Assessment of Whole-Genome Regression for Type II Diabetes

Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable.     

Heritability of lung function in severe alpha-1 antitrypsin deficiency

Severe alpha-1 antitrypsin (AAT) deficiency is a proven genetic risk factor for COPD, but there is marked variation in the development of COPD among AAT deficient subjects. To investigate familial aggregation of lung function in subjects with AAT deficiency, we estimated heritability for forced expiratory volume in 1 s (FEV1) and FEV1/forced vital capacity (FVC) in 378 AAT deficient subjects from 167 families in the AAT Genetic Modifiers Study; all subjects were verified homozygous for the Z AAT deficiency allele. Heritability was evaluated for models that included and excluded an ascertainment correction, as well as for models that excluded, included and were stratified by a cigarette smoking covariate. In models without an ascertainment correction, and in all models without a covariate for smoking, no evidence for familial aggregation of lung function was observed. In models conditioned on the index proband with covariates for smoking, post-bronchodilator FEV1/FVC demonstrated significant heritability (0.26 +/- 0.14, p = 0.03). When we limited the analysis to subjects with a smoking history, post-bronchodilator FEV1 demonstrated significant heritability (0.47 +/- 0.21, p = 0.02). Severity rate phenotypes were also assessed as potential phenotypes for genetic modifier studies. Significant heritability was found with all age-of-onset threshold models that included smoking and ascertainment adjustments. Using the t-distribution, the heritability estimates ranged from 0.43 to 0.64, depending on the age-of-onset of FEV1 decline used for the severity rate calculation. Correction for ascertainment and consideration of gene-by-smoking interactions will be crucial for the identification of genes that may modify susceptibility for COPD in families with AAT deficiency.     

Model comparison for genetic evaluation of milk yield in crossbred Holsteins in the tropics

The objective of this study was to compare models for appropriate genetic parameter estimation for milk yield (305-day) in crossbred Holsteins in the tropics, where only records from crossbred cows were available. Eleven models with different effects of contemporary group (CG) at calving (herd-year-season or herd-year-month as fixed, and herd-year-month as random), age at calving (as linear or quadratic covariates, age-class, and age-class x lactation), and dominance were considered. On-farm records from small herds (n < 50) were included or excluded to validate the parameter estimates. Average Information Restricted Maximum Likelihood (AIREML) and Best Linear Unbiased Prediction (BLUP) were used to estimate variance components and breeding values. R-square (R2) and standard error of heritability (h2) were used to determine the appropriate model. The estimates of heritability from most models ranged from 0.18 to 0.22. CG formation of herd-year-month as a random effect slightly lowered the additive genetic variance but considerably decreased the permanent environmental variance. The model with age-class x lactation gave better R2 than other age adjustments. The models including records from smallholders gave similar estimates of heritability and a lower standard error than the models excluding them. The estimate of dominance variance as a proportion of total variance was close to zero. The low ratio of dominance to additive genetic variance suggested that the inclusion of dominance effects in the model was unjustified. In conclusion, the model including the effects of herd-year-month, age-class x lactation, as well as additive genetic, permanent environmental and residual effects, was the most appropriate for genetic evaluation in crossbred Holsteins, where records from smallholders could be included.     

An Effective Method to Identify Heritable Components from Multivariate Phenotypes

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.     

Linkage and heritability analysis of migraine symptom groupings: a comparison of three different clustering methods on twin data

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.     

Heritability of anthropometric phenotypes in caste populations of Visakhapatnam,India

In this study, we used anthropometric data from six Andhra caste populations to examine heritability patterns of 23 anthropometric phenotypes (linear, craniofacial, and soft tissue measures) with special reference to caste differences. We obtained anthropometric data from 342 nuclear families from Brahmin, Reddy, Telaga, Nagara, Ag. Kshatriya, and Mala castes of Visakhapatnam, India. These caste groups represent the existing hierarchical stratification of Indian populations. We used a variance components approach to determine the heritability (h2) of these 23 anthropometric phenotypes (height, weight, BMI, etc.). The sample consisted of 1918 individuals ranging in age from 6 to 72 years (mean = 21.5, S.D. = 13.8). The heritabilities (h2 +/- S.E.) for all anthropometric traits for the entire sample were significant (p < 0.0001) and varied from 0.25 +/- 0.05 (BMI) to 0.61 +/- 0.05 (bizygomatic breadth) after accounting for sex, age, and caste effects. Since data on socioeconomic and nutritional covariates were available for a subset of families, we repeated the genetic analyses using this subset, which has yielded higher heritabilities ranging from 0.21 +/- 0.16 (head breadth) to 0.72 +/- 0.18 (nasal breadth). In general, craniofacial measurements exhibited higher h2 compared to linear measures. Breadth measurements and circumferences yielded more or less similar heritabilities. Age and sex effects were significant (p < 0.0001 ) for most of the traits, while the effects of caste, socioeconomic status, and nutritional status were inconsistent across the traits. In conclusion, anthropometric phenotypes examined in this study are under appreciable additive genetic influences.     

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.     

A genetic contribution to circulating cytokines and obesity in children

Cytokines are considered to be involved in obesity-related metabolic diseases. Study objectives are to determine the heritability of circulating cytokine levels, to investigate pleiotropy between cytokines and obesity traits, and to present genome scan results for cytokines in 1030 Hispanic children enrolled in VIVA LA FAMILIA Study. Cytokine phenotypes included monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-alpha), leptin, adiponectin, soluble intercellular adhesion molecule-1 (sICAM-1), transforming growth factor beta 1 (TGF-beta1), C-reactive protein (CRP), regulated upon activation, normal T-cell expressed and secreted (RANTES) and eotaxin. Obesity-related phenotypes included body mass index (BMI), fat mass (FM), truncal FM and fasting serum insulin. Heritabilities ranged from 0.33 to 0.97. Pleiotropy was observed between cytokines and obesity traits. Positive genetic correlations were seen between CRP, leptin, MCP-1 and obesity traits, and negative genetic correlations with adiponectin, ICAM-1 and TGF-beta1. Genome-wide scan of sICAM-1 mapped to chromosome 3 (LOD=3.74) between markers D3S1580 and D3S1601, which flanks the adiponectin gene (ADIPOQ). Suggestive linkage signals were found in other chromosomal regions for other cytokines. In summary, significant heritabilities for circulating cytokines, pleiotropy between cytokines and obesity traits, and linkage for sICAM-1 on chromosome 3q substantiate a genetic contribution to circulating cytokine levels in Hispanic children.     

Identification of genes for complex disease using longitudinal phenotypes

Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quantitative phenotypes were computed and analyzed. Genome screen results were then compared for these longitudinal phenotypes and the results obtained using two cross-sectional designs: data collected near a single age (45 years) and data collected at a single time point. Significant linkage was obtained for nine regions (LOD scores ranging from 5.5 to 34.6) for six of the phenotypes. Using cross-sectional data, LOD scores were slightly lower for the same chromosomal regions, with two regions becoming nonsignificant and one additional region being identified. The magnitude of the LOD score was highly correlated with the heritability of each phenotype as well as the proportion of phenotypic variance due to that locus. There were no false-positive linkage results using the longitudinal data and three false-positive findings using the cross-sectional data. The three false positive results appear to be due to the kurtosis in the trait distribution, even after removing extreme outliers. Our analyses demonstrated that the use of simple longitudinal phenotypes was a powerful means to detect genes of major to moderate effect on trait variability. In only one instance was the power and heritability of the trait increased by using data from one examination. Power to detect linkage can be improved by identifying the most heritable phenotype, ensuring normality of the trait distribution and maximizing the information utilized through novel longitudinal designs for genetic analysis.     

Sex-specific genetic architecture of human fatness in Chinese: the SAPPHIRe Study

To dissect the genetic architecture of sexual dimorphism in obesity-related traits, we evaluated the sex–genotype interaction, sex-specific heritability and genome-wide linkages for seven measurements related to obesity. A total of 1,365 non-diabetic Chinese subjects from the family study of the Stanford Asia–Pacific Program of Hypertension and Insulin Resistance were used to search for quantitative trait loci (QTLs) responsible for the obesity-related traits. Pleiotropy and co-incidence effects from the QTLs were also examined using the bivariate linkage approach. We found that sex-specific differences in heritability and the genotype–sex interaction effects were substantially significant for most of these traits. Several QTLs with strong linkage evidence were identified after incorporating genotype by sex (G × S) interactions into the linkage mapping, including one QTL for hip circumference [maximum LOD score (MLS) = 4.22, empirical p = 0.000033] and two QTLs: for BMI on chromosome 12q with MLS 3.37 (empirical p = 0.0043) and 3.10 (empirical p = 0.0054). Sex-specific analyses demonstrated that these linkage signals all resulted from females rather than males. Most of these QTLs for obesity-related traits replicated the findings in other ethnic groups. Bivariate linkage analyses showed several obesity traits were influenced by a common set of QTLs. All regions with linkage signals were observed in one gender, but not in the whole sample, suggesting the genetic architecture of obesity-related traits does differ by gender. These findings are useful for further identification of the liability genes for these phenotypes through candidate genes or genome-wide association analysis.     

A framework for structural equation models in general pedigrees

Background/Aims: Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed. Methods: This framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used. Results: A simulation study shows that our method is computationally feasible, and has good statistical properties. Conclusion: Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.     

Prediction error variance and expected response to selection,when selection is based on the best predictor – for Gaussian and threshold characters,traits following a Poisson mixed model and survival traits

In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed or random effects). In the different models, expressions are given (when these can be found – otherwise unbiased estimates are given) for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non Gaussian traits are generalisations of the well-known formulas for Gaussian traits – and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part of the model (heritability on the normally distributed level of the model) or a generalised version of heritability plays a central role in these formulas.     

Evidence for linkage and association with reading disability on 6p21.3-22

Reading disability (RD), or dyslexia, is a common heterogeneous syndrome with a large genetic component. Several studies have consistently found evidence for a quantitative-trait locus (QTL) within the 17 Mb (14.9 cM) that span D6S109 and D6S291 on chromosome 6p21.3-22. To characterize further linkage to the QTL, to define more accurately the location and the effect size, and to identify a peak of association, we performed Haseman-Elston and DeFries-Fulker linkage analyses, as well as transmission/disequilibrium, total-association, and variance-components analyses, on 11 quantitative reading and language phenotypes. One hundred four families with RD were genotyped with a new panel of 29 markers that spans 9 Mb of this region. Linkage results varied widely in degree of statistical significance for the different linkage tests, but multipoint analysis suggested a peak near D6S461. The average 6p QTL heritability for the 11 reading and language phenotypes was 0.27, with a maximum of 0.66 for orthographic choice. Consistent with the region of linkage described by these studies and others, there was a peak of transmission disequilibrium with a QTL centered at JA04 (chi2=9.48; empirical P=.0033; orthographic choice), and there was strong evidence for total association at this same marker (chi2=11.49; P=.0007; orthographic choice). Although the boundaries of the peak could not be precisely defined, the most likely location of the QTL is within a 4-Mb region surrounding JA04.     

The SERPINE2 gene is associated with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a complex human disease likely influenced by multiple genes, cigarette smoking, and gene-by-smoking interactions, but only severe alpha 1-antitrypsin deficiency is a proven genetic risk factor for COPD. Prior linkage analyses in the Boston Early-Onset COPD Study have demonstrated significant linkage to a key intermediate phenotype of COPD on chromosome 2q. We integrated results from murine lung development and human COPD gene-expression microarray studies with human COPD linkage results on chromosome 2q to prioritize candidate-gene selection, thus identifying SERPINE2 as a positional candidate susceptibility gene for COPD. Immunohistochemistry demonstrated expression of serpine2 protein in mouse and human adult lung tissue. In family-based association testing of 127 severe, early-onset COPD pedigrees from the Boston Early-Onset COPD Study, we observed significant association with COPD phenotypes and 18 single-nucleotide polymorphisms (SNPs) in the SERPINE2 gene. Association of five of these SNPs with COPD was replicated in a case-control analysis, with cases from the National Emphysema Treatment Trial and controls from the Normative Aging Study. Family-based and case-control haplotype analyses supported similar regions of association within the SERPINE2 gene. When significantly associated SNPs in these haplotypic regions were included as covariates in linkage models, LOD score attenuation was observed most markedly in a smokers-only linkage model (LOD 4.41, attenuated to 1.74). After the integration of murine and human microarray data to inform candidate-gene selection, we observed significant family-based association and independent replication of association in a case-control study, suggesting that SERPINE2 is a COPD-susceptibility gene and is likely influenced by gene-by-smoking interaction.     

Analysis of affected sib pairs, with covariates--with and without constraints

Covariate models have previously been developed as an extension to affected-sib-pair methods in which the covariate effects are jointly estimated with the degree of excess allele sharing. These models can estimate the differences in sib-pair allele sharing that are associated with measurable environment or genes. When there are no covariates, the pattern of identical-by-descent allele sharing in affected sib pairs is expected to fall within a small triangular region of the potential parameter space, under most genetic models. By restriction of the estimated allele sharing to this triangle, improved power is obtained in tests for genetic linkage. When the affected-sib-pair model is generalized to allow for covariates that affect allele sharing, however, new constraints and new methods for the application of constraints are required. Three generalized constraint methods are proposed and evaluated by use of simulated data. The results compare the power of the different methods, with and without covariates, for a single-gene model with age-dependent onset and for quantitative and qualitative gene-environment and gene-gene interaction models. Covariates can improve the power to detect linkage and can be particularly valuable when there are qualitative gene-environment interactions. In most situations, the best strategy is to assume that there is no dominance variance and to obtain constrained estimates for covariate models under this assumption.     

Ocular refraction: heritability and genome-wide search for eye morphometry traits in an isolated Sardinian population

No genes influencing oculometric phenotypes have yet been identified, despite it being well known that eye morphometry is involved in refraction and that genetics may play an important role. We have therefore performed a heritability analysis and genome-wide search (GWS) of biometric ocular traits in an isolated Sardinian population, assessing the genetic contribution and identifying the associated genetic loci. A complete eye examination including refraction and ocular biometry measurements such as axial length (AL), anterior chamber depth (ACD) and corneal curvature (CC), was performed on 789 subjects. Heritability analysis was carried out by means of parent–offspring regression and variance component models. Univariate and bivariate linkage analysis was performed by using 654 microsatellite markers spanning the genome. CC showed a mean heritability of 57%. AL and ACD were found to have significantly different variances (P<0.01) in males and females, so that heritability was calculated separately for each sex. AL had an estimated heritability in females of 31% and in males of 60%, whereas ACD had an estimated heritability of 47% in females and of 44% in males. In the GWS, the most suggestive evidence of linkage was identified on chromosome 2 for AL (LOD 2.64), on chromosome 1 for ACD (LOD 2.32) and on chromosomes 7, 2 and 3 for CC (LOD 2.50, 2.44 and 2.34, respectively). High heritability of eye morphometry traits was thus revealed. The identified loci are the first linkage signals available in ocular biometry. Notably, the observed significant differences in parental transmission deserve further study.The authors Ginevra Biino and Maria Antonietta Palmas contributed equally to this work     

Optimal designs for linkage disequilibrium mapping and candidate gene association tests in livestock populations

Simulation was used to evaluate the performance of different selective genotyping strategies when using linkage disequilibrium across large half-sib families to position a QTL within a previously defined genomic region. Strategies examined included standard selective genotyping and different approaches of discordant and concordant sib selection applied to arbitrary or selected families. Strategies were compared as a function of effect and frequency of QTL alleles, heritability, and phenotypic expression of the trait. Large half-sib families were simulated for 100 generations and 2% of the population was genotyped in the final generation. Simple ANOVA was applied and the marker with the greatest F-value was considered the most likely QTL position. For traits with continuous phenotypes, genotyping the most divergent pairs of half-sibs from all families was the best strategy in general, but standard selective genotyping was somewhat more precise when heritability was low. When the phenotype was distributed in ordered categories, discordant sib selection was the optimal approach for positioning QTL for traits with high heritability and concordant sib selection was the best approach when genetic effects were small. Genotyping of a few selected sibs from many families was generally more efficient than genotyping many individuals from a few highly selected sires.     

The consequences of including non-additive effects on the genetic evaluation of harvest body weight in Coho salmon (Oncorhynchus kisutch)

Background

In this study, we used different animal models to estimate genetic and environmental variance components on harvest weight in two populations of Oncorhynchus kisutch, forming two classes i.e. odd- and even-year spawners.

Methods

The models used were: additive, with and without inbreeding as a covariable (A + F and A respectively); additive plus common environmental due to full-sib families and inbreeding (A + C + F); additive plus parental dominance and inbreeding (A + D + F); and a full model (A + C + D + F). Genetic parameters and breeding values obtained by different models were compared to evaluate the consequences of including non-additive effects on genetic evaluation.

Results

Including inbreeding as a covariable did not affect the estimation of genetic parameters, but heritability was reduced when dominance or common environmental effects were included. A high heritability for harvest weight was estimated in both populations (even = 0.46 and odd = 0.50) when simple additive models (A + F and A) were used. Heritabilities decreased to 0.21 (even) and 0.37 (odd) when the full model was used (A + C + D + F). In this full model, the magnitude of the dominance variance was 0.19 (even) and 0.06 (odd), while the magnitude of the common environmental effect was lower than 0.01 in both populations. The correlation between breeding values estimated with different models was very high in all cases (i.e. higher than 0.98). However, ranking of the 30 best males and the 100 best females per generation changed when a high dominance variance was estimated, as was the case in one of the two populations (even).

Conclusions

Dominance and common environmental variance may be important components of variance in harvest weight in O. kisutch, thus not including them may produce an overestimation of the predicted response; furthermore, genetic evaluation was seen to be partially affected, since the ranking of selected animals changed with the inclusion of non-additive effects in the animal model.     

Simultaneous Discovery,Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.