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1.  Refining genome-wide linkage intervals using a meta-analysis of genome-wide association studies identifies loci influencing personality dimensions 
Personality traits are complex phenotypes related to psychosomatic health. Individually, various gene finding methods have not achieved much success in finding genetic variants associated with personality traits. We performed a meta-analysis of four genome-wide linkage scans (N=6149 subjects) of five basic personality traits assessed with the NEO Five-Factor Inventory. We compared the significant regions from the meta-analysis of linkage scans with the results of a meta-analysis of genome-wide association studies (GWAS) (N∼17 000). We found significant evidence of linkage of neuroticism to chromosome 3p14 (rs1490265, LOD=4.67) and to chromosome 19q13 (rs628604, LOD=3.55); of extraversion to 14q32 (ATGG002, LOD=3.3); and of agreeableness to 3p25 (rs709160, LOD=3.67) and to two adjacent regions on chromosome 15, including 15q13 (rs970408, LOD=4.07) and 15q14 (rs1055356, LOD=3.52) in the individual scans. In the meta-analysis, we found strong evidence of linkage of extraversion to 4q34, 9q34, 10q24 and 11q22, openness to 2p25, 3q26, 9p21, 11q24, 15q26 and 19q13 and agreeableness to 4q34 and 19p13. Significant evidence of association in the GWAS was detected between openness and rs677035 at 11q24 (P-value=2.6 × 10−06, KCNJ1). The findings of our linkage meta-analysis and those of the GWAS suggest that 11q24 is a susceptible locus for openness, with KCNJ1 as the possible candidate gene.
doi:10.1038/ejhg.2012.263
PMCID: PMC3722675  PMID: 23211697
personality; KCNJ1; NEO; linkage; GSMA
2.  Analytical and simulation methods for estimating the potential predictive ability of genetic profiling: a comparison of methods and results 
European Journal of Human Genetics  2012;20(12):1270-1274.
Various modeling methods have been proposed to estimate the potential predictive ability of polygenic risk variants that predispose to various common diseases. However, it is unknown whether differences between them affect their conclusions on predictive ability. We reviewed input parameters, assumptions and output of the five most common methods and compared their estimates of the area under the receiver operating characteristic (ROC) curve (AUC) using hypothetical data representing effect sizes and frequencies of genetic variants, population disease risk and number of variants. To assess the accuracy of the estimated AUCs, we aimed to reproduce the AUCs of published empirical studies. All methods assumed that the combined effect of genetic variants on disease risk followed a multiplicative risk model of independent genetic effects, but they either assumed per allele, per genotype or dominant/recessive effects for the genetic variants. Modeling strategy and input parameters differed. Methods used simulation analysis or analytical formulas with effect sizes quantified by odds ratios (ORs) or relative risks. Estimated AUC values were similar for lower ORs (<1.2). When AUCs were larger (>0.7) due to variants with strong effects, differences in estimated AUCs between methods increased. The simulation methods accurately reproduced the AUC values of empirical studies, but the analytical methods did not. We conclude that despite differences in input parameters, the modeling methods estimate similar AUC for realistic values of the ORs. When one or more variants have stronger effects and AUC values are higher, the simulation methods tend to be more accurate.
doi:10.1038/ejhg.2012.89
PMCID: PMC3499740  PMID: 22643180
risk prediction; modeling; discriminative accuracy; AUC; complex disease
3.  Ethnic differences and parental beliefs are important for overweight prevention and management in children: a cross-sectional study in the Netherlands 
BMC Public Health  2012;12:867.
Background
The prevalence of obesity and overweight is highest among ethnic minority groups in Western countries. The objective of this study is to examine the contribution of ethnicity and beliefs of parents about overweight preventive behaviours to their child’s outdoor play and snack intake, and to the parents’ intention to monitor these behaviours.
Methods
A cross-sectional survey was conducted among parents of native Dutch children and children from a large minority population (Turks) at primary schools, sampled from Youth Health Care registers.
Results
Native Dutch parents observed more outdoor play and lower snack intake in their child and had stronger intentions to monitor these behaviours than parents of Turkish descent. In the multivariate analyses, the parents’ attitude and social norm were the main contributing factors to the parental intention to monitor the child’s outdoor play and snack intake. Parental perceived behavioural control contributed to the child’s outdoor play and, in parents who perceived their child to be overweight, to snacking behaviour. The associations between parents’ behavioural cognitions and overweight related preventive behaviours were not modified by ethnicity, except for perceived social norm. The relationship between social norm and intention to monitor outdoor play was stronger in Dutch parents than in Turkish parents.
Conclusions
As the overweight related preventive behaviours of both children and parents did differ between the native and ethnic minority populations of this study, it is advised that interventions pay attention to cultural aspects of the targeted population. Further research is recommended into parental behavioural cognitions regarding overweight prevention and management for different ethnicities.
doi:10.1186/1471-2458-12-867
PMCID: PMC3508795  PMID: 23057582
Child obesity; Overweight; Culture; Health promotion
4.  Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure 
Wain, Louise V | Verwoert, Germaine C | O’Reilly, Paul F | Shi, Gang | Johnson, Toby | Johnson, Andrew D | Bochud, Murielle | Rice, Kenneth M | Henneman, Peter | Smith, Albert V | Ehret, Georg B | Amin, Najaf | Larson, Martin G | Mooser, Vincent | Hadley, David | Dörr, Marcus | Bis, Joshua C | Aspelund, Thor | Esko, Tõnu | Janssens, A Cecile JW | Zhao, Jing Hua | Heath, Simon | Laan, Maris | Fu, Jingyuan | Pistis, Giorgio | Luan, Jian’an | Arora, Pankaj | Lucas, Gavin | Pirastu, Nicola | Pichler, Irene | Jackson, Anne U | Webster, Rebecca J | Zhang, Feng | Peden, John F | Schmidt, Helena | Tanaka, Toshiko | Campbell, Harry | Igl, Wilmar | Milaneschi, Yuri | Hotteng, Jouke-Jan | Vitart, Veronique | Chasman, Daniel I | Trompet, Stella | Bragg-Gresham, Jennifer L | Alizadeh, Behrooz Z | Chambers, John C | Guo, Xiuqing | Lehtimäki, Terho | Kühnel, Brigitte | Lopez, Lorna M | Polašek, Ozren | Boban, Mladen | Nelson, Christopher P | Morrison, Alanna C | Pihur, Vasyl | Ganesh, Santhi K | Hofman, Albert | Kundu, Suman | Mattace-Raso, Francesco US | Rivadeneira, Fernando | Sijbrands, Eric JG | Uitterlinden, Andre G | Hwang, Shih-Jen | Vasan, Ramachandran S | Wang, Thomas J | Bergmann, Sven | Vollenweider, Peter | Waeber, Gérard | Laitinen, Jaana | Pouta, Anneli | Zitting, Paavo | McArdle, Wendy L | Kroemer, Heyo K | Völker, Uwe | Völzke, Henry | Glazer, Nicole L | Taylor, Kent D | Harris, Tamara B | Alavere, Helene | Haller, Toomas | Keis, Aime | Tammesoo, Mari-Liis | Aulchenko, Yurii | Barroso, Inês | Khaw, Kay-Tee | Galan, Pilar | Hercberg, Serge | Lathrop, Mark | Eyheramendy, Susana | Org, Elin | Sõber, Siim | Lu, Xiaowen | Nolte, Ilja M | Penninx, Brenda W | Corre, Tanguy | Masciullo, Corrado | Sala, Cinzia | Groop, Leif | Voight, Benjamin F | Melander, Olle | O’Donnell, Christopher J | Salomaa, Veikko | d’Adamo, Adamo Pio | Fabretto, Antonella | Faletra, Flavio | Ulivi, Sheila | Del Greco, M Fabiola | Facheris, Maurizio | Collins, Francis S | Bergman, Richard N | Beilby, John P | Hung, Joseph | Musk, A William | Mangino, Massimo | Shin, So-Youn | Soranzo, Nicole | Watkins, Hugh | Goel, Anuj | Hamsten, Anders | Gider, Pierre | Loitfelder, Marisa | Zeginigg, Marion | Hernandez, Dena | Najjar, Samer S | Navarro, Pau | Wild, Sarah H | Corsi, Anna Maria | Singleton, Andrew | de Geus, Eco JC | Willemsen, Gonneke | Parker, Alex N | Rose, Lynda M | Buckley, Brendan | Stott, David | Orru, Marco | Uda, Manuela | van der Klauw, Melanie M | Zhang, Weihua | Li, Xinzhong | Scott, James | Chen, Yii-Der Ida | Burke, Gregory L | Kähönen, Mika | Viikari, Jorma | Döring, Angela | Meitinger, Thomas | Davies, Gail | Starr, John M | Emilsson, Valur | Plump, Andrew | Lindeman, Jan H | ’t Hoen, Peter AC | König, Inke R | Felix, Janine F | Clarke, Robert | Hopewell, Jemma C | Ongen, Halit | Breteler, Monique | Debette, Stéphanie | DeStefano, Anita L | Fornage, Myriam | Mitchell, Gary F | Smith, Nicholas L | Holm, Hilma | Stefansson, Kari | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Samani, Nilesh J | Preuss, Michael | Rudan, Igor | Hayward, Caroline | Deary, Ian J | Wichmann, H-Erich | Raitakari, Olli T | Palmas, Walter | Kooner, Jaspal S | Stolk, Ronald P | Jukema, J Wouter | Wright, Alan F | Boomsma, Dorret I | Bandinelli, Stefania | Gyllensten, Ulf B | Wilson, James F | Ferrucci, Luigi | Schmidt, Reinhold | Farrall, Martin | Spector, Tim D | Palmer, Lyle J | Tuomilehto, Jaakko | Pfeufer, Arne | Gasparini, Paolo | Siscovick, David | Altshuler, David | Loos, Ruth JF | Toniolo, Daniela | Snieder, Harold | Gieger, Christian | Meneton, Pierre | Wareham, Nicholas J | Oostra, Ben A | Metspalu, Andres | Launer, Lenore | Rettig, Rainer | Strachan, David P | Beckmann, Jacques S | Witteman, Jacqueline CM | Erdmann, Jeanette | van Dijk, Ko Willems | Boerwinkle, Eric | Boehnke, Michael | Ridker, Paul M | Jarvelin, Marjo-Riitta | Chakravarti, Aravinda | Abecasis, Goncalo R | Gudnason, Vilmundur | Newton-Cheh, Christopher | Levy, Daniel | Munroe, Patricia B | Psaty, Bruce M | Caulfield, Mark J | Rao, Dabeeru C | Tobin, Martin D | Elliott, Paul | van Duijn, Cornelia M
Nature genetics  2011;43(10):1005-1011.
Numerous genetic loci influence systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans 1-3. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N=74,064) and follow-up studies (N=48,607), we identified at genome-wide significance (P= 2.7×10-8 to P=2.3×10-13) four novel PP loci (at 4q12 near CHIC2/PDGFRAI, 7q22.3 near PIK3CG, 8q24.12 in NOV, 11q24.3 near ADAMTS-8), two novel MAP loci (3p21.31 in MAP4, 10q25.3 near ADRB1) and one locus associated with both traits (2q24.3 near FIGN) which has recently been associated with SBP in east Asians. For three of the novel PP signals, the estimated effect for SBP was opposite to that for DBP, in contrast to the majority of common SBP- and DBP-associated variants which show concordant effects on both traits. These findings indicate novel genetic mechanisms underlying blood pressure variation, including pathways that may differentially influence SBP and DBP.
doi:10.1038/ng.922
PMCID: PMC3445021  PMID: 21909110
5.  Meta-analyses identify 13 novel loci associated with age at menopause and highlights DNA repair and immune pathways 
Stolk, Lisette | Perry, John RB | Chasman, Daniel I | He, Chunyan | Mangino, Massimo | Sulem, Patrick | Barbalic, Maja | Broer, Linda | Byrne, Enda M | Ernst, Florian | Esko, Tõnu | Franceschini, Nora | Gudbjartsson, Daniel F | Hottenga, Jouke-Jan | Kraft, Peter | McArdle, Patick F | Porcu, Eleonora | Shin, So-Youn | Smith, Albert V | van Wingerden, Sophie | Zhai, Guangju | Zhuang, Wei V | Albrecht, Eva | Alizadeh, Behrooz Z | Aspelund, Thor | Bandinelli, Stefania | Lauc, Lovorka Barac | Beckmann, Jacques S | Boban, Mladen | Boerwinkle, Eric | Broekmans, Frank J | Burri, Andrea | Campbell, Harry | Chanock, Stephen J | Chen, Constance | Cornelis, Marilyn C | Corre, Tanguy | Coviello, Andrea D | d’Adamo, Pio | Davies, Gail | de Faire, Ulf | de Geus, Eco JC | Deary, Ian J | Dedoussis, George VZ | Deloukas, Panagiotis | Ebrahim, Shah | Eiriksdottir, Gudny | Emilsson, Valur | Eriksson, Johan G | Fauser, Bart CJM | Ferreli, Liana | Ferrucci, Luigi | Fischer, Krista | Folsom, Aaron R | Garcia, Melissa E | Gasparini, Paolo | Gieger, Christian | Glazer, Nicole | Grobbee, Diederick E | Hall, Per | Haller, Toomas | Hankinson, Susan E | Hass, Merli | Hayward, Caroline | Heath, Andrew C | Hofman, Albert | Ingelsson, Erik | Janssens, A Cecile JW | Johnson, Andrew D | Karasik, David | Kardia, Sharon LR | Keyzer, Jules | Kiel, Douglas P | Kolcic, Ivana | Kutalik, Zoltán | Lahti, Jari | Lai, Sandra | Laisk, Triin | Laven, Joop SE | Lawlor, Debbie A | Liu, Jianjun | Lopez, Lorna M | Louwers, Yvonne V | Magnusson, Patrik KE | Marongiu, Mara | Martin, Nicholas G | Klaric, Irena Martinovic | Masciullo, Corrado | McKnight, Barbara | Medland, Sarah E | Melzer, David | Mooser, Vincent | Navarro, Pau | Newman, Anne B | Nyholt, Dale R | Onland-Moret, N. Charlotte | Palotie, Aarno | Paré, Guillaume | Parker, Alex N | Pedersen, Nancy L | Peeters, Petra HM | Pistis, Giorgio | Plump, Andrew S | Polasek, Ozren | Pop, Victor JM | Psaty, Bruce M | Räikkönen, Katri | Rehnberg, Emil | Rotter, Jerome I | Rudan, Igor | Sala, Cinzia | Salumets, Andres | Scuteri, Angelo | Singleton, Andrew | Smith, Jennifer A | Snieder, Harold | Soranzo, Nicole | Stacey, Simon N | Starr, John M | Stathopoulou, Maria G | Stirrups, Kathleen | Stolk, Ronald P | Styrkarsdottir, Unnur | Sun, Yan V | Tenesa, Albert | Thorand, Barbara | Toniolo, Daniela | Tryggvadottir, Laufey | Tsui, Kim | Ulivi, Sheila | van Dam, Rob M | van der Schouw, Yvonne T | van Gils, Carla H | van Nierop, Peter | Vink, Jacqueline M | Visscher, Peter M | Voorhuis, Marlies | Waeber, Gérard | Wallaschofski, Henri | Wichmann, H Erich | Widen, Elisabeth | Gent, Colette JM Wijnands-van | Willemsen, Gonneke | Wilson, James F | Wolffenbuttel, Bruce HR | Wright, Alan F | Yerges-Armstrong, Laura M | Zemunik, Tatijana | Zgaga, Lina | Zillikens, M. Carola | Zygmunt, Marek | Arnold, Alice M | Boomsma, Dorret I | Buring, Julie E. | Crisponi, Laura | Demerath, Ellen W | Gudnason, Vilmundur | Harris, Tamara B | Hu, Frank B | Hunter, David J | Launer, Lenore J | Metspalu, Andres | Montgomery, Grant W | Oostra, Ben A | Ridker, Paul M | Sanna, Serena | Schlessinger, David | Spector, Tim D | Stefansson, Kari | Streeten, Elizabeth A | Thorsteinsdottir, Unnur | Uda, Manuela | Uitterlinden, André G | van Duijn, Cornelia M | Völzke, Henry | Murray, Anna | Murabito, Joanne M | Visser, Jenny A | Lunetta, Kathryn L
Nature Genetics  2012;44(3):260-268.
To identify novel loci for age at natural menopause, we performed a meta-analysis of 22 genome-wide association studies in 38,968 women of European descent, with replication in up to 14,435 women. In addition to four known loci, we identified 13 new age at natural menopause loci (P < 5 × 10−8). The new loci included genes implicated in DNA repair (EXO1, HELQ, UIMC1, FAM175A, FANCI, TLK1, POLG, PRIM1) and immune function (IL11, NLRP11, BAT2). Gene-set enrichment pathway analyses using the full GWAS dataset identified exodeoxyribonuclease, NFκB signalling and mitochondrial dysfunction as biological processes related to timing of menopause.
doi:10.1038/ng.1051
PMCID: PMC3288642  PMID: 22267201
6.  Genetic architecture of circulating lipid levels 
Serum concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs) and total cholesterol (TC) are important heritable risk factors for cardiovascular disease. Although genome-wide association studies (GWASs) of circulating lipid levels have identified numerous loci, a substantial portion of the heritability of these traits remains unexplained. Evidence of unexplained genetic variance can be detected by combining multiple independent markers into additive genetic risk scores. Such polygenic scores, constructed using results from the ENGAGE Consortium GWAS on serum lipids, were applied to predict lipid levels in an independent population-based study, the Rotterdam Study-II (RS-II). We additionally tested for evidence of a shared genetic basis for different lipid phenotypes. Finally, the polygenic score approach was used to identify an alternative genome-wide significance threshold before pathway analysis and those results were compared with those based on the classical genome-wide significance threshold. Our study provides evidence suggesting that many loci influencing circulating lipid levels remain undiscovered. Cross-prediction models suggested a small overlap between the polygenic backgrounds involved in determining LDL-C, HDL-C and TG levels. Pathway analysis utilizing the best polygenic score for TC uncovered extra information compared with using only genome-wide significant loci. These results suggest that the genetic architecture of circulating lipids involves a number of undiscovered variants with very small effects, and that increasing GWAS sample sizes will enable the identification of novel variants that regulate lipid levels.
doi:10.1038/ejhg.2011.21
PMCID: PMC3137496  PMID: 21448234
serum lipids; polygenic; genome-wide association; polygenic score; pathway analysis
7.  Discriminative accuracy of genomic profiling comparing multiplicative and additive risk models 
Genetic prediction of common diseases is based on testing multiple genetic variants with weak effect sizes. Standard logistic regression and Cox Proportional Hazard models that assess the combined effect of multiple variants on disease risk assume multiplicative joint effects of the variants, but this assumption may not be correct. The risk model chosen may affect the predictive accuracy of genomic profiling. We investigated the discriminative accuracy of genomic profiling by comparing additive and multiplicative risk models. We examined genomic profiles of 40 variants with genotype frequencies varying from 0.1 to 0.4 and relative risks varying from 1.1 to 1.5 in separate scenarios assuming a disease risk of 10%. The discriminative accuracy was evaluated by the area under the receiver operating characteristic curve. Predicted risks were more extreme at the lower and higher risks for the multiplicative risk model compared with the additive model. The discriminative accuracy was consistently higher for multiplicative risk models than for additive risk models. The differences in discriminative accuracy were negligible when the effect sizes were small (<1.2), but were substantial when risk genotypes were common or when they had stronger effects. Unraveling the exact mode of biological interaction is important when effect sizes of genetic variants are moderate at the least, to prevent the incorrect estimation of risks.
doi:10.1038/ejhg.2010.165
PMCID: PMC3025793  PMID: 21081969
discriminative accuracy; genomic profiles; sensitivity; specificity; additive models
8.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies, building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published on the EJHG website.
doi:10.1038/ejhg.2011.25
PMCID: PMC3172920  PMID: 21407265
9.  Meta-analysis of genome-wide association for migraine in six population-based European cohorts 
Migraine is a common neurological disorder with a genetically complex background. This paper describes a meta-analysis of genome-wide association (GWA) studies on migraine, performed by the Dutch–Icelandic migraine genetics (DICE) consortium, which brings together six population-based European migraine cohorts with a total sample size of 10 980 individuals (2446 cases and 8534 controls). A total of 32 SNPs showed marginal evidence for association at a P-value<10−5. The best result was obtained for SNP rs9908234, which had a P-value of 8.00 × 10−8. This top SNP is located in the nerve growth factor receptor (NGFR) gene. However, this SNP did not replicate in three cohorts from the Netherlands and Australia. Of the other 31 SNPs, 18 SNPs were tested in two replication cohorts, but none replicated. In addition, we explored previously identified candidate genes in the meta-analysis data set. This revealed a modest gene-based significant association between migraine and the metadherin (MTDH) gene, previously identified in the first clinic-based GWA study (GWAS) for migraine (Bonferroni-corrected gene-based P-value=0.026). This finding is consistent with the involvement of the glutamate pathway in migraine. Additional research is necessary to further confirm the involvement of glutamate.
doi:10.1038/ejhg.2011.48
PMCID: PMC3172930  PMID: 21448238
migraine; meta-analysis; genome-wide association; population-based
10.  Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability 
Genome Medicine  2011;3(7):51.
Background
Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.
Methods
We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.
Results
We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.
Conclusions
The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC.
doi:10.1186/gm267
PMCID: PMC3221548  PMID: 21797996
11.  Personal genome testing: Test characteristics to clarify the discourse on ethical, legal and societal issues 
BMC Medical Ethics  2011;12:11.
Background
As genetics technology proceeds, practices of genetic testing have become more heterogeneous: many different types of tests are finding their way to the public in different settings and for a variety of purposes. This diversification is relevant to the discourse on ethical, legal and societal issues (ELSI) surrounding genetic testing, which must evolve to encompass these differences. One important development is the rise of personal genome testing on the basis of genetic profiling: the testing of multiple genetic variants simultaneously for the prediction of common multifactorial diseases. Currently, an increasing number of companies are offering personal genome tests directly to consumers and are spurring ELSI-discussions, which stand in need of clarification. This paper presents a systematic approach to the ELSI-evaluation of personal genome testing for multifactorial diseases along the lines of its test characteristics.
Discussion
This paper addresses four test characteristics of personal genome testing: its being a non-targeted type of testing, its high analytical validity, low clinical validity and problematic clinical utility. These characteristics raise their own specific ELSI, for example: non-targeted genetic profiling poses serious problems for information provision and informed consent. Questions about the quantity and quality of the necessary information, as well as about moral responsibilities with regard to the provision of information are therefore becoming central themes within ELSI-discussions of personal genome testing. Further, the current low level of clinical validity of genetic profiles raises questions concerning societal risks and regulatory requirements, whereas simultaneously it causes traditional ELSI-issues of clinical genetics, such as psychological and health risks, discrimination, and stigmatization, to lose part of their relevance. Also, classic notions of clinical utility are challenged by the newer notion of 'personal utility.'
Summary
Consideration of test characteristics is essential to any valuable discourse on the ELSI of personal genome testing for multifactorial diseases. Four key characteristics of the test - targeted/non-targeted testing, analytical validity, clinical validity and clinical utility - together determine the applicability and the relevance of ELSI to specific tests. The paper identifies and discusses four areas of interest for the ELSI-debate on personal genome testing: informational problems, risks, regulatory issues, and the notion of personal utility.
doi:10.1186/1472-6939-12-11
PMCID: PMC3141793  PMID: 21672210
12.  Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration 
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.
doi:10.1038/ejhg.2011.27
PMCID: PMC3083630  PMID: 21407270
13.  Strengthening the reporting of genetic risk prediction studies: the GRIPS statement 
Genome Medicine  2011;3(3):16.
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of genetic risk prediction studies (the GRIPS statement), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published at http://www.plosmedicine.org.
doi:10.1186/gm230
PMCID: PMC3092101  PMID: 21410995
14.  Comparison of participant information and informed consent forms of five European studies in genetic isolated populations 
Family-based research in genetically isolated populations is an effective approach for identifying loci influencing variation in disease traits. In common with all studies in humans, those in genetically isolated populations need ethical approval; however, existing ethical frameworks may be inadequate to protect participant privacy and confidentiality and to address participants' information needs in such populations. Using the ethical–legal guidelines of the Council for International Organizations of Medical Sciences (CIOMS) as a template, we compared the participant information leaflets and consent forms of studies in five European genetically isolated populations to identify additional information that should be incorporated into information leaflets and consent forms to guarantee satisfactorily informed consent. We highlight the additional information that participants require on the research purpose and the reasons why their population was chosen; on the potential risks and benefits of participation; on the opportunities for benefit sharing; on privacy; on the withdrawal of consent and on the disclosure of genetic data. This research raises some important issues that should be addressed properly and identifies relevant types of information that should be incorporated into information leaflets for this type of study.
doi:10.1038/ejhg.2009.155
PMCID: PMC2987217  PMID: 19826451
informed consent; isolates; participation; EUROSPAN; information leaflets; ethics
15.  An epidemiological perspective on the future of direct-to-consumer personal genome testing 
Personal genome testing is offered via the internet directly to consumers. Most tests that are currently offered use data from genome-wide scans to predict risks for multiple common diseases and traits. The utility of these tests is limited, predominantly because they lack predictive ability and clear benefits for disease prevention that are specific for genetic risk groups. In the near future, personal genome tests will likely be based on whole genome sequencing, but will these technological advances increase the utility of personal genome testing? Whole genome sequencing theoretically provides information about the risks of both monogenic and complex diseases, but the practical utility remains to be demonstrated. The utility of testing depends on the predictive ability of the test, the likelihood of actionable test results, and the options available for the reduction of risks. For monogenic diseases, the likelihood of known mutations will be extremely low in the general population and it will be a challenge to recognize new causal variants among all rare variants that are found using sequencing. For complex diseases, the predictive ability of genetic tests will be mainly restricted by the heritability of the disease, but also by the genetic complexity of the disease etiology, which determines the extent to which the heritability can be understood. Given that numerous genetic and non-genetic risk factors interact in the causation of complex diseases, the predictive ability of genetic models will likely remain modest. Personal genome testing will have minimal benefits for individual consumers unless major breakthroughs are made in the near future.
doi:10.1186/2041-2223-1-10
PMCID: PMC2990732  PMID: 21092344
16.  Using family history information to promote healthy lifestyles and prevent diseases; a discussion of the evidence 
BMC Public Health  2010;10:248.
Background
A family history, reflecting genetic susceptibility as well as shared environmental and behavioral factors, is an important risk factor for common chronic multifactorial diseases such as cardiovascular diseases, type 2 diabetes and many cancers.
Discussion
The purpose of the present paper is to discuss the evidence for the use of family history as a tool for primary prevention of common chronic diseases, in particular for tailored interventions aimed at promoting healthy lifestyles. The following questions are addressed: (1) What is the value of family history information as a determinant of personal disease risk?; (2)How can family history information be used to motivate at-risk individuals to adopt and maintain healthy lifestyles in order to prevent disease?; and (3) What additional studies are needed to assess the potential value of family history information as a tool to promote a healthy lifestyle?
Summary
In addition to risk assessment, family history information can be used to personalize health messages, which are potentially more effective in promoting healthy lifestyles than standardized health messages. More research is needed on the evidence for the effectiveness of such a tool.
doi:10.1186/1471-2458-10-248
PMCID: PMC2875210  PMID: 20465810
17.  Genome-based prediction of common diseases: methodological considerations for future research 
Genome Medicine  2009;1(2):20.
The translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our understanding of the genetic basis of multifactorial diseases is improving, but the currently identified susceptibility variants contribute only marginally to the development of disease. At the same time, an increasing number of companies are offering personalized lifestyle and health recommendations on the basis of individual genetic profiles. This discrepancy between the limited predictive value and the commercial availability of genetic profiles highlights the need for a critical appraisal of the usefulness of genome-based applications in clinical and public health care. Anticipating the discovery of a large number of genetic variants in the near future, we need to prepare a framework for the design and analysis of studies aiming to evaluate the clinical validity and utility of genetic tests. In this article, we review recent studies on the predictive value of genetic profiling from a methodological perspective and address issues around the choice of the study population, the construction of genetic profiles, the measurement of the predictive value, calibration and validation of prediction models, and assessment of clinical utility. Careful consideration of these issues will contribute to the knowledge base that is needed to identify useful genome-based applications for implementation in clinical and public health practice.
doi:10.1186/gm20
PMCID: PMC2664953  PMID: 19341491

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