Search

Polygenic Scores (PSs) describe the genetic component of an individual’s quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied., Polygenic scores are believed to hold future promise for trait prediction and personalized medicine, but are sensitive to demographic history. Here, Marnetto et al. develop partial polygenic scores supplemented with local ancestry deconvolution which improves prediction accuracy into recently admixed European populations.

Background: Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years. Methods: We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration. Results: Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720–0.824) and Predict-AF (0.749–0.831) had largely comparable discrimination, both favorable to continuous age (0.675–0.801). Calibration was similar using CHARGE-AF (slope range, 0.67–0.87) and Predict-AF (0.65–0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024–0.057) but more favorable among individuals aged Conclusions: AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.

Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals. In this article, we propose a computationally efficient alternative that employs fractional relatedness of family members through a series of founding members. The primary goal of this study is to investigate whether the effect of determinants on health outcome variables differs with and without accounting for family structure. We compare a fixed-effects model without familial effects with several variance components models that account for heritability and shared environment structure. Our secondary goal is to apply the fractional relatedness model in a realistic setting. Lifelines is a three-generation cohort study investigating the biological, behavioral, and environmental determinants of healthy aging. We analyzed a sample of 89,353 participants from 32,452 reconstructed families. Our primary conclusion is that the effect of determinants on health outcome variables does not differ with and without accounting for family structure. However, accounting for family structure through fractional relatedness allows for estimating heritability in a computationally efficient way, showing some interesting differences between physical and mental quality of life heritability. We have shown through simulations that the proposed fractional relatedness model performs better than the standard kinship model, not only in terms of computational time and convenience of fitting using standard functions in R, but also in terms of bias of heritability estimates and coverage.

Longevity in commercial sows is often selected for through stayability traits measured in purebred animals. However, this may not be justifiable because longevity and stayability may be subject to both genotype by environment interaction (G × E) and genotype by genotype interaction (G × G). This study tested the hypothesis that stayability to service after first parity is more strongly genetically correlated with longevity in commercial herds when stayability is measured in commercial herds rather than multiplier herds. The analysis was based on farrowing- and service-records from 470,824 sows (189,263 multiplier; 281,561 commercial) and 300 herds (156 multiplier; 144 commercial sows). Multiplier sows were either purebred Landrace or Yorkshire and commercial sows were mainly rotationally crossbreds between the two breeds. Commercial longevity was defined as age in days when culled (LongC), and stayability to service after first parity was defined for both commercial sows (StayC) and multiplier sows (StayM). The genetic correlations between LongC, StayC, and StayM were estimated by restricted maximum likelihood using linear mixed models. Genetic parameters were estimated separately for Landrace and Yorkshire. In Landrace, the genetic correlations between LongC and StayC, LongC and StayM, and StayC and StayM were 0.86 ± 0.02, 0.24 ± 0.05, and 0.34 ± 0.06, respectively. In Yorkshire, the genetic correlations between LongC and StayC, LongC and StayM, and StayC and StayM were 0.81 ± 0.03, 0.17 ± 0.05, and 0.18 ± 0.7, respectively. Conclusively, longevity in commercial herds is more strongly correlated with stayability when stayability is measured in commercial herds rather than multiplier herds.

Background: Weight loss success is determined by genetic factors, which may differ according to treatment strategy. Methods: From a multidisciplinary obesity treatment program involving dietary advice, psychological counseling, and increased physical activity, 587 subjects (68% female; 46.1 ± 12.4 years; BMI 39.9 ± 6.3) were recruited. At baseline, a blood sample was drawn for DNA isolation. Genotypes were determined for 30 polymorphisms in 25 candidate genes. The association between genotypes and weight loss was assessed after 3 months (short-term) and after 12 months of treatment (long-term). Weight loss was categorized as ≥5% or Results: The G/G genotype of PLIN1 (rs2289487) and PLIN1 (rs2304795), the T/T genotype of PLIN1 (rs1052700), and the C/C genotype of MMP2 predicted ≥5% weight loss in the first 3 months. The C/G-G/G genotype of PPARγ (rs1801282) and the T/C genotype of TIMP4 (rs3755724) predicted ≥5% weight loss after 12 months. Subjects with the combination of PPARγ (rs1801282) C/G-G/G and TIMP4 (rs3755724) T/C lost even more weight. Conclusion: Polymorphisms in genes related to regulation of fat storage and structural adaptation of the adipocytes are predictors for weight loss success with different genes being relevant for short-term and long-term weight loss success.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers. Tis work was supported by Fondation pour la Recherche Médical (ref DEI20151234405) and Investissements d’Avenir programme ANR-11-IDEX-0005-02, Sorbonne Paris Cite, Laboratoire d’excellence INFLAMEX. Te authors thank the students that participated to the wisdom of the crowd exercise.

Birth weight (BW) variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. These associations have been proposed to reflect the lifelong consequences of an adverse intrauterine environment. In earlier work, we demonstrated that much of the negative correlation between BW and adult cardio-metabolic traits could instead be attributable to shared genetic effects. However, that work and other previous studies did not systematically distinguish the direct effects of an individual’s own genotype on BW and subsequent disease risk from indirect effects of their mother’s correlated genotype, mediated by the intrauterine environment. Here, we describe expanded genome-wide association analyses of own BW (n=321,223) and offspring BW (n=230,069 mothers), which identified 278 independent association signals influencing BW (214 novel). We used structural equation modelling to decompose the contributions of direct fetal and indirect maternal genetic influences on BW, implicating fetal- and maternal-specific mechanisms. We used Mendelian randomization to explore the causal relationships between factors influencing BW through fetal or maternal routes, for example, glycemic traits and blood pressure. Direct fetal genotype effects dominate the shared genetic contribution to the association between lower BW and higher type 2 diabetes risk, whereas the relationship between lower BW and higher later blood pressure (BP) is driven by a combination of indirect maternal and direct fetal genetic effects: indirect effects of maternal BP-raising genotypes act to reduce offspring BW, but only direct fetal genotype effects (once inherited) increase the offspring’s later BP. Instrumental variable analysis using maternal BW-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring BP. In successfully separating fetal from maternal genetic effects, this work represents an important advance in genetic studies of perinatal outcomes, and shows that the association between lower BW and higher adult BP is attributable to genetic effects, and not to intrauterine programming.

Motivation Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection. Results We propose a Bayesian Gaussian process (GP) approach for functional QTL mapping. We use GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over time. We use an efficient gradient based algorithm to estimate the tuning parameters of GPs. Notably, the GP approach is directly applicable to the incomplete datasets having even larger than 50% missing data rate (among phenotypes). We further develop a stepwise algorithm to search through the model space in terms of genetic variants, and use a minimal increase of Bayesian posterior probability as a stopping rule to focus on only a small set of putative QTL. We also discuss the connection between GP and penalized B-splines and wavelets. On two simulated and three real datasets, our GP approach demonstrates great flexibility for modeling different types of phenotypic trajectories with low computational cost. The proposed model selection approach finds the most likely QTL reliably in tested datasets. Availability and implementation Software and simulated data are available as a MATLAB package ‘GPQTLmapping’, and they can be downloaded from GitHub (https://github.com/jpvanhat/GPQTLmapping). Real datasets used in case studies are publicly available at QTL Archive. Supplementary information Supplementary data are available at Bioinformatics online.

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother’s, and, to a lesser extent, with father’s TL having the strongest influence on the offspring. In this cohort, mother’s, but not father’s age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait.

The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections. Nesting cross-validation within bootstrapping could provide further improvements in precision, but this has not been investigated systematically. We performed simulation studies of Lasso variable selection precision (VSP) with and without nesting cross-validation within bootstrapping. Data were simulated to represent genomic data under a polygenic model as well as under a model with effect sizes representative of typical GWAS results. We compared these approaches to each other as well as to software defaults for the Lasso. Nested cross-validation had the most precise variable selection at small effect sizes. At larger effect sizes, there was no advantage to nesting. We illustrated the nested approach with empirical data comprising SNPs and SNP-SNP interactions from the most significant SNPs in a GWAS of borderline personality symptoms. In the empirical example, we found that the default Lasso selected low-reliability SNPs and interactions which were excluded by bootstrapping.

Predicting metabolizer phenotype (MP) is typically performed using data from a single gene. Cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is considered the primary gene for predicting MP in reference to approximately 30% of marketed drugs and endogenous toxins. CYP2D6 predictions have proven clinically effective but also have well-documented inaccuracies due to relatively high genotype-phenotype discordance in certain populations. Herein, a pathway-driven predictive model employs genetic data from uridine diphosphate glucuronosyltransferase, family 1, polypeptide B7 (UGT2B7), adenosine triphosphate (ATP)-binding cassette, subfamily B, number 1 (ABCB1), opioid receptor mu 1 (OPRM1), and catechol-O-methyltransferase (COMT) to predict the tramadol to primary metabolite ratio (T:M1) and the resulting toxicologically inferred MP (t-MP). These data were then combined with CYP2D6 data to evaluate performance of a fully combinatorial model relative to CYP2D6 alone. These data identify UGT2B7 as a potentially significant explanatory marker for T:M1 variability in a population of tramadol-exposed individuals of Finnish ancestry. Supervised machine learning and feature selection were used to demonstrate that a set of 16 loci from 5 genes can predict t-MP with over 90% accuracy, depending on t-MP category and algorithm, which was significantly greater than predictions made by CYP2D6 alone.

Background— Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown. Methods and Results— We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (>1.6 per allele) and assuming the sum of all nongenetic hazard ratios was Conclusions— In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal.

Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset.

Sund, Reijo/0000-0002-6268-8117; Heikkila, Kauko/0000-0002-9256-8028; Knafo-Noam, Ariel/0000-0003-0613-1960; Montgomery, Grant W/0000-0002-4140-8139; de Freitas, Duarte Luis/0000-0001-6642-3370; ONCEL, Sevgi YURT/0000-0002-0990-292X; Fagnani, Corrado/0000-0001-5771-7772; Loos, Ruth J F/0000-0002-8532-5087; Kaprio, Jaakko/0000-0002-3716-2455; Huibregtse, Brooke M./0000-0003-0977-7249; ROMERA, JUAN FRANCISCO SANCHEZ/0000-0002-5405-6216; Colodro-Conde, Lucia/0000-0002-9004-364X; Tan, Qihua/0000-0003-3194-0030; Ordonana, Juan R./0000-0001-7779-6017; Kandler, Christian/0000-0002-9175-235X; Aliev, Fazil/0000-0001-8357-4699; Siribaddana, Sisira/0000-0001-5821-2557; Busjahn, Andreas/0000-0001-9650-6919; Tan, Qihua/0000-0003-3194-0030; Rasmussen, Finn/0000-0001-7915-7809; Silventoinen, Karri/0000-0003-1759-3079; Pedersen, Nancy/0000-0001-8057-3543; van Beijsterveldt, Toos/0000-0002-6617-4201; Franz, Carol/0000-0002-8987-1755; Rebato, Esther/0000-0003-1221-8501; Pahlen, Shandell/0000-0003-0753-4155; Harden, Kathryn/0000-0002-1557-6737; Bartels, Meike/0000-0002-9667-7555; Willemsen, Gonneke/0000-0003-3755-0236; Latvala, Antti/0000-0001-5695-117X WOS: 000412597600005 PubMed: 28975875 Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990-1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected. CODATwins project (Academy of Finland) [266592]; National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1079102]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 HD068435, R01 MH062375, 1R01ESO15150-01, R21 AG039572, AA023322, HD081437, NIA R01 AG018384, R01 AG018386, R01 AG022381, R01 AG022982]; California Tobacco-Related Disease Research ProgramUniversity of California System [7RT-0134H, 8RT-0107H, 6RT-0354H]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [1RO1-AG13662-01A2]; Swedish Research Council through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Stockholm County Council (ALF)Stockholm County Council; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation; Swedish Asthma and Allergy Association's Research Foundation; NIDAUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [DA011015]; Longitudinal Twin Study [HD10333]; Fund of Scientific Research, Flanders and Twins; ENGAGE European Network for Genetic and Genomic Epidemiology [201413]; National Institute of Alcohol Abuse and AlcoholismUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse & Alcoholism (NIAAA) [AA-12502, AA-00145, AA-09203]; Academy of Finland Center of Excellence in Complex Disease GeneticsAcademy of Finland [213506, 129680]; Academy of FinlandAcademy of Finland [100499, 205585, 118555, 141054, 265240, 263278, 264146]; Cancer Research UKCancer Research UK [C1418/A7974]; Medexpert Ltd., Budapest, Hungary; Global Research Network Program of the National Research Foundation [NRF 2011-220-E00006]; European Research Council (ERC)European Research Council (ERC) [240994]; Michigan State University; National Institute of Mental Health (NIMH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01-MH081813, R01-MH0820-54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01]; Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) [R01-HD066040]; MSU Foundation [11-SPG-2518]; Fundacion SenecaFundacion Seneca; Regional Agency for Science and Technology, Murcia, Spain [08633/PHCS/08, 15302/PHCS/10, 19479/PI/14]; Ministry of Science and Innovation, SpainSpanish Government [PSI2009-11560, PSI2014-56680-R]; MagW/ZonMW [904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192]; VU University's Institute for Health and Care Research (EMGO+); European Research CouncilEuropean Research Council (ERC) [ERC - 230374]; Avera Institute, Sioux Falls, South Dakota (USA); National Research Foundation of KoreaNational Research Foundation of Korea [NRF-371-2011-1 B00047]; Krkkale University Research Grant [2009/43]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114C117]; Cooperative Studies Program of the Office of Research AMP; Development of the United States Department of Veterans Affairs; Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science [15H05105]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH58354]; JSPS KAKENHI JP [23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261]; [5T32DA017637-11]; [POCI/DES/56834/2004]; [NIH RC2 HL103416] This study was conducted within the CODATwins project (Academy of Finland #266592). The Australian Twin Registry is supported by a Centre of Research Excellence (grant ID 1079102) from the National Health and Medical Research Council administered by the University of Melbourne. The Boston University Twin Project is funded by grants (#R01 HD068435 #R01 MH062375) from the National Institutes of Health to K. Saudino. California Twin Program was supported by The California Tobacco-Related Disease Research Program (7RT-0134H, 8RT-0107H, 6RT-0354H) and the National Institutes of Health (1R01ESO15150-01). The Carolina African American Twin Study of Aging (CAATSA) was funded by a grant from the National Institute on Aging (grant 1RO1-AG13662-01A2) to K. E. Whitfield. The CATSS-Study is supported by the Swedish Research Council through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) framework grant no 340-2013-5867, grants provided by the Stockholm County Council (ALF-projects), the Swedish Heart-Lung Foundation and the Swedish Asthma and Allergy Association's Research Foundation. Colorado Twin Registry is funded by NIDA funded center grant DA011015 and Longitudinal Twin Study HD10333; Author Huibregtse is supported by 5T32DA017637-11. Since its origin, the East Flanders Prospective Survey has been partly supported by grants from the Fund of Scientific Research, Flanders and Twins, a non-profit Association for Scientific Research in Multiple Births (Belgium). Data collection and analyses in Finnish twin cohorts have been supported by ENGAGE European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, and AA-09203 to R J Rose, the Academy of Finland Center of Excellence in Complex Disease Genetics (grant numbers: 213506, 129680), and the Academy of Finland (grants 100499, 205585, 118555, 141054, 265240, 263278, and 264146 to J Kaprio). Gemini was supported by a grant from Cancer Research UK (C1418/A7974). Anthropometric measurements of the Hungarian twins were supported by Medexpert Ltd., Budapest, Hungary. Korean Twin-Family Register was supported by the Global Research Network Program of the National Research Foundation (NRF 2011-220-E00006). Longitudinal Israeli Study of Twins was funded by the Starting Grant no. 240994 from the European Research Council (ERC) to Ariel Knafo. The Michigan State University Twin Registry has been supported by Michigan State University, as well as grants R01-MH081813, R01-MH0820-54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01 from the National Institute of Mental Health (NIMH), R01-HD066040 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD), and 11-SPG-2518 from the MSU Foundation. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIMH, the NICHD, or the National Institutes of Health. The Murcia Twin Registry is supported by Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain (08633/PHCS/08, 15302/PHCS/10 & 19479/PI/14) and Ministry of Science and Innovation, Spain (PSI2009-11560 & PSI2014-56680-R). The NAS-NRC Twin Registry acknowledges financial support from the National Institutes of Health grant number R21 AG039572.; r Netherlands Twin Register acknowledges the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192; VU University's Institute for Health and Care Research (EMGO+); the European Research Council (ERC - 230374), the Avera Institute, Sioux Falls, South Dakota (USA). Madeira data comes from the following project: Genetic and environmental influences on physical activity, fitness, and health: the Madeira family study. Project reference: POCI/DES/56834/2004. Founded by the Portuguese agency for research (The Foundation for Science and Technology [FCT]). South Korea Twin Registry is supported by National Research Foundation of Korea (NRF-371-2011-1 B00047). The Texas Twin Project is currently funded by grants AA023322 and HD081437 from the National Institutes of Health. S. Y. oncel and F. Aliev are supported by Krkkale University Research Grant: KKU, 2009/43 and TUBITAK grant 114C117. Washington State Twin Registry (formerly the University of Washington Twin Registry) was supported in part by grant NIH RC2 HL103416 (D. Buchwald, PI). Vietnam Era Twin Study of Aging was supported by National Institute of Health grants NIA R01 AG018384, R01 AG018386, R01 AG022381, and R01 AG022982, and, in part, with resources of the VA San Diego Center of Excellence for Stress and Mental Health. The Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs has provided financial support for the development and maintenance of the Vietnam Era Twin (VET) Registry. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA/NIH, or the VA. The West Japan Twins and Higher Order Multiple Births Registry was supported by Grant-in-Aid for Scientific Research (B) (grant number 15H05105) from the Japan Society for the Promotion of Science. The University of Southern California Twin Study is funded by a grant from the National Institute of Mental Health (R01 MH58354). Osaka University Aged Twin Registry is supported by grants from JSPS KAKENHI JP (23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261).

A novel haplotype association method is presented, and its power is demonstrated. Relying on a statistical model for linkage disequilibrium (LD), the method first infers ancestral haplotypes and their loadings at each marker for each individual. The loadings are then used to quantify local haplotype sharing between individuals at each marker. A statistical model was developed to link the local haplotype sharing and phenotypes to test for association. We devised a novel method to fit the LD model, reducing the complexity from putatively quadratic to linear (in the number of ancestral haplotypes). Therefore, the LD model can be fitted to all study samples simultaneously, and, consequently, our method is applicable to big data sets. Compared to existing haplotype association methods, our method integrated out phase uncertainty, avoided arbitrariness in specifying haplotypes, and had the same number of tests as the single-SNP analysis. We applied our method to data from the Wellcome Trust Case Control Consortium and discovered eight novel associations between seven gene regions and five disease phenotypes. Among these, GRIK4, which encodes a protein that belongs to the glutamate-gated ionic channel family, is strongly associated with both coronary artery disease and rheumatoid arthritis. A software package implementing methods described in this article is freely available at http://www.haplotype.org.

Item does not contain fulltext Recent historical periods in Europe have been characterized by severe epidemic events such as plague, smallpox, or influenza that shaped the immune system of modern populations. This study aims to identify signals of convergent evolution of the immune system, based on the peculiar demographic history in which two populations with different genetic ancestry, Europeans and Rroma (Gypsies), have lived in the same geographic area and have been exposed to similar environments, including infections, during the last millennium. We identified several genes under evolutionary pressure in European/Romanian and Rroma/Gipsy populations, but not in a Northwest Indian population, the geographic origin of the Rroma. Genes in the immune system were highly represented among those under strong evolutionary pressures in Europeans, and infections are likely to have played an important role. For example, Toll-like receptor 1 (TLR1)/TLR6/TLR10 gene cluster showed a strong signal of adaptive selection. Their gene products are functional receptors for Yersinia pestis, the agent of plague, as shown by overexpression studies showing induction of proinflammatory cytokines such as TNF, IL-1beta, and IL-6 as one possible infection that may have exerted evolutionary pressures. Immunogenetic analysis showed that TLR1, TLR6, and TLR10 single-nucleotide polymorphisms modulate Y. pestis-induced cytokine responses. Other infections may also have played an important role. Thus, reconstruction of evolutionary history of European populations has identified several immune pathways, among them TLR1/TLR6/TLR10, as being shaped by convergent evolution in two human populations with different origins under the same infectious environment.

Previous epidemiologic studies have evaluated the use of immunological markers as possible tools for measuring ageing and predicting age-related pathology. The importance of both genetic and environmental influences in regulation of these markers has been emphasized. In order to further evaluate this relationship, the present study aims to investigate the relative influence of genetic and environmental factors on four key cytokines involved in the human immune response (Interleukin (IL.)-1 beta, IL-6, IL-10 and Tumor Necrosis Factor (TNF)-alpha). In addition, the role of age as a possible moderator on these influences was evaluated.Methods: The study was conducted in 1603 females from the Twins UK registry, with mean age +/- SD of 60.4 +/- 12.2 years, including 863 monozygotic twins (385 pairs and 93 singletons) and 740 dizygotic twins (321 pairs and 98 singletons). Heritability was estimated using structural equation modeling. The role of age as a moderator was evaluated using gene-age interaction models.Results: Heritabilities were moderate for IL-10 (range: 0.27-0.32) and IL-10 (0.30) and low for IL-6 (range: 0.15-0.16) and TNF-alpha (range: 0.17-0.23). For IL-1 beta, heritability declines with age due to an increase in unique environmental factors. For TNE-alpha, heritability increases with age due to a decrease in unique environmental factors.Conclusion: The current findings illustrate the importance of genetic and environmental influences on four cytokines involved in the human immune response. For two of these there is evidence that heritability changes with age owing to changes in environmental factors unique to the individual. (C) 2012 Elsevier Ltd. All rights reserved.

In addition to APOE and FOXO3, AKT1 has recently been suggested as a third consistent longevity gene, with variants in AKT1 found to be associated with human lifespan in two previous studies. Here, we evaluated AKT1 as a longevity-associated gene across populations by attempting to replicate the previously identified variant rs3803304 as well as by analyzing six additional AKT1 single-nucleotide polymorphisms, thus capturing more of the common variation in the gene. The study population was 2996 long-lived individuals (nonagenarians and centenarians) and 1840 younger controls of Danish and German ancestry. None of the seven SNPs tested were significantly associated with longevity in either a case-control or a longitudinal setting, although a supportive nominal indication of a disadvantageous effect of rs3803304 was found in a restricted group of Danish centenarian men. Overall, our results do not support AKT1 as a universal longevity-associated gene.

The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/ site/honcheongso/software/robustsnp. © The Author(s) 2011., published_or_final_version, Springer Open Choice, 21 Feb 2012

Several genome-wide association studies (GWAS) have been published on various complex diseases. Although, new loci are found to be associated with these diseases, still only very little of the genetic risk for these diseases can be explained. As GWAS are still underpowered to find small main effects, and gene-gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this study, we evaluated alternative methods to study GWAS data. Instead of focusing on the single nucleotide polymorphisms (SNPs) with the highest statistical significance, we took advantage of prior biological information and tried to detect overrepresented pathways in the GWAS data. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology. In this study, we present the various benefits and limitations of pathway-classification tools in analyzing GWAS data. We show multiple differences in outcome between pathway tools analyzing the same dataset. Furthermore, analyzing randomly selected SNPs always results in significantly overrepresented pathways, large pathways have a higher chance of becoming statistically significant and the bioinformatics tools used in this study are biased toward detecting well-defined pathways. As an example, we analyzed data from two GWAS on type 2 diabetes (T2D): the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC). Occasionally the results from the DGI and the WTCCC GWAS showed concordance in overrepresented pathways, but discordance in the corresponding genes. Thus, incorporating gene networks and pathway classification tools into the analysis can point toward significantly overrepresented molecular pathways, which cannot be picked up using traditional single-locus analyses. However, the limitations discussed in this study, need to be addressed before these methods can be widely used. Geriet. Epidemiol. 33:419-431, 2009. (C) 2009 Wiley-Liss, Inc.

Recent studies have provided some clues with regard to the relationship existing between uncoupling protein 1 (UCP1) and blood pressure in animal experiments. In an attempt to determine the genetic polymorphisms that are associated with blood pressure in humans, we have analyzed genetic polymorphisms in UCP1 gene. In this study, we assessed the association between UCP1 genotypes and systolic blood pressure (SBP) and diastolic blood pressure (DBP), in a population comprised of 832 Korean female subjects, using a general linear model, which was adjusted for age and body mass index (BMI). Among 4 genetic polymorphisms and the haplotypes constructed from them, haplotype3 of UCP1, UCP1-ht3[GAGA], evidenced significant associations with SBP (p=0.005) and DBP (p=0.013). However, this haplotype was not significantly associated with obesity phenotypes, including BMI or fat mass (p>0.05), thereby suggesting that its association with blood pressure was independent of obesity phenotypes.

Personalised, genotype-based nutrition is a concept that links genotyping with specific nutritional advice in order to improve the prevention of nutrition-associated, chronic diseases. This review describes the current scientific basis of the concept and discusses its problems. There is convincing evidence that variant genes may indeed determine the biological response to nutrients. The effects of single-gene variants on risk or risk factor levels of a complex disease are, however, usually small and sometimes inconsistent. Thus, information on the effects of combinations of relevant gene variants appears to be required in order to improve the predictive precision of the genetic information. Furthermore, very few associations between genotype and response have been tested for causality in human intervention studies, and little is known about potential adverse effects of a genotype-derived intervention. These issues need to be addressed before genotyping can become an acceptable method to guide nutritional recommendations. © The Authors 2007.

In a small chromosomal region, a number of polymorphisms may be both linked to and associated with a disease. Distinguishing the potential causal sites from those indirectly associated due to linkage disequilibrium (LD) with a causal site is an important problem. This problem may be approached by determining which of the associations can explain the observed linkage signal. Recently, several methods have been proposed to aid in the identification of disease associated polymorphisms that may explain an observed linkage signal, using genotype data from affected sib pairs (ASPs) [Li et al. [2005] Am. J. Hum. Genet. 76:934–949; Sun et al. [2002] Am. J. Hum. Genet. 70:399–411]. These methods can be used to test the null hypothesis that a candidate single nucleotide polymorphism (SNP) is the sole causal variant in the region, or is in complete LD with the sole causal variant in the region. We extend variations of these methods to test for complete LD between a disease locus and haplotypes composed of two or more tightly linked candidate SNPs. We study properties of the proposed methods by simulation and apply them to type 1 diabetes data for ASPs and their parents at candidate SNP and microsatellite marker loci in the Insulin (INS) gene region. Genet. Epidemiol. 31:2727–740, 2007. © 2007 Wiley-Liss, Inc.

We investigated whether women show larger heart rate variability (HRV) than men after controlling for a large number of health-related covariates, using two indices of HRV, namely respiratory sinus arrhythmia (RSA) and approximate entropy (ApEn). In a twin design, the heritability of both indices was examined. The covariation between RSA and ApEn, a measure of heart rate dynamics derived from nonlinear dynamical systems theory, was decomposed into genetic and environmental components. Subjects were 196 male and 210 female middle-aged twins. Females showed larger HRV than men before (ApEn: p < .001; RSA: p = .052) and after adjustment for covariates (ApEn: p < .001; RSA: p = .015). This sex difference was confirmed by significant intrapair differences in the opposite-sex twin pairs for both ApEn (p < .001) and RSA (p = .03). In addition to sex, only heart period and age (both p < .001) were found to be independent predictors of ApEn, whereas RSA was also influenced by respiration rate and smoking (both p < .001). Age explained 16% and 6% of the variance in RSA and ApEn, respectively. Oral contraceptive use and menopausal status had no effect on HRV. Genetic model fitting yielded moderate heritability estimates for RSA (30%) and ApEn (40%) for both males and females. The correlation between RSA and ApEn (r = .60) could be attributed to genetic factors (48%), environmental factors (36%) and age (16%). The present study found support for a gender difference in HRV with women having greater HRV than men even after controlling for a large number of potential confounders. Indices of heart rate dynamics derived from nonlinear dynamical systems theory are moderately heritable and may be more sensitive than traditional indices of HRV to reveal subtle sex differences with important implications for health and disease.

Genetic diversity in worldwide population of beets is strongly affected by the domestication history, and the comparison of linkage disequilibrium in worldwide and elite populations highlights strong selection pressure. Genetic relationships and linkage disequilibrium (LD) were evaluated in a set of 2035 worldwide beet accessions and in another of 1338 elite sugar beet lines, using 320 and 769 single nucleotide polymorphisms, respectively. The structures of the populations were analyzed using four different approaches. Within the worldwide population, three of the methods gave a very coherent picture of the population structure. Fodder beet and sugar beet accessions were grouped together, separated from garden beets and sea beets, reflecting well the origins of beet domestication. The structure of the elite panel, however, was less stable between clustering methods, which was probably because of the high level of genetic mixing in breeding programs. For the linkage disequilibrium analysis, the usual measure (r 2) was used, and compared with others that correct for population structure and relatedness (r 2 , r 2 , r 2 ). The LD as measured by r 2 persisted beyond 10 cM within the elite panel and fell below 0.1 after less than 2 cM in the worldwide population, for almost all chromosomes. With correction for relatedness, LD decreased under 0.1 by 1 cM for almost all chromosomes in both populations, except for chromosomes 3 and 9 within the elite panel. In these regions, the larger extent of LD could be explained by strong selection pressure.

Large exploratory studies, including candidate-gene–association testing, genomewide linkage-disequilibrium scans, and array-expression experiments, are becoming increasingly common. A serious problem for such studies is that statistical power is compromised by the need to control the false-positive rate for a large family of tests. Because multiple true associations are anticipated, methods have been proposed that combine evidence from the most significant tests, as a more powerful alternative to individually adjusted tests. The practical application of these methods is currently limited by a reliance on permutation testing to account for the correlated nature of single-nucleotide polymorphism (SNP)–association data. On a genomewide scale, this is both very time-consuming and impractical for repeated explorations with standard marker panels. Here, we alleviate these problems by fitting analytic distributions to the empirical distribution of combined evidence. We fit extreme-value distributions for fixed lengths of combined evidence and a beta distribution for the most significant length. An initial phase of permutation sampling is required to fit these distributions, but it can be completed more quickly than a simple permutation test and need be done only once for each panel of tests, after which the fitted parameters give a reusable calibration of the panel. Our approach is also a more efficient alternative to a standard permutation test. We demonstrate the accuracy of our approach and compare its efficiency with that of permutation tests on genomewide SNP data released by the International HapMap Consortium. The estimation of analytic distributions for combined evidence will allow these powerful methods to be applied more widely in large exploratory studies.

In the study of complex traits, the utility of linkage analysis and single marker association tests can be limited for researchers attempting to elucidate the complex interplay between a gene and environmental covariates. For these purposes, tests of gene-environment interactions are needed. In addition, recent studies have indicated that haplotypes, which are specific combinations of nucleotides on the same chromosome, may be more suitable as the unit of analysis for statistical tests than single genetic markers. The difficulty with this approach is that, in standard laboratory genotyping, haplotypes are often not directly observable. Instead, unphased marker phenotypes are collected. In this article, we present a method for estimating and testing haplotype-environment interactions when linkage phase is potentially ambiguous. The method builds on the work of Schaid et al. [2002] and is applicable to any trait that can be placed in the generalized linear model framework. Simulations were run to illustrate the salient features of the method. addition, the method was used to test for haplotype-smoking exposure interaction with data from the Childhood Asthma Management Program. Copyright (C) 2003 S. Karger AG, Basel.

The transmission/disequilibrium test (TDT) is a popular, simple, and powerful test of linkage, which can be used to analyze data consisting of transmissions to the affected members of families with any kind pedigree structure, including affected sib pairs (ASPs). Although it is based on the preferential transmission of a particular marker allele across families, it is not a valid test of association for ASPs. Martin et al. devised a similar statistic for ASPs, Tsp, which is also based on preferential transmission of a marker allele but which is a valid test of both linkage and association for ASPs. It is, however, less powerful than the TDT as a test of linkage for ASPs. What I show is that the differences between the TDT and Tsp are due to the fact that, although both statistics are based on preferential transmission of a marker allele, the TDT also exploits excess sharing in identity-by-descent transmissions to ASPs. Furthermore, I show that both of these statistics are members of a family of “TDT-like” statistics for ASPs. The statistics in this family are based on preferential transmission but also, to varying extents, exploit excess sharing. From this family of statistics, we see that, although the TDT exploits excess sharing to some extent, it is possible to do so to a greater extent—and thus produce a more powerful test of linkage, for ASPs, than is provided by the TDT. Power simulations conducted under a number of disease models are used to verify that the most powerful member of this family of TDT-like statistics is more powerful than the TDT for ASPs.

Optimal design of quantitative-trait loci (QTL) mapping studies requires a precise understanding of the power of QTL linkage versus QTL association analysis, under a range of different conditions. In this article, we investigate the power of QTL linkage and association analyses for simple random sibship samples, under the variance-components model proposed by Fulker et al. After a brief description of an extension of this variance-components model, we show that the powers of both linkage and association analyses are crucially dependent on the proportion of phenotypic variance attributable to the QTL. The main difference between the two tests is that, whereas the power of association is directly related to the QTL heritability, the power of linkage is related more closely to the square of the QTL heritability. We also describe both how the power of linkage is attenuated by incomplete linkage and incomplete marker information and how the power of association is attenuated by incomplete linkage disequilibrium.

The gamete-competition model is an application of the Bradley-Terry model for ranking of sports teams. If allele i of a marker locus is assigned parameter taui0, then the probability that a parent with heterozygous genotype i/j transmits allele i is Pr(i/j--)=tau(i)/(tau(i) + tau(j). Mendelian segregation corresponds to the choice tau(i)=1 for all i. To test whether Mendelian segregation is true, one can estimate the tau(i) from pedigree data and perform a likelihood-ratio test under the constraint that one tau(i) equals 1. Although this procedure generates an interesting method for performance of segregation analysis with a marker locus, its real promise lies in generalization of the transmission/disequilibrium test. Quantitative as well as qualitative outcomes can be considered. The gamete-competition model uses full pedigree data and gives an estimate of the strength of transmission distortion to affected children for each allele. Covariates are incorporated by rewriting of tau(i)=exp(beta(t)x(k)), where beta is a parameter vector and xk is a covariate vector for the kth transmitted gamete. Examples of covariates include disease-severity indicators for the child, sex of the child, or repeat number for tandem-repeat alleles.

Summary One strategy for localization of a quantitative-trait locus (QTL) is to test whether the distribution of a quantitative trait depends on the number of copies of a specific genetic-marker allele that an individual possesses. This approach tests for association between alleles at the marker and the QTL, and it assumes that association is a consequence of the marker being physically close to the QTL. However, problems can occur when data are not from a homogeneous population, since associations can arise irrespective of a genetic marker being in physical proximity to the QTL—that is, no information is gained regarding localization. Methods to address this problem have recently been proposed. These proposed methods use family data for indirect stratification of a population, thereby removing the effect of associations that are due to unknown population substructure. They are, however, restricted in terms of the number of children per family that can be used in the analysis. Here we introduce tests that can be used on family data with parent and child genotypes, with child genotypes only, or with a combination of these types of families, without size restrictions. Furthermore, equations that allow one to determine the sample size needed to achieve desired power are derived. By means of simulation, we demonstrate that the existing tests have an elevated false-positive rate when the size restrictions are not followed and that a good deal of information is lost as a result of adherence to the size restrictions. Finally, we introduce permutation procedures that are recommended for small samples but that can also be used for extensions of the tests to multiallelic markers and to the simultaneous use of more than one marker.

Summary Linkage disequilibrium has been used to help in the identification of genes predisposing to certain qualitative diseases. Although several linkage-disequilibrium tests have been developed for localization of genes influencing quantitative traits, these tests have not been thoroughly compared with one another. In this report we compare, under a variety of conditions, several different linkage-disequilibrium tests for identification of loci affecting quantitative traits. These tests use either single individuals or parent-child trios. When we compared tests with equal samples, we found that the truncated measured allele (TMA) test was the most powerful. The trait allele frequencies, the stringency of sample ascertainment, the number of marker alleles, and the linked genetic variance affected the power, but the presence of polygenes did not. When there were more than two trait alleles at a locus in the population, power to detect disequilibrium was greatly diminished. The presence of unlinked disequilibrium ( D ′ * ) increased the false-positive error rates of disequilibrium tests involving single individuals but did not affect the error rates of tests using family trios. The increase in error rates was affected by the stringency of selection, the trait allele frequency, and the linked genetic variance but not by polygenic factors. In an equilibrium population, the TMA test is most powerful, but, when adjusted for the presence of admixture, Allison test 3 becomes the most powerful whenever D ′* >.15.

SummaryGenomewide association studies have been advocated as a promising alternative to genomewide linkage scans for detection of small-effect genes in complex diseases. Comparisons of power and sample size between the two strategies have shown considerable advantages for the association studies. These comparisons assume that the set of markers includes the exact disease-related polymorphism. A concern, however, is that the power of an association study decreases when this is not the case, because of discrepant allele frequencies and less-than-maximum disequilibrium between the disease-related polymorphism and its nearest marker. Here, we quantify this concern by comparing the sample sizes needed by the two strategies when the markers exclude the disease-related polymorphism. For affected sib pairs and their parents, we found that incomplete disequilibrium and differing allele frequencies can have substantial negative impact on the power of association studies, resulting, in some circumstances, in little gain and even in loss of power, compared with linkage analysis. We provide some guidelines for choosing between strategies, for the detection of genes for complex diseases.

SummaryPopulation-based tests of association have used data from either case-control studies or studies based on trios (affected child and parents). Case-control studies are more prone to false-positive results caused by inappropriate controls, which can occur if, for example, there is population admixture or stratification. An advantage of family-based tests is that cases and controls are well matched, but parental data may not always be available, especially for late-onset diseases. Three recent family-based tests of association and linkage utilize unaffected siblings as surrogates for untyped parents. In this paper, we propose an extension of one of these tests. We describe and compare the four tests in the context of a complex disease for both biallelic and multiallelic markers, as well as for sibships of different sizes. We also examine the consequences of having some parental data in the sample.

SummaryFamily-based tests of association provide the opportunity to test for an association between a disease and a genetic marker. Such tests avoid false-positive results produced by population stratification, so that evidence for association may be interpreted as evidence for linkage or causation. Several methods that use family-based controls have been proposed, including the haplotype relative risk, the transmission-disequilibrium test, and affected family–based controls. However, because these methods require genotypes on affected individuals and their parents, they are not ideally suited to the study of late-onset diseases. In this paper, we develop several family-based tests of association that use discordant sib pairs (DSPs) in which one sib is affected with a disease and the other sib is not. These tests are based on statistics that compare counts of alleles or genotypes or that test for symmetry in tables of alleles or genotypes. We describe the use of a permutation framework to assess the significance of these statistics. These DSP-based tests provide the same general advantages as parent-offspring trio–based tests, while being applicable to essentially any disease; they may also be tailored to particular hypotheses regarding the genetic model. We compare the statistical properties of our DSP-based tests by computer simulation and illustrate their use with an application to Alzheimer disease and the apolipoprotein E polymorphism. Our results suggest that the discordant-alleles test, which compares the numbers of nonmatching alleles in DSPs, is the most powerful of the tests we considered, for a wide class of disease models and marker types. Finally, we discuss advantages and disadvantages of the DSP design for genetic association mapping.

Background: Many QTL studies have two common features: (1) often there is missing marker information, (2) among many markers involved in the biological process only a few are causal. In statistics, the second issue falls under the headings "sparsity" and "causal inference". The goal of this work is to develop a two-step statistical methodology for QTL mapping for markers with binary genotypes. The first step introduces a novel imputation method for missing genotypes. Outcomes of the proposed imputation method are probabilities which serve as weights to the second step, namely in weighted lasso. The sparse phenotype inference is employed to select a set of predictive markers for the trait of interest.Results: Simulation studies validate the proposed methodology under a wide range of realistic settings. Furthermore, the methodology outperforms alternative imputation and variable selection methods in such studies. The methodology was applied to an Arabidopsis experiment, containing 69 markers for 165 recombinant inbred lines of a F8 generation. The results confirm previously identified regions, however several new markers are also found. On the basis of the inferred ROC behavior these markers show good potential for being real, especially for the germination trait G(max).Conclusions: Our imputation method shows higher accuracy in terms of sensitivity and specificity compared to alternative imputation method. Also, the proposed weighted lasso outperforms commonly practiced multiple regression as well as the traditional lasso and adaptive lasso with three weighting schemes. This means that under realistic missing data settings this methodology can be used for QTL identification.

Contains fulltext : 116599.pdf (Publisher’s version ) (Closed access) Background Recent research has shown that loneliness, a common problem in adolescence, may have a genetic basis. The evidence, though, was limited mostly to serotonin-related and dopamine-related genes. In the present study, we focused on the oxytocin receptor gene (OXTR).Methods Associations were examined in a longitudinal study spanning five annual waves (N=307). The relations between OXTR and loneliness were examined, as well as interactions between OXTR and sex, parental support, 5-HTTLPR genotype, and DRD2 genotype.Results Using Latent Growth Curve Modeling, the OXTR genotype was not directly related to loneliness. An OXTRxsex interaction was found. Girls showed a steeper decline in loneliness when they had an A allele compared with girls who were homozygous for the G allele. In addition, a gene-gene interaction or epistasis was observed. Both boys and girls who had at least one A1 allele for the DRD2 gene and also had the GG genotype for the OXTR gene showed stable levels of loneliness over time.Conclusion The present study is the first to show that the GG genotype for the OXTR gene is linked to the development of loneliness in adolescence and that this association is moderated by participants' sex and their genotype for a dopamine-related gene. 10 p.

The neuregulin 1 (NRG1) receptor ErbB4 is involved in the development of cortical inhibitory GABAergic circuits and NRG1-ErbB4 signaling has been implicated in schizophrenia (SCZ). A magnetic resonance spectroscopy (H-1-MRS) study has demonstrated that a single-nucleotide polymorphism in ERBB4, rs7598440, influences human cortical GABA concentrations. Other work has highlighted the significant impact of this genetic variant on expression of ERBB4 in the hippocampus and dorsolateral prefrontal cortex in human post mortem tissue. Our aim was to examine the association of rs7598440 with cerebrospinal fluid (CSF) GABA levels in healthy volunteers (n = 155). We detected a significant dose-dependent association of the rs7598440 genotype with CSF GABA levels (G-allele standardized beta = -0.23; 95% CIs: -0.39 to -0.07; P=0.0066). GABA concentrations were highest in A homozygous, intermediate in heterozygous, and lowest in G homozygous subjects. When excluding subjects on psychotropic medication (three subjects using antidepressants), the results did not change (G-allele standardized beta=-0.23; 95% CIs: -0.40 to -0.07; P=0.0051). The explained variance in CSF GABA by rs7598440 in our model is 5.2% (P=0.004). The directionality of our findings agrees with the aforementioned H-1-MRS and gene expression studies. Our observation therefore strengthens the evidence that the A-allele of rs7598440 in ERBB4 is associated with increased GABA concentrations in the human central nervous system (CNS). To our knowledge, our finding constitutes the first confirmation that CSF can be used to study genotype-phenotype correlations of GABA levels in the CNS. Such quantitative genetic analyses may be extrapolated to other CSF constituents relevant to SCZ in future studies. Neuropsychopharmacology (2012) 37, 2088-2092; doi:10.1038/npp.2012.57; published online 2 May 2012

Accuracy of genomic selection depends on the accuracy of prediction of single nucleotide polymorphism effects and the proportion of genetic variance explained by markers. Design of the reference population with respect to its family structure may influence the accuracy of genomic selection. The objective of this study was to investigate the effect of various relationship levels within the reference population and different level of relationship of evaluated animals to the reference population on the reliability of direct genomic breeding values (DGV). The DGV reliabilities, expressed as squared correlation between estimated and true breeding value, were calculated for evaluated animals at 3 heritability levels. To emulate a trait that is difficult or expensive to measure, such as methane emission, reference populations were kept small and consisted of females with own performance records. A population reflecting a dairy cattle population structure was simulated. Four chosen reference populations consisted of all females available in the first genotyped generation. They consisted of highly (HR), moderately (MR), or lowly (LR) related animals, by selecting paternal half-sib families of decreasing size, or consisted of randomly chosen animals (RND). Of those 4 reference populations, RND had the lowest average relationship. Three sets of evaluated animals were chosen from 3 consecutive generations of genotyped animals, starting from the same generation as the reference population. Reliabilities of DGV predictions were calculated deterministically using selection index theory. The randomly chosen reference population had the lowest average relationship within the reference population. Average reliabilities increased when average relationship within the reference population decreased and the highest average reliabilities were achieved for RND (e.g., from 0.53 in HR to 0.61 in RND for a heritability of 0.30). A higher relationship to the reference population resulted in higher reliability values. At the average squared relationship of evaluated animals to the reference population of 0.005, reliabilities were, on average, 0.49 (HR) and 0.63 (RND) for a heritability of 0.30; 0.20 (HR) and 0.27 (RND) for a heritability of 0.05; and 0.07 (HR) and 0.09 (RND) for a heritability of 0.01. Substantial decrease in the reliability was observed when the number of generations to the reference population increased [e.g., for heritability of 0.30, the decrease from evaluated set I (chosen from the same generation as the reference population) to II (one generation younger than the reference population) was 0.04 for HR, and 0.07 for RND]. In this study, the importance of the design of a reference population consisting of cows was shown and optimal designs of the reference population for genomic prediction were suggested.

Item does not contain fulltext The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 x 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across approximately 115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci., Author Summary This work describes a new methodology for analyzing genome-wide case-control association studies of diseases with strong correlations to clinical covariates, such as age in prostate cancer and body mass index in type 2 diabetes. Currently, researchers either ignore these clinical covariates or apply approaches that ignore the disease's prevalence and the study's ascertainment strategy. We take an alternative approach, leveraging external prevalence information from the epidemiological literature and constructing a statistic based on the classic liability threshold model of disease. Our approach not only improves the power of studies that ascertain individuals randomly or based on the disease phenotype, but also improves the power of studies that ascertain individuals based on both the disease phenotype and clinical covariates. We apply our statistic to seven datasets over six different diseases and a variety of clinical covariates. We found that there was a substantial improvement in test statistics relative to current approaches at known associated variants. This suggests that novel loci may be identified by applying our method to existing and future association studies of these diseases.

Although complex diseases and traits are thought to have multifactorial genetic basis, the common methods in genome-wide association analyses test each variant for association independent of the others. This computational simplification may lead to reduced power to identify variants with small effect sizes and requires correcting for multiple hypothesis tests with complex relationships. However, advances in computational methods and increase in computational resources are enabling the computation of models that adhere more closely to the theory of multifactorial inheritance. Here, a Bayesian variable selection and model averaging approach is formulated for searching for additive and dominant genetic effects. The approach considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait. We first characterize the behavior of the approach in simulations. The results indicate a gain in the causal variant identification performance when additive and dominant variation are simulated, with a negligible loss of power in purely additive case. An application to the analysis of high- and low-density lipoprotein cholesterol levels in a dataset of 3895 Finns is then presented, demonstrating the feasibility of the approach at the current scale of single-nucleotide polymorphism data. We describe a Markov chain Monte Carlo algorithm for the computation and give suggestions on the specification of prior parameters using commonly available prior information. An open-source software implementing the method is available at http://www.lce.hut.fi/research/mm/bmagwa/ and https://github.com/to-mi/.

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.

Methods for single marker association analysis are presented for binary and quantitative traits. For a binary trait, we focus on the analysis of retrospective case-control data using Pearson's chi-squared test, the trend test, and a robust test. For a continuous trait, typical methods are based on a linear regression model or the analysis of variance. We illustrate how these tests can be applied using a public available R package "Rassoc" and some existing R functions. Guidelines for choosing these test statistics are provided. © 2012 Springer Science+Business Media, LLC.

Background Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.

Information-theoretic metrics have been proposed for studying gene-gene and gene-environment interactions in genetic epidemiology. Although these metrics have proven very promising, they are typically interpreted in the context of communications and information transmission, diminishing their tangibility for epidemiologists and statisticians.In this paper, we clarify the interpretation of information-theoretic metrics. In particular, we develop the methods so that their relation to the global properties of probability models is made clear and contrast them with log-linear models for multinomial data. Hopefully, a better understanding of their properties and probabilistic implications will promote their acceptance and correct usage in genetic epidemiology. Our novel development also suggests new approaches to model search and computation.

The rapid and continuing progress in gene discovery for complex diseases is fuelling 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 prior 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. © 2011 The Authors. European Journal of Clinical Investigation © 2011 Stichting European Society for Clinical Investigation Journal Foundation.

Genomic copy number variants (CNVs) are a common, heritable source of inter-individual differences in genomic sequence. Their influence on phenotypic variability and their involvement in the pathogenesis of several common diseases is well established and the object of many current studies. In the course of examining CNV association to various quantitative traits in a general population, we have detected a strong association of CNVs over the four TCR genes to lymphocyte and neutrophil numbers in blood. In a small replication series, we have further characterized the nature of these CNVs and found them not to be germline, but dependent on the origin of analysed DNA. Germline deletion and rearrangement around the T-cell receptor (TCR) genes naturally occurs in white blood cells. Blood DNA derived from persons with high lymphocyte counts generates variable intensity signals which behave like germline CNVs over these genes. As DNA containing a relative high proportion of these CNV-like events involving the TCR genes has the ability to influence genotype counts of SNPs in the regions of these genes, care should be taken in interpreting and replicating association signals on variants within these genes when blood-derived DNA is the only source of data.