Jennifer A Smith

University of Michigan, Ann Arbor, Michigan, United States

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Publications (41)310.74 Total impact

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    ABSTRACT: Epigenetic changes, such as DNA methylation, have been hypothesized to provide a link between the social environment and disease development. The purpose of this study was to examine associations between life course measures of socioeconomic status (SES) and DNA methylation (DNAm) in 18 genes related to stress reactivity and inflammation using a multi-level modeling approach that treats DNAm measurements as repeat measures within an individual. DNAm and gene expression were assessed in purified monocytes for a random subsample of 1,264 non-Hispanic white, African-American, and Hispanic participants aged 55-94 from the Multi-Ethnic Study of Atherosclerosis (MESA). After correction for multiple testing, we found that low childhood SES was associated with DNAm in three stress-related genes (AVP, FKBP5, OXTR) and two inflammation-related genes (CCL1, CD1D), low adult SES was associated with DNAm in one stress-related gene (AVP) and five inflammation-related genes (CD1D, F8, KLRG1, NLRP12, TLR3), and social mobility was associated with DNAm in three stress-related genes (AVP, FKBP5, OXTR) and seven inflammation-related genes (CCL1, CD1D, F8, KLRG1, NLRP12, PYDC1, TLR3). In general, low SES was associated with increased DNAm. Expression data was available for seven genes that showed a significant relationship between SES and DNAm. In five of these seven genes (CD1D, F8, FKBP5, KLRG1, NLRP12), DNAm was associated with gene expression for at least one transcript, providing evidence of the potential functional consequences of alterations in DNAm related to SES. The results of this study reflect the biological complexity of epigenetic data and underscore the need for multi-disciplinary approaches to study how DNAm may contribute to the social patterning of disease.
    Epigenetics: official journal of the DNA Methylation Society 08/2015; DOI:10.1080/15592294.2015.1085139 · 5.11 Impact Factor
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    ABSTRACT: Complex illnesses, like depression, are thought to arise from the interplay between psychosocial stressors and genetic predispositions. Approaches that take into account both personal and neighborhood factors and that consider gene regions as well as individual SNPs may be necessary to capture these interactions across race and ethnic groups. We used novel gene-region based analysis methods [Sequence Kernel Association Test (SKAT) and meta-analysis (MetaSKAT), gene-environment set association test (GESAT)], as well as traditional linear models to identify gene region and SNP × psychosocial factor interactions at the individual- and neighborhood-level, across multiple race/ethnicities. Multiple regions identified in SKAT analyses showed evidence of a significant gene-region association with averaged depressive symptom scores across race/ethnicity (MetaSKAT p values <0.001). One region × neighborhood-environment interaction was significantly associated with averaged depressive symptom score across race/ethnicity after multiple testing correction (chr 18:21454070-21494070, Fisher's combined p value = 0.001). The examination of gene regions jointly with environmental factors measured at multiple levels (individuals and their contexts) may shed light on the etiology of depressive illness across race/ethnicities.
    Behavior Genetics 08/2015; DOI:10.1007/s10519-015-9734-6 · 2.84 Impact Factor
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    ABSTRACT: To investigate the effect of demographics including age and sex on excretion of four key urinary factors (calcium (Ca), magnesium (Mg), oxalate (Ox) and uric acid (UA)) related to kidney stone risk. Twenty-four hour urine samples were collected from non-Hispanic white sibships in Rochester, MN. Height, weight, blood pressure, serum creatinine and cystatin C (CC) were measured. Diet was assessed using the Viocare food frequency questionnaire. Effects of demographics and dietary elements on urinary excretions were evaluated in univariate, multivariate, and interaction models that included age, sex, and body mass index (BMI). Samples were available from 709 individuals. In multivariate models, sex was a significant predictor of all four urinary factors, age was significant for all but UA excretion, and serum creatinine was significant only for Ca and Mg excretion (p<0.05). BMI or weight positively correlated with Mg, Ox and UA excretion (p<0.05). Use of a thiazide diuretic (lower) and dietary protein (higher) were associated with Ca excretion, while dietary Ca was associated with higher Mg excretion. Urinary UA excretion increased with animal protein intake and CC estimated glomerular filtration rate (eGFR), and was lower with concurrent loop diuretic use. Significant interaction effects on urinary UA excretion were observed for loop diuretic use and sex, eGFR and sex, age and animal protein intake, and BMI and eGFR (p<0.05). Age and sex influence excretion of key urinary factors related to kidney stone risk, and should be taken into account when evaluating kidney stone patients. Copyright © 2015 Elsevier Inc. All rights reserved.
    Urology 07/2015; DOI:10.1016/j.urology.2015.07.012 · 2.13 Impact Factor
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    ABSTRACT: Homozygosity has long been associated with rare, often devastating, Mendelian disorders1, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3, 4. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10−300, 2.1 × 10−6, 2.5 × 10−10 and 1.8 × 10−10, respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months’ less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5, 6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.
    Nature 07/2015; DOI:10.1038/nature14618 · 42.35 Impact Factor
  • Peter K. Joshi · Tonu Esko · Hannele Mattsson · Niina Eklund · Ilaria Gandin · Teresa Nutile · Anne U. Jackson · Claudia Schurmann · Albert V. Smith · Weihua Zhang · [...] · Vilmundur Gudnason · Atsushi Takahashi · John C. Chambers · Jaspal S. Kooner · David P. Strachan · Harry Campbell · Joel N. Hirschhorn · Markus Perola · Ozren Polašek · James F. Wilson
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    ABSTRACT: Homozygosity has long been associated with rare, often devastating, Mendelian disorders1, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3, 4. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10−300, 2.1 × 10−6, 2.5 × 10−10 and 1.8 × 10−10, respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months’ less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5, 6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.
    Nature 07/2015; · 42.35 Impact Factor
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    ABSTRACT: Genetic risk scores are a useful tool for examining the cumulative predictive ability of genetic variation on cardiovascular disease. Important considerations for creating genetic risk scores include the choice of genetic variants, weighting, and comparability across ethnicities. Genetic risk scores that use information from genome-wide meta-analyses can successfully predict cardiovascular outcomes and subclinical phenotypes, yet there is limited clinical utility of these scores beyond traditional cardiovascular risk factors in many populations. Novel uses of genetic risk scores include evaluating the genetic contribution of specific intermediate traits or risk factors to cardiovascular disease, risk prediction in high-risk populations, gene-by-environment interaction studies, and Mendelian randomization studies. Though questions remain about the ultimate clinical utility of the genetic risk score, further investigation in high-risk populations and new ways to combine genetic risk scores with traditional risk factors may prove to be fruitful.
    07/2015; DOI:10.1007/s40471-015-0046-4
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    ABSTRACT: The hormone cortisol is likely to be a key mediator of the stress response that influences multiple physiologic systems that are involved in common chronic disease, including the cardiovascular system, the immune system, and metabolism. In this paper, a candidate gene approach was used to investigate genetic contributions to variability in multiple correlated features of the daily cortisol profile in a sample of European Americans, African Americans, and Hispanic Americans from the Multi-Ethnic Study of Atherosclerosis (MESA). We proposed and applied a new gene-level multiple-phenotype analysis and carried out a meta-analysis to combine the ethnicity specific results. This new analysis, instead of a more routine single marker-single phenotype approach identified a significant association between one gene (ADRB2) and cortisol features (meta-analysis p-value=0.0025), which was not identified by three other commonly used existing analytic strategies: 1. Single marker association tests involving each single cortisol feature separately; 2. Single marker association tests jointly testing for multiple cortisol features; 3. Gene-level association tests separately carried out for each single cortisol feature. The analytic strategies presented consider different hypotheses regarding genotype-phenotype association and imply different costs of multiple testing. The proposed gene-level analysis integrating multiple cortisol features across multiple ethnic groups provides new insights into the gene-cortisol association.
    PLoS ONE 05/2015; 10(5):e0126637. DOI:10.1371/journal.pone.0126637 · 3.23 Impact Factor
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    ABSTRACT: Kidney stones and their risk factors aggregate in families, yet few studies have estimated the heritability of known risk factors. Estimate the heritability of dietary risk factors for kidney stones. Dietary intakes were assessed using the Viocare Food Frequency Questionnaire in sibships enrolled in the Rochester, MN cohort of the Genetic Epidemiology Network of Arteriopathy. Measures of urinary supersaturation were determined using 24 h urine samples. Heritabilities and genetic correlations were estimated using variance components methods. Samples were available from 620 individuals (262 men, 358 women, mean (SD) age 65 (9) years). Dietary intakes of protein, sucrose, and calcium had strong evidence for heritability (p < 0.01) after adjustment for age, sex, height and weight. Among the significantly heritable dietary intakes (p < 0.05), genetic factors explained 22-50 % of the inter-individual variation. Significant genetic correlations were observed among dietary protein, dietary sucrose, and dietary calcium intakes (p < 0.001). Evidence from this relatively large cohort suggests a strong heritable component to dietary intakes of protein, sucrose and calcium that contributes to nephrolithiasis risk. Further efforts to understand the interplay of genetic and environmental risk factors in kidney stone pathogenesis are warranted.
    Journal of nephrology 05/2015; DOI:10.1007/s40620-015-0204-2 · 2.00 Impact Factor
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    ABSTRACT: Recent genome-wide association studies (GWAS) have shown that single nucleotide polymorphisms (SNPs) in the Chr9p21 region are associated with coronary artery disease (CAD). Most of the SNPs identified in this region are non-coding SNPs, suggesting that they may influence gene expression by cis or trans mechanisms to affect disease susceptibility. Since all cells from an individual have the same DNA sequence variations, levels of gene expression in immortalized cell lines can reflect the functional effects of DNA sequence variations that influence or regulate gene expression. The objective of this study is to evaluate the functional consequences of the risk variants in the Chr9p21 region on gene expression. We examined the association between the variants in the Chr9p21 region and the transcript-level mRNA expression of the adjacent genes (cis) as well as all other genes across the whole genome (trans) from transformed beta-lymphocytes in 801 non-Hispanic white participants from The Genetic Epidemiology Network of Arteriopathy (GENOA) study. We found that the CAD risk variants in the Chr9p21 region were significantly associated with the mRNA expression of the ANRIL transcript ENST00000428597 (p = 8.58E-06). Importantly, a few distant transcripts were also found to be associated with the variants in this region, including the well-known CAD risk gene ABCA1 (p = 1.01E-05). Gene enrichment testing suggests that retinol metabolism, N-Glycan biosynthesis, and TGF signaling pathways may be involved. These results suggest that the effect of risk variants in the Chr9p21 region on susceptibility to CAD is likely to be mediated through both cis and trans mechanisms.
    BMC Medical Genomics 05/2015; 8(1):21. DOI:10.1186/s12920-015-0094-0 · 3.91 Impact Factor
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    ABSTRACT: Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within-subject correlation, a feature that is desirable for longitudinal analysis. In addition, a joint test incorporating gene-time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large-scale genome-wide association studies (GWAS). The proposed methods are evaluated through extensive simulation studies and illustrated using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate substantial gain in power using LGRF when compared with two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing gene-based tests using the average value of repeated measurements as the outcome. © 2015, The International Biometric Society.
    Biometrics 04/2015; DOI:10.1111/biom.12310 · 1.52 Impact Factor
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    ABSTRACT: Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical by descent. We propose both burden and variance-component tests of rare variation that are applicable to affected sibships of arbitrary size and that do not require genotype information from unaffected siblings or independent controls. Our approaches are robust to population stratification and produce analytic p values, thereby enabling our approach to scale easily to genome-wide studies of rare variation. We illustrate our methods by using simulated data and exome chip data from sibships ascertained for hypertension collected as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) study. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
    The American Journal of Human Genetics 03/2015; 96(4). DOI:10.1016/j.ajhg.2015.01.020 · 10.99 Impact Factor
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    ABSTRACT: Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity (e.g. body mass index [BMI]≥40 kg/m(2)), but their contribution to common obesity (BMI≥30 kg/m(2)) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331,175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CI) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR=1.15, 95% CI 1.06-1.24, P=6.08x10(-6)) and rs6234/rs6235 (OR=1.07, 95% CI 1.04-1.10, P=3.00x10(-7)). Similarly, significant associations were found with continuous BMI for rs6232 (beta=0.03, 95% CI 0.00-0.07; P=0.047) and rs6234/rs6235 (beta=0.02, 95% CI 0.00-0.03; P=5.57x10(-4)). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
    Human Molecular Genetics 03/2015; 24(12). DOI:10.1093/hmg/ddv097 · 6.68 Impact Factor
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    ABSTRACT: Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis
    Nature 02/2015; 518(7538). DOI:10.1038/nature14177 · 42.35 Impact Factor
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    ABSTRACT: -The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multi-ethnic genome-wide association studies. -We included 21,079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (N=17,936), African (N=1,943), Hispanic (N=795), and Asian (N=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each SNP and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (p=2.7×10(-19)) and identified novel loci on chr10q24 (p=1.6×10(-9)) and chr2p21 (p=4.4×10(-8)). In the multi-ethnic meta-analysis, we identified two additional loci, on chr1q22 (p=2.0×10(-8)) and chr2p16 (p=1.5×10(-8)). The novel loci contained genes that have been implicated in Alzheimer's disease (chr2p21, chr10q24), intracerebral hemorrhage (chr1q22), neuro-inflammatory diseases (chr2p21), and glioma (chr10q24, chr2p16). -We identified four novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of white matter hyperintensities in addition to previously-proposed ischemic mechanisms.
    Circulation Cardiovascular Genetics 02/2015; 8(2). DOI:10.1161/CIRCGENETICS.114.000858 · 5.34 Impact Factor
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    ABSTRACT: Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
    Nature Communications 01/2015; 6:5897. DOI:10.1038/ncomms6897 · 10.74 Impact Factor
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    ABSTRACT: Background It has been well-established, both by population genetics theory and direct observation in many organisms, that increased genetic diversity provides a survival advantage. However, given the limitations of both sample size and genome-wide metrics, this hypothesis has not been comprehensively tested in human populations. Moreover, the presence of numerous segregating small effect alleles that influence traits that directly impact health directly raises the question as to whether global measures of genomic variation are themselves associated with human health and disease.ResultsWe performed a meta-analysis of 17 cohorts followed prospectively, with a combined sample size of 46,716 individuals, including a total of 15,234 deaths. We find a significant association between increased heterozygosity and survival (P¿=¿0.03). We estimate that within a single population, every standard deviation of heterozygosity an individual has over the mean decreases that person¿s risk of death by 1.57%.Conclusions This effect was consistent between European and African ancestry cohorts, men and women, and major causes of death (cancer and cardiovascular disease), demonstrating the broad positive impact of genomic diversity on human survival.
    BMC Genetics 12/2014; 15(1):1274. DOI:10.1186/s12863-014-0159-7 · 2.36 Impact Factor
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    ABSTRACT: Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
    The American Journal of Human Genetics 12/2014; 96(1). DOI:10.1016/j.ajhg.2014.11.011 · 10.99 Impact Factor
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    ABSTRACT: Memory performance in older persons can reflect genetic influences on cognitive function and dementing processes. We aimed to identify genetic contributions to verbal declarative memory in a community setting. We conducted genome-wide association studies for paragraph or word list delayed recall in 19 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, comprising 29,076 dementia- and stroke-free individuals of European descent, aged ≥45 years. Replication of suggestive associations (p < 5 × 10(-6)) was sought in 10,617 participants of European descent, 3811 African-Americans, and 1561 young adults. rs4420638, near APOE, was associated with poorer delayed recall performance in discovery (p = 5.57 × 10(-10)) and replication cohorts (p = 5.65 × 10(-8)). This association was stronger for paragraph than word list delayed recall and in the oldest persons. Two associations with specific tests, in subsets of the total sample, reached genome-wide significance in combined analyses of discovery and replication (rs11074779 [HS3ST4], p = 3.11 × 10(-8), and rs6813517 [SPOCK3], p = 2.58 × 10(-8)) near genes involved in immune response. A genetic score combining 58 independent suggestive memory risk variants was associated with increasing Alzheimer disease pathology in 725 autopsy samples. Association of memory risk loci with gene expression in 138 human hippocampus samples showed cis-associations with WDR48 and CLDN5, both related to ubiquitin metabolism. This largest study to date exploring the genetics of memory function in ~40,000 older individuals revealed genome-wide associations and suggested an involvement of immune and ubiquitin pathways. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
  • 43rd Annual Meeting of the Child-Neurology-Society; 10/2014
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    ABSTRACT: The genetic contribution to longevity in humans has been estimated to range from 15% to 25%. Only two genes, APOE and FOXO3, have shown association with longevity in multiple independent studies.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 09/2014; 70(1). DOI:10.1093/gerona/glu166 · 4.98 Impact Factor