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205 results on '"Williams-Blangero, S"'

4. Attitudes towards helminthic infection in the Jirel population of eastern Nepal

Author

Williams-Blangero, S., Subedi, J., Upadhayay, R.P., Manral, D.B., Khadka, K., Jirel, S., Robinson, E.S., and Blangero, J.

Subjects
Nepal -- Health aspects, Nepalese -- Diseases, Health attitudes -- Nepal, Health behavior -- Nepal, Worms, Intestinal and parasitic -- Health aspects, Health, Social sciences
Published
1998

6. Quantitative HLA-class-II/factor VIII (FVIII) peptidomic variation in dendritic cells correlates with the immunogenic potential of therapeutic FVIII proteins in hemophilia A

Author

Diego, VP, Luu, BW, Hofmann, M, Dinh, LV, Almeida, M, Powell, JS, Rajalingam, R, Peralta, JM, Kumar, S, Curran, JE, Sauna, ZE, Kellerman, R, Park, Y, Key, NS, Escobar, MA, Huy, H, Verhagen, AM, Williams-Blangero, S, Lehmann, PV, Maraskovsky, E, Blangero, J, Howard, TE, Diego, VP, Luu, BW, Hofmann, M, Dinh, LV, Almeida, M, Powell, JS, Rajalingam, R, Peralta, JM, Kumar, S, Curran, JE, Sauna, ZE, Kellerman, R, Park, Y, Key, NS, Escobar, MA, Huy, H, Verhagen, AM, Williams-Blangero, S, Lehmann, PV, Maraskovsky, E, Blangero, J, and Howard, TE

Abstract

BACKGROUND: Plasma-derived (pd) or recombinant (r) therapeutic factor VIII proteins (FVIIIs) are infused to arrest/prevent bleeding in patients with hemophilia A (PWHA). However, FVIIIs are neutralized if anti-FVIII-antibodies (inhibitors) develop. Accumulating evidence suggests that pdFVIIIs with von Willebrand factor (VWF) are less immunogenic than rFVIIIs and that distinct rFVIIIs are differentially immunogenic. Since inhibitor development is T-helper-cell-dependent, human leukocyte antigen (HLA)-class-II (HLAcII) molecules constitute an important early determinant. OBJECTIVES: Use dendritic cell (DC)-protein processing/presentation assays with mass-spectrometric and peptide-proteomic analyses to quantify the DP-bound, DQ-bound, and DR-bound FVIII-derived peptides in individual HLAcII repertoires and compare the immunogenic potential of six distinct FVIIIs based on their measured peptide counts. PATIENTS/METHODS: Monocyte-derived DCs from normal donors and/or PWHA were cultured with either: Mix-rFVIII, a VWF-free equimolar mixture of a full-length (FL)-rFVIII [Advate® (Takeda)] and four distinct B-domain-deleted (BDD)-rFVIIIs [Xyntha® (Pfizer), NovoEight® (Novo-Nordisk), Nuwiq® (Octapharma), and Afstyla® (CSL Behring GmBH)]; a pdFVIII + pdVWF [Beriate® (CSL Behring GmBH)]; Advate ± pdVWF; Afstyla ± pdVWF; and Xyntha + pdVWF. RESULTS: We showed that (i) Beriate had a significantly lower immunogenic potential than Advate ± pdVWF, Afstyla - pdVWF, and Mix-rFVIII; (ii) distinct FVIIIs differed significantly in their immunogenic potential in that, in addition to (i), Afstyla + pdVWF had a significantly lower immunogenic potential than Beriate, while the immunogenic potential of Beriate was not significantly different from that of Xyntha + pdVWF; and (iii) rFVIIIs with pdVWF had significantly lower immunogenic potentials than the same rFVIIIs without pdVWF. CONCLUSIONS: Our results provide HLAcII peptidomic level explanations for several important clinical observations

Published
2020

8. Skin color variation in eastern Nepal

Author

Williams-Blangero, S. and Blangero, J.

Subjects
Indigenous peoples -- Nepal, Human population genetics -- Research, Skin color -- Demographic aspects, Anthropology/archeology/folklore
Abstract

The purpose of this paper is to provide skin color data on several endogamous groups of eastern Nepal and to demonstrate genetic microdifferentiation in skin color. Skin reflectance measures were taken at the upper inner arm and forehead sites, using the British DSL Model 99 Reflectance Spectrophotometer fitted with blue, green, and red filters. Measurements on 484 males representing six endogamous groups (Jirel, Sunwar, Sherpa, Tamang, Brahman, Chetri) were utilized. After adjusting for group-specific age effects, multivariate likelihood ratio permutation tests were used to assess ethnic differences in skin reflectance means and covariance matrices. Ethnic group membership had a highly significant effect on skin color at the upper inner arm site and at the forehead site. Differential tanning responses among groups were also detected and may represent the influence of genotye-environment interaction on reflectance traits. Mahalanobis distance analysis revealed patterns of microdifferentiation that are inconsistent with oral historical accounts. Little support was found for the ethnohistorical belief that Jirels are a hybrid group descended from Sunwars and Sharpas, even though this scenario is supported by linguistic, anthropometric, and dermatoglyphic data. We suggest that while reflectometric studies of skin pigmentation may be useful in assessing the macrodifferentiation of human populations, the use of skin color in differentiation studies at the local population level needs to be carefully evaluated.

Published
1991

10. Population structure of the Jirels: patterns of mate choice

Author

Williams-Blangero, S.

Subjects
Human population genetics -- Research, Mate selection -- Research, Anthropology/archeology/folklore
Abstract

The influence of mating practices on genetic structure has been an area of great interest for anthropologists. In this paper, the techniques of potential mates analysis are employed to explore the mating patterns observed among the Jirels, a tribal population of eastern Nepal. Genealogical, anthropometric, dermatoglyphic, and demographic data for members of seven Jirel villages are used. Potential mate pools for a sample of 268 females are enumerated by village. Age structure and the Jirel restriction against clan endogamy are found to severely limit the number of males who are potential mates for a given female. The mating structure of the population is illuminated by statistical analysis of the characteristics of 160 actual mate pairs and all corresponding potential mate pairs. Using this approach several general mate choice practices were verified: 1) biological kin tend to be avoided as mates, 2) members of the same clan are excluded as potential mates, 3) mate exchange between clans is nonrandom, 4) individuals similar in age tend to be selected as mates, and 5) mates are drawn from the natal village more often than random expectation. A multivariate phenotypic distance measure between individuals did not reveal any evidence for assortative mating for either anthropometric or dermatoglyphic characters.

Published
1990

20. Pleiotropy of cardiometabolic syndrome with obesity-related anthropometric traits determined using empirically derived kinships from the Busselton Health Study

Author

Cadby, G., Melton, P., McCarthy, N., Almeida, M., Williams-Blangero, S., Curran, J., VandeBerg, J., Hui, J., Beilby, J., Musk, A., James, A., Hung, J., Blangero, J., Moses, Eric, Cadby, G., Melton, P., McCarthy, N., Almeida, M., Williams-Blangero, S., Curran, J., VandeBerg, J., Hui, J., Beilby, J., Musk, A., James, A., Hung, J., Blangero, J., and Moses, Eric

Abstract

Over two billion adults are overweight or obese and therefore at an increased risk of cardiometabolic syndrome (CMS). Obesity-related anthropometric traits genetically correlated with CMS may provide insight into CMS aetiology. The aim of this study was to utilise an empirically derived genetic relatedness matrix to calculate heritabilities and genetic correlations between CMS and anthropometric traits to determine whether they share genetic risk factors (pleiotropy). We used genome-wide single nucleotide polymorphism (SNP) data on 4671 Busselton Health Study participants. Exploiting both known and unknown relatedness, empirical kinship probabilities were estimated using these SNP data. General linear mixed models implemented in SOLAR were used to estimate narrow-sense heritabilities (h 2 ) and genetic correlations (r g ) between 15 anthropometric and 9 CMS traits. Anthropometric traits were adjusted by body mass index (BMI) to determine whether the observed genetic correlation was independent of obesity. After adjustment for multiple testing, all CMS and anthropometric traits were significantly heritable (h 2 range 0.18–0.57). We identified 50 significant genetic correlations (r g range: - 0.37 to 0.75) between CMS and anthropometric traits. Five genetic correlations remained significant after adjustment for BMI [high density lipoprotein cholesterol (HDL-C) and waist–hip ratio; triglycerides and waist–hip ratio; triglycerides and waist–height ratio; non-HDL-C and waist–height ratio; insulin and iliac skinfold thickness]. This study provides evidence for the presence of potentially pleiotropic genes that affect both anthropometric and CMS traits, independently of obesity.

Published
2017

21. Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts

Author

Mamtani, M, Kulkarni, H, Wong, G, Weir, JM, Barlow, CK, Dyer, TD, Almasy, L, Mahaney, MC, Comuzzie, AG, Glahn, DC, Magliano, DJ, Zimmet, P, Shaw, J, Williams-Blangero, S, Duggirala, R, Blangero, J, Meikle, PJ, Curran, JE, Mamtani, M, Kulkarni, H, Wong, G, Weir, JM, Barlow, CK, Dyer, TD, Almasy, L, Mahaney, MC, Comuzzie, AG, Glahn, DC, Magliano, DJ, Zimmet, P, Shaw, J, Williams-Blangero, S, Duggirala, R, Blangero, J, Meikle, PJ, and Curran, JE

Abstract

BACKGROUND: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. METHODS: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia--the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. RESULTS: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76%. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p

Published
2016

22. The transcriptional landscape of age in human peripheral blood

Author

Peters, MJ, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Göring, HHH, Fornage, M, Liu, Y, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, A, Uitterlinden, AG, Rivadeneira, F, Van Rooij, J, Stolk, L, Broer, L, Verbiest, MMPJ, Jhamai, M, Arp, P, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, C, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Völzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, McRae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE, Helmer, Q, Den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, Blangero, J, Peters, MJ, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Göring, HHH, Fornage, M, Liu, Y, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, A, Uitterlinden, AG, Rivadeneira, F, Van Rooij, J, Stolk, L, Broer, L, Verbiest, MMPJ, Jhamai, M, Arp, P, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, C, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Völzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, McRae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE, Helmer, Q, Den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, and Blangero, J

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.

Published
2015

23. The transcriptional landscape of age in human peripheral blood

Author

Peters, M.J. (Marjolein), Joehanes, R. (Roby), Pilling, L.C. (Luke), Schurmann, C. (Claudia), Conneely, K.N. (Karen N.), Powell, J.E. (Joseph), Reinmaa, E. (Eva), Sutphin, G.L. (George L.), Zhernakova, A. (Alexandra), Schramm, K. (Katharina), Wilson, Y.A. (Yana A.), Kobes, S. (Sayuko), Tukiainen, T. (Taru), Ramos, Y.F.M. (Yolande), Göring, H.H.H. (Harald H.), Fornage, M. (Myriam), Liu, Y. (YongMei), Gharib, S.A. (Sina), Stranger, B.E. (Barbara), Jager, P.L. (Philip) de, Aviv, A. (Abraham), Levy, D. (Daniel), Murabito, J. (Joanne), Munson, P.J. (Peter J.), Huan, T. (Tianxiao), Hofman, A. (Albert), Uitterlinden, A.G. (André), Rivadeneira Ramirez, F. (Fernando), Rooij, J.G.J. (Jeroen) van, Stolk, L. (Lisette), Broer, L. (Linda), Verbiest, M.M.P.J. (Michael), Jhamai, M. (Mila), Arp, P.P. (Pascal), Metspalu, A. (Andres), Tserel, L. (Liina), Milani, L. (Lili), Samani, N.J. (Nilesh), Peterson, P. (Pärt), Kasela, S. (Silva), Codd, V. (Veryan), Peters, A. (Annette), Ward-Caviness, C.K. (Cavin K.), Herder, C. (Christian), Waldenberger, M. (Melanie), Roden, M. (Michael), Singmann, P. (Paula), Zeilinger, S. (Sonja), Illig, T. (Thomas), Homuth, G. (Georg), Grabe, H.J. (Hans Jörgen), Völzke, H. (Henry), Steil, L. (Leif), Kocher, T. (Thomas), Murray, A. (Anna), Melzer, D. (David), Yaghootkar, H. (Hanieh), Bandinelli, S., Moses, E.K. (Eric), Kent, J.W. (Jack), Curran, J.E. (Joanne), Johnson, M.P. (Matthew), Williams-Blangero, S. (Sarah), Westra, H.J. (Harm-Jan), McRae, A.F. (Allan F.), Smith, J.A. (Jennifer A), Kardia, S.L.R. (Sharon), Hovatta, I. (Iiris), Perola, M. (Markus), Ripatti, S. (Samuli), Salomaa, V. (Veikko), Henders, A.K. (Anjali), Martin, N.G. (Nicholas), Smith, A.K. (Alicia K.), Mehta, D. (Divya), Binder, E.B. (Elisabeth B.), Nylocks, K.M. (K. Maria), Kennedy, E.M. (Elizabeth M.), Klengel, T. (Torsten), Ding, J. (Jingzhong), Suchy-Dicey, A. (Astrid), Enquobahrie, D., Brody, J.A. (Jennifer A.), Rotter, J.I. (Jerome I.), Chen, Y.-D.I. (Yii-Der I.), Houwing-Duistermaat, J.J. (Jeanine), Kloppenburg, M. (Margreet), Slagboom, P.E. (Eline), Helmer, Q. (Quinta), Hollander, W. (Wouter) den, Bean, S. (Shannon), Raj, T. (Towfique), Bakhshi, N. (Noman), Wang, Q.P. (Qiao Ping), Oyston, L.J. (Lisa J.), Psaty, B.M. (Bruce), Tracy, R.P. (Russell), Montgomery, G.W. (Grant), Turner, S.T. (Stephen), Blangero, J. (John), Meulenbelt, I. (Ingrid), Ressler, K.J. (Kerry), Yang, J. (Jian), Franke, L. (Lude), Kettunen, J. (Johannes), Visscher, P.M. (Peter), Neely, G.G. (G. Gregory), Korstanje, R. (Ron), Hanson, R.L. (Robert L.), Prokisch, H. (Holger), Ferrucci, L. (Luigi), Esko, T. (Tõnu), Teumer, A. (Alexander), Meurs, J.B.J. (Joyce) van, Johnson, A.D. (Andrew D.), Nalls, M.A. (Michael), Hernandez, D.G. (Dena), Cookson, M.R. (Mark), Gibbs, R.J. (Raphael J.), Hardy, J. (John), Ramasamy, A. (Adaikalavan), Zonderman, A.B. (Alan B.), Dillman, A. (Allissa), Traynor, B. (Bryan), Smith, C. (Colin), Longo, D.L. (Dan L.), Trabzuni, D. (Danyah), Troncoso, J.C. (Juan), Brug, M.P. (Marcel) van der, Weale, M.E. (Michael), O'Brien, R. (Richard), Johnson, R. (Robert), Walker, R. (Robert), Zielke, R.H. (Ronald H.), Arepalli, S. (Sampath), Ryten, M. (Mina), Singleton, A., Peters, M.J. (Marjolein), Joehanes, R. (Roby), Pilling, L.C. (Luke), Schurmann, C. (Claudia), Conneely, K.N. (Karen N.), Powell, J.E. (Joseph), Reinmaa, E. (Eva), Sutphin, G.L. (George L.), Zhernakova, A. (Alexandra), Schramm, K. (Katharina), Wilson, Y.A. (Yana A.), Kobes, S. (Sayuko), Tukiainen, T. (Taru), Ramos, Y.F.M. (Yolande), Göring, H.H.H. (Harald H.), Fornage, M. (Myriam), Liu, Y. (YongMei), Gharib, S.A. (Sina), Stranger, B.E. (Barbara), Jager, P.L. (Philip) de, Aviv, A. (Abraham), Levy, D. (Daniel), Murabito, J. (Joanne), Munson, P.J. (Peter J.), Huan, T. (Tianxiao), Hofman, A. (Albert), Uitterlinden, A.G. (André), Rivadeneira Ramirez, F. (Fernando), Rooij, J.G.J. (Jeroen) van, Stolk, L. (Lisette), Broer, L. (Linda), Verbiest, M.M.P.J. (Michael), Jhamai, M. (Mila), Arp, P.P. (Pascal), Metspalu, A. (Andres), Tserel, L. (Liina), Milani, L. (Lili), Samani, N.J. (Nilesh), Peterson, P. (Pärt), Kasela, S. (Silva), Codd, V. (Veryan), Peters, A. (Annette), Ward-Caviness, C.K. (Cavin K.), Herder, C. (Christian), Waldenberger, M. (Melanie), Roden, M. (Michael), Singmann, P. (Paula), Zeilinger, S. (Sonja), Illig, T. (Thomas), Homuth, G. (Georg), Grabe, H.J. (Hans Jörgen), Völzke, H. (Henry), Steil, L. (Leif), Kocher, T. (Thomas), Murray, A. (Anna), Melzer, D. (David), Yaghootkar, H. (Hanieh), Bandinelli, S., Moses, E.K. (Eric), Kent, J.W. (Jack), Curran, J.E. (Joanne), Johnson, M.P. (Matthew), Williams-Blangero, S. (Sarah), Westra, H.J. (Harm-Jan), McRae, A.F. (Allan F.), Smith, J.A. (Jennifer A), Kardia, S.L.R. (Sharon), Hovatta, I. (Iiris), Perola, M. (Markus), Ripatti, S. (Samuli), Salomaa, V. (Veikko), Henders, A.K. (Anjali), Martin, N.G. (Nicholas), Smith, A.K. (Alicia K.), Mehta, D. (Divya), Binder, E.B. (Elisabeth B.), Nylocks, K.M. (K. Maria), Kennedy, E.M. (Elizabeth M.), Klengel, T. (Torsten), Ding, J. (Jingzhong), Suchy-Dicey, A. (Astrid), Enquobahrie, D., Brody, J.A. (Jennifer A.), Rotter, J.I. (Jerome I.), Chen, Y.-D.I. (Yii-Der I.), Houwing-Duistermaat, J.J. (Jeanine), Kloppenburg, M. (Margreet), Slagboom, P.E. (Eline), Helmer, Q. (Quinta), Hollander, W. (Wouter) den, Bean, S. (Shannon), Raj, T. (Towfique), Bakhshi, N. (Noman), Wang, Q.P. (Qiao Ping), Oyston, L.J. (Lisa J.), Psaty, B.M. (Bruce), Tracy, R.P. (Russell), Montgomery, G.W. (Grant), Turner, S.T. (Stephen), Blangero, J. (John), Meulenbelt, I. (Ingrid), Ressler, K.J. (Kerry), Yang, J. (Jian), Franke, L. (Lude), Kettunen, J. (Johannes), Visscher, P.M. (Peter), Neely, G.G. (G. Gregory), Korstanje, R. (Ron), Hanson, R.L. (Robert L.), Prokisch, H. (Holger), Ferrucci, L. (Luigi), Esko, T. (Tõnu), Teumer, A. (Alexander), Meurs, J.B.J. (Joyce) van, Johnson, A.D. (Andrew D.), Nalls, M.A. (Michael), Hernandez, D.G. (Dena), Cookson, M.R. (Mark), Gibbs, R.J. (Raphael J.), Hardy, J. (John), Ramasamy, A. (Adaikalavan), Zonderman, A.B. (Alan B.), Dillman, A. (Allissa), Traynor, B. (Bryan), Smith, C. (Colin), Longo, D.L. (Dan L.), Trabzuni, D. (Danyah), Troncoso, J.C. (Juan), Brug, M.P. (Marcel) van der, Weale, M.E. (Michael), O'Brien, R. (Richard), Johnson, R. (Robert), Walker, R. (Robert), Zielke, R.H. (Ronald H.), Arepalli, S. (Sampath), Ryten, M. (Mina), and Singleton, A.

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.

Published
2015
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24. The transcriptional landscape of age in human peripheral blood

Author

Peters, Marjolein, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Goring, HHH, Fornage, M, Liu, YM, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, Bert, Uitterlinden, André, Rivadeneira, Fernando, van Rooij, J, Stolk, Lisette, Broer, Linda, Verbiest, Michael, Jhamai, M, Arp, Pascal, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, Cindy, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Voelzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, Mcrae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE (Eline), Helmer, Q, den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, Blangero, J, Meulenbelt, I, Ressler, KJ, Yang, Jiaqi, Franke, L, Kettunen, J, Visscher, PM, Neely, GG, Korstanje, R, Hanson, RL, Prokisch, H, Ferrucci, L, Esko, T, Teumer, A, van Meurs, Joyce, Andrew, D, Peters, Marjolein, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Goring, HHH, Fornage, M, Liu, YM, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, Bert, Uitterlinden, André, Rivadeneira, Fernando, van Rooij, J, Stolk, Lisette, Broer, Linda, Verbiest, Michael, Jhamai, M, Arp, Pascal, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, Cindy, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Voelzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, Mcrae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE (Eline), Helmer, Q, den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, Blangero, J, Meulenbelt, I, Ressler, KJ, Yang, Jiaqi, Franke, L, Kettunen, J, Visscher, PM, Neely, GG, Korstanje, R, Hanson, RL, Prokisch, H, Ferrucci, L, Esko, T, Teumer, A, van Meurs, Joyce, and Andrew, D

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.

Published
2015

25. The transcriptional landscape of age in human peripheral blood

Author

Peters, M., Joehanes, R., Pilling, L., Schurmann, C., Conneely, K., Powell, J., Reinmaa, E., Sutphin, G., Zhernakova, A., Schramm, K., Wilson, Y., Kobes, S., Tukiainen, T., Ramos, Y., Göring, H., Fornage, M., Liu, Y., Gharib, S., Stranger, B., De Jager, P., Aviv, A., Levy, D., Murabito, J., Munson, P., Huan, T., Hofman, A., Uitterlinden, A., Rivadeneira, F., Van Rooij, J., Stolk, L., Broer, L., Verbiest, M., Jhamai, M., Arp, P., Metspalu, A., Tserel, L., Milani, L., Samani, N., Peterson, P., Kasela, S., Codd, V., Peters, A., Ward-Caviness, C., Herder, C., Waldenberger, M., Roden, M., Singmann, P., Zeilinger, S., Illig, T., Homuth, G., Grabe, H., Völzke, H., Steil, L., Kocher, T., Murray, A., Melzer, D., Yaghootkar, H., Bandinelli, S., Moses, Eric, Kent, J., Curran, J., Johnson, M., Williams-Blangero, S., Westra, H., McRae, A., Smith, J., Kardia, S., Hovatta, I., Perola, M., Ripatti, S., Salomaa, V., Henders, A., Peters, M., Joehanes, R., Pilling, L., Schurmann, C., Conneely, K., Powell, J., Reinmaa, E., Sutphin, G., Zhernakova, A., Schramm, K., Wilson, Y., Kobes, S., Tukiainen, T., Ramos, Y., Göring, H., Fornage, M., Liu, Y., Gharib, S., Stranger, B., De Jager, P., Aviv, A., Levy, D., Murabito, J., Munson, P., Huan, T., Hofman, A., Uitterlinden, A., Rivadeneira, F., Van Rooij, J., Stolk, L., Broer, L., Verbiest, M., Jhamai, M., Arp, P., Metspalu, A., Tserel, L., Milani, L., Samani, N., Peterson, P., Kasela, S., Codd, V., Peters, A., Ward-Caviness, C., Herder, C., Waldenberger, M., Roden, M., Singmann, P., Zeilinger, S., Illig, T., Homuth, G., Grabe, H., Völzke, H., Steil, L., Kocher, T., Murray, A., Melzer, D., Yaghootkar, H., Bandinelli, S., Moses, Eric, Kent, J., Curran, J., Johnson, M., Williams-Blangero, S., Westra, H., McRae, A., Smith, J., Kardia, S., Hovatta, I., Perola, M., Ripatti, S., Salomaa, V., and Henders, A.

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.

Published
2015

26. Effects of copy number variable regions on local gene expression in white blood cells of Mexican Americans

Author

Blackburn, A., Almeida, M., Dean, A., Curran, J., Johnson, M., Moses, Eric, Abraham, L., Carless, M., Dyer, T., Kumar, S., Almasy, L., Mahaney, M., Comuzzie, A., Williams-Blangero, S., Blangero, J., Lehman, D., Göring, H., Blackburn, A., Almeida, M., Dean, A., Curran, J., Johnson, M., Moses, Eric, Abraham, L., Carless, M., Dyer, T., Kumar, S., Almasy, L., Mahaney, M., Comuzzie, A., Williams-Blangero, S., Blangero, J., Lehman, D., and Göring, H.

Abstract

Only few systematic studies on the contribution of copy number variation to gene expression variation have been published to date. Here we identify effects of copy number variable regions (CNVRs) on nearby gene expression by investigating 909 CNVRs and expression levels of 12059 nearby genes in white blood cells from Mexican-American participants of the San Antonio Family Heart Study. We empirically evaluate our ability to detect the contribution of CNVs to proximal gene expression (presumably in cis) at various window sizes (up to a 10 Mb distance) between the gene and CNV. We found a ~1-Mb window size to be optimal for capturing cis effects of CNVs. Up to 10% of the CNVs in this study were found to be significantly associated with the expression of at least one gene within their vicinity. As expected, we find that CNVs that directly overlap gene sequences have the largest effects on gene expression (compared with non-overlapping CNVRs located nearby), with positive correlation (except for a few exceptions) between estimated genomic dosage and expression level. We find that genes whose expression level is significantly influenced by nearby CNVRs are enriched for immunity and autoimmunity related genes. These findings add to the currently limited catalog of CNVRs that are recognized as expression quantitative trait loci, and have implications for future study designs as well as for prioritizing candidate causal variants in genomic regions associated with disease.

Published
2015

27. Phenotypic consequences of nonrandom migration in the Jirels of Nepal

Author

Williams-Blangero, S.

Subjects
Phenotype -- Research, Human population genetics -- Research, Anthropology/archeology/folklore
Abstract

The phenotypic structure of human populations is shaped by a number of factors such as population size and martial migration. This paper examines the impact of migration on the between-village phenotypic differentiation of the Jirels, a tribal group of eastern Nepal. Data on stature and five cranial measurements for 526 individuals (males and females) are utilized to illustrate the patterns of phenotypic variation. A permutation method is used to generate the phenotypic consequences of random migration constrained to observed levels of movement. The results suggest that Jirel migration is nonrandom and that it produces higher levels of phenotypic differentiation than would result from a random migration process.

Published
1989

28. Systems genetics of the nuclear factor-?B signal transduction network. I. Detection of several quantitative trait loci potentially relevant to aging

Author

Diego, V., Curran, J., Charlesworth, J., Peralta, J., Voruganti, V., Cole, S., Dyer, T., Johnson, M., Moses, Eric, Göring, H., Williams, J., Comuzzie, A., Almasy, L., Blangero, J., Williams-Blangero, S., Diego, V., Curran, J., Charlesworth, J., Peralta, J., Voruganti, V., Cole, S., Dyer, T., Johnson, M., Moses, Eric, Göring, H., Williams, J., Comuzzie, A., Almasy, L., Blangero, J., and Williams-Blangero, S.

Abstract

A theory of aging holds that senescence is caused by a dysregulated nuclear factor kappa B (NF-κB) signal transduction network (STN). We adopted a systems genetics approach in our study of the NF-κB STN. Ingenuity Pathways Analysis (IPA) was used to identify gene/gene product interactions between NF-κB and the genes in our transcriptional profiling array. Principal components factor analysis (PCFA) was performed on a sub-network of 19 genes, including two initiators of the toll-like receptor (TLR) pathway, myeloid differentiation primary response gene (88) (MyD88) and TIR (Toll/interleukin-1 receptor)-domain-containing adapter-inducing interferon-β (TRIF). TLR pathways are either MyD88-dependent or TRIF-dependent. Therefore, we also performed PCFA on a subset excluding the MyD88 transcript, and on another subset excluding two TRIF transcripts. Using linkage analysis we found that each set gave rise to at least one factor with a logarithm of the odds (LOD) score greater than 3, two on chromosome 15 at 15q12 and 15q22.2, and another two on chromosome 17 at 17p13.3 and 17q25.3. We also found several suggestive signals (2 < LOD score < 3) at 1q32.1, 1q41, 2q34, 3q23, and 7p15.3. We are currently examining potential associations with single nucleotide polymorphisms within the 1-LOD intervals of our linkage signals.

Published
2012

29. Genotype × age interaction in human transcriptional ageing

Author

Kent, J., Göring, H., Charlesworth, J., Drigalenko, E., Diego, V., Curran, J., Johnson, M., Dyer, T., Cole, S., Jowett, J., Mahaney, M., Comuzzie, A., Almasy, L., Moses, Eric, Blangero, J., Williams-Blangero, S., Kent, J., Göring, H., Charlesworth, J., Drigalenko, E., Diego, V., Curran, J., Johnson, M., Dyer, T., Cole, S., Jowett, J., Mahaney, M., Comuzzie, A., Almasy, L., Moses, Eric, Blangero, J., and Williams-Blangero, S.

Abstract

Individual differences in biological ageing (i.e., the rate of physiological response to the passage of time) may be due in part to genotype-specific variation in gene action. However, the sources of heritable variation in human age-related gene expression profiles are largely unknown. We have profiled genome-wide expression in peripheral blood mononuclear cells from 1240 individuals in large families and found 4472 human autosomal transcripts, representing ~4349 genes, significantly correlated with age. We identified 623 transcripts that show genotype by age interaction in addition to a main effect of age, defining a large set of novel candidates for characterization of the mechanisms of differential biological ageing. We applied a novel SNP genotype × age interaction test to one of these candidates, the ubiquilin-like gene UBQLNL, and found evidence of joint cis-association and genotype by age interaction as well as trans-genotype by age interaction for UBQLNL expression. Both UBQLNL expression levels at recruitment and cis genotype are associated with longitudinal cancer risk in our study cohort.

Published
2012

34. Erratum

Author

Beall, C.M., Brittenham, G.M., Strohl, K.P., Blangero, J., Williams-Blangero, S., Goldstein, M.C., and Gonzales, C.

Subjects
Hemoglobin -- Research, Tibetans -- Physiological aspects, Anthropology/archeology/folklore
Published
1998

43. Exposure to Schistosoma mansoni infection in a rural area in Brazil. II: household risk factors.

Author

Bethony, Jeffrey, Williams, Jeff T., Kloos, Helmut, Blangero, John, Alves-Fraga, Lucia, Buck, Germaine, Michalek, Arthur, Williams-Blangero, Sarah, LoVerde, Philip T., Correa-Oliveira, Rodrigo, Gazzinelli, Andrea, Bethony, J, Williams, J T, Kloos, H, Blangero, J, Alves-Fraga, L, Buck, G, Michalek, A, Williams-Blangero, S, and Loverde, P T

Subjects
SCHISTOSOMA mansoni, HOUSEHOLDS
Abstract

A number of studies have pointed out the potential importance of the household in the transmission of schistosomiasis. The clustering of domestic activities associated with water collection, storage, and usage can result in the sharing of transmission sites and infective water contact behaviours. In this study, we employed a variance component method to estimate effects due to individual risk factors and shared residence on the variance in faecal egg counts during Schistosoma mansoni infection. A suite of covariates, which included demographic, socioeconomic, water supply, and water contact behaviour terms, contributed 15% to the variance in faecal egg counts. Shared residence alone accounted for 28% of the variance in faecal egg excretion. When both the suite of covariates and shared residence were considered in the same model, shared residence still contributed 22% to the variance in infection intensity. These results point to the importance of shared residence as a means of capturing the complex interrelationship between shared demographic, socioeconomic, physical environmental, and behavioural factors that influence transmission of schistosomiasis at the household level. [ABSTRACT FROM AUTHOR]

Published
2001
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50. Genotype-by-Environment Interactions in Nonalcoholic Fatty Liver Disease and Chronic Illness among Mexican Americans: The Role of Acculturation Stress.

Author

Manusov EG, Diego VP, Almeida M, Ortiz D, Curran JE, Galan J, Leandro AC, Laston S, Blangero J, and Williams-Blangero S

Subjects
Humans, Male, Female, Adult, Middle Aged, Chronic Disease, Genotype, Stress, Psychological genetics, Genetic Predisposition to Disease, Obesity genetics, Texas epidemiology, Gene-Environment Interaction, Non-alcoholic Fatty Liver Disease genetics, Non-alcoholic Fatty Liver Disease pathology, Non-alcoholic Fatty Liver Disease ethnology, Mexican Americans genetics, Acculturation
Abstract

This study examines the complex interplay of genetic and environmental interactions that shape chronic illness risk. Evidence is mounting for the role of genetic expression and the immune response in the pathogenesis of chronic disease. In the Rio Grande Valley of south Texas, where 90% of the population is Mexican American, chronic illnesses (including obesity, diabetes, nonalcoholic liver disease, and depression) are reaching epidemic proportions. This study leverages an ongoing family study of the genetic determinants of risk for obesity, diabetes, hypertension, hyperlipidemia, and depression in a Mexican American population. Data collected included blood pressure, BMI, hepatic transaminases, HbA1c, depression (BDI-II), acculturation/marginalization (ARSMA-II), and liver health as assessed by elastography. Heritability and genotype-by-environment (G×E) interactions were analyzed, focusing on the marginalization/separation measure of the ARSMA-II. Significant heritabilities were found for traits such as HbA1c (h 2 = 0.52), marginalization (h 2 = 0.30), AST (h 2 = 0.25), ALT (h 2 = 0.41), and BMI (h 2 = 0.55). Genotype-by-environment interactions were significant for HbA1c, AST/ALT ratio, BDI-II, and CAP, indicating that genetic factors interact with marginalization to influence these traits. This study found that acculturation stress exacerbates the genetic response to chronic illness. These findings underscore the importance of considering G×E interactions in understanding disease susceptibility and may inform targeted interventions for at-risk populations. Further research is warranted to elucidate the underlying molecular pathways and replicate these findings in diverse populations.

Published
2024
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