GWAS of longitudinal trajectories at biobank scale.

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

Multivariate genome-wide association analysis by iterative hard thresholding.

MOTIVATION: In a genome-wide association study, analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate genome-wide association study operate marker-by-marker and are computationally intensive. RESULTS: We present a sparsity constrained regression algorithm for multivariate genome-wide association study based on iterative hard thresholding and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, iterative hard thresholding exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470 228 variants, MendelIHT completed a three-trait joint analysis (n=185 656) in 20 h and an 18-trait joint analysis (n=104 264) in 53 h with an 80 GB memory footprint. In short, MendelIHT enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits. AVAILABILITY AND IMPLEMENTATION: Software, documentation, and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelIHT.jl.

The Undiagnosed Diseases Network: Characteristics of solvable applicants and diagnostic suggestions for non-accepted ones.

PURPOSE: Can certain characteristics identify as solvable some undiagnosed patients who seek extensive evaluation and thorough record review, like by the Undiagnosed Diseases Network (UDN)? METHODS: The UDN is a national research resource to solve medical mysteries through team science. Applicants provide informed consent to access to their medical records. After review, expert panels assess if applicants meet inclusion and exclusion criteria to select participants. When not accepting applicants, UDN experts may offer suggestions for diagnostic efforts. Using minimal information from initial applications, we compare features in applicants not accepted with those accepted and either solved or still not solved by the UDN. The diagnostic suggestions offered to non-accepted applicants and their clinicians were tallied. RESULTS: Non-accepted applicants were more often female, older at first symptoms and application, and longer in review than accepted applicants. The accepted and successfully diagnosed applicants were younger in ages, shorter in review time, more often non-white, of Hispanic ethnicity, and presenting with nervous system features. Half of non-accepted applicants were given suggestions for further local diagnostic evaluation. A few seemed to have two major diagnoses or a provocative environmental exposure history. CONCLUSION: Comprehensive UDN record review generates possibly helpful advice.

Patterns of reproduction and autozygosity distinguish the breeding from non-breeding gray wolves of Yellowstone National Park.

For species of management concern, accurate estimates of inbreeding and associated consequences on reproduction are crucial for predicting their future viability. However, few studies have partitioned this aspect of genetic viability with respect to reproduction in a group-living social mammal. We investigated the contributions of foundation stock lineages, putative fitness consequences of inbreeding, and genetic diversity of the breeding versus non-reproductive segment of the Yellowstone National Park gray wolf population. Our dataset spans 25 years and seven generations since reintroduction, encompassing 152 nuclear families and 329 litters. We found over 87% of the pedigree foundation genomes persisted and report influxes of allelic diversity from two translocated wolves from a divergent source in Montana. As expected for group-living species, mean kinship significantly increased over time but with minimal loss of observed heterozygosity. Strikingly, the reproductive portion of the population carried a significantly lower genome-wide inbreeding coefficients, autozygosity, and more rapid decay for linkage disequilibrium relative to the non-breeding population. Breeding wolves had significantly longer lifespans and lower inbreeding coefficients than non-breeding wolves. Our model revealed that the number of litters was negatively significantly associated with heterozygosity (R=-0.11). Our findings highlight genetic contributions to fitness, and the importance of the reproductively active individuals in a population to counteract loss of genetic variation in a wild, free-ranging social carnivore. It is crucial for managers to mitigate factors that significantly reduce effective population size and genetic connectivity, which supports the dispersion of genetic variation that aids in rapid evolutionary responses to environmental challenges.

Among men, androgens are associated with a decrease in Alzheimer's disease risk.

INTRODUCTION: Increased levels of sex hormones have been hypothesized to decrease Alzheimer's disease (AD) risk. We assessed the association between sex steroid hormones with AD using a Mendelian randomization (MR) approach. METHODS: An inverse-variance weighting (IVW) MR analysis was performed using effect estimates from external genome-wide association study (GWAS) summary statistics. We included independent variants (linkage disequilibrium R2  < 0.001) and a p-value threshold of 5 × 10-8 . RESULTS: An increase in androgens was associated with a decreased AD risk among men: testosterone (odds ratio [OR]: 0.53; 95% confidence interval [CI]: 0.32-0.88; p-value: 0.01; false discovery rate [FDR] p-value: 0.03); dehydroepiandrosterone sulfate (DHEAS; OR: 0.56; 95% CI: 0.38-0.85; p-value: 0.01; FDR p-value: 0.03); and androsterone sulfate (OR: 0.69; 95% CI: 0.46-1.02; p-value: 0.06; FDR p-value: 0.10). There was no association between sex steroid hormones and AD among women, although analysis for estradiol had limited statistical power. DISCUSSION: A higher concentration of androgens was associated with a decreased risk of AD among men of European ancestry, suggesting that androgens among men might be neuroprotective and could potentially prevent or delay an AD diagnosis. HIGHLIGHTS: Sex hormones are hypothesized to play a role in developing Alzheimer's disease (AD). The effect of sex hormones on AD was assessed using Mendelian randomization (MR) analysis. Among women, genetically determined effects of sex hormones were limited or null. Among men, a higher concentration of androgens decreased AD risk. This study suggests a causal relationship between androgens and AD among men.

A fast data-driven method for genotype imputation, phasing and local ancestry inference: MendelImpute.jl.

MOTIVATION: Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers. RESULTS: We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs (single nucleotide polymorphisms). Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing. AVAILABILITY AND IMPLEMENTATION: Software, documentation and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelImpute.jl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

WiSER: Robust and scalable estimation and inference of within-subject variances from intensive longitudinal data.

The availability of vast amounts of longitudinal data from electronic health records (EHRs) and personal wearable devices opens the door to numerous new research questions. In many studies, individual variability of a longitudinal outcome is as important as the mean. Blood pressure fluctuations, glycemic variations, and mood swings are prime examples where it is critical to identify factors that affect the within-individual variability. We propose a scalable method, within-subject variance estimator by robust regression (WiSER), for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. It is robust against the misspecification of the conditional distribution of responses or the distribution of random effects. It shows similar performance as the correctly specified likelihood methods but is 103 ∼ 105 times faster. The estimation algorithm scales linearly in the total number of observations, making it applicable to massive longitudinal data sets. The effectiveness of WiSER is evaluated in extensive simulation studies. Its broad applicability is illustrated using the accelerometry data from the Women's Health Study and a clinical trial for longitudinal diabetes care.

Modern simulation utilities for genetic analysis.

BACKGROUND: Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools. RESULTS: We present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research. CONCLUSION: The TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.

Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale.

Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many phenotypes more naturally take ordered, discrete values. Examples include (a) subtypes defined from multiple sources of clinical information and (b) derived phenotypes generated by specific phenotyping algorithms for electronic health records (EHR). GWAS of ordinal traits have been problematic. Dichotomizing can lead to a range of arbitrary cutoff values, generating inconsistent, hard to interpret results. Using multinomial regression ignores trait value hierarchy and potentially loses power. Treating ordinal data as quantitative can lead to misleading inference. To address these issues, we analyze ordinal traits with an ordered, multinomial model. This approach increases power and leads to more interpretable results. We derive efficient algorithms for computing test statistics, making ordinal trait GWAS computationally practical for Biobank scale data. Our method is available as a Julia package OrdinalGWAS.jl. Application to a COPDGene study confirms previously found signals based on binary case-control status, but with more significance. Additionally, we demonstrate the capability of our package to run on UK Biobank data by analyzing hypertension as an ordinal trait.

Increased Menopausal Age Reduces the Risk of Parkinson's Disease: A Mendelian Randomization Approach.

BACKGROUND: Studies of Parkinson's disease (PD) and the association with age at menarche or menopause have reported inconsistent findings. Mendelian randomization (MR) may address measurement errors because of difficulties accurately reporting the age these life events occur. OBJECTIVE: We used MR to assess the association between age at menopause and age at menarche with PD risk. METHODS: We performed inverse variant-weighted (IVW) MR analysis using external genome-wide association study (GWAS) summary data from the United Kingdom biobank, and the effect estimates between genetic variants and PD among two population-based studies (Parkinson's disease in Denmark (PASIDA) study, Denmark, and Parkinson's Environment and Gene study [PEG], United States) that enrolled 1737 female and 2430 male subjects of European ancestry. We, then, replicated our findings for age at menopause using summary statistics from the PD consortium (19 773 women), followed by a meta-analysis combining all summary statistics. RESULTS: For each year increase in age at menopause, the risk for PD decreased (odds ration [OR], 0.84; 95% confidence interval [CI], 0.73-0.98; P = 0.03) among women in our study, whereas there was no association among men (OR, 0.98; 95% CI, 0.85-1.11; P = 0.71). A replication using summary statistics from the PD consortium estimated an OR of 0.94 (95% CI, 0.90-0.99; P = 0.01), and we calculated a meta-analytic OR of 0.93 (95% CI, 0.89-0.98; P = 0.003). There was no indication for an association between age at menarche and PD (OR, 0.75; 95% CI, 0.44-1.29; P = 0.29). CONCLUSIONS: A later age at menopause was associated with a decreased risk of PD in women, supporting the hypothesis that sex hormones or other factors related to late menopause may be neuroprotective in PD. © 2021 International Parkinson and Movement Disorder Society.

Computational tools for assessing gene therapy under branching process models of mutation.

Multitype branching processes are ideal for studying the population dynamics of stem cell populations undergoing mutation accumulation over the years following transplant. In such stochastic models, several quantities are of clinical interest as insertional mutagenesis carries the potential threat of leukemogenesis following gene therapy with autologous stem cell transplantation. In this paper, we develop a three-type branching process model describing accumulations of mutations in a population of stem cells distinguished by their ability for long-term self-renewal. Our outcome of interest is the appearance of a double-mutant cell, which carries a high potential for leukemic transformation. In our model, a single-hit mutation carries a slight proliferative advantage over a wild-type stem cells. We compute marginalized transition probabilities that allow us to capture important quantitative aspects of our model, including the probability of observing a double-hit mutant and relevant moments of a single-hit mutation population over time. We thoroughly explore the model behavior numerically, varying birth rates across the initial sizes and populations of wild type stem cells and single-hit mutants, and compare the probability of observing a double-hit mutant under these conditions. We find that increasing the number of single-mutants over wild-type particles initially present has a large effect on the occurrence of a double-mutant, and that it is relatively safe for single-mutants to be quite proliferative, provided the lentiviral gene addition avoids creating single mutants in the original insertion process. Our approach is broadly applicable to an important set of questions in cancer modeling and other population processes involving multiple stages, compartments, or types.

Serum lipids are associated with nonalcoholic fatty liver disease: a pilot case-control study in Mexico.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and cirrhosis. NAFLD is mediated by changes in lipid metabolism and known risk factors include obesity, metabolic syndrome, and diabetes. The aim of this study was to better understand differences in the lipid composition of individuals with NAFLD compared to controls, by performing direct infusion lipidomics on serum biospecimens from a cohort study of adults in Mexico. METHODS: A nested case-control study was conducted with a sample of 98 NAFLD cases and 100 healthy controls who are participating in an on-going, longitudinal study in Mexico. NAFLD cases were clinically confirmed using elevated liver enzyme tests and liver ultrasound or liver ultrasound elastography, after excluding alcohol abuse, and 100 controls were identified as having at least two consecutive normal alanine aminotransferase (ALT) and aspartate aminotransferase (AST) (< 40 U/L) results in a 6-month period, and a normal liver ultrasound elastography result in January 2018. Samples were analyzed on the Sciex Lipidyzer Platform and quantified with normalization to serum volume. As many as 1100 lipid species can be identified using the Lipidyzer targeted multiple-reaction monitoring list. The association between serum lipids and NAFLD was investigated using analysis of covariance, random forest analysis, and by generating receiver operator characteristic (ROC) curves. RESULTS: NAFLD cases had differences in total amounts of serum cholesterol esters, lysophosphatidylcholines, sphingomyelins, and triacylglycerols (TAGs), however, other lipid subclasses were similar to controls. Analysis of individual TAG species revealed increased incorporation of saturated fatty acyl tails in serum of NAFLD cases. After adjusting for age, sex, body mass index, and PNPLA3 genotype, a combined panel of ten lipids predicted case or control status better than an area under the ROC curve of 0.83. CONCLUSIONS: These preliminary results indicate that the serum lipidome differs in patients with NAFLD, compared to healthy controls, and suggest that assessing the desaturation state of TAGs or a specific lipid panel may be useful clinical tools for the diagnosis of NAFLD.

Family genetic result communication in rare and undiagnosed disease communities: Understanding the practice.

Genetic results have implications not only for the individual, but also for their family members. Research on family communication of genetic results has primarily focused on families affected by adult-onset, dominant conditions as well as more common genetic conditions such as familial hypercholesterolemia, cardiomyopathies, and genetic hearing loss. This study therefore aimed to characterize genetic result communication in families with rare and undiagnosed conditions and identify factors that influence communication. One hundred and forty-two individuals who received a diagnosis from the Undiagnosed Diseases Network (UDN), a study focused on providing diagnoses to individuals with undiagnosed conditions, were eligible to complete a survey assessing genetic results communication. Survey items assessed if communication was discussed with healthcare providers, with whom participants communicated genetic testing, why they chose to communicate with these family members, and what information they communicated. All respondents (5 adult UDN participants, 38 parents/guardians of UDN participants, and 2 identifying as both) shared genetic results with at least one family member. Individuals who identified as both were considered exclusively adult participants for the purpose of these analyses. Adult participants and parents/guardians of participants reported high levels of understanding (96%), utility (96%), and comfort communicating genetic results (89%). Additionally, parents/guardians were more likely to disclose genetic results due to a general desire to share (60% of parents/guardians vs. 14% adult participants), while adult participants reported that they shared results to communicate risk to family members (86% of adult participants vs. 24% of parents/guardians). Many respondents did not recall discussing with a healthcare provider how (64%) or what (42%) to communicate about results. The results of this study provide insight into the practice of result communication by individuals with rare and previously undiagnosed conditions, which can ideally inform development of more effective counseling strategies and guidelines to aid family communication.

OPENMENDEL: a cooperative programming project for statistical genetics.

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

Diagnostic utility of transcriptome sequencing for rare Mendelian diseases.

PURPOSE: We investigated the value of transcriptome sequencing (RNAseq) in ascertaining the consequence of DNA variants on RNA transcripts to improve the diagnostic rate from exome or genome sequencing for undiagnosed Mendelian diseases spanning a wide spectrum of clinical indications. METHODS: From 234 subjects referred to the Undiagnosed Diseases Network, University of California-Los Angeles clinical site between July 2014 and August 2018, 113 were enrolled for high likelihood of having rare undiagnosed, suspected genetic conditions despite thorough prior clinical evaluation. Exome or genome sequencing and RNAseq were performed, and RNAseq data was integrated with genome sequencing data for DNA variant interpretation genome-wide. RESULTS: The molecular diagnostic rate by exome or genome sequencing was 31%. Integration of RNAseq with genome sequencing resulted in an additional seven cases with clear diagnosis of a known genetic disease. Thus, the overall molecular diagnostic rate was 38%, and 18% of all genetic diagnoses returned required RNAseq to determine variant causality. CONCLUSION: In this rare disease cohort with a wide spectrum of undiagnosed, suspected genetic conditions, RNAseq analysis increased the molecular diagnostic rate above that possible with genome sequencing analysis alone even without availability of the most appropriate tissue type to assess.

Heritability of interpack aggression in a wild pedigreed population of North American grey wolves.

Aggression is a quantitative trait deeply entwined with individual fitness. Mapping the genomic architecture underlying such traits is complicated by complex inheritance patterns, social structure, pedigree information and gene pleiotropy. Here, we leveraged the pedigree of a reintroduced population of grey wolves (Canis lupus) in Yellowstone National Park, Wyoming, USA, to examine the heritability of and the genetic variation associated with aggression. Since their reintroduction, many ecological and behavioural aspects have been documented, providing unmatched records of aggressive behaviour across multiple generations of a wild population of wolves. Using a linear mixed model, a robust genetic relationship matrix, 12,288 single nucleotide polymorphisms (SNPs) and 111 wolves, we estimated the SNP-based heritability of aggression to be 37% and an additional 14% of the phenotypic variation explained by shared environmental exposures. We identified 598 SNP genotypes from 425 grey wolves to resolve a consensus pedigree that was included in a heritability analysis of 141 individuals with SNP genotype, metadata and aggression data. The pedigree-based heritability estimate for aggression is 14%, and an additional 16% of the phenotypic variation was explained by shared environmental exposures. We find strong effects of breeding status and relative pack size on aggression. Through an integrative approach, these results provide a framework for understanding the genetic architecture of a complex trait that influences individual fitness, with linkages to reproduction, in a social carnivore. Along with a few other studies, we show here the incredible utility of a pedigreed natural population for dissecting a complex, fitness-related behavioural trait.

A candidate gene study of intermediate histopathological phenotypes in HIV-associated neurocognitive disorders.

HIV-associated neurocognitive disorders (HAND) describe a spectrum of neuropsychological impairment caused by HIV-1 infection. While the sequence of cellular and physiological events that lead to HAND remains obscure, it likely involves chronic neuroinflammation. Host genetic markers that increase the risk for HAND have been reported, but replication of such studies is lacking, possibly due to inconsistent application of a behavioral phenotype across studies. In the current study, we used histopathologic phenotypes in order to validate putative risk alleles for HAND. The National NeuroAIDS Tissue Consortium, a longitudinal study of the neurologic manifestations of HIV. Data and specimens were obtained from 175 HIV-infected adults. After determining several potential covariates of neurocognitive functioning, we quantified levels of six histopathological markers in the frontal lobe in association with neurocognitive functioning: SYP, MAP 2, HLA-DR, Iba1, GFAP, and ß-amyloid. We then determined alleles of 15 candidate genes for their associations with neurocognitive functioning and histopathological markers. Finally, we identified the most plausible causal pathway based on our data using a multi-stage linear regression-based mediation analysis approach. None of the genetic markers were associated with neurocognitive functioning. Of the histopathological markers, only MAP 2 and SYP were associated with neurocognitive functioning; however, MAP 2 and SYP did not vary as a function of genotype. Mediation analysis suggests a causal pathway in which presynaptic degeneration (SYP) leads to somatodendritic degeneration (MAP 2) and ultimately neurocognitive impairment. This study did not support the role of host genotype in the histopathology underlying HAND. The findings lend further support for synaptodendritic degeneration as the proximal underlying neuropathological substrate of HAND.

Correction to: A candidate gene study of intermediate histopathological phenotypes in HIV-associated neurocognitive disorders.

Due to a production error data in Table 1 were not presented correctly.

Subtypes of Native American ancestry and leading causes of death: Mapuche ancestry-specific associations with gallbladder cancer risk in Chile.

Latin Americans are highly heterogeneous regarding the type of Native American ancestry. Consideration of specific associations with common diseases may lead to substantial advances in unraveling of disease etiology and disease prevention. Here we investigate possible associations between the type of Native American ancestry and leading causes of death. After an aggregate-data study based on genome-wide genotype data from 1805 admixed Chileans and 639,789 deaths, we validate an identified association with gallbladder cancer relying on individual data from 64 gallbladder cancer patients, with and without a family history, and 170 healthy controls. Native American proportions were markedly underestimated when the two main types of Native American ancestry in Chile, originated from the Mapuche and Aymara indigenous peoples, were combined together. Consideration of the type of Native American ancestry was crucial to identify disease associations. Native American ancestry showed no association with gallbladder cancer mortality (P = 0.26). By contrast, each 1% increase in the Mapuche proportion represented a 3.7% increased mortality risk by gallbladder cancer (95%CI 3.1-4.3%, P = 6×10-27). Individual-data results and extensive sensitivity analyses confirmed the association between Mapuche ancestry and gallbladder cancer. Increasing Mapuche proportions were also associated with an increased mortality due to asthma and, interestingly, with a decreased mortality by diabetes. The mortality due to skin, bladder, larynx, bronchus and lung cancers increased with increasing Aymara proportions. Described methods should be considered in future studies on human population genetics and human health. Complementary individual-based studies are needed to apportion the genetic and non-genetic components of associations identified relying on aggregate-data.

The Roles of Physical Activity and Inflammation in Mortality, Cognition, and Depressive Symptoms Among Older Mexican Americans.

A higher level of physical activity (PA) is associated with decreased risk of mortality, dementia, and depression, yet the mechanisms involved are not well understood, and little evidence exists for Mexican Americans. With data from the Sacramento Area Latino Study on Aging (1998-2007), we used Cox proportional hazards regression to separately evaluate associations of baseline PA level with mortality, dementia/cognitive impairment without dementia (CIND), and depressive symptoms, and we estimated the mediating effects of inflammatory markers in additive hazard models. A low level of PA (<35 metabolic equivalent of task-hours/week) was associated with increased mortality (hazard ratio (HR) = 1.50, 95% confidence interval (CI): 1.20, 1.88), dementia/CIND (HR = 1.37, 95% CI: 0.96, 1.96), and depressive symptoms (HR = 1.23, 95% CI: 1.00, 1.52). A low PA level added 512 (95% CI: -34, 1,058) cases of dementia/CIND per 100,000 person-years at risk (direct effect), while, through a mediating path, interleukin 6 (IL-6) added another 49 (95% CI: 5, 94) cases, or 9% of the total effect. For mortality, 8%-10% of the PA total effect was mediated through IL-6, tumor necrosis factor α (TNF-α), or TNF-α receptors. None of the inflammatory markers mediated the association between PA and depressive symptoms. Our results suggest that antiinflammation (especially as assessed by IL-6 and TNF-α levels) may partly explain how PA protects against dementia/CIND and mortality.