

LRG1 Promotes Diabetic Kidney Disease Progression by Enhancing TGF-β-Induced Angiogenesis

BACKGROUND

Glomerular endothelial dysfunction and neoangiogenesis have long been implicated in the pathogenesis of diabetic kidney disease (DKD). However, the specific molecular pathways contributing to these processes in the early stages of DKD are not well understood. Our recent transcriptomic profiling of glomerular endothelial cells identified a number of proangiogenic genes that were upregulated in diabetic mice, including leucine-rich α-2-glycoprotein 1 (LRG1). LRG1 was previously shown to promote neovascularization in mouse models of ocular disease by potentiating endothelial TGF-β/activin receptor-like kinase 1 (ALK1) signaling. However, LRG1's role in the kidney, particularly in the setting of DKD, has been unclear.

METHODS

We analyzed expression of LRG1 mRNA in glomeruli of diabetic kidneys and assessed its localization by RNA in situ hybridization. We examined the effects of genetic ablation of Lrg1 on DKD progression in unilaterally nephrectomized, streptozotocin-induced diabetic mice at 12 and 20 weeks after diabetes induction. We also assessed whether plasma LRG1 was associated with renal outcome in patients with type 2 diabetes.

RESULTS

LRG1 localized predominantly to glomerular endothelial cells, and its expression was elevated in the diabetic kidneys. LRG1 ablation markedly attenuated diabetes-induced glomerular angiogenesis, podocyte loss, and the development of diabetic glomerulopathy. These improvements were associated with reduced ALK1-Smad1/5/8 activation in glomeruli of diabetic mice. Moreover, increased plasma LRG1 was associated with worse renal outcome in patients with type 2 diabetes.

CONCLUSIONS

These findings identify LRG1 as a potential novel pathogenic mediator of diabetic glomerular neoangiogenesis and a risk factor in DKD progression.

Plasma endostatin predicts kidney outcomes in patients with type 2 diabetes

Novel biomarkers are needed to predict kidney function decline in patients with type 2 diabetes, especially those with preserved glomerular filtration rate (GFR). There are limited data on the association of markers of endothelial dysfunction with longitudinal GFR decline. We used banked specimens from a nested case-control study in the Action to Control Cardiovascular Disease (ACCORD) trial (n=187 cases: 187 controls) and from a diverse contemporary cohort of type 2 diabetic patients from the Mount Sinai BioMe Biobank (n=871) to assess the association of plasma endostatin and kidney outcomes. We measured plasma endostatin at enrollment and examined its association with a composite kidney outcome of sustained 40% decline in estimated GFR or end-stage renal disease. Baseline plasma endostatin levels were higher in participants with the composite outcome. Each log2 increment in plasma endostatin was associated with approximately 2.5-fold higher risk of the kidney outcome (adjusted odds ratio [OR] 2.5; 95% confidence interval [CI] 1.5-4.3 in ACCORD and adjusted hazard ratio [HR] 2.6; 95% CI 1.8-3.8 in BioMe). Participants in the highest vs. lowest quartile of plasma endostatin had approximately four-fold higher risk for the kidney outcome (adjusted OR 3.6; 95% CI 1.8-7.3 in ACCORD and adjusted HR 4.4; 95% CI 2.3-8.5 in BioMe). The AUC for the kidney outcome improved from 0.74 to 0.77 in BioMe with the addition of endostatin to a base clinical model. Plasma endostatin was strongly associated with kidney outcomes in type 2 diabetics with preserved eGFR and improved risk discrimination over traditional predictors.

Physician Knowledge of Human Genetic Variation, Beliefs About Race and Genetics, and Use of Race in Clinical Decision-making

BACKGROUND

Race in the USA has an enduring connection to health and well-being. It is often used as a proxy for ancestry and genetic variation, although self-identified race does not establish genetic risk of disease for an individual patient. How physicians reconcile these seemingly paradoxical facts as they make clinical decisions is unknown.

OBJECTIVE

To examine physicians' genetic knowledge and beliefs about race with their use of race in clinical decision-making DESIGN: Cross-sectional survey of a national sample of clinically active general internists RESULTS: Seven hundred eighty-seven physicians completed the survey. Regression models indicate that genetic knowledge was not significantly associated with use of race. However, physicians who agreed with notions of race as a biological phenomenon and those who agreed that race has clinical importance were more likely to report using race in their decision-making.

CONCLUSIONS

Genomic and precision medicine holds considerable promise for narrowing the gap in health among racial groups in the USA. For this promise to be realized, our findings suggest that future research and education efforts related to race, genomics, and health must go beyond educating health care providers about common genetic conditions to delving into assumptions about race and genetics.

Plasma biomarkers are associated with renal outcomes in individuals with APOL1 risk variants

G1/G2 variants in the Apolipoprotein L1 (APOL1) gene are associated with end-stage renal disease (ESRD) in people with African ancestry. Plasma biomarkers may have utility for risk stratification in APOL1 high-risk individuals of African ancestry. To evaluate this, we measured tumor necrosis factor receptor 1/2 (TNFR1/2) and kidney injury molecule-1 (KIM1) in baseline plasma specimens from individuals of African ancestry with high-risk APOL1 genotype. Biomarker association with a composite renal outcome of ESRD or 40% sustained decline in estimated glomerular filtration rate (eGFR) was then determined and then assessed as improvement in area under curve. Among the 498 participants, the median age was 56 years, 67.7% were female, and the baseline eGFR was 83.3 ml/min/1.73 m2 with 80 reaching outcome over 5.9 years. TNFR1, TNFR2, and KIM1 at enrollment were independently associated with renal outcome continuously (adjusted hazard ratio 2.0 [95% confidence interval 1.3-3.1]; 1.5 [1.2-1.9]; and 1.6 [1.3-1.9] per doubling in levels, respectively) or by tertiles. The area under the curve significantly improved from 0.75 with the clinical model to 0.79 with the biomarker-enhanced model. The event rate was 40% with all 3 biomarkers elevated (adjusted odds ratio of 5.3 (2.3-12.0) vs. 17% (adjusted odds ratio 1.8 [0.9-3.6] with 1 or 2 elevated and 7% with no biomarkers elevated. Thus, plasma concentrations of TNFR1, TNFR2, and KIM1 are independently associated with renal outcome and improve discrimination or reclassification of African ancestry individuals with a high-risk APOL1 genotype and preserve renal function. Elevation of all markers had higher risk of outcome and can assist with better clinical prediction and improved clinical trial efficiency by enriching event rates.

Serum 6-Bromotryptophan Levels Identified as a Risk Factor for CKD Progression

Background Metabolite levels reflect physiologic homeostasis and may serve as biomarkers of disease progression. Identifying metabolites associated with APOL1 risk alleles-genetic variants associated with CKD risk commonly present in persons of African descent-may reveal novel markers of CKD progression relevant to other populations.Methods We evaluated associations between the number of APOL1 risk alleles and 760 serum metabolites identified via untargeted profiling in participants of the African American Study of Kidney Disease and Hypertension (AASK) (n=588; Bonferroni significance threshold P<6.5×10^-5) and replicated findings in 678 black participants with CKD in BioMe, an electronic medical record-linked biobank. We tested the metabolite association with CKD progression in AASK, BioMe, and the Modification of Diet in Renal Disease (MDRD) Study.Results One metabolite, 6-bromotryptophan, was significant in AASK (P=4.7×10^-5) and replicated in BioMe (P=5.7×10^-3) participants, with lower levels associated with more APOL1 risk alleles. Lower levels of 6-bromotryptophan were associated with CKD progression in AASK and BioMe participants and in white participants in the MDRD Study, independent of demographics and clinical characteristics, including baseline GFR (adjusted hazard ratio per two-fold higher 6-bromotryptophan level, AASK, 0.76; 95% confidence interval [95% CI], 0.64 to 0.91; BioMe, 0.61; 95% CI, 0.43 to 0.85; MDRD, 0.52; 95% CI, 0.34 to 0.79). The interaction between the APOL1 risk alleles and 6-bromotryptophan was not significant. The identity of 6-bromotryptophan was confirmed in experiments comparing its molecular signature with that of authentic standards of other bromotryptophan isomers.Conclusions Serum 6-bromotryptophan is a consistent and novel risk factor for CKD progression.

Location-Specific Oral Microbiome Possesses Features Associated With CKD

INTRODUCTION

Chronic kidney disease (CKD), a progressive loss of renal function, can lead to serious complications if underdiagnosed. Many studies suggest that the oral microbiota plays important role in the health of the host; however, little is known about the association between the oral microbiota and CKD pathogenesis.

METHODS

In this study, we surveyed the oral microbiota in saliva, the left and right molars, and the anterior mandibular lingual area from 77 participants (18 with and 59 without CKD), and tested their association with CKD to identify microbial features that may be predictive of CKD status.

RESULTS

The overall oral microbiota composition significantly differed by oral locations and was associated with CKD status in saliva and anterior mandibular lingual samples. In CKD patients, we observed a significant enrichment of Neisseria and depletion of Veillonella in both sample types and a lower prevalence of Streptococcus in saliva after adjustment for other comorbidities. Furthermore, we detected a negative association of Neisseria and Streptococcus genera with the kidney function as measured by estimated glomerular filtration rate. Neisseria abundance also correlated with plasma interleukin-18 levels.

CONCLUSION

We demonstrate the association of the oral microbiome with CKD and inflammatory kidney biomarkers, highlighting a potential role of the commensal bacteria in CKD pathogenesis. A better understanding of the interplay between the oral microbiota and CKD may help in the development of new strategies to identify at-risk individuals or to serve as a novel target for therapeutic intervention.

Leveraging functional annotations in genetic risk prediction for human complex diseases

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.

Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci

Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m2) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.

Genome-wide association of white blood cell counts in Hispanic/Latino Americans: the Hispanic Community Health Study/Study of Latinos

Circulating white blood cell (WBC) counts (neutrophils, monocytes, lymphocytes, eosinophils, basophils) differ by ethnicity. The genetic factors underlying basal WBC traits in Hispanics/Latinos are unknown. We performed a genome-wide association study of total WBC and differential counts in a large, ethnically diverse US population sample of Hispanics/Latinos ascertained by the Hispanic Community Health Study and Study of Latinos (HCHS/SOL). We demonstrate that several previously known WBC-associated genetic loci (e.g. the African Duffy antigen receptor for chemokines null variant for neutrophil count) are generalizable to WBC traits in Hispanics/Latinos. We identified and replicated common and rare germ-line variants at FLT3 (a gene often somatically mutated in leukemia) associated with monocyte count. The common FLT3 variant rs76428106 has a large allele frequency differential between African and non-African populations. We also identified several novel genetic loci involving or regulating hematopoietic transcription factors (CEBPE-SLC7A7, CEBPA and CRBN-TRNT1) associated with basophil count. The minor allele of the CEBPE variant associated with lower basophil count has been previously associated with Amerindian ancestry and higher risk of acute lymphoblastic leukemia in Hispanics. Together, these data suggest that germline genetic variation affecting transcriptional and signaling pathways that underlie WBC development and lineage specification can contribute to inter-individual as well as ethnic differences in peripheral blood cell counts (normal hematopoiesis) in addition to susceptibility to leukemia (malignant hematopoiesis).

Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case–control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case–control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.

Expanded carrier screening in an infertile population: how often is clinical decision making affected?

PURPOSE

Options for preconception genetic screening have grown dramatically. Expanded carrier screening (ECS) now allows for determining carrier status for hundreds of genetic mutations by using a single sample, and some recommend ECS prior to in vitro fertilization. This study seeks to evaluate how often ECS alters clinical management when patients present for infertility care.

METHODS

All patients tested with ECS at a single infertility care center from 2011 to 2014 were evaluated. The overall rate of positive ECS results and the number of couples who were carriers of the same genetic disorder were evaluated.

RESULTS

A total of 6,643 individuals were tested, representing 3,738 couples; 1,666 (25.1%) of the individuals had a positive test result for at least one disorder. In 8 of the 3,738 couples, both members of the couple were positive for the same genetic disorder or had a test result that placed them at risk of having an affected child. Three of eight cases were cystic fibrosis. In this cohort, ECS affected clinical care eight times after 6,643 tests (0.12%, confidence interval: 0.05-0.24%) in 3,738 couples (0.21%, confidence interval: 0.09-0.42%).

CONCLUSIONS

ECS is becoming more widespread. In a large case series, ECS affected clinical decision making for patients presenting for infertility care in 0.21% of cases. This information must be weighed when utilizing these tests and may be a helpful part of patient counseling.Genet Med 18 11, 1097-1101.

Analytical Validation of a Personalized Medicine APOL1 Genotyping Assay for Nondiabetic Chronic Kidney Disease Risk Assessment

The incidence of chronic kidney disease (CKD) varies by ancestry, with African Americans (AA) having a threefold to fourfold higher rate than whites. Notably, two APOL1 alleles, termed G1 [c.(1072A>G; 1200T>G)] and G2 (c.1212_1217del6), are strongly associated with higher rates of nondiabetic CKD and an increased risk for hypertensive end-stage renal disease. This has prompted the opportunity to implement APOL1 testing to identify at-risk patients and modify other risk factors to reduce the progression of CKD to end-stage renal disease. We developed an APOL1 genotyping assay using multiplex allele-specific primer extension, and validated using 58 positive and negative controls. Genotyping results were completely concordant with Sanger sequencing, and both triplicate interrun and intrarun genotyping results were completely concordant. Multiethnic APOL1 allele frequencies were also determined by genotyping 7059 AA, Hispanic, and Asian individuals from the New York City metropolitan area. The AA, Hispanic, and Asian APOL1 G1 and G2 allele frequencies were 0.22 and 0.13, 0.037 and 0.025, and 0.013 and 0.004, respectively. Notably, approximately 14% of the AA population carried two risk alleles and are at increased risk for CKD, compared with <1% of the Hispanic and Asian populations. This novel APOL1 genotyping assay is robust and highly accurate, and represents one of the first personalized medicine clinical genetic tests for disease risk prediction.

Pharmacogenetics of drug-metabolizing enzymes in US Hispanics

Although the Hispanic population is continuously growing in the United States, they are underrepresented in pharmacogenetic studies. This review addresses the need for compiling available pharmacogenetic data in US Hispanics, discussing the prevalence of clinically relevant polymorphisms in pharmacogenes encoding for drug-metabolizing enzymes. CYP3A5*3 (0.245-0.867) showed the largest frequency in a US Hispanic population. A higher prevalence of CYP2C9*3, CYP2C19*4, and UGT2B7 IVS1+985 A>G was observed in US Hispanic vs. non-Hispanic populations. We found interethnic and intraethnic variability in frequencies of genetic polymorphisms for metabolizing enzymes, which highlights the need to define the ancestries of participants in pharmacogenetic studies. New approaches should be integrated in experimental designs to gain knowledge about the clinical relevance of the unique combination of genetic variants occurring in this admixed population. Ethnic subgroups in the US Hispanic population may harbor variants that might be part of multiple causative loci or in linkage-disequilibrium with functional variants. Pharmacogenetic studies in Hispanics should not be limited to ascertain commonly studied polymorphisms that were originally identified in their parental populations. The success of the Personalized Medicine paradigm will depend on recognizing genetic diversity between and within US Hispanics and the uniqueness of their genetic backgrounds.

Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans

Platelets play an essential role in hemostasis and thrombosis. We performed a genome-wide association study of platelet count in 12,491 participants of the Hispanic Community Health Study/Study of Latinos by using a mixed-model method that accounts for admixture and family relationships. We discovered and replicated associations with five genes (ACTN1, ETV7, GABBR1-MOG, MEF2C, and ZBTB9-BAK1). Our strongest association was with Amerindian-specific variant rs117672662 (p value = 1.16 × 10(-28)) in ACTN1, a gene implicated in congenital macrothrombocytopenia. rs117672662 exhibited allelic differences in transcriptional activity and protein binding in hematopoietic cells. Our results underscore the value of diverse populations to extend insights into the allelic architecture of complex traits.

A minimum set of ancestry informative markers for determining admixture proportions in a mixed American population: the Brazilian set

The Brazilian population is considered to be highly admixed. The main contributing ancestral populations were European and African, with Amerindians contributing to a lesser extent. The aims of this study were to provide a resource for determining and quantifying individual continental ancestry using the smallest number of SNPs possible, thus allowing for a cost- and time-efficient strategy for genomic ancestry determination. We identified and validated a minimum set of 192 ancestry informative markers (AIMs) for the genetic ancestry determination of Brazilian populations. These markers were selected on the basis of their distribution throughout the human genome, and their capacity of being genotyped on widely available commercial platforms. We analyzed genotyping data from 6487 individuals belonging to three Brazilian cohorts. Estimates of individual admixture using this 192 AIM panels were highly correlated with estimates using ~370 000 genome-wide SNPs: 91%, 92%, and 74% of, respectively, African, European, and Native American ancestry components. Besides that, 192 AIMs are well distributed among populations from these ancestral continents, allowing greater freedom in future studies with this panel regarding the choice of reference populations. We also observed that genetic ancestry inferred by AIMs provides similar association results to the one obtained using ancestry inferred by genomic data (370 K SNPs) in a simple regression model with rs1426654, related to skin pigmentation, genotypes as dependent variable. In conclusion, these markers can be used to identify and accurately quantify ancestry of Latin Americans or US Hispanics/Latino individuals, in particular in the context of fine-mapping strategies that require the quantification of continental ancestry in thousands of individuals.

[Personalized medicine: equity and access]

Personalized medicine has seen a recent increase in popularity amongst medical researchers and policymakers. Nevertheless, there are persistent legal, ethical, and social questions that need to be explored, particularly related to the criticism that personalized medicine constitutes an elitist model of healthcare. Investigating this critique the current manuscript argues that personalized medicine has the potential to become a positive force for equitable access to better healthcare at a national and international level.

Effect of Genetic African Ancestry on eGFR and Kidney Disease

Self-reported ancestry, genetically determined ancestry, and APOL1 polymorphisms are associated with variation in kidney function and related disease risk, but the relative importance of these factors remains unclear. We estimated the global proportion of African ancestry for 9048 individuals at Mount Sinai Medical Center in Manhattan (3189 African Americans, 1721 European Americans, and 4138 Hispanic/Latino Americans by self-report) using genome-wide genotype data. CKD-EPI eGFR and genotypes of three APOL1 coding variants were available. In admixed African Americans and Hispanic/Latino Americans, serum creatinine values increased as African ancestry increased (per 10% increase in African ancestry, creatinine values increased 1% in African Americans and 0.9% in Hispanic/Latino Americans; P≤1x10(-7)). eGFR was likewise significantly associated with African genetic ancestry in both populations. In contrast, APOL1 risk haplotypes were significantly associated with CKD, eGFR<45 ml/min per 1.73 m(2), and ESRD, with effects increasing with worsening disease states and the contribution of genetic African ancestry decreasing in parallel. Using genetic ancestry in the eGFR equation to reclassify patients as black on the basis of ≥50% African ancestry resulted in higher eGFR for 14.7% of Hispanic/Latino Americans and lower eGFR for 4.1% of African Americans, affecting CKD staging in 4.3% and 1% of participants, respectively. Reclassified individuals had electrolyte values consistent with their newly assigned CKD stage. In summary, proportion of African ancestry was significantly associated with normal-range creatinine and eGFR, whereas APOL1 risk haplotypes drove the associations with CKD. Recalculation of eGFR on the basis of genetic ancestry affected CKD staging and warrants additional investigation.

The association of the vanin-1 N131S variant with blood pressure is mediated by endoplasmic reticulum-associated degradation and loss of function

High blood pressure (BP) is the most common cardiovascular risk factor worldwide and a major contributor to heart disease and stroke. We previously discovered a BP-associated missense SNP (single nucleotide polymorphism)–rs2272996–in the gene encoding vanin-1, a glycosylphosphatidylinositol (GPI)-anchored membrane pantetheinase. In the present study, we first replicated the association of rs2272996 and BP traits with a total sample size of nearly 30,000 individuals from the Continental Origins and Genetic Epidemiology Network (COGENT) of African Americans (P = 0.01). This association was further validated using patient plasma samples; we observed that the N131S mutation is associated with significantly lower plasma vanin-1 protein levels. We observed that the N131S vanin-1 is subjected to rapid endoplasmic reticulum-associated degradation (ERAD) as the underlying mechanism for its reduction. Using HEK293 cells stably expressing vanin-1 variants, we showed that N131S vanin-1 was degraded significantly faster than wild type (WT) vanin-1. Consequently, there were only minimal quantities of variant vanin-1 present on the plasma membrane and greatly reduced pantetheinase activity. Application of MG-132, a proteasome inhibitor, resulted in accumulation of ubiquitinated variant protein. A further experiment demonstrated that atenolol and diltiazem, two current drugs for treating hypertension, reduce the vanin-1 protein level. Our study provides strong biological evidence for the association of the identified SNP with BP and suggests that vanin-1 misfolding and degradation are the underlying molecular mechanism.

Hypertension (HTN) or high blood pressure (BP) is common worldwide and a major risk factor for cardiovascular disease and all-cause mortality. Identification of genetic variants of consequence for HTN serves as the molecular basis for its treatment. Using admixture mapping analysis of the Family Blood Pressure Program data, we recently identified that the VNN1 gene (encoding the protein vanin-1), in particular SNP rs2272996 (N131S), was associated with BP in both African Americans and Mexican Americans. Vanin-1 was reported to act as an oxidative stress sensor using its pantetheinase enzyme activity. Because a linkage between oxidative stress and HTN has been hypothesized for many years, vanin-1's pantetheinase activity offers a physiologic rationale for BP regulation. Here, we first replicated the association of rs2272996 with BP in the Continental Origins and Genetic Epidemiology Network (COGENT), which included nearly 30,000 African Americans. We further demonstrated that the N131S mutation in vanin-1 leads to its rapid degradation in cells, resulting in loss of function on the plasma membrane. The loss of function of vanin-1 is associated with reduced BP. Therefore, our results indicate that vanin-1 is a new candidate to be manipulated to ameliorate HTN.

Social diversity in humans: implications and hidden consequences for biological research

Humans are both similar and diverse in such a vast number of dimensions that for human geneticists and social scientists to decide which of these dimensions is a worthy focus of empirical investigation is a formidable challenge. For geneticists, one vital question, of course, revolves around hypothesizing which kind of social diversity might illuminate genetic variation-and vice versa (i.e., what genetic variation illuminates human social diversity). For example, are there health outcomes that can be best explained by genetic variation-or for social scientists, are health outcomes mainly a function of the social diversity of lifestyles and social circumstances of a given population? Indeed, what is a "population," how is it bounded, and are those boundaries most appropriate or relevant for human genetic research, be they national borders, religious affiliation, ethnic or racial identification, or language group, to name but a few? For social scientists, the matter of what constitutes the relevant borders of a population is equally complex, and the answer is demarcated by the goal of the research project. Although race and caste are categories deployed in both human genetics and social science, the social meaning of race and caste as pathways to employment, health, or education demonstrably overwhelms the analytic and explanatory power of genetic markers of difference between human aggregates.

Race in biological and biomedical research

The concept of race has had a significant influence on research in human biology since the early 19th century. But race was given its meaning and social impact in the political sphere and subsequently intervened in science as a foreign concept, not grounded in the dominant empiricism of modern biology. The uses of race in science were therefore often disruptive and controversial; at times, science had to be retrofitted to accommodate race, and science in turn was often used to explain and justify race. This relationship was unstable in large part because race was about a phenomenon that could not be observed directly, being based on claims about the structure and function of genomic DNA. Over time, this relationship has been characterized by distinct phases, evolving from the inference of genetic effects based on the observed phenotype to the measurement of base-pair variation in DNA. Despite this fundamental advance in methodology, liabilities imposed by the dual political-empirical origins of race persist. On the one hand, an optimistic prediction can be made that just as geology made it possible to overturn the myth of the recent creation of the earth and evolution told us where the living world came from, molecular genetics will end the use of race in biology. At the same time, because race is fundamentally a political and not a scientific idea, it is possible that only a political intervention will relieve us of the burden of race.

CYP2C8*3 increases risk of neuropathy in breast cancer patients treated with paclitaxel

BACKGROUND

Paclitaxel-induced neuropathy is an adverse event that often leads to therapeutic disruption and patient discomfort. We attempted to replicate a previously reported association between increased neuropathy risk and CYP2C8*3 genotype.

PATIENTS AND METHODS

Demographic, treatment, and toxicity data were collected for paclitaxel-treated breast cancer patients who were genotyped for the CYP2C8*3 K399R (rs10509681) variant. A log-rank test was used in the primary analysis of European-American patients. An additional independent replication was then attempted in a cohort of African-American patients, followed by modeling of the entire patient cohort with relevant covariates.

RESULTS

In the primary analysis of 209 European patients, there was an increased risk of paclitaxel-induced neuropathy related to CYP2C8*3 status [HR (per allele) = 1.93 (95% CI: 1.05-3.55), overall log-rank P = 0.006]. The association was replicated in direction and magnitude of effect in 107 African-American patients (P = 0.043). In the Cox model using the entire mixed-race cohort (n = 411), each CYP2C8*3 allele approximately doubled the patient's risk of grade 2+ neuropathy (P = 0.004), and non-Europeans were at higher neuropathy risk than Europeans of similar genotype (P = 0.030).

CONCLUSIONS

The increased risk of paclitaxel-induced neuropathy in patients who carry the CYP2C8*3 variant was replicated in two racially distinct patient cohorts.

A Method for Inferring an Individual's Genetic Ancestry and Degree of Admixture Associated with Six Major Continental Populations

The determination of the ancestry and genetic backgrounds of the subjects in genetic and general epidemiology studies is a crucial component in the analysis of relevant outcomes or associations. Although there are many methods for differentiating ancestral subgroups among individuals based on genetic markers only a few of these methods provide actual estimates of the fraction of an individual’s genome that is likely to be associated with different ancestral populations. We propose a method for assigning ancestry that works in stages to refine estimates of ancestral population contributions to individual genomes. The method leverages genotype data in the public domain obtained from individuals with known ancestries. Although we showcase the method in the assessment of ancestral genome proportions leveraging largely continental populations, the strategy can be used for assessing within-continent or more subtle ancestral origins with the appropriate data.

After the revolution? Ethical and social challenges in 'personalized genomic medicine'

Personalized genomic medicine (PGM) is a goal that currently unites a wide array of biomedical initiatives, and is promoted as a 'new paradigm for healthcare' by its champions. Its promissory virtues include individualized diagnosis and risk prediction, more effective prevention and health promotion, and patient empowerment. Beyond overcoming scientific and technological hurdles to realizing PGM, proponents may interpret and rank these promises differently, which carries ethical and social implications for the realization of PGM as an approach to healthcare. We examine competing visions of PGM's virtues and the directions in which they could take the field, in order to anticipate policy choices that may lie ahead for researchers, healthcare providers and the public.

Personalizing medicine with clinical pharmacogenetics

Clinical genetic testing has grown substantially over the past 30 years as the causative mutations for Mendelian diseases have been identified, particularly aided in part by the recent advances in molecular-based technologies. Importantly, the adoption of new tests and testing strategies (e.g., diagnostic confirmation, prenatal testing, and population-based carrier screening) has often been met with caution and careful consideration before clinical implementation, which facilitates the appropriate use of new genetic tests. Although the field of pharmacogenetics was established in the 1950s, clinical testing for constitutional pharmacogenetic variants implicated in interindividual drug response variability has only recently become available to help clinicians guide pharmacotherapy, in part due to US Food and Drug Administration-mediated product insert revisions that include pharmacogenetic information for selected drugs. However, despite pharmacogenetic associations with adverse outcomes, physician uptake of clinical pharmacogenetic testing has been slow. Compared with testing for Mendelian diseases, pharmacogenetic testing for certain indications can have a lower positive predictive value, which is one reason for underutilization. A number of other barriers remain with implementing clinical pharmacogenetics, including clinical utility, professional education, and regulatory and reimbursement issues, among others. This review presents some of the current opportunities and challenges with implementing clinical pharmacogenetic testing.

Conceptual approaches to the study of health disparities

Scientific and policy interest in health disparities, defined as systematic, plausibly avoidable health differences adversely affecting socially disadvantaged groups, has increased markedly over the past few decades. Like other research, research in health disparities is strongly influenced by the underlying conceptual model of the hypothetical causes of disparities. Conceptual models are important and a major source of debate because multiple types of factors and processes may be involved in generating disparities, because different disciplines emphasize different types of factors, and because the conceptual model often drives what is studied, how results are interpreted, and which interventions are identified as most promising. This article reviews common conceptual approaches to health disparities including the genetic model, the fundamental cause model, the pathways model, and the interaction model. Strengths and limitations of the approaches are highlighted. The article concludes by outlining key elements and implications of an integrative systems-based conceptual model.