Human sexual orientation is influenced by genetic and non-shared environmental factors as are two important psychological correlates – childhood gender typicality (CGT) and adult gender identity (AGI). However, researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation between these traits, particularly in women.N = 4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a common pathway model best fitted the data.Here we performed a multivariate genetic analysis in a large sample of British female twins (This indicated that a single latent variable influenced by a genetic component and common non-shared environmental component explained the association between the three traits but there was substantial measurement error. These findings highlight common developmental factors affecting differences in sexual orientation. Understanding of the origins of sexual orientation can help narrow competing developmental explanations for behavioral sex differences in general and is of increasing importance to researchers concerned with the physical and mental health of sexual minorities Several early family and twin studies provide evidence for a genetic component to both male and female sexual orientation Early criticisms of previous studies focused on the possibility that their reliance on self-selected volunteers  may have biased the results by increasing twin resemblance. But it is not clear how this would inflate concordance rates or overestimate genetic or non-genetic effects (similarity would be increased for both MZ and DZ twins). However, two studies of attraction components of sexual orientation, and one of same-sex sexual behavior, were population-based and all reported lower concordance rates than previously found at around 30% - although Bailey et al. 2000 were unable to resolve genetic, shared and non-shared environmental factors in their univariate models Sexual orientation, like many complex behaviors, comes as a “package” of covarying traits. Critical among these are childhood gender typicality (or CGT), which are sex-typed behaviors, activities and interests that are statistically atypical for biological sex during childhood) and gender identity . CGT is robustly correlated with adult homosexuality as demonstrated in prospective and retrospective studies and has been observed cross-culturally Taken together the data suggest that CGT  might be considered as a possible “sex atypicality” endophenotype for trait sexual orientation which could be more powerful in future gene discovery. Trait sexual orientation is notoriously skewed. Thus, a broad research strategy which includes CGT and AGI with their more favorable statistical distributions will enhance our ability to resolve the molecular genetics of sexuality.nd to 4th finger digits  revealed a significant association between male-typical digit ratios and sexual orientation in women A related conceptual issue concerns the putative etiological factor(s) which explain the link between CGT, AGI and sexual orientation and thus may constitute the “sex atypicality” endophenotype. A good candidate is prenatal sexual differentiation under the action of androgens In the present study, we analyzed questionnaire data from a large volunteer register of female twins in the United Kingdom to test the hypotheses that (1) genetic factors significantly influence variation in measures of sexual orientation and it's two covariates – CGT and AGI; (2) that these three traits correlate significantly at the phenotypic level; and (3) that the covariation among the three traits is also due to a genetic correlation. This is the first study of its kind in a British sample.The study was approved by the St. Thomas' Hospital research ethics committee. All study participants involved in this study provided informed written consent.Subjects were monozygotic (MZ) and dizygotic (DZ) volunteer female twins drawn from the “TwinsUK” registry In 2002, twins were sent a questionnaire asking about general sexual behavior and sexual orientation . Of the 8,418 questionnaires sent, 4,725 (56.1%) were returned. In a 2005 follow-up survey, an anonymous questionnaire assessing CGT and AGI was also sent to 6,934 female twins in the registry and returned by 4,850 (69.9%). The questionnaires were developed previously based on scales in the published literature but shortened for the purposes of practicality within a large twin register. Twins were not selected on the basis of variables being studied and were unaware of any hypothesis being tested.N = 44; 0.99%) and/or reported never having had sexual experiences  were excluded from the analyses as were 228 (5.15%) females with missing values for any items assessing CGT and AGI. Also, 32 (0.72%) women were excluded because of unknown zygosity. After applying exclusion criteria, a total of 4,066 women were eligible for analysis, comprising 906 complete MZ pairs, 806 complete DZ pairs and 642 women whose co-twins did not participate (15.35%). However, sample sizes varied somewhat in the different analyses because of missing scale data.Final questionnaire data relating to sexual orientation and its psychological correlates, CGT and AGI, was available on a total of 4,426 female twin individuals - a 49% response rate. Females who reported never having felt sexually attracted to anyone else  to 5  with a supplementary option of “no sexual attraction” (numbered 6) t-tests (two-tailed) were used to examine differences between MZ and DZ twins on age, years of education, CGT, AGI and mean sexual orientation scores. Two-sample tests of proportions were used to test for differences in marital status and SES. Pearson's correlation coefficients were used to explore patterns of association between CGT, AGI and sexual orientation scores.For descriptive and genetic analysis CGT, AGI and sexual attraction were treated as continuous traits based on women's responses to the specific questions. Unpaired P value less than 0.05 or odds ratios with a 95% confidence interval not including “1” were considered statistically significant, unless stated otherwise. Data handling and descriptive analyses were undertaken using STATA  while all genetic modelling was carried out with Mx software For all analyses, a The present study used the classical twin design where population variance in phenotypes, as well as covariance between them, can be dissected into genetic and environmental sources. The twin design assumes that MZ twins share 100% of both their genes and shared environment, whereas DZ twins share - on average - 50% of their genes and 100% of shared environment. Presuming that both types of twins share equally similar family environments, any greater similarity between MZ as compared with DZ twin pairs is attributed to genetic factors.In the present study maximum likelihood genetic modeling was used to model latent genetic and environmental factors influencing sibling covariance in CGT, AGI and sexual orientation for MZ and DZ twins. Bivariate normality was given for the measures of CGT and AGI after the variables were transformed. However, for trait such as sexual orientation, normality cannot be achieved.Genetic model fitting was used to decompose the observed phenotypic variance (P) into additive (A) and dominant (D) genetic effects, and shared (C) and non-shared environmental (E) effects h2) can be obtained by comparing similarities in scores using intra-class correlation coefficients (ICCs) for MZ and DZ twin pairs. Depending on the correlations between the MZ and DZ twins, either an ACE or an ADE model is fitted. For univariate models in the present study, an ACE model was applied when DZ correlations were more than half the MZ correlations. When the DZ correlations were less than half the MZ correlations, both ACE and ADE were estimated for comparative purposes df) of the full model and subtracting it from the fit function and the df of the nested restricted models. The subtraction gives an χ2 value and associated df that can be tested for significance. In addition, the Akaike Information Criteria (AIC = χ2-2df) was considered, with lower values indicating the most suitable model. The most parsimonious model was then used to estimate the heritability. Note the assumption that trait-related environments are similar to the same degree in MZ and DZ pairs is valid for trait sexual orientation . More detailed descriptions of twin modeling analyses can be found in Posthuma et al. For continuous phenotypes, evidence for a genetic contribution  and the proportion of the total genetic variance for the traits (genetic correlation).To test our hypothesis that the covariation among CGT, AGI, sexual orientation (attraction) can be explained by genetic correlation between the traits, we further fitted the following three multivariate models to the data The suitability of the multivariate models was determined by comparing the models AIC, BIC (Bayesian Information Criterion) and their goodness of fit as measured with the likelihood ratio chi-square test (−2LL).P<0.01). Also DZ twins, more than MZ twins, reported being in a relationship  or being widowed .Most women displayed “average” AGI scores with the peak score being at 4.25 . A small fraction of women had values at both extreme ends of the distribution. Overall, AGI showed less variability compared with CGT. Whilst only a negligible proportion of subjects reported a high degree of childhood gender nonconforming behavior, most of the women scored in the upper third of the distribution, with peak scores at 5.5 and 7. We also observed the previously documented general tendency for women to show more non-heterosexuality at the predominantly heterosexual end of the two scales see .Intra-class correlations for MZ and DZ twins in the three measures are reported in Based on the intra-class correlations, ACE and ADE models were fitted for all phenotypes. For all measures the best-fitting model was an AE model . The higr = −0.21 to r = 0.05, with the highest correlation being between CGT and sexual attraction and the lowest between AGI and sexual attraction . The parameter estimates derived from the common pathway model are shown in c and unique A, D and E make to the variance in a trait, squares of the path coefficient are taken. Thus, the model postulates the existence of an underlying sexual orientation phenotype (Pc) with a heritability 24% (0.492) that chiefly explains the co-occurrence of CGT, AGI and sexual attraction. For CGT Pc accounts for 43% (0.652) of the variation, for AGI it accounts for 4% (0.192) and for sexual attraction it accounts for 11% (−0.322) of the variation. The heritability of the variation in the phenotype that is not accounted for by Pc is 10% (−0.322) for CGT, 10% (0.322) for AGI and 19% (0.442) for sexual attraction. No influence of D on the variation of Pc and the phenotypes could be detected. Overall, these results suggest that the common sexual orientation phenotype does not account for significant variation in AGI in this model.When comparing the independent and common pathway models with the Cholesky model, the common pathway model was found to offer the most suitable explanation of the data with the lowest value of AIC at 3871.1 and the lowest BIC at −22609.7 . This coOur results show that sexual attraction and CGT are influenced by genetic factors (accounting for 25% and 32% of the variance respectively). Genetic contributions as estimated in the univariate analyses had a much weaker impact on AGI (11%). The effect of non-shared environmental factors (including measurement error) on all traits was large. However, there was no effect of the shared family environment on any trait.These findings are broadly consistent with previous population-level twin studies demonstrating a heritable basis to male and female sexual orientation. The heritability estimates reported here for female sexual attractions were larger than those reported by Bailey et al. The effect of E on all traits was large. E includes phenotypic variation accounted for by non-shared environment and measurement error. A variety of sources can cause measurement error, including inadequate or imprecise assessment instruments and phenotype description, and a variety of response styles, specifically acquiescence, disacquiescence, extreme response, midpoint responding, and noncontingent responding h2 estimates generally reported for sexual orientation . This adds further support to the notion that CGT may be the main heritable component or endophenotype of sexual orientation Consistent with several studies the highest heritability was found for CGT (32%) The results from the multivariate analyses presented here provide evidence of a genetic overlap between CGT and sexual attraction but less for AGI. We detected a common latent phenotype with a heritability of 24% underlying sexual orientation, CGT and AGI as well as moderate phenotype-specific additive genetic factors and large phenotype-specific non-shared environmental factor loading on these traits. These data are supportive of those from Bailey et al. Insofar as our genetic estimates are additive, these data do not suggest a major role for epistatic or dominant allelic effects. Our results are also silent on the sources of non-shared environmental effects. However, what is clear from several twin studies, including the present, is that shared factors such as the home environment and parenting styles have little impact on human sexual orientation. Nevertheless, as we cannot be certain that our measures, particularly AGI, were robust  there is a necessary degree of imprecision to our parameter estimates and the model fitting results should be treated with caution. Future studies should be particularly careful in using measures of AGI. Several other limitations weaken overly strong conclusions from the present study. The response rate was lower than three other large twin studies  In summary, we found genetic influences on female sexual orientation as measured via attractions and on CGT . A moderate effect of a common latent phenotype suggests that there are some overlapping mechanisms which may be responsible for sexual orientation. However, stronger conclusions are not warranted at this stage because of substantial measurement error. Future research efforts should focus on “sex-atypicality” as a possible intermediate phenotype for trait sexual orientation which may be more amenable to gene-mapping approaches.


Bayes-C threshold models were used to estimate SNP effects by classifying IBK into two, three or nine ordered categories. Magnitudes of genetic variances estimated in localized regions across the genome indicated that SNP within the most informative regions accounted for much of the genetic variance of IBK and pointed out some degree of association to IBK. There are many candidate genes in these regions which could include a gene or group of genes associated with bacterial disease in cattle.Infectious bovine keratoconjunctivitis (IBK), also known as pinkeye, is characterized by damage to the cornea and is an economically important, lowly heritable, categorical disease trait in beef cattle. Scores of eye damage were collected at weaning on 858 Angus cattle. SNP genotypes for each animal were obtained from BovineSNP50 Infinium-beadchips. Simultaneous associations of all SNP with IBK phenotype were determined using Bayes-C that treats SNP effects as random with equal variance for an assumed fraction ( Moraxella bovis. IBK is characterized by excessive tearing, inflammation of the conjunctiva, and ulceration of the cornea. In severe cases, perforation of the cornea may occur which leads to permanent blindness. IBK is considered the most important ocular disease in cattle, due to the decreased performance of infected individuals and its subsequent economic effects. IBK is an economically important, lowly heritable (0.10 - 0.25), categorical disease trait. Breed differences in susceptibility of IBK have been established. In a number of studies, Hereford, Jersey and Holstein breeds appear to be more susceptible to infection than Bos Indicus breeds [Infectious Bovine Keratoconjunctivitis (IBK), commonly known as pinkeye, is a contagious bacterial disease caused in beef cattle by itable 0.0 - 0.25,s breeds  to be asThe objective of the current study was to detect single-nucleotide polymorphism (SNP) markers in linkage disequilibrium (LD) with QTL associated with IBK in Angus cattle.Infectious bovine keratoconjunctivitis records were collected from 858 animals born and raised in the Iowa State University Angus research herd. Both eyes were scored individually. The IBK scoring system for one eye used a scale from 0 to 4. A score of 0 denotes a cornea with no apparent lesions, 1 for a cornea with a lesion covering less than 1/3 of the cornea, 2 for a lesion covering 1/3 to 2/3 of the cornea, 3 for a lesion covering more than 2/3 of the cornea, and 4 for perforation of the cornea  and as 1 otherwise (37.3 %).IBK classified in three categories: Incidence was scored as 0 for both unaffected eyes (63.7 %), 1 for single affected eye (26.4 %) and 2 for both affected eyes (10.9 %).IBK classified in nine categories: Incidence was scored from 0 (63.7 %) to 8 (0.58 %) by adding the scores of the left and right eyes to utilize more information. For example, an animal with right eye score of 2 and a left eye score of 1 was scored as a 3.High-density SNP genotypes were obtained using the Bovine SNP50 Infinium II BeadChip . The Illumina A/B allele calls were used to compute a covariate for each locus that had values 0, 1, or 2 representing the number of B alleles. Missing genotypes represented less than 0.2% of total observations and were replaced with average covariate values. All genotypes were retained regardless of minor allele frequency.icl):The Bayes-C model described by Kizilkaya et al.,  was modiicl is the underlying liability to IBK  of individual i; b is a vector of contemporary group effects defined as the interaction of year and pasture management group; xi is an incidence vector for b; K is the number of SNP marker loci; ijZ is the covariate at locus j for individual i; jβ is the random substitution effect for locus j, which is conditional on jδ=1 but jβ=0 when jδ=0, jδ is a random 0/1 variable indicating the absence (jδ=0) with probability π or presence (jδ=1) with probability 1 — π of locus j in the model; and ei is the random residual effect assumed normally distributed with µ=0 and known where GenSel software [π=0.999) of SNP markers have no effect on IBK scores. In the Markov Chain Monte Carlo (MCMC) implementation, a burn-in period of 5,000 MCMC cycles was used before saving samples from each of an additional 40,000 MCMC cycles. SNP marker effects jβ were estimated by computing Monte-Carlo means of the posterior distribution of these effects using a Gibbs sampling strategy described in Kizilkaya et al., [The Bayes-C threshold model was implemented in software  by assumGenetic variances explained by SNP were calculated in the chromosomal regions defined by 5 contiguous SNP sliding windows. The 1000 largest genetic variance windows across the genome are shown in Figure The regions on chromosomes 2 , 13 (30900) and 23 (48300) were found to be common across threshold model analyses and to have the highest genetic variances in the whole genome analysis of IBK scores for two, three or nine categories. Thereby, these regions could be indicative of QTL locations which are associated with IBK.Comparison of genetic variances from analyses of IBK indicated that analysis of IBK scored into nine categories attributed smaller genetic variances to the regions on chromosomes 2, 13 and 23 than those with IBK defined in two and three categories. However, results from the nine category analysis additionally identified regions on chromosomes 1 and 20 which accounted for more genetic variance than these regions from analyses of IBK with two and three categories. SNP within the regions on chromosomes 1 and 20 were also found to reside within QTL regions identified by Casas and Stone . FurtherWhole genome analysis of IBK using a marker-based threshold model identified genomic regions that were associated with IBK. Degree of associations determined in the analyses of IBK with two, three or nine classification were different for regions on chromosome 2, 13 and 23, which resulted from varying power across threshold model analyses depending on different number of observations in IBK categories with two, three or nine classification. Analysis of IBK with nine categories estimated smaller genetic variances for certain regions; however, it seemed to compensate for the loss of information by providing more indicative regions of QTL across the genome, especially on chromosomes 1 and 20. Analyses herein also provided confirmatory evidence of SNP associated with putative QTL on chromosome 1 and 20 for IBK. These findings should motivate future studies in whole genome analysis to identify the genetic basis of IBK.The authors declare that they have no competing interests.KK developed the model and part of the program, and carried out the statistical analysis. RGT did the data preparation and helped with the statistical analysis. DJG and RLF developed the GenSel program used for analysis and JMR conceived the study, participated in its design and coordination. KK, RGT, DJG, RLF and JMR helped to draft the manuscript. All authors read and approved the final manuscript.


The same individuals were tested at age 11, enabling us the rare opportunity to measure cognitive change across most of their lifespan. 547,750 SNPs ranked by position are divided into 10, 908 overlapping regions of 101 SNPs to estimate the genetic variance each region explains, an approach that resembles classical linkage methods. We also estimate the genetic variation explained by individual autosomes and by SNPs within genes. Empirical significance thresholds are estimated separately for each trait from whole genome scans of 500 permutated data sets. The 5% significance threshold for the likelihood ratio test of a single region ranged from 17–17.5 for the three traits. This is the equivalent to nominal significance under the expectation of a chi-squared distribution (between 1df and 0) of P<1.44×10−5. These thresholds indicate that the distribution of the likelihood ratio test from this type of variance component analysis should be estimated empirically. Furthermore, we show that estimates of variation explained by these regions can be grossly overestimated. After applying permutation thresholds, a region for gf on chromosome 5 spanning the PRRC1 gene is significant at a genome-wide 10% empirical threshold. Analysis of gene methylation on the temporal cortex provides support for the association of PRRC1 and fluid intelligence (P = 0.004), and provides a prime candidate gene for high throughput sequencing of these uniquely informative cohorts.Combining information from multiple SNPs may capture a greater amount of genetic variation than from the sum of individual SNP effects and help identifying missing heritability. Regions may capture variation from multiple common variants of small effect, multiple rare variants or a combination of both. We describe regional heritability mapping of human cognition. Measures of crystallised (g Loss of cognitive function is one of the most feared aspects of growing old. Intelligence and the rate of age related cognitive change vary widely in healthy individuals and have been associated with health status, longevity and quality of life gc) is typically assessed using vocabulary and knowledge-based tests, and tends to remain stable with age. Fluid intelligence (gf) is assessed using tests that require on-the-spot thinking — often with abstract materials and under time pressure — and tends to peak in early adulthood and decline thereafter Here we analyse the genetic contributions to complex variation in three measures of intelligence: (i) crystallised intelligence; (ii) fluid general intelligence; and (iii) lifetime change in intelligence. We use three Scottish birth cohorts whose intelligence was measured in childhood (age 11 years) and again in late adulthood (age 65 to 79 years). Crystallised intelligence  have generally failed to capture a large proportion of the genetic variation in complex traits Ethical approval for all the projects was obtained from the Lothian Research Ethics Committee. Data were gathered from three longitudinal studies of relatively healthy older individuals with detailed cognitive phenotypes: the Lothian Birth Cohorts of 1921  and 1936 , and the Aberdeen Birth Cohort of 1936 . The years 1921 and 1936 refer to the participant's year of birth. Participants took a validated intelligence test at a mean age of 11 years: the Moray House Test No. 12 (MHT), which is a test of general intelligence Selection of individuals, ethical consent, and full details of the assessments have been described in previous studies Following informed consent, venesected whole blood was collected for DNA extraction. A total of 599,011 single nucleotide polymorphisms (SNPs) were genotyped using the Illumina610-Quadv1 chip as described previously n×n genomic relationship matrix (GRM) of relatedness at a population level between n individuals gives the covariance structure for the phenotype based on the premise that the more related two individuals are, or the greater the amount of the genome they share in common, the greater the expectation of phenotypic similarity.In a population of unrelated individuals, SNP genotypes can be used to estimate shared co-ancestry or identity by state between individuals with rare SNPs weighted more heavily. Under certain assumptions it can be shown that a region that is shown to be identical by state will also be identical by descent m SNPs can be used to solve a linear mixed model Y is An extension of this to a bivariate analysis  was usedYang et al. uR is a vector of n additive genomic random effects from the region, n is the number of individuals and I is the identity matrix as described above. RG is a GRM derived only from SNPs within the defined region.By combining information on multiple SNPs within a genomic region we aim to capture a substantial part of the heritability missed by traditional ‘one SNP at a time’ approaches. Identifying those regions of the genome that capture most variation is an efficient way of selecting candidate regions for high throughput sequencing that could complement whole-exome sequencing experiments until whole genome sequencing is feasible for large numbers of samples. Here, autosomal SNPs were ranked by genomic location and divided into regions spanning 101 consecutive SNPs. Regions were overlapping to allow for the possibility that genetic variation is distributed among two or more windows, with a shared region between two consecutive regions spanning 50 SNPs, resulting in 10,908 overlapping regions from 547,750 SNPs. Each region was fitted individually in the linear mixed model . and those that mapped outside these limits. Genome build 37 was used to identify genes and gene limits. A linear mixed model was used to fit forty-four variance components simultaneously, capturing SNPs within genes and SNPs outside genes on each of the 22 human autosomes . statistic calculated as twice the difference between the log likelihoods of the full model and a null or reduced model that did not fit a genetic component. For a single test, the expectation of the LRT for testing one extra variance component is a 50∶50 mixture of a point mass of 0 and a chi square distribution with 1df Results from the 10,908 regions were ranked by likelihood ratio test statistic. The top ten non –overlapping or approximately 0.1% of regions were fitted back into a linear model with an eleventh ‘polygenic’ variance component comprising all the available autosomal SNPs. This model was tested against a null model containing only the polygenic variance component under the expectation that the likelihood ratio test is distributed as a chi-square with ten degrees of freedom. We repeated the analyses without the ‘polygenic’ variance component and obtained virtually the same results.Finally, the contribution of the identified top ten regions for each of the traits were analysed for putative pleiotropic effects across cognitive phenotypes.f to 18.8 for gc for a type 1 error rate of 5%. As 100 permutations is not sufficient to ensure a stable estimate of the threshold, but testing 10,908 regions for three traits hundreds of times is computationally intensive, we repeated the analyses using non-overlapping windows and carried out a further 500 permutations. A permutation involved randomly permuting the phenotypic and genotypic data and testing 5454 alternate or non-over-lapping regions on the permuted data set. For each set of permuted data; i) regional population-sense heritabilities were estimated for all regions (each spanning 101 SNPs) and ii) The top ten regions ranked by LRT test statistic from each permutation were simultaneously fitted into a linear model to determine their combined contribution. These were fitted with and without a ‘polygenic’ component. This gave the empirical distribution of the test statistic under the null hypothesis for fitting a single region and for when the ten top ranking regions are fitted simultaneously.To date there is little evidence for the empirical distribution of a suitable threshold for the LRT statistic when testing multiple genomic regions. Rowe et al. The software package PLINK 2ps) to distinguish it from the often used narrow and broad sense heritability A = 0.95 (s.e. 0.25), and to crystallised intelligence rA = 0.66 (s.e. 0.34) (i.e. the amount of correlation emerging from pleiotropy is high). There was little genetic correlation between crystallised intelligence and cognitive change rA = 0.008 (s.e. 0.53).For simplicity we define the proportion of phenotypic variance captured by SNP genotypes in unrelated individuals as population-sense heritability  are given in 2ps>1%  for SNP association in each region and −log10P-value from the LRT test for each region was 0.52 . This region on chromosome 5 associated with fluid intelligence was the only region for all three traits to exceed genome-wide significance at the P<0.10 threshold. When the top ten regions (perm = 0.58), 15% (Pperm = 0.11) and 18% (Pperm = 0.43) of the phenotypic variation for crystallised intelligence, fluid intelligence, and cognitive change respectively. The likelihood ratio test statistic for the regional heritability scan across the genome and the most significant hits from the genome wide association analyses  were 17.2 for crystallised intelligence, 17.5 for fluid intelligence and 17.08 for cognitive change To estimate empirical thresholds, phenotypic data for each of the three traits were permuted 500 times to attain an estimate of the null distribution when genotype and phenotype were randomly assorted. We performed 5,454 REML analyses across the genome for each of the permuted data sets resulting in over 8.2 million single tests. The results were ranked by log likelihood and compared to a null model using an LRT. The resulting genome-wide significance thresholds for the LRT .Genetic variation of the traits differed across autosomes and for SNPs within or outside genes. SNPs within genes explained 48, 64 and 38% of the total genetic variation for f was genome-wide significant at the 10%, however, even if the locus was truly associated with gf we were not expecting a high level of statistical support due to the small sample size of the study. In order to gather further independent evidence that could support or reject the association of the locus with gf we assembled previously published data of brain-measured intermediate phenotypes f. h2ps was estimated with ACTA The top region associated with g2ps of cg04431054 measured in the CRBL, FCTX, PONS and TCTX brain regions was 0.46, 0.24, 0.28 and 0.33, respectively.The 86 SNPs located on chromosome 5 between 126711782–127335370 base-pairs explain a significant (P<0.0001) proportion of the phenotypic variation of cg04431054 for each of the four brain tissues . h2ps of is associated with cg04431054 levels in the CRBL, FCTX, PONS and TCTX brain regions. However, we have not yet shown a direct link between cg04431054 levels and gf. To do that, we estimate the effect of the 86 SNPs on the brain phenotypes and construct a genetic score f phenotypes. A significant regression of genetic score for cg04431054 with gf would indicate a link between the levels of cg04431054 and gf. Only one of the four brain regions (TCTX) showed a significant association with gf (P = 0.004), and explained 0.5% of the phenotypic variance. The regression coefficient was positive  indicating a positive correlation between methylation levels and gf. Hence, our analyses of brain-related intermediate phenotypes provides supporting evidence of the region being truly associated with gf, uncovers the likely target region of the brain associated with gf and identifies PRRC1 as a candidate gene for gf.So far, we have shown that the 623 kb region of chromosome 5 associated with g2ps). 2ps) than 95% of the ranked permutation analyses. Despite this the LRT for the comparison of the linear models did not achieve genome wide significance for either of these regions.We implemented a recently proposed method of genome scanning by expanding single SNP analysis to the estimation of genetic variance explained by regions spanning 101 co-located SNPs. After deriving empirical thresholds by permutation analysis we show that stringent thresholds close to that of a bonferroni correction are necessary for evaluating the likelihood ratio test statistic and that the distribution of multiple tests is highly inflated when compared to the null distribution for a single test. This is also true for estimates of heritability (hCTXN3 gene (cortexin 3) (rs790837 (P<10−6) is located at position 127004506, 10 kb away from this gene.We did find a genome wide significant region (P<0.10) for the LRT statistic on chromosome 5 associated with fluid intelligence. The region spans the texin 3) , a brainCTXN3-SLC12A2 region is a strong candidate region and has been linked to brain function and schizophrenia in multiple studies SLC12A2 ranked within the top ten regions with an LRT of 9.33 (cortexin 3 (LRT = 16) was the only region to achieve genome wide significance (P<0.10). Nonetheless, the strongest evidence suggests that variation of methylation levels at the promoter region of PRRC1 are mediating variation if gf. The function of the Golgi-associated PRRC1 gene in the brain is unknown and will require follow-up functional studies.The  of 9.33 , here thThe population-sense heritabilities for fluid intelligence are lower than those previously reported possibly due to an older demographic. Family based (narrow-sense) estimates of heritability for IQ related traits have been shown to decline somewhat with age In general the estimates of genomic heritability for chromosomes reflected the analysis of smaller regions in that the regions with the highest test statistics are located on chromosomes explaining the greatest variance. The sum of heritability estimates for individual chromosomes was inflated by 20–50% compared to estimating the heritability for the entire genome. When heritabilities were estimated from SNPs inside and outside genes (i.e. fitting 44 variance components) heritability for fluid intelligence was doubled when compared to fitting the 22 autosomes  were compared across traits and gender using a Kolmogorov-Smirnov test. Cognitive change differed from fluid and crystallised intelligence . Differences between crystallised and fluid intelligence were less marked (P<0.01). Interestingly, we found some evidence that the distribution of heritability across the genome for cognitive change differs in males and females. Genetic variation was higher in females and the Kolmogorov-Smirnov test of the distributions of heritabilities for the 10,908 regions in males (n = 871) and females (n = 933) was suggestive at P-value of 0.06, although the test does not account for the correlation of the regions and is likely to be inflated. A previous study showed higher variation within males for a measure of general intelligence Distributions of the regional heritability test statistic Click here for additional data file.


It has been shown that if genetic relationships among individuals are not taken into account for genome wide association studies, this may lead to false positives. To address this problem, we used Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification analyses. To account for linkage disequilibrium among the significant markers, principal components loadings obtained from top markers can be included as covariates. Estimation of Bayesian networks may also be useful to investigate linkage disequilibrium among SNPs and their relation with environmental variables.For the quantitative trait we first estimated residuals while taking polygenic effects into account. We then used a single SNP approach to detect the most significant SNPs based on the residuals and applied principal component regression to take linkage disequilibrium among these SNPs into account. For the categorical trait we used principal component stratification methodology to account for background effects. For correction of linkage disequilibrium we used principal component logit regression. Bayesian networks were estimated to investigate relationship among SNPs.Using the Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification approach we detected around 100 significant SNPs for the quantitative trait (p<0.05 with 1000 permutations) and 109 significant  SNPs for the categorical trait. With additional principal component regression we reduced the list to 16 and 50 SNPs for the quantitative and categorical trait, respectively.GRAMMAR could efficiently incorporate the information regarding random genetic effects. Principal component stratification should be cautiously used with stringent multiple hypothesis testing correction to correct for ancestral stratification and association analyses for binary traits when there are systematic genetic effects such as half sib family structures. Bayesian networks are useful to investigate relationships among SNPs and environmental variables. It has been shown that ignoring genetic relationships among cases and controls may lead to false positives in genome wide association analyses (GWAS). Genome-wide Rapid Association using Mixed Model and Regression, GRAMMAR,  approach is a one solution based on correction of quantitative trait for both polygenic and fixed effects. Another approach, introduced by Price et al (2006), uses principal components loadings (PCL) as covariate in linear regression models for detecting and correcting ancestral stratifications for categorical traits in GWAS.GWAS analyses result in a list of significant SNPs. Some of these SNPs will be in linkage disequilibrium (LD) due to the dense set of markers and this may lead to collinearity among these SNPs. Collinearity among genotype scores could raise problems when applying model selection procedures and different solutions have been proposed from non parametric methods to selective algorithms . Wang and Abbott (2008) suggested using a principal component regression (PCReg) approach to break the collinearity among marker genotypes using top principal components loadings (PCL) as covariates in the linear regression model. Pant et al (2010) extended the PCReg approach to the categorical traits via logistic regression and model selection strategies.Most of the GWAS studies do not take into consideration the possible relations among SNPs and/or with other explanatory variables. Bayesian networks are models that present statistical dependencies and independencies in the joint probability distribution of the data. As such they do not state causality, but it is certainly possible to speculate what kind of causal mechanisms would be compatible with the observed dependencies and independencies. Rodin et al (2005) used Bayesian networks to do joint inference on marker variation in the human APOE gene and plasma apolipoprotein E levels. Sebastiani et al (2005) used Bayesian networks in association studies.The main aim of this study was to apply GRAMMAR, Bayesian Network and principal component stratification models to QTL-MAS 2010 dataset.http://www.vsni.co.uk/software/asreml);In the first step of the GRAMMAR analysis, we estimated the heritabilities and residuals for the quantitative trait using an animal model as was implemented in Asreml (Xb + Za + e (1)y = X and Z are incidence matrices, and e is a vector containing residuals., where y contains the observations, b is the fixed effects of sex, a is the additive genetic effect, matrices A is the additive genetic relationship matrix for the animals; I is an identity matrix,For the random effects, it is assumed that y=Xf + η + e (2)y represents vector of n observations ), η is intercept, X is a design matrix relating observations with f regression coefficients vector to be estimated, e is a vector of residuals assumed to be normally distributed. We applied GRAMMAR  with 1000 permutation to detect most significant SNPs for quantitative trait using residuals obtained (1) and used in (2) as response variable.where Principal components analyses can be used to decompose the genomic matrix into a set of new orthogonal variables which account for the total variance of the original variables  in decreasing proportions. For the binary trait, we used principal component stratification  methodology to account for ancestral stratification in the QTL-MAS2010 dataset, as was implemented in SAS and JMP Genomics software  version 9.1. In order to take LD among significant markers into account for GRAMMAR and principal component stratification approaches we applied principal component analyses with 21 (PCL) as covariate (about 80% of variance explained) for the quantitative trait and 20 PCL as covariate (about 75% of variance explained) for the binary trait in regression models .1, …, Xn) . Bayesian networks consist of two components: a directed acyclic graph G= and the parameters Θ=. The graph G determines for each variable Xi a set Gi of parent variables (i.e. variables from which there are directed arcs to Xi). A hypothesis conveyed by such a graph is that the probability of vector X can be expressed as a product of conditional probabilities parameterised by the components of Θ:Bayesian networks are multivariate models for determining the probability of an n-dimensional discrete data vector X=. More interestingly, in a Bayesian setting, under certain technical assumptions, after giving a prior distribution for the parameters Θ, one may calculate the marginal likelihood P, where α denotes the hyperparameters for the prior distribution of the Θ. This allows us to compare Bayesian network structures by their posterior probability P α PP(G) . It is well known that finding the most probable Bayesian network structure is an NP-hard problem (Chikering 2002). Therefore it is customary to resort to search heuristics such as a local greedy search. However, it has been shown that the most probable forest structured Bayesian network (i.e. a network in which each variable has at most one parent) can be found in quadratic time with respect to number of variables n .We used the significant markers found from GRAMMAR and principal component approaches to train the Bayesian network. We learned both general Bayesian network and Bayesian Forests using different search algorithms to compare the results with LD measures. Details of the LD measures used in this paper could be found in Devlin and Risch (1995).We excluded 263 SNPs due to minor allele frequency <1%, leaving 9768 SNPs in the analyses. We excluded 8 individuals with too high IBS (Identity By State) (>95%) leaving 2318 individuals in the dataset. Normality for the quantitative trait was confirmed by Kolmogorow Smirnow test, P > 0.150. Based on an animal model (1) we estimated heritabilities as 0.44 (±0.05) for the binary trait and 0.58 (±0.12) for the quantitative trait.We used 20 PCL to take possible ancestral stratification into account for the binary trait. Visual inspection of the scree plot showed that the sharpest reduction was obtained from the first 10 principal components. We detected the top 109 SNPs based on local FDR  (p<0.0006). In order to take linkage disequilibrium into account we additionally applied PCReg with normal and logit functions. Using the first 20 PCL as covariate, we applied PCReg to 109 markers with a logit function, and obtained the top 50 SNPs from this approach. We also investigated loading plots for the top SNPs. Although some clusters were found related with location of the SNPs and LD among them, this was not observed consistently for the entire genome  Figure . We usedWe constructed the general Bayesian Networks using non-informative priors and did not use any prior biological information  as response variable in a linear model. When we used phenotypes as response variable linear model tend to overestimate the total explanatory variation. We estimated QTL variance for the top marker (5488) as 7.7 % and 2.9 % using linear models with phenotypic and residual values, respectively. This QTL was simulated with 4.49 % variance suggesting that using residuals gave more correct estimates. When residuals are not normally distributed, orthogonal models are robust compared with a linear model from deviation of normality .When cases and controls have genetic relations, GRAMMAR could efficiently incorporate the information regarding random genetic effects. Principal component stratification could be used to correct for ancestral stratification and association analyses for binary traits although if there is systematic genetic effects stringent multiple hypothesis test corrections should be used. Bayesian networks are useful to investigate relationships among SNPs and environmental variables. Although a learned network does not have to show causal relationships, it is still informative and creates hypotheses based on interactions among SNP’s.The authors declare that they have no competing interests.BK compiled the dataset and done genetic, GRAMMAR, principal component stratification and PCReg analyses. TS and BK estimated the Bayesian networks. BK and TS wrote the manuscript. JAC, CH and DJK advised on analysis and data interpretation and revised the manuscript.Loadings of first 2 principal component of binary trait from top 109(AXX) markers using principal component stratification model. Although some markers cluster according to high linkage disequilibrium and by chromosome, this is not consistently true over the genome. Loadings of first 2 principal component of binary trait from top 109(AXX) markers using principal component stratification model. Although some markers cluster according to high linkage disequilibrium and by chromosome, this is not consistently true over the genome.Click here for fileLearned general Bayesian network for binary trait using top 109 markers obtained from principal component stratification methodology. Learned general Bayesian network for binary trait using top 109 markers obtained from principal component stratification methodology.Click here for fileLearned Bayesian Forest for binary trait using top 109 markers obtained from principal component stratification methodology. Learned Bayesian Forest for binary trait using top 109 markers obtained from principal component stratification methodology.Click here for file


GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent–offspring trios include adult individuals ranging in age from 19 to 87 years  from birth cohorts 1910–1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14–15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of  Despite major successes in gene discovery, for many traits the percentage of variance explained by GWAS loci is relatively low, leaving a substantial part of the variation to be explained. Approaches that estimate genetic variance based on a large series of GWAS variants such as GCTA3 show that aggregate effects of genome-wide significant common variants and those that do not reach this criterion explain substantially larger amounts of variance for, for example, human height,4 major depression5 or type 1 and 2 diabetes.6 Clearly, GWAS have not yet reached their limits, as is demonstrated by the many new loci detected after increasing sample sizes.The last decade has seen rapid developments and breakthroughs in unraveling the genetic etiology of complex genetic disorders.10 Despite progress, our knowledge of the regions identified by GWAS is still limited. Considering that even for ‘simple' genetic diseases such as cystic fibrosis thousands of relevant mutations have occurred in a single gene, there is a need for in-depth sequencing and characterization of the genomic regions identified thus far. In a selected, relatively small, group of 256 Sardinians with extreme LDL-C values who were resequenced and reimputed to the 1000 Genomes Project reference at six GWAS loci, the variance explained for blood lipid levels at known loci doubled through the identification of both rare and common variants.11The use of dedicated arrays such as the metaboChip and immunoChip, which include new common and rare variants from the established GWAS regions, confirm the findings of modeling studies that the heritability not yet explained is to be found in a mixture of unmapped and untagged common, and not yet discovered rare variants.12 sequencing across a range of different populations is mandatory for translation into public health and clinical benefits. One aim of the 1000 Genomes Project is to create maps of genetic variation across multiple populations, but the number of individuals per population is modest. Tennessen et al12 identified >500 000 single-nucleotide variants (SNVs) in a sequencing project of 15 585 human protein-coding genes. The major part of these SNVs were rare , unknown (82%) and population-specific (82%). A deep-sequencing study13 of 202 putative drug targets confirmed that most genetic variants occur at very low frequencies. To characterize a population, it is crucial to sequence as many individuals as possible to maximize the probability of capturing rare variants.Thus, identifying low frequency and rare variants by resequencing will contribute to resolving the missing heritability. As rarer variants are more likely to be population specific,http://www.nlgenome.nl) project was initiated as a part of BBMRI-NL14 (Biobanking and Biomolecular Research Infrastructure-Netherlands). The aim of GoNL was to sequence 1000 independent genomes from the Dutch population. These 1000 Genomes come from a representative sample of 250 parent-pairs with 1 or 2 offspring. Sequencing was done using genomic DNA to an intended average depth of 12x for each sample. Here, we describe the design of GoNL within the demographic setting of the Netherlands, the sampling frame and basic characteristics of the participants.Findings from sequencing studies are also important for clinical applications and public health genetics. The interpretation of the ‘potential causality' of a novel variant after it has been identified in patients in a clinical setting, requires knowledge of its frequency in the population the patient belonged to. In view of its potential research and clinical applications, the Genome of the Netherlands , the number of non-western immigrants increased from ∼1.2% of the population to 10.4% in 2005 and is now stable at 11.6%. Immigrants have been of various genetic backgrounds, with Turkish and Moroccan origin being the largest groups followed by those of Asian and African origin from (former) Dutch overseas areas . At the design stage of the GoNL project, we decided to include participants whose ancestors are born in the Netherlands. Sequencing of other groups was not possible without compromising the power for all the groups. However, extending sequencing beyond Caucasians remains a desirable aim.Geographically, the Netherlands is a small country of about 41 500 km15 Also studies of the Y-chromosome clearly pinpoint differences in genetic origin.Another key decision concerned the selection of subjects over geographic regions. The Netherlands are historically divided into provinces, which are geographical units with their own governance dating back from the period that the Netherlands were a republic (1588–1795). Geographically, the distribution of the population in the Netherlands is heavily skewed with the majority (75%) of the population living in the Western and Middle provinces of the country including North-Holland , South-Holland , North-Brabant , Gelderland  and Utrecht 10% see ; only a Representative sampling from the provinces of the Netherlands according to sample size would lead to major differences in numbers of subjects sequenced per region. Although this reflects the size and distribution of the population, many population genetic analyses are influenced by sample size, including linkage disequilibrium and founder effects. We, therefore, chose to include an approximately equal representation from the original 11 Dutch provinces see , oversamA key characteristic of GoNL is its trio design. The sample unit consists of two parents and one offspring yielding information on four genomes (two per parent). There were three considerations that led to the choice of the trio design for GoNL despite the fact that a trio design reduces the effective sample size by one-third compared with a design that includes only unrelated individuals. First, a major aim is to provide a facility for imputation, rapid and valid haplotyping, and the trio design allowed GoNL to reduce the computational burden of haplotyping. Second, a trio design would enable reducing the number of false-positive variant calls by screening for violation of Mendelian segregation in the trio-aware calling of genotypes. Third, the trio design would allow for a characterization of new mutations within the Dutch population, in that novel variants that occur in offspring but not in parents can be assessed. This latter approach requires a separate calling and storage of variants in which non-Mendelian transmission is allowed. Our sequencing at intermediate coverage was considered to be optimal to address these aims.http://www.bbmri.nl/). In BBMRI-NL,14 ∼180 biobanks from all over the Netherlands have been united, including over 600 000 subjects with biological samples and phenotype information. For 100 000 to 150 000 subjects genome-wide SNP data are available, or are currently being generated. For the GoNL trio study, four population-based biobanks provided samples:http://lifelines.nl/lifelines-research/general), which is a three-generation population-based cohort now including more than 100 000 participants (target 160 000) representing ∼10% of the northern part of the Netherlands.17LifeLines , which includes 3500 persons from families in which at least two long-lived siblings were alive and their parents were of Caucasian descent.18The Leiden Longevity Study (http://www.tweelingenregister.org), which ascertains two- and three-generation Dutch families from the entire country based on the presence of twins or higher order multiples in the family and includes nearly 180 000 participants.20 The NTR Biobank collected biological samples in >10 000 participants.21The Netherlands Twin Registry (NTR: 22 and its surrounding area (http://www.erasmus-epidemiology.nl/rotterdamstudy) and the Genetic Research in Isolated Populations program (GRIP: http://www.epib.nl/research/geneticepi/research.html#gip) targeting the south western part of the Province North-Brabant.The Rotterdam studies, which consist of a population-based long-term follow-up study including 11 800 persons from RotterdamIn the Netherlands, several large-scale collaborative Biobank consortia have been established, that bring together patient samples and population-based collections of biological samples. GoNL is part of the Dutch branch of BBMRI  and 500 haplotypes at half of that depth . This design should significantly contribute to the globally existing information, as, for example, the 1000 Genomes Project has a 4x coverage. Paired-end sequencing of genomic DNA was done on the Illumina HiSeq 2000 platform  at the Beijing Genomics Institute (BGI), starting in October 2010 and finished in June of 2011. Additionally, for each sample micro-array SNP data  were generated by the biobanks. Sequencing was performed on 2–3 DNA libraries per sample which were run on 2–4 lanes of HiSeq 2000. The raw reads were subsequently analyzed following 1000 Genomes best practices, in collaboration with the Broad Institute and BGI. When reads were aligned against build 37 of the human genome coverage was, as expected, randomly Poisson distributed over the genome with a coverage peak in between 14–15x. The reads were cleaned by indel realignment, duplicate read marking and quality score recalibration, and SNP variants were called with a trio-aware caller. In parallel, a wide variety of short indels and complex structural variants were called and the SNPs were phased for imputation. Data processing involved 200 000 h of computation and was run on the Dutch national Target and BigGrid compute facilities supported by CIT/Groningen and SARA/Amsterdam. This effort resulted in 60 TB of aligned reads and variant calls . While the sequencing technology and data processing pipelines are being improved continually, variant detection, including short indels and complex structural variants in low-complexity regions continue to pose a challenge. The GoNL data analysis group applied Genome Analysis Toolkit (GATK) BreakDancer, Pindel,24 GenomeSTRiP, CNVnator, 123SV and DWAC-seq to discover and genotype diverse types of variants such as SNPs, insertions, deletions, tandem duplications, inversions and translocations.25 Each of these tools showed its own variation detection characteristics, and the joint use of the tools turned out most powerful. Detailed analytical descriptions of SNP and SV calling, and de novo and loss-of-function variants will be reported separately. Detection of de novo mutations and accurate genotyping from pedigree data is challenging, especially at low or intermediate depth of coverage. Both require accurate and quantitative calibration of the evidence supporting the individuals' genotypes using all the available information, including their familial relationship and population allele frequency.A coverage of 12x in both parents and the offspring was chosen based on balancing sensitivity  and accuracy , within budgetary limits.26 The PCA was first run on 4441 unrelated Dutch individuals15 typed on the Affymetrix Human Genome-Wide SNP 6.0 Array , with a SNP set excluding 24 long-range LD regions27 and LD-based SNP pruning (based on a variance inflation factor of 2), resulting in a set of 130 248 SNPs. These PCs were then projected on the 769 GoNL samples . versus PC 2 and the PC 1 versus PC 3 plots. Three PCs correlated significantly with geographic location and distinguished between: (1) the North and South of the Netherlands; (2) between the East and West; and (3) between the middle-band of the Netherlands and the rest of the country. When projected on the GoNL individuals, for whom we have the province of the current living address available, it is clear from To describe the regional genomic differences in the genetic make-up across the Netherlands, principal component analysis (PCA) was run using the EIGENSOFT package.de novo variation . For 19 families, additional monzygotic or dizygotic twin siblings were sequenced, which allows in-depth research targeting new variants. Finally, the additional data on clinical phenotypes, epigenetics, RNA sequencing and the metabolome of the sequenced individuals will allow functional studies of new variants.The goal of GoNL is to establish a reference set of variants in the Dutch human genome that have a frequency of at least 0.5% by whole-genome sequencing of 1000 independent genomes. Our pilot analyses reveal that new variants are likely to emerge compared with 1000 Genomes and may thus contribute to sequence data acquired by others. The GoNL project includes a number of unique aspects compared with other ongoing projects see . The seqOne of the main purposes is to catalog both common and rare variants, and to use the Dutch reference genome as a basis to computationally infer the corresponding information in a much larger data set of 100–150 thousand Dutch GWAS samples that have been generated in the Dutch population. The creation of an imputation pipeline is an important deliverable of GoNL. An imputation pipeline was created on the European Grid network for cohorts participating in BBMRI-NL, which can also be used by cohorts from elsewhere. The pipeline requires cohorts to upload their imputation-ready genotype data. Next, the process aligns the data with the Dutch and/or combined reference set and imputation will be done following recent protocols like the ones for the GIANT (the Genetic Investigation of ANthropometric Traits) consortium, using parallel grid computing. Afterwards, the full results including parameters for imputation quality can be downloaded by the cohort. The quality of imputation depends on several features of the input data and on the quality of the haplotype structure in the reference genome. The trio design with medium sequence coverage enables us to establish an accurately haplotyped Dutch reference genome, as the presence of Mendelian transmissions of variants provided an extra level of quality control and aided in the correct estimation of both genotypes and haplotypes.de novo variation . It was recently shown that there is considerable variation in mutation rates within and between families, which in part can be attributed to the age of the father at the time of conception.28 With a cohort of 250 families, we will be able to further our understanding of mutational processes, in particular as GoNL also includes 19 families with two twin offspring. The twin pairs will allow us to address questions related to the developmental timing of de novo mutations and the frequency of germline mosaicism.The GoNL trio design allows calling a substantial amount of 29 DNA and Y-chromosome will become available. Of immediate utility to the Dutch scientific community is the use of GoNL as a reference for clinical and medical-sequencing projects. Several founder mutations have previously been identified among the Dutch16 including founder mutations in the BRCA1/2 (hereditary breast cancer), LDLR (dyslipidemia) and Tau (dementia) genes. These findings predict that many genes may incorporate region and population-specific variants. The geographical variation observed in our preliminary analyses of substructure corroborates the importance of the original selection of GoNL samples dispersed throughout the Netherlands. The Dutch samples thus far included in multi-center GWAS studies originate from several different places in the Netherlands and may thus optimally benefit from imputation when the reference material properly reflects this provenance.The GoNL data can also be used to address questions related to the demographic history of the Dutch as in-depth information on both mitochondrial14 To fully understand the functional consequences of genetic variation, we have to integrate across these levels of information. The Dutch biobanks also contain a wealth of phenotypic information, which will become instrumental in pinpointing genes and genetic variants in disease. GoNL will be an excellent starting point to enrich the GWAS samples with more functional DNA variation, and with the BBMRI-NL infrastructure there is an excellent basis to further explain the functional consequences of disease-associated variants.Within BBMRI-NL, several additional projects aim at harmonization and enrichment of biobanks within the Netherlands at a national scale. These projects include epigenetic studies, RNA sequencing and metabolomics projects.http://www.ncbi.nlm.nih.gov/gap). Requests for data can be addressed to the steering committee of GoNL (see http://www.nlgenome.nl).GoNL aims to be an open research source for researchers in the Netherlands and the rest of the world. The sequence data will be made available through dbGaP (


Natural genetic variation can have a pronounced influence on human taste perception, which in turn may influence food preference and dietary choice. Genome-wide association studies represent a powerful tool to understand this influence. To help optimize the design of future genome-wide-association studies on human taste perception we have used the well-known TAS2R38-PROP association as a tool to determine the relative power and efficiency of different phenotyping and data-analysis strategies. The results show that the choice of both data collection and data processing schemes can have a very substantial impact on the power to detect genotypic variation that affects chemosensory perception. Based on these results we provide practical guidelines for the design of future GWAS studies on chemosensory phenotypes. Moreover, in addition to the TAS2R38 gene past studies have implicated a number of other genetic loci to affect taste sensitivity to PROP and the related bitter compound PTC. None of these other locations showed genome-wide significant associations in our study. To facilitate further, target-gene driven, studies on PROP taste perception we provide the genome-wide list of p-values for all SNPs genotyped in the current study. An identical taste stimulus may elicit substantially different perceptions in different individuals. Some individuals may detect the same stimulus at a lower concentration than others. And, at concentrations above the detection threshold, the perceived intensity of the stimulus may vary between individuals. This variation in taste sensitivity may affect food liking as well as food choice e.g. dietary habits or childhood experiences). Traditionally the study of environmental factors has garnered much of the attention. But, with the help of high-throughput genotyping arrays and genome-wide-association studies (GWAS) Variations in taste sensitivity are driven both by genetic and by environmental factors  chemosensory phenotypes are relatively difficult to measure precisely. While simple phenotyping approaches for chemosensory perception exist - for example subjects may simply be asked to rate the intensity of a single tastant solution on an intensity scale – these simple measurements are prone to be diluted by substantial measurement uncertainties. Potential sources of these uncertainties have been identified and increasingly powerful - but also increasingly complex - procedures to account for these uncertainties have been identified Here we report on a study that uses the well-known genotype-phenotype association between the detection thresholds for the bitter compound PROP and a specific variation in the TAS2R38 bitter taste receptor (HGNC:9584) as a benchmark to compare the relative power of different taste phenotyping strategies for GWAS applications.In 1932 Arthur Fox The availability of a simple test - placing a PTC-soaked paper-strip on subjects' tongues and recording if they perceived its bitter taste – allowed the taster phenotype to be determined quickly, on large panels and with reasonable accuracy. Based on these tests, it soon became clear that the ability to taste PTC at low concentrations was a genetic trait that was inherited in near-mendelian fashion Using classical linkage surveys the genetic variation underlying PTC sensitivity was initially mapped to a region on chromosome 7q3 In more recent studies PTC is often replaced by propylthiouracil (PROP). PROP displays nearly the same sensory properties as PTC and, due to its status as an approved drug, PROP poses fewer safety concerns.10[thresh1] – log10[thresh2]|) equals 0.32 log units.To characterize the taste sensitivity of the study's subjects to PROP we determined both detection thresholds and supra-threshold intensity ratings for aqueous solutions of this compound. Determinations of generalized labeled magnitude (gLMS) intensity ratings were performed in triplicate. As part of the preparation for the GWAS analysis we assessed how the PROP detection threshold is affected by the key demographic parameters gender, age, body mass index and ethnicity. To assess the effect of ethnicity we performed linear regression against the first two principal components from a principal-component analysis of the entire genotyping data set. These principal components have been shown to be highly effective in describing and correcting for ethnic origin 10 (p)) of the probability p that an association of the observed strength could have occurred by chance.The results of genome-wide association studies are commonly displayed in “Manhattan plots”. These plots show, for each of the ∼800,000 probed SNPs, the strength of the statistical association between the subjects' genotype and phenotype. The strength of the association is plotted as the negative decadic logarithm (-log−52 and 1.5×10−52 respectively for the top two SNPs) is a reflection of the near Mendelian inheritance of the PROP taster phenotype.The profound statistical strength of the association  of the observed variability in detection thresholds. Distribution of PROP detection thresholds according to SNP rs10246939 genotype (The regression plot  of subjegenotype  show thai.e. non log-transformed) detection threshold, ii. the log-transform of the average of the raw detection thresholds, iii. a scenario where the two observations of the detection threshold were both log-transformed but then treated as if they were independent observations on two separate, but genetically identical, subjects and finally iv. classification of subjects into tasters and non-tasters with the resulting binary phenotype analyzed by logistic regression (see next section). In this context the p-value of association serves as a measure of the statistical power provided by a particular phenotype representation. The lower the p-value achieved by a particular phenotype representation in this analysis, the greater is the statistical power afforded by it and the better it is suited to identify associated SNPs in future studies.The input phenotype for the GWAS on the PROP detection threshold was the average of the two log-transformed replicate measurements of the detection threshold. This specific treatment of the threshold data was chosen to take into account the well-known, near-logarithmic response of humans to taste stimuli. Still, the decision for this particular treatment of the data represented a discreet choice. To investigate, if this choice was indeed optimal, or if there may be different phenotype representations that would increase the statistical power of the sensory data, we tested the performance of four other parameter representations in separate GWA analyses: i. the average of the raw (i.e. non-log-transformed) detection thresholds . Also, averaging the detection thresholds before log-transformation noticeably weakens the association (pmin =  4.4×10−49)  .i.e. the treatment of the replicate threshold measurements as independent observations, is rather interesting. While this choice of parameter representation results in a very strong association (pmin = 6.6×10−69) prior to genomic control, the uncorrected QQ-plot (not shown) indicates substantial non-specific inflation of p-values for all SNPs. After genomic control −45  compared to the linear regression analysis (pmin =  1.2×10−52). To ensure this weak association was not due to the choice of the cutoff concentration used for taster-vs.-non-taster classification we repeated the analysis by varying the cutoff from 0.1mM to 0.3mM in 0.025 mM steps. Still this “optimization” of the cutoff value could not enhance the association (pmin = 2.25×10−12 with a 0.175 mM cutoff). These results illustrate the very substantial loss of statistical power that results when noisy quantitative phenotypes are converted into binary phenotypes.While the logistic approach was able to identify the TAS2R38 SNPs, the association is dramatically weakened (pmin = 9.4×10−41) clearly outperformed that on the log-averaged detection threshold of 113 subjects (log-averaged pmin = 8×10−27). These results are fully in line with the general rule that replicate measurements add less power to an association study than the addition of an equivalent number of new subjects.The results described in the previous section answer the question of how best to treat replicated measurements of detection thresholds for use as input phenotypes in a GWAS. But, is the collection of repeat measurements useful? More precisely, is it better to perform replicate measurements on a smaller number of subjects, thus increasing phenotype quality, or, is it better to perform just a single measurement on as many subjects as possible? This latter option may, for example, be attractive when phenotyping is performed as an add-on to an existing genotype-phenotype association study. In such a scenario recruitment and genotyping costs are not a factor and the only question is how to best use the available phenotyping resources. To address this question we performed a GWAS analysis on all 225 subjects of our study but used only the first measurement of the detection threshold. We then compared the result from that GWAS to results from GWASs performed by randomly selecting sub-panels of half the number of subjects (i.e.113) and using the average of the two detection threshold replicates as the phenotype. To avoid sampling bias, we repeated the sub-panel selection 1000 times and averaged the results. The comparison shows that the GWAS using a single threshold determination on 225 subjects . For each of the three concentrations where genome-wide significant associations were obtained the two most strongly associated SNPs are the same SNPs in the TAS2R38 receptor (rs713598 and rs10246939) that were identified in the GWAS with the PROP detection threshold.To compare the relative power of objective taste sensitivity measurements (see above) to measurements obtained by subjective methods we performed GWAS on the supra-threshold intensity ratings obtained with the gLMS method. Specifically, we performed separate GWAS's for the intensity ratings for each of the five concentrations of PROP included in our gLMS tests as well as for the water control. As input phenotype we used the average of the three intensity ratings for each of the concentrations . As the min = 8.2×10−28 is weaker than for the straight gLMS rating of the 1mM solution alone (see above) indicating that pooling of intensity ratings across different concentrations does not necessarily result in a increased association signal.To evaluate the usefulness of phenotyping strategies that integrate all six data points from an individual's gLMS curve into a single parameter we calculated an “area-under-the-curve” phenotype . It could be reasoned that this phenotype effectively pools the signal across all six sample concentrations and might therefore increase the strength of the association signal. At the same time, not all concentrations add an equal amount of signal, but all of them add noise. Along this line of thinking pooling of the signal across different concentrations might actually lead to a loss of power in the phenotype. The results of a GWAS with the area-under-the-curve phenotype show that, while this phenotype also correctly identifies rs713598 and rs10246939 as the top SNPs the overall strength of the association p−8 to denote genome-wide significance and r-square values for PROP detection thresholds (49%) and the supra-threshold intensity ratings of the 1mM PROP solution (38%) as determined in our study (see above). The resulting power-to-detect curves (−8) with greater than 90% probability. To provide an indication for the number of subjects that would have been required to detect additional genetic factors with weaker influence on PROP sensitivity we also computed power to detect curves for a number of smaller r-square values. As can be seen from these curves, our panel size of 225 subjects would not be sufficient to detect genetic factors that explain less than ∼15% of overall phenotype variance.To compare the relative power of detection-threshold and supra-threshold measurements as input phenotypes for GWA studies we calculated power-to-detect curves for the two phenotypes. For these calculations we used a p-value cutoff of 5×10t curves  show theThe results of the psychophysics measurements reported here, both in their average values and in the observed distribution of values are in agreement with recent observations by others 10 units, which corresponds to a greater than two-fold change in the detection threshold. And, for 17% of subjects the variability between replicate measurements was >0.62 log10 units corresponding to a greater than four-fold change in detection threshold. As a consequence, some subjects that would have been classified as tasters based on the first measurement later displayed detection thresholds that would classify them as non-tasters and vice versa.The intra-subject variability between those repeat measurements is substantial – both for the detection thresholds and for the supra-threshold intensity ratings. In the case of the detection thresholds the average difference between the two measurements is 0.32 logThis intra-subject variation in phenotype far exceeds the nominal resolution of the stair-case method that was used to measure this phenotype. Therefore it is unlikely that this variation is simply the result of measurement error. Instead, other factors which are not easily controlled do appear to have a substantial effect on day-to-day variations in taste sensitivity.The intra-subject variability we observed in our study over a rather short period of just a few days is comparable in magnitude to the intra-subject variability reported in studies with substantially longer time periods of weeks to months between repeat measurements Together the data underline that neither a subject's detection threshold nor its taster/non-taster status should be viewed as a single, fixed parameter that can be ascertained in a single measurement session. Instead, a subject's detection threshold appears to be a dynamic parameter that is subject to substantial variation over the period of just a few days.The same caveat applies to the supra-threshold intensity ratings. In fact, intra-subject variability of the supra-threshold ratings was even greater than that of the detection thresholds. Intensity ratings involve a larger degree of subjectivity than do the threshold determinations via the staircase method. Correspondingly, the gLMS scores were somewhat less powerful in identifying TAS2R38-PROP association in our GWA studies. Still, the GWA studies based on the gLMS scores easily identified the correct SNPs. And gLMS scores do have key advantages that, despite this reduced power, may make them attractive phenotyping parameters for future GWA studies in the field of psychophysics. First, after an initial training of participants, gLMS intensity curves can be recorded much faster and with lower personnel effort than detection thresholds. In cases where the costs of genotyping and subject recruitment are not limiting, but testing time is at a premium, gLMS intensity measurements may be particularly attractive. Such a situation may be encountered, for example, when psychophysics measurements are performed as an “add on” to another study. Also, gLMS intensity curves provide both a rough estimate of a compound's detection threshold as well as supra-threshold intensity ratings. In cases where variations in detection thresholds and variations in the perceived intensity of supra-threshold stimuli are driven by two distinct genetic variations, gLMS intensity curves may then allow the identification of both of these genetic variations.−5) in the GWAS on the PROP detection threshold phenotype. Only four of these SNPs have genome-wide significant associations (p<5×10−8) and all four of these SNPs are located within the TAS2R38 gene itself or within its immediate vicinity. Incidentally the same four SNPs  were the only SNPs that achieved genome-wide significance for any of our studied phenotype representation.Past studies have suggested that genetic factors, other than TAS2R38, may also play a role in driving the observed natural variation in the bitter taste perception of PTC and PROP. In An additional 14 SNPs are located very near TAS2R38. To test, if these SNPs are simply in linkage disequilibrium with the causal TAS2R38 SNPs, or if they make an independent functional contribution, we corrected each subject's phenotype for their genotype at the top TAS2R38 SNP and then used this corrected phenotype in an additional GWAS. In this second GWAS the p-value for these 14 SNPs decreased to well below statistical significance indicating that they do not make an independent contribution to the PROP detection phenotype.−5 are distributed throughout the genome with 13 of them within less than 100kb of an annotated gene. None of these genes suggested a mechanistic link to taste perception.The remaining 16 SNPs with suggestive p-values of <10Previous studies had specifically implicated loci on chromosome 16p −4 is observed for rs9933117 which is located in an intron of XYLT1, a xylosyltransferase gene. After Bonferroni correction for the large number of SNPs in this region the p-value is non-significant (pcorr  =  1). A similar situation is observed for the 5p15 region. Here the minimum p-value of 3.6×10−4 is observed for SNP rs1395093. This SNP falls into a gene-free region and after Bonferroni correction the p-value also drops to a non-significant level (pcorr  =  1).On the p-arm of chromosome 16 the minimal p-value of 2.8×10in vitro experiments on functionalized bitter receptors We further find that variations in both detection threshold and supra-threshold intensities are dominated by the same genetic variations in the TAS2R38 receptor. These observations are entirely consistent with recent results from e.g. studies on larger panels or different cohorts) may identify such associations in the future. Still, we can use the data from the current study to get a rough idea about the likely upper limit for the impact that such genetic variants could have. Using power-to-detect calculations we can see that with a panel of 225 subjects and a genome-wide-significance cutoff of log(p)  =  5×10−8 a genetic variation that explains more than 11.5% of overall phenotype variance would have been detected in our GWAS with greater than 50% probability. In other words, failure to find additional genetic factors in our study makes the existence of multiple genetic factors with substantial impact on PROP taste perception (r-square >> 11.5%) seem unlikely.At the same time we like to make clear that failure of our study to detect additional genetic variants that impact PROP perception in no way precludes the existence of such genetic variants. It is very well possible that more powerful studies  on PROP perception via the hypothesis-free GWAS approach. Still, this data set may enable other researchers to study such subtle genotype-PROP-taste associations using a target-gene approach. The complete set of the association p-values for the PROP detection threshold phenotype for all genotyped SNPs is included in Given the very substantial differences in the frequency of the relevant TAS2R38 genotypes in different ethnic groups  into a binary phenotype (e.g. taster/non-taster status) generally leads to a substantial loss of statistical power and should be avoided. While this loss of power is well-known and well-understood from a statistical perspective, conversion of taste thresholds into taster/non-taster status continues to be such a common practice in the taste field that we feel compelled to point out the resulting loss of statistical power in this context.Conversion of a continuous phenotype , we recommend to perform such replicate measurements only on a subset of the panelists. Typically a rather modest number of subjects will already be sufficient to get a reliable estimate of intra-subject variability so that the bulk of the phenotyping effort can be dedicated to the inclusion of additional subjects.Detection thresholds measured via the staircase method are a more powerful phenotype than suprathreshold intensity ratings obtained with the gLMS approach. Threshold measurements represent an attractive phenotyping strategy when panel size is limited.Determination of suprathreshold intensity ratings using the gLMS approach is a more cost effective phenotyping strategy. Use gLMS-based approaches for phenotyping when large, already genotyped subject panels are available.Detection threshold data should be log-transformed and then -if replicate measurements were obtained- averaged to generate the input phenotype for the GWAS. This log-transformation accounts for the log-linear relationship between taste stimulus and taste response, leads to normally-distributed residuals in genotype-phenotype regression during GWAS and ultimately boosts the power to find genotype-phenotype associations.Given that taste perception of PROP bitterness follows the prototypical stimulus-response relationship  found acIn conclusion, we have used the well-known association between variations in the TAS2R38 taste receptor gene and variations in taste perception of the bitter compound PROP to evaluate the relative power and efficiency of different chemosensory phenotyping strategies for genome-wide association studies (GWAS). The performance of the GWAS was surprisingly robust to the specific choice of data processing procedures and both detection threshold as well as supra-threshold intensity ratings reproduced the TAS2R38 association unequivocally. Still, careful choice of phenotyping method and parameter representation can provide a substantial boost in the strength of the genotype-phenotype associations. We anticipate that the lessons learned in this study will be valuable for future GWA studies on chemosensory phenotypes where associations between genotype and phenotype are less pronounced than is the case for TAS2R38 and PROP detection.All procedures were approved by the Institutional Review Board of the Sírio Libanês Hospital, where tests were administered, and by the National Committee of Research Ethics at the Brazilian Ministry of Health (HSL 2007/25 Process no. 25000.114841/2007-17).All subjects gave informed consent by signing a written informed consent statement.A group of 225 subjects was recruited from the general population of the São Paulo metropolitan area of Brazil. Subjects were aged 18-47 (mean 32.8). BMIs ranged from 16.6 to 46.1 (mean 25.5). In the panel 49% of subjects were male and 51% female. The Sao Paulo metropolitan area is known for its high degree of ethnic diversity and this diversity is reflected in the panel. Subjects were required not to smoke 3 hours prior to testing. A detailed breakdown of subject gender, age, BMI and smoking status is provided in Genotyping services were outsourced to Expression Analysis Inc. . Briefly, genomic DNA was extracted from whole blood and genotyping was performed on the Illumina Human Omni-Quad1 platform following standard protocols. Genotype calling was performed with Beadstudio software (Illumina). Calls with a genotyping score below 0.2 were excluded from further analysis. Single nucleotide polymorphisms (SNPs) with a call rate below 90% and individuals with a call rate below 95% were also excluded.Genotyping data was of high quality with an average call rate of 99.8% for all SNPs. 99.4% of SNPs had a call rate of greater than the cutoff value (95%) set for the rejection of individual SNPs. The average Q-score for all SNPs was 0.71 and for 99.6% of called SNPs the Q-score passed the cutoff (0.2) for inclusion.th log steps  was used to generate 25 test solutions ranging in concentration from 3.2 mM to 0.32 microM. Based on the results of a preliminary PTC paper-strip test, the staircase procedure was started at a PROP concentration of 0.047 mM for PTC-tasters and 0.689 mM for non-tasters. Between samples subjects rinsed their mouth three times with milli-Q water. The staircase procedure was repeated after one week to obtain a second measurement.Detection thresholds for PROP were determined via the staircase method Supra-threshold taste intensities were determined using a general labeled-magnitude scale (gLMS) procedure The two repeats of the detection threshold determined by the staircase method were log-transformed and then averaged. For comparison purposes we also calculated other representations of the detection threshold  code optimized for GWAS scenarios.i.e. the corrected phenotype parameters were then regressed against SNP allele dosage using a linear, additive model.Population stratification and relatedness was assessed using the ancestry principal components as previously described Logistic regression was performed using the same set of covariates. To determine the binary phenotype for logistic regression, subjects were classified into PROP-tasters and non-tasters according to their log-averaged detection threshold determined via the staircase method.Genomic control A complete GWAS including, phenotype correction and genomic control required less than 3 minutes of CPU time on an 8 processor 2.3 GHz Xenon-based computer with 8 GB of RAM running a RedHat implementation of Linux version 2.6.2 is the fraction of phenotypic variance explained by a SNP, −8 for a GWAS scenario).Power to detect curves, for details see To calculate the area-under-the-curve phenotype from our gLMS data we averaged the three repeat measurements for each concentration and then added those average scores across the 6 measured samples (i.e. 5 PROP concentrations plus water) to obtain a representation of the “area” under the taste-intensity-vs.-concentration curve. See Supporting Information S1−5), call rate (>95%), q-score (>0.2) and minor allele frequency (>5%) and have undergone correction for age, gender, BMI and genetic ancestry principal components as well as genomic-control. Individual data items are snp-identifier (e.g. rs number), chromosome number, position on chromosome, and p-value. The data is organized with one SNP per row and individual data items are separated by a semicolon. Chromosome number and position are based on the NCBI B36 build of the human genome.The file “genome_wide_p-value_table” contains a list of p-values for associations to the PROP detection threshold phenotype for ∼790′000 SNPs that were measured in our study. The SNPs have been quality controlled for Hardy-Weinberg Equilibrium p-value (>10(CSV)Click here for additional data file.