Jennifer A Smith

University of Michigan, Ann Arbor, Michigan, United States

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Publications (48)370.82 Total impact

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    ABSTRACT: Demographics influence kidney stone risk and the type of stone that is more likely to form. Common kidney stone risk factors include having a low urine volume and a high urine concentration. The goal of the current study was to evaluate the effect of demographics on urinary concentration and osmole excretion. Twenty-four-hour urine samples were collected from non-Hispanic white sibships in Rochester, MN. Height, weight, blood pressure, serum creatinine, and cystatin C were measured. Diet was assessed using the Viocare food frequency questionnaire. Effects of demographics and dietary elements on urine osmolality and volume were evaluated in bivariate and multivariable models, as well as models that included dietary interactions with age, sex, and weight. Samples were available from 709 individuals (mean age 66 ± 9 years, 59 % female). Across the age spectrum, males had higher urine osmolality (~140 mOsm/kg, p < 0.0001) and total osmole excretion (~270 mOsm, p < 0.0001) compared to females. For any given urine volume, males had a consistently higher urine osmolality (~140 mOsm/kg, p < 0.0001). In multivariable models, urine osmolality declined with age and water intake and remained higher in males than females. Urine osmolality positively associated with weight and animal protein intake. Higher urine volume associated with larger water intake. An interaction revealed that greater body weight was associated with larger changes in urine osmolality as oxalate intake increased (p = 0.04). Data from this study support the hypothesis that there are sex differences in thirst and vasopressin action. This trend in urine concentration is also consistent with known epidemiologic patterns of urinary stone disease risk.
    No preview · Article · Dec 2016 · Biology of Sex Differences
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    ABSTRACT: Time-varying phenotypes have been studied less frequently in the context of genome-wide analyses across ethnicities, particularly for mood disorders. This study uses genome-wide association studies of depressive symptoms in a longitudinal framework and across multiple ethnicities to find common variants for depressive symptoms. Ethnicity-specific GWAS for depressive symptoms were conducted using three approaches: a baseline measure, longitudinal measures averaged over time, and a repeated measures analysis. We then used meta-analysis to jointly analyze the results across ethnicities within the Multi-ethnic Study of Atherosclerosis (MESA, n = 6,335), and then within ethnicity, across MESA and a sample from the Health and Retirement Study African- and European-Americans (HRS, n = 10,163). This study uses genome-wide association studies of depressive symptoms in a longitudinal framework and across multiple ethnicities to find common variants for depressive symptoms. Ethnicity-specific GWAS for depressive symptoms were conducted using three approaches: a baseline measure, longitudinal measures averaged over time, and a repeated measures analysis. We then used meta-analysis to jointly analyze the results across ethnicities within the Multi-ethnic Study of Atherosclerosis (MESA, n = 6,335), and then within ethnicity, across MESA and a sample from the Health and Retirement Study African- and European-Americans (HRS, n = 10,163). Several novel variants were identified at the genome-wide suggestive level (5×10 −8 < p-value ≤ 5×10 −6 ) in each ethnicity for each approach to analyzing depressive symptoms. The repeated measures analyses resulted in typically smaller p-values and an increase in the number of single-nucleotide polymorphisms (SNP) reaching genome-wide suggestive level. For phenotypes that vary over time, the detection of genetic predictors may be enhanced by repeated measures analyses.
    Preview · Article · Dec 2015 · BMC Genetics
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    Full-text · Dataset · Nov 2015
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    ABSTRACT: Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.
    Full-text · Article · Oct 2015 · Nature Communications
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    ABSTRACT: Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms.
    No preview · Article · Sep 2015 · Nature Genetics
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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.
    Full-text · Article · Aug 2015 · Epigenetics: official journal of the DNA Methylation Society
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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.
    No preview · Article · Aug 2015 · Behavior Genetics
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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.
    No preview · Article · Jul 2015 · Urology
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    ABSTRACT: Objectives: SLC2A9 gene variants have been associated with urinary uric acid (UA) concentration, but little is known about the functional mechanism linking these gene variants with UA. SLC2A9 encodes a UA transporter present in the proximal tubule of the kidney, and gene expression levels of SLC2A9 and other genes in the uricosuric pathway (ABCG2, SLC17A1, SLC17A3, and SLC22A12) could potentially mediate the relationship between SLC2A9 gene variants and urinary UA excretion. Methods: The association between urinary UA concentrations and single nucleotide polymorphisms (SNPs) within the SLC2A9 gene region, expression levels of genes in the uricosuric pathway, and dietary protein intake were analyzed for a sample of non-Hispanic white participants from the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort. The SLC2A9 SNP most significantly associated with urinary UA concentration was then tested for associations with gene expression levels from uric acid absorption/secretion associated genes. Models including interactions between dietary protein (total, animal, and vegetable) and genetic factors were also assessed. Results: The most significant SLC2A9 SNP associated with urinary UA (rs12509955, corrected p = 0.001) was also associated with SLC2A9 gene expression levels (corrected p = 0.0084); however, SLC2A9 gene expression levels were not significantly associated with urinary UA concentrations (p = 0.509). The interactions between rs12509955 and total dietary protein, and SLC2A9 gene-level gene expression and dietary vegetable protein on the outcome of urinary UA were marginally significant (p = 0.11 and p = 0.07, respectively). Gene expression level of one SLC2A9 transcript had a significant interaction with dietary animal protein (SLC2A9-001 ENST00000506583, p = 0.01) and a marginally significant interaction with total dietary protein (p = 0.07) on urinary UA. Conclusion: Our results illustrate that SNPs in the SLC2A9 gene influence SLC2A9 gene expression as well as urinary UA excretion. Evidence is also suggestive that gene-by-diet interactions may disproportionately increase urinary UA in genetically susceptible individuals that consume higher amounts of protein.
    Preview · Article · Jul 2015 · PLoS ONE
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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.
    Full-text · Article · Jul 2015 · Nature
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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.
    Full-text · Article · Jul 2015 · Nature
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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.
    Full-text · Article · Jul 2015
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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.
    Preview · Article · May 2015 · PLoS ONE
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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.
    No preview · Article · May 2015 · Journal of nephrology
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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.
    Preview · Article · May 2015 · BMC Medical Genomics
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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.
    No preview · Article · Apr 2015 · Biometrics

  • No preview · Conference Paper · Apr 2015
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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.
    No preview · Article · Mar 2015 · The American Journal of Human Genetics
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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.
    No preview · Article · Mar 2015 · Human Molecular Genetics
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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
    Full-text · Article · Feb 2015 · Nature