This genetic association is thus upheld in spite of a known genotype-by-genotype interaction and environmental variation.Association and linkage mapping have become important tools in understanding the genetics of complex traits, including diseases in humans. As the success of association mapping is reduced by small effect sizes and limited power, linkage studies in laboratory-based model systems are still heavily used. But whether the results of these studies can be replicated in natural populations has been questioned. Here, we show that a polymorphism in the gene  However, in these studies both the power to detect association and the effect size of particular polymorphisms on disease phenotypes are typically low Pref greatly reduces transmission of the virus . To measure the male transmission rate, we added two uninfected virgin females to every male. These virgins were collected from an outbred laboratory population of D. melanogaster that we produced by mixing isofemale lines from the same population. The flies were sheltered from rain and direct sunlight but were otherwise kept under field conditions. The flies were allowed to lay eggs and were removed from the vial after 2–5 days to limit larval competition. The parental flies were then maintained on agar .We measured the rate at which males and females transmit the sigma virus to their offspring in a field population of 2 at 12°C for 15 min and recording their status 2 h later. While uninfected flies quickly recover from the CO2-induced anaesthesia, it causes paralysis or death in infected flies ((2)Pref that is associated with resistance, identified females as D. melanogaster by polymerase chain reaction (PCR), and confirmed the infection status of flies with low transmission rates by reverse transcriptase-PCR . Differences in transmission rate were analysed using generalized linear models with a quasi-binomial error distribution.The wild flies and their offspring vials were transferred to the laboratory and tested for sigma virus infection by exposing them to pure COed flies . We geno3.D. melanogaster for infection with the sigma virus. The prevalence was 10.3 per cent , and did not differ between females and males . The mean female transmission rate was 83.3 per cent (95% CI = 71.2–93.1%), and mean male transmission was considerably lower at 46.9 per cent (95% CI = 35–59%), a difference that was highly significant  with quasi-binomial error structure, t79 = 3.975, nfemale = 39, nmale = 48, p < 0.001).We tested 900 wild-caught (2)Pref was at a frequency of 24.0 per cent (n = 835), with 7.1 per cent of the population being homozygous for the resistant allele and 33.8 per cent heterozygous. As transmission rates are sex-specific, we analysed the sexes separately. We also pooled heterozygous and homozygous individuals carrying the resistant allele, as there were very few infected homozygous flies. The rate of female transmission was affected by the mother's genotype Pref-mediated resistance is common in European populations Pref reduces both paternal and maternal transmission by similar amounts ((2)Pref explaining all of the genetic variation in this trait, male transmission is continuously distributed and affected by other genes (Bangham et al. The effect size of QTLs and polymorphisms identified by association mapping can vary and even differ in direction in males and females, a fact that has long been puzzling (The results from our study are encouraging for laboratory-based association and linkage mapping studies, showing that this approach can yield results that are directly replicable in the field. At the same time, they demonstrate that such replication in the field is feasible and that quantitative and evolutionary biologists should endeavour to validate results from laboratory experiments on genetic model systems in natural populations whenever possible. This is a crucial, but often overlooked, step in understanding the genetics of complex traits such as disease and parasite resistance.


Complexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects.We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus.Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus.Plasmodium falciparum  and Saccharomyces cerevisiae (yeast). We demonstrate the prioritization of traits with few strong genetic effects through Gene Ontology (GO) analysis of Yeast. Our results are strongly consistent with results gathered using genome-wide methods and identify additional hotspots and eQTLs.We demonstrate our method by identifying eQTL hotspots in New eQTLs and hotspots found with this method may represent regions of the genome or biological processes that are controlled through few relatively strong genetic interactions. These points of interest warrant experimental investigation. In the context of eQTL studies an 'expression trait' refers to the quantity of labeled (c)DNA hybridizing to a single probe on a microarray. An eQTL is a strong association between one locus in the genome and one expression trait. eQTLs describe the global relationships, or regulatory architecture between expression levels and genotypes in an organism . eQTL stIt is common among eQTL studies to compensate for multiple testing by using a permutation test ,5. A perp-values and allows us to simultaneously utilize corrections for multiple testing and detect associations that are moderate on a genome-wide scale but significant for individual expression traits.The stringent thresholds imposed by error correcting methods such as the permutation test limit the ability of traditional eQTL techniques to identify moderate genetic effects. Finding false negatives by simply lowering the threshold for significance would undermine the error correction so we focus our approach on measuring significance at the individual expression trait scale. Our approach capitalizes on the fact that false negatives are most likely to occur near the cutoff for significance and should therefore be very significant relative to the vast majority of observations. This information allows us to create a model distribution that we expect describes the 'association profile' of an expression trait with interesting genetic effects. Our approach uses Hellinger distance to determine which traits most closely match this model distribution. The approach builds on genome-wide techniques by measuring the similarity between distributions of genome-wide corrected et al. who performed eQTL analysis across the progeny of the Hb3 drug resistant and the Dd2 drug sensitive malaria parasites 18 hours post erythrocyte invasion . Hellinger distance is symmetric and non-negative, implying that dH = dH. Finally, squared Hellinger distance is the lower bound of KL divergence. Hellinger distance essentially compares the shape but not the scale of the magnitude of the two distributions.This is achieved by first splitting each distribution into an equal number of bins. This step is essentially building a histogram of each distribution. Each bin contains some proportion of the total values in one distribution. The next step compares the proportion held in each bin to the proportion held in the corresponding bin in the other distribution. This proportional comparison is how Hellinger distance measures divergence without regard to scale. A more precise definition follows:a Pand a Qare the counts for corresponding bins for the two distributions and |P| and |Q| indicate the total number of values in the distributions.Where p-value distribution and a reference distribution. As Hellinger distance does not make any assumptions about the shape or scale of the distributions being compared any reference distribution can be used while preserving the meaning of the statistic.eQTL mapping calculates the association between each expression trait and each locus. The result is a set or distribution of associations for each expression trait. The permutation test provides a genome-wide corrected p-value for each association. Our method is based on calculating the Hellinger distance between each expression trait's y = x3 over the integers from 1 to the number of loci to model this expectation. This reference distribution provides a balance between linear ordination and ease of interpretation. It allows the Hellinger distance statistic to be interpreted as evidence that a trait is controlled by a single locus or few loci.However, the fact that false negatives are more likely to occur near the cutoff for significance allows us to tailor the reference distribution to reflect our expectation for false negatives. Associations near the cutoff for significance, while not statistically significant, are still a great deal more significant than the vast majority of the associations. Therefore we expect there to be a large, relatively empty range between the strongest association and the majority of the associations. We use the reference distribution of values defined by We calculated the Hellinger distance using numbers of bins ranging from 10 to 100 in intervals of 10. Over that inverval there are between 30 and 3 observations in each bin for the Plasmodium data. As the bin number approaches either extreme of the interval the hellinger distance becomes less able to reliably distinguish differences between distributions. This occurs because either too many or too few observations fall in each bin. The number of bins used did not make a significant difference to the results. We use thirty bins to provide an empirically acceptable binning granularity. The bin width is calculated as:The bin-width for each distribution is calculated separately.This approach to determining the number of bins must be repeated for each additional data set. A potential alternative and more general method of determining the number of bins would be to use a kernel density bandwidth optimization technique .p-values and typical p-values causes the bulk of the values in the distribution to appear in the smaller bins in the histogram. The effect is that a greater difference between the most significant association and the bulk of the associations results in a lower Hellinger distance. The degree to which the strongest association in the distribution is exceptional is the primary factor in the shape of the distribution and therefore the Hellinger distance. In other words, the Hellinger distance statistic describes the extent to which an expression trait's most significant association is exceptional among all of its associations. Our choice of reference distribution reflects the expectation that the highest frequency occurs in the smallest bins and tapers off towards the largest association. Because we chose a reference distribution that we expect describes an expression trait that is controlled primarily at a single locus, the Hellinger distance measures the importance of that locus to the trait's expression.The large difference between exceptional p-values. Though Hellinger distances measure significance at an expression trait level, the elements of the underlying distribution are already corrected for multiple testing error at a genom-wide level.One key difference between the genome-wide approach and our Hellinger distance based approach is that they measure significance on different scales. The genome-wide approach, in combination with a permutation test, provides statistics measuring the significance of each trait/locus association with respect to all associations between expression traits and loci. Our approach differs in that, for each expression trait, the Hellinger distance approach measures significance with respect to all the associations between a single trait and every locus. The result is that the genome-wide approach provides a statistic for each indvidual trait/marker association while the Hellinger distance provides a single statistic per expression trait. However, in the interpretation of Hellinger distance statistics it is important to consider that the calculation is based on distributions of genome-wide corrected AKR contributed to the conception of methodology, led the design and implementation of the methodology, wrote the manuscript, and performed the analysis. GS carried out the GO similarity and essentiality analysis. NVC conceived of the methodology, contributed to the design of the methodology and study, and contributed to the revision of the manuscript. MF provided access to expression data and expertise in genetics. SE conceived the study and contributed to the design of the method and revision of the manuscript. All authors read and approved the final manuscript.Probes and the corresponding Hellinger distances for Plasmodium falciparum.Click here for fileComparison of eQTL hotspots using the genome-wide and Hellinger distance approaches . Traits with large Hellinger distance versus traits with eQTLs. Marker positions on the genome versus the frequency of significant associations or eQTLs. The dashed line represents the cutoff for significance. The first bar graph shows the frequency of eQTLs by the genome-wide method while the second shows the frequency of expression traits with small Hellinger distance. Traits with significant Hellinger distance are assigned to the marker they are most strongly associated with.Click here for file


It has been argued that the limited genetic diversity and reduced allelic heterogeneity observed in isolated founder populations facilitates discovery of loci contributing to both Mendelian and complex disease. A strong founder effect, severe isolation, and substantial inbreeding have dramatically reduced genetic diversity in natives from the island of Kosrae, Federated States of Micronesia, who exhibit a high prevalence of obesity and other metabolic disorders. We hypothesized that genetic drift and possibly natural selection on Kosrae might have increased the frequency of previously rare genetic variants with relatively large effects, making these alleles readily detectable in genome-wide association analysis. However, mapping in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We performed genome-wide association analysis for 15 quantitative traits in 2,906 members of the Kosrae population, using novel approaches to manage the extreme relatedness in the sample. As positive controls, we observe association to known loci for plasma cholesterol, triglycerides, and C-reactive protein and to a compelling candidate loci for thyroid stimulating hormone and fasting plasma glucose. We show that our study is well powered to detect common alleles explaining ≥5% phenotypic variance. However, no such large effects were observed with genome-wide significance, arguing that even in such a severely inbred population, common alleles typically have modest effects. Finally, we show that a majority of common variants discovered in Caucasians have indistinguishable effect sizes on Kosrae, despite the major differences in population genetics and environment. Isolated populations have contributed to the discovery of loci with simple Mendelian segregation and large effects on disease risk or trait variation. We hypothesized that the use of isolated populations might also facilitate the discovery of common alleles contributing to complex traits with relatively larger effects. However, the use of association analyses to map common loci influencing trait variation in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We developed an analytic strategy to perform genome-wide association studies in an inbred family containing over 2,800 individuals from the island of Kosrae, Federated States of Micronesia. No alleles with large effect were observed with strong statistical support in any of the 15 traits examined, suggesting that the contribution of individual common variants to complex trait variation in Kosraens is typically not much greater than that observed in other populations. We show that the effects of many loci previously identified in Caucasian populations are indistinguishable in Caucasians and Kosraens, despite very different population genetics and environmental influences. The use of isolated populations has a long history in genetic mapping, with benefits including founder effects, reduced genetic diversity, reduced genetic and environmental heterogeneity, and large, multi-generational pedigrees We have been studying the native population of Kosrae, Federated States of Micronesia, under the hypothesis that power to detect mutant alleles might be enhanced by reduced allelic heterogeneity, and that different genes  might be obtained. Our initial analyses of genotyping data from 30 Kosraen trios and ∼110,000 genome-wide SNPs showed that Kosraens exhibit strikingly reduced haplotype diversity and extended LD, likely resulting from a strong founder effect and repeated population bottlenecks We also previously observed that native Kosraens exhibit elevated rates of obesity and diabetes, as seen in other indigenous populations The genetic architecture of isolated populations introduces analytic challenges which confound traditional association tests We ascertained over 3,100 Kosraen adults in three screens spanning a decade and performed genome-wide association studies for 15 quantitative traits in this cohort. To do so, we developed analytic strategies to address the complexities of studying a population in which the majority of subjects are related. Our work includes: extensive validation of the extended Kosrae pedigree; identifying an analytic approach to maximize power; calibrating the association score to correct for relatedness in the cohort; and application of this method to the analysis of 15 quantitative traits. Results from the genome-wide association analyses validate our approach by detecting previously known loci for LDL-C, HDL-C, triglycerides and C-reactive protein. Additionally, our data suggest novel loci contributing to phenotypic variation in thyroid stimulating hormone (TSH) and fasting plasma glucose (FPG). While empirical power calculations suggest our study is well-powered to detect common variants of relatively large effect (≥5% variance explained) with genome-wide significance, no such effects were observed in our data with convincing statistical support.e.g., r2>0.7 between BMI and waist circumference). Any values that could not be reconciled were excluded from the analysis. Heritability estimates for each trait are typically within published ranges; mean values, distribution, number of phenotyped individuals, and heritability estimates for each trait can be found in We performed a population-based screen of native Kosraens over three separate visits to the island . The 199A total of 2,906 individuals were successfully genotyped using the Affymetrix 500 k mapping assay  . SNPs weWe next used data from 2,906 individuals genotyped for 400,301 polymorphic, autosomal SNPs to validate the Kosrae pedigree.Genetic accuracy of the Kosrae pedigree was assessed using pairwise identity-by-descent (IBD) estimates generated in PLINK Changes to the pedigree were made based on data from a related pair in which both individuals were genotyped. However, successive iterations of pedigree validation and correction for fully genotyped, first-degree relatives produced a “ripple effect,” also improving the accuracy of relationships involving individuals not genotyped with the 500 k assay , and secAfter extensive comparisons with genetic data, the extended Kosrae pedigree spans eight generations and includes over 4,300 individuals (living or deceased), averaging four individuals per sibship (range 1–12). Nearly all  of the subjects successfully genotyped with the Affymetrix 500 k assay can be joined in a single extended pedigree, with an additional six individuals forming three independent nuclear families. We count 58 consanguineous offspring as well as numerous marriage loops. Nearly 30% of all genotyped individuals have two genotyped parents. Fifty-six individuals appear distantly related or unrelated to any other study participants.Our goal was to develop an analytic framework that accommodates the complex familial relationships in the Kosraen cohort while maximizing power to detect association. We were unable to identify or develop software capable of simultaneously computing over a complex pedigree of 2,900 individuals and >330,000 SNPs. Thus, our strategy became to break the pedigree into smaller units; a similar approach was recently taken by Przeworski and colleagues in their study of recombination in the Hutterites We broke the extended pedigree to create smaller units that could be feasibly analyzed by existing software packages for large numbers of markers, while maximizing the number of genotyped individuals included in the analysis  and maintaining some degree of information about relatedness between study participants. Alternative methods for breaking the pedigree were systematically explored, as described in the We selected sibships-without-parents as the unit of analysis . The 2,8The actual number of individuals included in the association analysis varies with the availability of phenotypic data, as individuals lacking phenotype data were omitted from the analysis of each trait. The extended Kosrae pedigree was thus broken into sibships for analysis of each of 15 quantitative traits. For example, in LDL-C, 560 sibships size ≥2 and 240 sibships of size 1  were analyzed for association. For BMI, the analysis was limited to individuals who had reached full adult height  Since the Kosrae cohort spans multiple generations, members of one sibship are frequently parents or cousins of other sibships. Because traditional association tests assume independence between family groups, we anticipated that relatedness between sibships would inflate the association test statistic We used simulation to evaluate association tests and the distribution of association statistics, with the goal of selecting an association test that maximized power for our chosen family configuration of sibships-without-parents. We compared two different approaches for association analyses of quantitative traits: a within-family test vs. a combined within- and between-family test. We selected the FBAT software to represent within-family test statistics, with the expectation that it would be robust to population stratification and relatedness between families 2 = 0.47 on Kosrae) and near-complete phenotyping in our cohort. Genotype data for 1,000 SNPs were randomly drawn from the larger dataset. After omitting rare SNPs (MAF<0.01), 770 SNPs remained. For each simulation, we modified the BMI phenotype to contain an association to a single SNP contributing an additional 1% to the total phenotypic variance. While this constitutes a fairly substantial single locus effect, it constitutes a small influence on the trait as a whole that does not distort the overall heritability and genome-wide relationships between genotype and phenotype. A total of 770 modified phenotypes were generated, each containing an artificial association to a different SNP in addition to the heritable and other variation in the observed BMI phenotype. Across datasets, the randomly-selected SNP associated with the spiked-in effect spanned a range of allele frequencies greater than 0.01.We used a modified simulation framework to evaluate and compare performance of the association approaches. An effect of known size was spiked into a Kosraen phenotype (BMI) and analyzed using observed Kosraen genotypes and family structure. We chose to modify an observed phenotype instead of simulating genotypes in order to preserve the complex familial correlations between genotype and phenotype on Kosrae. We selected BMI as a representative quantitative phenotype for its moderate heritability . As expected, p-values for the within-family test follow the null distribution while the combined within- and between-family  test exhibited a systematic deviation from the null. Such inflation of the nominal association score is typical for genotyping artifacts as well as excess known or cryptic relatedness in a between-family test of association, and was anticipated from the known relatedness between sibships. We determined that the major source of score inflation in the combined within- and between-family association test  was relatedness in the cohort . Concept−6 . For reference, −5 for each trait, including genome-wide significant association (p≤5×10−8) between SNPs on chromosome 11 and triglycerides. Quantile-quantile (QQ) plots and the respective genomic control correction factors (λ) for select traits are shown in We applied our strategy for association analyses to the examination of 15 quantitative traits, using measurements from clinical screenings in 1994, 2001 and 2003. As anticipated from the known relatedness between sibships, scores were inflated compared to the null distribution. Score inflation ranged from λ = 1.10 for fasting plasma glucose to λ = 2.05 for HDL-C . Score iCETP , with the same allele and direction of effect as reported in Caucasian populations APOE/C1/C4/C2 gene cluster on chromosome 19 (p = 1.89×10−7), a known locus influencing plasma levels of LDL-C and total cholesterol HMGCR), the target for cholesterol-lowering statin drugs −20−4 to 1.8×10−4CRP and HNF1A gene loci show association and the same direction of effect on Kosrae  as previously observed in a Caucasian cohort, in which the associated SNPs were either directly genotyped or are in perfect correlation (r2 = 1 in both HapMap CEU and ASN) APOE gene  We observed strong association between SNPs in previously established loci and HDL-C, LDL-C, triglyceride levels, TSH and CRP, supporting the validity of our analytic approach. For HDL-C, we observe association with rs4783962 and rs1800775 near APOC3/A5 have genome-wide significant association (p<5×10−8) with triglyceride levels (p = 1.2×10−9 to 8.6×10−9), including specific variants not previously implicated in Caucasian cohorts. In Kosrae, the best-associated SNP for triglyceride levels  is ∼23 kb downstream of the variant recognized in Caucasians (rs2266788). Correlation between these two SNPs has not been evaluated in Kosraens, since rs2266788 is neither directly genotyped nor well-covered by other SNPs in our 500 k dataset. However, rs2266788 is uncorrelated in HapMap Asian or Caucasian samples  with any of the seven SNPs associated with triglycerides in Kosraens. As the causal variant for this locus has not been identified, it remains to be determined whether these seven SNPs tag a causal allele common to both populations or whether independent causal variants segregate in these two ethnic groups. Besides SNPs near APOC3/A5 and triglycerides, no other loci across all 15 traits achieved genome-wide significance (Seven SNPs near ificance .−7 to 4.8×10−6) map to chromosome 9 at 97.6–97.8 Mb, a region encompassing the gene thyroid transcription factor 2 (TTF-2) (MIM 602617). Missense mutations in TTF-2 have been reported in conjunction with thyroid agenesis and congenital hypothyroidism in humans TTF-2 coding region, respectively. Analyses conditioning on the best-associated SNP, rs755109, suggest that a single association signal underlies association between SNPs in this region and plasma levels of TSH in Kosraens (data not shown).The most compelling evidence for a novel finding is observed in the association results for thyroid stimulating hormone (TSH). Among the top 20 results for TSH, 10 SNPs (p = 9.9×10MAWBP (MIM 612189) is modestly associated with fasting plasma glucose in Kosraens (p = 1.12×10−4). Upregulated expression of this gene has been reported in rat models of insulin resistance −6, with 7 other neighboring SNPs producing combined p-values of 2.53×10−6 to 5.73×10−6. While extended linkage disequilibrium limits our ability to identify the causal variant in Kosraens or to exclude association to other genes in the associated region, these loci represent promising results for follow-up in other cohorts.In another example of association observed near a strong biological candidate, rs10998046 on chromosome 10 near −6. Given these power calculations, and the evidence for a series of known associated loci as described above, the consistent lack of strong association across the majority of traits argues strongly that common variants of large effect are unusual on Kosrae (−5 for fasting plasma glucose (rs10745259) to 8.4×10−7 for waist circumference (rs2222328). Only two of fifteen traits, TSH and waist circumference, have strong, novel associations with p≤10−6. No novel associations were detected in any trait with p<8×10−7. Interestingly, these data indicate that on Kosrae, common variants in LD with SNPs on the genotyping array are of small effect in this founder population, similar to the genetic architecture observed in Caucasian populations.Our study was motivated by the hypothesis that the reduction in allelic heterogeneity resulting from the founder effect on Kosrae, combined with drift and natural selection, might produce common variants with relatively large effects segregating through the population. Empirical power calculations demonstrated that effects ≥5% should be readily detected (95% power) at p≤10n Kosrae , as theyLPL  or LIPC ; triglycerides and GCKR , and LDL-C and APOB . These and other common variants of small effect identified in Caucasian populations may also influence trait variation on Kosrae, despite lack of genome-wide statistical significance for association.If the effect sizes for common variants are similar in Kosraens and Europeans, then the lack of strong associations is unsurprising given the modest size of our cohort. We observe modest evidence for SNPs near multiple other loci which have been convincingly replicated in other cohorts 2≥0.95) in HapMap Caucasians and Asians We examined data for previously associated SNPs not under the null hypothesis of no effect on trait value, but rather under the alternative hypothesis that the effect seen in Europeans was also observed on Kosrae. We compared the effect sizes (β-coefficients) and allele frequencies for known loci in Caucasians to those observed in our study. Specifically, we identified 45 established associations for BMI, height, lipids, fasting plasma glucose, TSH and CRP  where th−42 to 6.8×10−4), of which 4 SNPs were associated with height in Caucasians. Over 70% of the loci evaluated (n = 28) had effects which were of indistinguishable magnitude and direction (p≥0.1) in the two populations.Of the 39 SNPs examined for effect sizes on Kosrae, only 6 loci had significantly different (p≤0.01) effects in Caucasians and Kosraens , a strong biological candidate with no previously known association. Associations near APOC3/A5 for triglycerides and near TTF-2 for TSH also highlight the possibility of island-specific variants or differences in LD between Kosraens and Caucasians that may be useful in identifying causal variants common to both populations.Suggestive evidence for association was observed for thyroid stimulating factor (TSH) to SNPs in the gene encoding thyroid transcription factor 2 , Massachusetts Institute of Technology (COUHES protocol #0602110607) and Massachusetts General Hospital . All patients provided written informed consent (in English or Kosraen) for the collection of samples and subsequent analysis.During screenings performed in 1994, 2001 and 2003, patients were recruited by public announcements and came to the clinic following an overnight fast. The 1994 screen was described previously Quantitative trait measurements were log- or square root- transformed to approach normality, adjusted for age and gender where applicable, and converted to Z-scores. An average Z-score was used for patients screened in multiple collection years or monozygotic twins. Individual trait descriptions are available in the Supplemental materials .Genotypes were analyzed for association with fifteen quantitative traits: body mass index (BMI), height, weight, waist circumference, plasma leptin, percent body fat, diastolic and systolic blood pressure, fasting plasma glucose, thyroid stimulating hormone, HDL-C, LDL-C, total plasma cholesterol, triglycerides and high-sensitivity plasma C-reactive protein.Data from the Affymetrix 500 k assay were generated at Affymetrix, South San Francisco, CA. Genotypes were called with the BRLMM algorithm. A minimum call rate of 95% was required for each chip . The twoAutosomal SNPs with MAF≥0.01 were analyzed for association with each trait, where MAF was calculated using the individuals phenotyped for that trait. Sibling relationships were accounted for according to default procedures in PLINK. The number of SNPs analyzed ranged from 332,890 (TSH) to 345,026 (Height).Study participants provided names and birthdates of their relatives during the patient interview. Information from multiple patient records was cross-referenced and used to reconstruct extended pedigrees. Relationships reported by subjects in the 1994 screen were validated genetically using Mendelian inheritance checks and identity-by-state analyses with microsatellite markers and the pedigree was modified to reflect the genetically accurate relationships SNP genotyping data were subjected to identity-by-descent estimation using PLINK Thirteen pairs of monozygotic twins and thirty-five duplicate sample pairs were identified by genotype similarity. Sample identity was confirmed from patient records  and one subject from each pair was included in the association analyses.We identified 58 offspring from consanguineous matings, where a common ancestor could be identified in the extended Kosrae pedigree; these consanguineous offspring were excluded from association analyses. An additional nine individuals were excluded from the dataset, as they self-reported non-Micronesian ethnicities and could not be connected to the pedigree.Three approaches were considered to break the extended pedigree into smaller units.Founders consist of a filtered subset of sibships-without-parents, such that any genotyped offspring of a founder sibship are removed from the dataset. Founder sibships are drawn primarily from the upper levels of the Kosrae family tree. For example, 582 “founders” in 247 sibships were identified for the BMI phenotype.Sibships without parents consist of two or more individuals known to share both parents. Since the Kosrae cohort spans multiple generations, members of one family group are frequently parents or cousins of other family groups. Information about parents is used to define a sibship; however, any genotyped parents are considered only in the context of their own siblings. For BMI, 1,871 individuals were included in 467 sibships of size ≥2.Nuclear families consist of two genotyped parents and one or more offspring. Where one genotyped parent is available, offspring are included as a sibship without parents and the genotyped parent is included in the context of its own siblings. Where no genotyped parents are available, individuals contribute as members of a sibship without parents.Genome-wide estimates of identity-by-descent (pihat) were used to select a subset of distantly related individuals. Genotyped individuals were randomly ordered, and individuals were selected if they were related to every other member of the group below a set threshold. A threshold of pihat ≤0.125 was used for individuals included in the association analyses (“sibships of size one”), corresponding to a relationship of first cousin or less. This selection process was repeated for 1,000 iterations, after which the largest set of individuals was identified for further use. These individuals contribute in the Between-family association score. For example, 202 “sibships of size one” contribute to the analysis for BMI.e.g., calculating allele frequencies, LD).A more stringent threshold of pihat ≤0.08, applied to the entire dataset of 2,906 study participants, resulted in n = 133 individuals related as less than first cousins. These individuals are treated as independent observations (“unrelateds”), suitable for use in any analysis requiring unrelated individuals ; minimal inflation of the association test statistic; and the practical consideration of retaining similar family structures across multiple traits.“Founders” (n = 582) have the fewest relationships with other individuals in the complete pedigree and were expected to minimize association score inflation due to excess relatedness between families. However, the exclusion of over 2,300 genotyped individuals from the analysis and concomitant loss of power persuaded us against this family configuration.Sibships-without-parents and nuclear families include similar numbers of individuals for a given trait. We note that the optimal configuration of nuclear families varies across phenotypes, whereas sibships-without-parents minimize differences in family structure across multiple traits. Individuals lacking a phenotype are simply omitted from a sibship and do not radically change the number of sibships available for analysis. The extended Kosrae pedigree was broken into sibships-without-parents separately for each trait and analyzed for association.We performed empirical evaluations of power for each association method using simulated datasets. We “spiked” an effect of known size  into an existing Kosraen phenotype (BMI) and analyzed this modified phenotype with observed Kosraen genotypes, thereby retaining the true genotype-phenotype correlation between related study subjects. A subset of 1,000 SNPs across the genome were randomly selected and filtered to retain SNPs with MAF>0.01. The remaining 770 SNPs were analyzed for association with the spiked phenotype. A total of 770 spiked phenotypes were generated, in which each phenotype was altered to reflect association to a different SNP of the random subset. These 770 phenotypic datasets were analyzed for association to the spiked SNP using FBAT and PLINK/QFAM under an additive model We used empirical power estimates from the BMI “spiking” experiment and the module for variance components QTL association for sibships in the Genetic Power Calculator Quantitative trait data were analyzed under an additive model using the QFAM module of PLINK −8 for genome-wide significance, following the work of Pe'er et al (2008) and Dudbridge and Gusnanto (2008) 6 independent tests across the genome and requires an additional p≤0.05. This significance threshold is likely conservative on Kosrae, where the true number of independent tests is likely to be smaller because of the extended LD, and so alleviates the multiple testing burden.We account for multiple testing by assuming a threshold of p≤5×10Association results for known, associated loci were drawn from studies in large Caucasian cohorts for multiple traits. For each Caucasian risk allele where the SNP was directly genotyped in Kosraens, we determined its frequency in HapMap CEU. We identified SNPs on the Affymetrix array that have frequencies in HapMap CEU within 2% of the frequency of the risk allele in HapMap CEU. For each SNP in that set, we calculated the difference in allele frequency between HapMap CEU and Kosrae. These values were used to generate an empirical distribution of allele frequency differences. For the risk allele, we calculate the difference in allele frequency between HapMap CEU and Kosrae, and place this difference on the empirical distribution to determine significance.k and βc are the effect sizes for Kosrae and Caucasian populations, k, and is distributed like a T.Association results for known, associated loci were drawn from studies in large Caucasian cohorts for multiple traits. Caucasian loci were limited to SNPs directly genotyped in Kosraens, or where a strong proxy was genotyped in Kosraens (r2≥0.95 in HapMap CEU and ASN). SNPs with MAF<0.05 on Kosrae were omitted from comparison, as power is low to estimate effect sizes accurately for rare SNPs. We assumed the Caucasian β estimates for each of the traits to be a fixed value. A test of heterogeneity for the magnitude and direction of the effect in Caucasians and Kosraens was performed as follows Figure S1Marker quality control for SNPs in the Affymetrix 500 k assay.(0.01 MB PDF)Click here for additional data file.Figure S2Excess relatedness is the major source of association score inflation. Association score inflation was evaluated in three subsets of the Kosrae cohort using the BMI phenotype. Score inflation is greatly reduced in “unrelated” (less than first cousins) individuals and in a set of sibships filtered to remove all parent-child relationships between sibships, as compared to all available sibships in the cohort.(0.06 MB PDF)Click here for additional data file.Figure S3Quantile-quantile plots showing genome-wide association results for 15 quantitative traits. For each trait, the number of individuals used in the analysis, heritability, and genomic control correction factor (lambda) are given.(0.17 MB PDF)Click here for additional data file.Table S1Genotyping statistics for the Affymetrix 500 k assay.(0.04 MB DOC)Click here for additional data file.Dataset S1Supplemental Trait Descriptions.(0.81 MB DOC)Click here for additional data file.


CATSPER4, combined P-value = 6.1×10−8 and rs910316 in TMED10, P-value = 1.4×10−7) and two had previously been described with weak statistical support . One locus (rs1182188 at GNA12) identifies the first height eQTL. We also assessed the contribution of height loci to the upper- (trunk) and lower-body (hip axis and femur) skeletal components of height. We find evidence for several loci associated with trunk length , hip axis length , and femur length . Finally, we used conditional analyses to explore a possible differential contribution of the height loci to these different skeletal size measurements. In addition to validating four novel loci controlling adult stature, our study represents the first effort to assess the contribution of genetic loci to three skeletal components of height. Further statistical tests in larger numbers of individuals will be required to verify if the height loci affect height preferentially through these subcomponents of height.Recent genome-wide (GW) scans have identified several independent loci affecting human stature, but their contribution through the different skeletal components of height is still poorly understood. We carried out a genome-wide scan in 12,611 participants, followed by replication in an additional 7,187 individuals, and identified 17 genomic regions with GW-significant association with height. Of these, two are entirely novel (rs11809207 in  The first genetic association studies of adult height have confirmed a role of many common variants in influencing human height, but to date, the genetic basis of differences between different skeletal components of height have not been addressed. Here, we take advantage of recent technical and methodological advances to examine the role of common genetic variants on both height and skeletal components of height. By examining nearly 20,000 individuals from the UK and the Netherlands, we provide statistically significant evidence that 17 genomic regions are associated with height, including four novel regions. We also examine, for the first time, the association of these 17 regions with skeletal size measurements of spine, femur, and hip axis length, a measurement of hip geometry known to influence the risk of osteoporotic fractures. We find that some height loci are also associated with these skeletal components, although further statistical tests will be required to verify if these genetic variants act differentially on the individual skeletal measurements. The knowledge generated by this and other studies will not only inform the genetics of human quantitative variation, but will also lead to the potential discovery of many medically important polymorphisms. Body height is determined by several biological processes that occur throughout the life of an individual and involve both normal and pathological growth. Most skeletal bones are formed by endochondral ossification, the process of replacing hyaline cartilage with bony tissue. Ossification starts shortly after gestation at the diaphysis and extends along the end of the long bones. Growth continues throughout childhood via mitotic division of the cartilage at the distal surface of the epiphysis and the epiphyseal plate, accelerates during the adolescence growth spurt and slows down in the early twenties, when the epiphyseal plate completely ossifies and peak body height is achieved. Subsequent decreases in peak body height throughout life are mainly the consequence of vertebral bone deformities such as crush fractures (osteoporosis) and cartilage degeneration (osteoarthritis).Epidemiological studies have revealed marked differences in the growth patterns for the lower and upper portions of the body. Both trunk and lower limb length are associated with parental height, birth weight and weight at age 4 years Recent, well-powered genome-wide association (GWA) scans have already identified 43 independent loci associated with height, revealing a significant overrepresentation of genes controlling DNA replication, intracellular signalling, cell division and mesoderm and skeletal development −5 in the full set of 12,611 individuals; in the independent discovery set of 9,400 individuals  57 SNPs from 20 genomic regions were significant at this threshold. We selected 52 SNPs from the 35 regions for replication genotyping in an additional 4,275 samples from three population cohorts . In addition, we genotyped a subset of 31 SNPs highly suggestive of true association but that did not reach GW-significance in the first replication set in additional 2,912 individuals with height data from the CBR cohort (−7) in the combined dataset, and seven additional loci had suggestive evidence for association with height  data for 299,216 SNPs from a combined sample of 12,611 adults of Caucasian origin from the UK  and the Netherlands  genotyped with Human300 and 550 bead arrays (Illumina) . We noteR cohort . A totalCATSPER4 gene . The A allele at rs11809207 was associated with an increase in height of 0.071 (95% C.I. 0.044–0.098) standard deviations, corresponding to an effect size of 0.46 cm per copy of the allele. Two nearby SNPs are in high LD with rs11809207 and have marginally higher P-values in the meta-analysis  is located in the first intron of TMED10  and UQCC  both of which have been replicated in multiple studies HHIP had a nominal P-value in the replication sample but did not reach GW-significance in the combined analysis , rs6817306 in LCORL , rs849141 in JAZF1  and rs10472828 in NPR3   and vertebral body heights (Rotterdam Study). We combined study-specific summary statistics using z-scores and found that nine of the 17 loci were significantly associated with trunk length at the nominal level. The strongest associations with spine were at rs6570507 in  0.0018) .LCORL  and UQCC .We next tested association of the 17 confirmed height loci with hip axis length (HAL) in 2,341 individuals from the Rotterdam Study . HAL is PRKG2, P-value = 2.4×10−5) and rs10946808   in 3,505 individuals from two cohorts . The strongest associations with femur length were observed at rs710841 (.4×10−6) .We used the following qualitative approach to explore a possible differential contribution of height loci to skeletal size measurements. We selected a homogeneous set of measurements  to avoid bias deriving from heterogeneous measurements among cohorts. Although this is expected to reduce the power to detect statistically significant associations, the magnitude of the betas is unlikely to be materially affected. Secondly, we re-calculated associations of the 17 GW-significant height loci with each skeletal size measurement as described before, only in this case we restricted the analysis to the homogeneous set of measurements. We then performed an analysis where association with each skeletal size measurement was assessed after adding height as an additional term to the linear regression model. We finally compared qualitatively the magnitude of the association, expressed as regression coefficients and SE, in the two models (univariate and height-adjusted). In cases where the locus acts prevalently through the given skeletal size measurement, we expect the regression coefficients of the height-adjusted analysis to show the least reduction compared to the original unadjusted analysis. For loci showing associations in the height-adjusted analysis, we then recalculated associations with height after adding skeletal size in the linear regression model. A null regression coefficient in this case suggests that the height association may be explained by the prevalent effect of the locus on the skeletal measurement under exam. Collectively, these conditional analyses, with relevant caveats DLEU7) or strongly reduced (e.g. for the SNPs in HLA-B). The GPR126 locus (rs6570507 and rs12189801) displayed the least reduction in regression coefficients between the two models  to 0.091 (0.04) for rs6830062) was observed after addition of height in the model  in at least one HapMap population. The rankings of the CEU expression and height association p-values at the 27 tested SNPs correlate well, suggesting a potential common functional variant underlying both the height and expression signal . Simulation of random significant eQTLs at this interval  and comparison of the height-expression correlation strength to the observed data indicates that the probability of obtaining such a relationship between the two phenotypes by chance is very small . We detected one cis signal that reached significance in the eQTL analysis in a 340 Kb region of chromosome 7 centred on rs1182188. The rs1182188 SNP is located in the first intron of GNA12 . Two of the regions with GW-significant associations with stature are novel and were centred on an intronic SNP in CATSPER4 , and on the first intron of TMED10 . CATSPER4 encodes for a receptor membrane ligand ion channel with a role in acrosome reaction and male fertility TMED10 (TMP21) encodes for a presenilin complex component with a role in vesicular protein trafficking TMED10 is in high linkage disequilibrium with three SNPs in NEK9 displaying marginally lower association P-values in the genome-wide scan. NEK9 is a regulator of cellular processes essential for interphase progression We carried out a genome-wide scan for stature in 12,611 adults of Caucasian origin and identified seventeen independent regions having GW-significant associations at the 5×10NPR3  and rs849141 in JAZF1  both of which were identified with low confidence  in one recent GWA scan also using Illumina genome chips NPR3 encodes for natriuretic peptide (NCP), a protein class that elicit a number of vascular, renal, and endocrine effects that are important in the maintenance of blood pressure and extracellular fluid volume JAZF1 is a transcriptional repressor associated with a role in endometrial stromal tumors JAZF1 were also recently implicated in type 2 diabetes and prostate cancer susceptibility HMGA2  and UQCC  as the loci with the strongest overall association with height We also identified two additional loci showing genome-wide statistical association with height, namely rs10472828 in In this study we also attempted, for the first time, to study the contribution of height loci to the components of trunk and leg length using measurements of skeletal size by directly testing associations of the 17 validated height regions with skeletal size parameters of trunk, leg and skeletal frame size, and by conducting an exploratory conditional analysis. Some intrinsic limitations of our study design and analytical approaches need to be taken into consideration in the interpretation of results for skeletal size measurements.Firstly, although we assembled the largest dataset of this kind, the small sample size available for skeletal size associations will have affected our ability to conclusively confirm or rule out associations for some loci. However, the composite nature of height as a phenotype, and an overall higher accuracy and specificity of the skeletal measurements in assessing the contribution of genetic loci to limb length, suggests that smaller sample sizes might be sufficient to detect associations for these intermediate traits with high confidence. For example, the seventeen GW-significant height loci jointly explain approximately 5% of total variance in femur length, which is more than twice the variance in height explained by the same loci combined.In order to increase power, we combined trunk size measurements from different cohorts that, despite being highly correlated, were not identical. Radiographic measurements of vertebral height size (Rotterdam Study) provide a more accurate measure of the skeletal component of trunk height compared to the DXA-derived measurements of the total spine (TwinsUK and Chuvasha), which include inter-vertebral disk heights and potential measurement error due to vertebral crush fractures. Measuring skeletal associations requires methods that are relatively expensive and low-throughput compared to height, making it difficult to assemble large homogeneous samples for analysis. To provide more comparable estimates of association, we focused our analyses on a subset of homogeneous standardised measurements of vertebral size and HAL in the largest available cohort, the Rotterdam Study.To help assess whether components of skeletal height mediated the association between relevant genetic loci and height, and to examine the independency of associations between genetic loci and components of height, we conducted an exploratory conditional analysis. In this and related contexts, conditional analysis to infer conditional independence and mediation has limitations. Conditional analysis of highly correlated traits  can lead to an attenuation of effect sizes for relevant genetic loci even when there is no underlying causal network between genetic variants and mediating traits HMGA2 (rs1042725) with sitting height  in a cohort of approximately 2,000 children. Although this may suggest a differential effect on trunk length, in our study such association was not replicated in an adult sample of 6,509 individuals for a highly correlated SNP in the same locus . Yet, direct spine length measures in adults are likely to be more precise and specific than measures of sitting height since they are less affected by sources of measurement error that may explain such discordant results. For instance, sitting height is prone to measurement errors affected by head dimensions which are disproportionate in children . In addition, radiographic measures of spine length will also be less affected by other artefacts arising from posture differences, age-specific growth patterns and/or possible common age-related effects of inter-vertebral disk degeneration, all of which can play a key role in this discrepancy.With the above caveats in mind, our results provide some interesting first insights into the potential contribution of height loci and possible differential effects on skeletal size measurements. These results should be considered exploratory and will require replication in larger cohorts to better understand their role and address potential sources of heterogeneity including the impact of measurement error. For instance, in a previous study Weedon and colleagues GDF5 and UQCC (rs4911494 and rs6088813) were strongly associated with HAL in the Rotterdam study , but not with femur length . GDF5 is a member of the TGF-beta superfamily of growth factors/signalling molecules that act as regulators of cell growth and differentiation in both embryonic and adult tissues. Mutations in this gene are associated with severe skeletal malformations including acromesomelic dysplasia, Hunter-Thompson type, brachydactyly, type C and chondrodysplasia Grebe type GDF5  has been associated with osteoarthritis (OA), the commonest form of human arthritis characterized by degeneration of articular cartilage and bone remodelling 2 = 0.93), indicating a possible role of this gene in cartilage metabolism and/or bone shape and alignment in determining height.The clinical relevance of the effects observed for these height loci is interesting and merits further exploration. Several loci displayed significant association with HAL, a measurement shown to vary between ethnic groups and to have substantial heritability. In addition HAL is highly correlated with long limb growth and represents an important predictor for osteoporotic fracture In summary, this study extended by four the list of loci with confirmed association to adult height, which now comprises 47 independent regions. The use of fine mapping through genotype imputation and resequencing will be important for refining the association signal in each locus and for identifying the true causative variants. The potential differential effects that we observed of height loci to lower limb and trunk growth are consistent with some genes potentially acting as regulators of long-bone growth, while others appear to be specific to different bone sites or to influence cartilage growth. Further analytical and experimental approaches to assess the contribution of height loci to skeletal measurements and intermediate phenotypes will be important to understand the physiology of human growth, and may lead to the identification of genetic variants relevant to diverse musculoskeletal pathologies in humans.Note: While this manuscript was in review the JAZF1 locus was confirmed by an independent study The initial discovery sample included 9,400 samples of European origin, including 1,430 British individuals  from the British 1958 Birth Cohort, 2,224 individuals from the TwinsUK cohort  and 5,746 individuals  from a Dutch cohort (Rotterdam Study). After applying quality filters, 299,216 SNPs remained for analysis with data in at least 9,000 individuals. Further details of individual cohorts are given below and in www.twinsuk.ac.uk) is an adult twin British registry shown to be representative of singleton populations and the United Kingdom population Sample: the TwinsUK cohort  was measured as described in −6 ; MAF≥5% ; SNP call rate ≥95% (777 SNPs removed); 307,040 SNPs were retained for analysis.Genotyping: All samples were typed with Infinium assays  as described in Richards et al. Sample: Samples in this cohort were derived from the Rotterdam Study , a single-center prospective population-based study of determinants of chronic disabling diseases in elderly individuals (age 55 years and over) Height and skeletal measurements: Standing height (cm) measurements were attained by a trained research assistant at the baseline visit using a standard wall-mounted statiometer. Thoracolumbar spine lengths (cm) extending from T7 to L4 were calculated by summing the mean of anterior, medial and posterior vertebral heights assessed radiographically in 2550 genotyped subjects after a mean follow-up of 6.4±0.4(SD) years. X-rays were scored for the presence of vertebral fractures using the MCCloskey/Kanis method −6 ; MAF≥5% ; SNP call rate ≥95% ; 306,942 SNPs overlapping with the TwinsUK and 1958 Birth Cohort data were retained for analysis.Genotyping: Genotyping of the samples from the Rotterdam study was carried out at the Genetic Laboratory of the Department of Internal Medicine of Erasmus MC, Rotterdam, The Netherlands using the HumanHap500 V.3 Illumina SNP Array. The Beadstudio GenCall algorithm was used for data calling an quality control (QC) procedures were performed as described earlier http://www.b58cgene.sgul.ac.uk/).Sample: The British 1958 Birth Cohort is a national population sample of individuals born within a single week in 1958, and followed periodically from birth to age 44–45 years, when a DNA bank was established as a national reference series for case-control studies. The samples selected for genotyping correspond to 1502 cohort members included as population controls in the Wellcome Trust Case-Control Consortium Height measurements: Standing height was measured in the home, and adjusted throughout for instrument and survey nurse . Z-scores were calculated on untransformed height data (cm), standardising height by gender. Field protocols, informed consent and this within-cohort genetic association analysis were approved by the South East NHS Multi-Centre Research Ethics Committee.−6 (404 SNPs removed); MAF≥5% ; SNP call rate ≥95% ; 309,549 SNPs were retained for analysis.Genotyping: Genomic DNAs from 1958 Birth Cohort samples were genotyped at WTSI using the HumanHap500 Illumina array. Genotypes were called using the GenCall algorithm and two rounds of manual curation. The same QC filters were applied to the SNPs overlapping with HumanHap300 chip. Hardy-Weinberg p-value≥10Sample: The European Prospective Investigation into Cancer and Nutrition study (EPIC-Norfolk) is a prospective population study of 25,663 men and women aged between 40 and 79 years, resident in Norfolk, UK. Participants are recruited from general practice registers between 1993 and 1997 and represent an ethnically homogeneous Caucasian population. Details of recruitment, anthropometric measurements, and health examinations following standardized protocols have been published Height measurements: Height and weight were measured using standard anthropometric techniques. Height z-scores were calculated by standardising untransformed height by gender and age decades. The Norwich Local Research Ethics Committee granted ethical approval for the study. All participants gave written informed consent.−6 ; MAF≥5% ; SNP call rate ≥95% ; 295,512 SNPs were retained for analysis.Genotyping: The EPIC samples were genotyped at WTSI on whole-genome amplified DNA. Genotype data calling of the EPIC cohort was carried using the Illuminus algorithm. Prior to analysis the SNPs were subject to the QC criteria used before: Hardy-Weinberg p-value≥10Sample: An additional 2,294 samples  from the TwinsUK cohort described previously were genotyped as part of the replication set. 174 individuals were siblings (DZ twins) of individuals genotyped in the discovery cohort. Height and skeletal measurements were obtained as described previously.Sample: The Chingford Study is a prospective population-based longitudinal cohort. The Chingford study includes 1,003 women derived from the age/sex register of a large general practice in North London, who are representative of the general UK population in terms of weight, height and smoking characteristics Height measurements: Height measurements were obtained using standard anthropometric techniques.Sample: Chuvashians are a Caucasian Finno-Ugric speaking population residing in the Chuvasha and Bashkortostan autonomous regions of the Russian Federation Height and skeletal measurements: Body length was measured using a statiometer and steel rulers (with 1 mm gradations) following standard anthropometric techniques Sample: The Cambridge BioResource is a collection of 4,000 pseudo-anonymised DNA samples. The collection of DNA samples from 4,000 healthy blood donors has been established by the Cambridge Biomedical Research Centre in collaboration with NHS Blood and Transplant for use in genotype-phenotype association studies.Height measurements: Information on height (cm) self-declared and obtained by questionnaire was available for 1,912 individuals.Genotyping of SNPs selected for replication was carried out using mass spectrometry (Sequenom iPLEX) at the Wellcome Trust Sanger Institute following standard procedures. Details of genotyping assays are available upon request from the authors.Associations with z-scores for the EPIC, Rotterdam and 1958 Birth Cohort were tested using a generalised linear model (1df) assuming an additive effect for the presence of each additional minor allele. Analyses were carried out using the PLINK software package http://www.sph.umich.edu/csg/abecasis/Metal/). In the case of related samples the effective sample size may inflate slightly the summary statistics, although by simulation this effect has been shown to be negligible. Rather than assigning an arbitrary effect size to the sample, the true sample sizes were used for calculation.GW-meta-analysis statistics for height and skeletal size were obtained using a weighted z-statistics method, where weights were proportional to the square root of the number of individuals examined in each sample and selected such as the squared weights sum to be 1 www.r-project.org) or in StatsDirect. Quantile-quantile plots were produced in R using custom scripts based on the SNPassoc package All individual-level analyses were implemented in the R statistical package  and gender (Chuvasha), or analyzing sex-stratified standardized residuals adjusted for age (Rotterdam study). Associations in the TwinsUK set were tested on the combined sample of GWAS and replication genotypes, for all samples with skeletal measurements available . Analyses were carried out using a score test implemented in the Merlin software. Associations in the Chuvasha sample were tested using pedigree disequilibrium test (PDT) For calculation of effect sizes for height, in order to obtain comparable estimates across all cohorts we converted height data from the family-based and twin cohorts to z-scores corrected by age and gender in a subset of unrelated individuals . We then combined the summary statistics (beta and standard deviation) using inverse-variance meta-analysis and calculated the average effect size by multiplying the betas and standard errors by the average height standard deviation in our sample .For skeletal size measurements, we note that individual cohorts were measured using slightly different  metrics for spine  and lower limb  it was the largest of the three cohorts, and thus likely the best powered; (ii) the vertebral size measurements available for this cohort are a better proxy of trunk length, as they are not affected by vertebral fractures and intervertebral disk degradation; (iii) this cohort, unlike TwinsUK, contains similar numbers of males and females. For femur we used the TwinsUK collection, which represents the largest collection with available data.−7 as follows. We retrieved gene expression data measured in lymphoblastoid cell lines derived from the HapMap II individuals as detailed in We carried out cis-association analysis for all the 17 height signals with meta-analysis P-values<5×10In cases where the P-value rankings correlated well in the observed data , we evaluated the significance of the correlation in the CEU population by simulating random significant eQTLs. Expression values of each gene were shuffled 100,000 times and SNP associations recomputed. We considered a randomized interval significant if it contained at least one association with a smaller p-value than the most significant observed eQTL for that interval. For every significant randomized interval, the comparisons of the rankings of the shuffled expression and height associations were also recalculated. Significance was assigned by comparing the correlation coefficients at the significant randomized intervals with the initial observed values. After simulations and comparisons with the observed data, we identified one region with significant evidence for cis-effects.http://www.twinsuk.ac.uk/TwinsUK Cohort: http://www.chingfordstudy.org.uk/Chingford Cohort: http://www.b58cgene.sgul.ac.uk/1958 Birth Cohort: http://www.epib.nl/ergo.htmRotterdam Study: http://www.r-project.orgR statistical package: http://www.sph.umich.edu/csg/abecasis/Metal/METAL: Figure S1Quantile-Quantile plots for association with height, calculated for (top row) the combined sample of males and females and for (medium row) females and (bottom row) males alone, with step-wise addition of additional cohorts (left to right): (i) TwinsUK; (ii) Rotterdam Study; (iii) 1958 Birth Cohort; (iv) EPIC Norfolk (Cohort); (v) EPIC Norfolk (High-BMI cases). The TwinsUK cohort is not represented in the male-specific analysis since the cohort contains only females.(0.09 MB PDF)Click here for additional data file.Figure S22 values from HapMap CEU). Red diamonds indicate high LD with the lead SNP (r2>0.8), orange diamonds indicate moderate LD with the lead SNP (0.5<r2<0.8), yellow indicates markers in weak LD with the lead SNP (0.2<r2<0.5), white indicates either no LD with the lead SNP (r2<0.2), or loci where such information was not available.Regional plot of the 17 confirmed associations with height for SNPs genotyped in the TwinsUK, Rotterdam, 1958 Birth Cohort, EPIC cohort and EPIC cases. Meta-analysis −log10 P-values are plotted as a function of genomic position (NCBI Build 36). The GWAS P-value for the lead SNP is denoted by a red diamond. A blue diamond indicates the P-value for the lead SNP in the replication sample. Proxies are indicated with diamonds of smaller size, with colours determined from their pairwise r(0.69 MB PDF)Click here for additional data file.Table S1Distribution of height and skeletal size measurements in the cohorts included in the study.(0.09 MB PDF)Click here for additional data file.Table S2Calculation of effect size for height associations by cohort. Beta and SE for height values are given in z-score units for each copy of A1 allele. For family-based cohorts (TwinsUK Discovery (KCL) and replication (KCL_RP) and Chuvasha) height z-scores were calculated in a subset of unrelated individuals.(0.15 MB PDF)Click here for additional data file.Table S3Gender-specific associations at the 17 validated height loci.(0.11 MB PDF)Click here for additional data file.Table S4Association of height loci with femur length. For each locus, the meta-analysis P-value was calculated for best analysis on two cohorts . Univariate and conditional analyses were carried out on a subset of unrelated TwinsUK samples with available height and femur length.(0.12 MB PDF)Click here for additional data file.


For genome-wide association studies in family-based designs, we propose a powerful two-stage testing strategy that can be applied in situations in which parent-offspring trio data are available and all offspring are affected with the trait or disease under study. In the first step of the testing strategy, we construct estimators of genetic effect size in the completely ascertained sample of affected offspring and their parents that are statistically independent of the family-based association/transmission disequilibrium tests (FBATs/TDTs) that are calculated in the second step of the testing strategy. For each marker, the genetic effect is estimated  and the conditional power of the corresponding FBAT/TDT is computed. Based on the power estimates, a weighted Bonferroni procedure assigns an individually adjusted significance level to each SNP. In the second stage, the SNPs are tested with the FBAT/TDT statistic at the individually adjusted significance levels. Using simulation studies for scenarios with up to 1,000,000 SNPs, varying allele frequencies and genetic effect sizes, the power of the strategy is compared with standard methodology . In all considered situations, the proposed testing strategy demonstrates substantial power increases over the standard approach, even when the true genetic model is unknown and must be selected based on the conditional power estimates. The practical relevance of our methodology is illustrated by an application to a genome-wide association study for childhood asthma, in which we detect two markers meeting genome-wide significance that would not have been detected using standard methodology. The current state of genotyping technology has enabled researchers to conduct genome-wide association studies of up to 1,000,000 SNPs, allowing for systematic scanning of the genome for variants that might influence the development and progression of complex diseases. One of the largest obstacles to the successful detection of such variants is the multiple comparisons/testing problem in the genetic association analysis. For family-based designs in which all offspring are affected with the disease/trait under study, we developed a methodology that addresses this problem by partitioning the family-based data into two statistically independent components. The first component is used to screen the data and determine the most promising SNPs. The second component is used to test the SNPs for association, where information from the screening is used to weight the SNPs during testing. This methodology is more powerful than standard procedures for multiple comparisons adjustment . Additionally, as only one data set is required for screening and testing, our testing strategy is less susceptible to study heterogeneity. Finally, as many family-based studies collect data only from affected offspring, this method addresses a major limitation of previous methodologies for multiple comparisons in family-based designs, which require variation in the disease/trait among offspring. Recent advances in mapping array technology and the increasing content from SNP databases A brute-force way to address the multiple comparison problem is to design studies with sample sizes large enough to test all genotyped SNPs with standard association tests and adjust for multiple comparison using the Bonferroni correction For the analysis of quantitative traits in family-based designs, Van Steen et al. Van Steen type testing strategies have three key advantages: 1.) The method achieves statistical power levels which can be substantially higher than those of standard approaches Although the approach has recently been significantly improved and now allows family studies to achieve power levels that are comparable to population-based studies with the same number of probands In this manuscript, we propose an extension of Van Steen type testing strategies to family-based designs in which all probands are affected. The strategy also uses the same data set for both stages, which we will refer to as the rank-weighting step and the testing step. In the first stage of the testing strategy, the genetic relative risk effect sizes are estimated for each SNP. We show that it is possible to derive four estimating equations that depend only on the observed parental mating types, but not on any unknown parameters. The estimating equations can be solved analytically, allowing for the construction of effect size estimators that do not depend on the marker allele frequency or offspring genotypes. This is in contrast to effect size estimators/association test statistics for study designs with only affected subjects in population-based studies Based on the genetic effect size estimates obtained from the estimating equations, we compute the conditional power of the FBAT/TDT for all SNPs. The relative rank of the SNPs by conditional power is then used in a weighted Bonferroni approach Using extensive simulation studies, the statistical power of the testing strategy is assessed for over a range of genetic effect sizes, different numbers of trios, when the mode of inheritance is known and unknown, and in the absence and presence of linkage disequilibrium (LD). The practical relevance of the approach is illustrated by an application to a genome-wide association study of childhood asthma.Y, and the parental genotypes, P1, P2X, as random, conditioning upon the offspring phenotype, Y, and the parental genotypes P1, P2. The evidence for SNP-trait association is evaluated by comparing the observed offspring genotype with the expected offspring genotype, which are computed by conditioning upon the parental genotypes, assuming Mendelian transmissions. Since the offspring genotype is the only random component of the FBAT/TDT statistic, the implication is that other information in the FBAT/TDT statistic  may be used to assess the evidence for association without biasing the significance level of the FBAT/TDT statistic.Van Steen testing strategies for genome-wide association studies partition the data set into two statistically independent, but overlapping parts X, Y, and P1, P2 can then be partitioned into two statistically independent components p, and the density of the second step, the FBAT/TDT testing step, is p, likelihood decomposition (Equation 1) implies that the two steps of the testing strategy are independent. The “evidence of association”  for each marker from the rank-weighting step can be utilized in the second stage without having to adjust the overall significance level for the estimation of the genetic effect size in the first stage. There are various ways in which the information from the rank-weighting step can inform the application of the FBAT/TDT statistic in the second step. The effect size estimate from the screening step can be used to select a small subset of “very promising” markers for FBAT/TDT testing Based on the two information sources about association in family-based designs, the density of the joint distribution for We assume that trios are given , and that SNP data are analyzed. If the parental data are missing/unavailable, the parental genotypes can be replaced in all equations below by the sufficient statistic by Rabinowitz & Laird ix be the coded genotype of the ith proband, counting the number of minor alleles for the SNP of interest. The variables ip1 and ip2 denote the parental genotypes for both parents at the locus. The phenotype of the ith proband is defined by iy. For trio samples in which all probands are affected, the phenotype is coded as “y = 1”. The FBAT statistic, For each marker locus of interest, let iy = 1”. The parameter xf denotes the penetrance probability ), and Ψx, the genotype relative risk . The probability Pr is defined by Mendelian transmission and can be computed straightforwardly, conditional on parental genotypes, without any additional knowledge/assumptions. The penetrance probabilities In order to develop a Van Steen type testing strategy Pr(iy = 1|ix) x.In the original Van Steen approach p1 = 2, p2 = 2), , , ,  and , where 0, 1, and 2 denote the number of copies of the minor allele for the marker of interest. The frequencies of the parental mating types in the ascertained sample (iy = 1) can be computed using Bayes' rule,p, denotes the minor allele frequency for the marker in the general population, and again, as above, the probabilities Pr are the paternal mating type frequencies in the general population, and k and l are given by one of the six distinct mating types defined above. Under the assumption of random mating and Hardy-Weinberg equilibrium at the marker locus in the general population, the probabilities Pr will be defined by the actual mating type and the minor allele frequency, p.Due to the lack of variation in the phenotype, the only variation that can be utilized for the estimation of the relative risk probabilities are the parental genotypes. In the trio design, there are six distinct parental mating types:  over all unknown parameters, i.e., the genotype relative risks, Ψ1 and Ψ2, and the minor allele frequency of the marker, p. However, due to the structure of the likelihood function, the Fisher information matrix is ill conditioned Based on these assumptions, the likelihood of the parental mating types in the ascertained sample is given by 1 and Ψ2, that are independent of the minor allele frequency, p, and have a closed analytical form, facilitating a numerically fast and robust implementation in genome-wide association studies. We consider the following four possible ratios of parental mating types:p, drops out of the mating type ratios, and one can show that the ratios R1, R2, R3, and R4 are given by:To avoid issues related to the estimation of the allele frequency, we will construct estimators for the genotype relative risks, ΨR1, R2, R3, and R4 do not depend on the unknown minor allele frequency, p, and can be estimated based on the parental genotypes, e.g., p, would have to be estimated.It is important to note that the four ratios 1 = (1+Ψ2)/2), each equation in (Equation 6) will depend only on one unknown genotype relative risk parameter. Each equation can then be solved for the unknown parameter and four estimates for the genotype relative risk are obtained. Alternatively, an overall effect size estimate can be constructed by averaging over all four estimates for the genetic effect size. The selected estimate for the genotype relative risk can then be used to calculate the marker distribution under the alternative hypothesis (Equation 3), which is the final component needed in calculating the conditional power of the FBAT/TDT statistic. Using simulation studies, we will assess which of the four ratios (or the average) for the proposed testing strategy generally achieves the highest and most stable power estimates.If a genetic model is specified  and can be used in the rank-weighting step of the testing strategy without biasing the significance level of the FBAT/TDT statistic in the second stage. The independence of the mating type ratios from the allele frequency makes the approach particularly attractive in the presence of population admixture.While we have outlined the concept of genotype relative risk estimation in the context of ascertained family samples for the trio designs, the genetic effect size estimators can be constructed in the same way for more complex nuclear family structures. Using the algorithm by Rabinowitz & Laird It is important to note that the proposed genetic effect size estimators are derived under the assumption of Hardy-Weinberg equilibrium at the marker locus in the general population, but not in the ascertained sample. Since it is common practice to filter out SNPs that are strongly out of Hardy-Weinberg equilibrium when the genotype data are cleaned prior to analysis, only SNPs with mild to moderate violations of the Hardy-Weinberg assumption will reach the association analysis step. The effects of SNPs with Hardy-Weinberg violations on the proposed testing strategy are thereby limited. However, the genetic effect size estimation in the first step will be biased for such SNPs. In the presence of SNPs that are out of Hardy-Weinberg equilibrium and that are not associated with affection status, the proposed testing strategy is likely to have reduced power. If the Hardy-Weinberg assumption does not hold at the disease susceptibility locus (DSL), the power of the proposed testing strategy can be either increased or decreased, depending on whether the signal that is caused by the true genetic effect at the DSL locus is amplified by the Hardy-Weinberg violation or not. Further, it is important to note that, while violations of the Hardy-Weinberg assumption will have an effect on the rank-weighting step, the validity of the FBAT/TDT-testing step and, consequently, the validity of the entire approach will not be affected by departures from Hardy-Weinberg.iα, to each marker locus based on its conditional power ranking. Essentially, iα is the type 1 error apportioned to the ith test on the basis of its power ranking relative to all of the other tests. The individual significance levels are selected so that the overall significance level is maintained, e.g., iα, and its association with affection status is declared as genome-wide significant if its FBAT/TDT statistic p-value is less than the individual significance level iα.In the first phase of the testing strategy, the genetic effect size estimates for each marker are used to compute the conditional power at each locus, and all markers are ranked by power. A weighted Bonferroni approach iα, we must select a weighting scheme to apply to the weighted Bonferroni method iα) of the overall significance level, α. Many different weighting schemes to select bin/weight sizes may be applied, as long as α is maintained. We selected an exponential weighting scheme, which uses weights that decrease exponentially and bin sizes that increase exponentially as the power rankings decrease jk be the size of the thj partition, and let k and r be user-defined partitioning parameters with an integer value. Then the sizes of the subsequent partitions can be defined by k1 = k and jk = k*rj−1) between the SNP to be simulated and the SNP immediately preceding the SNP that is simulated  was used to recapitulate local LD patterns on a genome-wide scale.The data were simulated under two separate scenarios. In the first scenario, independence among all markers ) is assumed. In the second scenario, we simulated local LD between the SNPs. In order to obtain realistic local LD patterns, we utilized a 550 K scan in the CAMP study  that consists of 400 trios. Based on the observed local LD patterns in CAMP, we simulated the correlated SNPs for the second scenario. Specifically, we applied a ‘moving window’ algorithm, where the observed correlation  or by the average of the four estimates. In simulation studies comparing the performance of the estimators (data not shown), we observed that the genotype relative risk estimator based on equation R4 consistently generated the highest power estimates  >0.1), and was stable, even with modest effect sizes  and lower allele frequencies . Thus, all estimated power levels for the proposed method that are shown here are based on the genotype relative risk estimator for mating type ratio R4.K) of 10% is selected throughout the simulations. The trio sample size varies between 500–2000 trios. To accurately depict the degree of LD between markers, 500,000 markers are simulated. Under the independence scenario, the power was assessed as the proportion of replicates where the FBAT test statistic p-value was less than the required weighted Bonferroni alpha level, based on its power ranking from the rank-weighting step. Under the LD scenario, the power was computed in two ways. First, we defined a positive result identically to the procedure used for the independence scenario . Secondly, we more broadly defined a positive result to include a significant finding in the DSL or in any markers in strong LD (r2>0.8) and within the same physical region,  with the DSL. For the standard Bonferroni correction, power was defined as the proportion of replicates with an FBAT statistic p-value<10−7 .In the first set of simulations, we assume an additive mode of inheritance at the DSL. The genetic effect size is defined in terms of an odds ratio and ranges between 1.25 and 2.5, depending on the number of trios. A disease prevalence  is required for genome-wide significance. Columns 4–5 (Independence scenario”) reflect the scenario in which all markers are independent . Columns 6–9 (“LD scenario”) display the power estimates when LD is present among markers, where the power represents either detecting the DSL only (Column 6–7), or the DSL/markers in strong LD with the DSL (Columns 8–9). The power estimates are based on at least 1,000 replicates for each (DSL) minor allele frequency and odds ratio.The results of the first set of simulations are displayed in For genome screens of 500 K SNPs, regardless of the sample size or degree of correlation among markers, the use of power-driven weights from the rank-weighting step shows a considerable improvement in power over the standard methodology. For the lowest power estimates (<40% power for the standard Bonferroni), the power estimates for the weighted method are typically at least twofold greater than the standard approach. For low to moderate power estimates, (40–70% power for Bonferroni), the weighted method outperforms the standard correction by to 15–40%. For SNPs with greater than 70% power with the standard approach, the improvement ranges between 7 and 11%, unless the power estimates are near one. However, even in these scenarios, the power estimates for the weighted Bonferroni method are always higher, though the differences between the two methods are more modest.With respect to trio sample size, we note that even with smaller sample sizes , there is still power to detect a DSL (or SNP in LD with the DSL), and the power gains over standard Bonferroni correction are maintained, although a more pronounced effect size is required (OR = 2.25–2.5) to achieve adequate power. Based on the results of our simulation studies, we would not recommend genome-wide association studies of fewer than 300 trios unless extremely large effect sizes (OR>3) were anticipated.To verify that the proposed testing strategy maintains the overall alpha level, the simulations were repeated under the null hypothesis of no linkage/no association, with a sample size of 500 trios. Based on over 10,000 replicates, the observed overall type 1 error rate was maintained at 4.66%.Finally, in examining the impact that LD has on power, when considering a positive finding to be the detection of the DSL only, the power of the approach was slightly reduced in comparison to the scenario in which the SNPs were independent. However, the proposed testing strategy still outperforms the standard approach by differences that are of practical relevance. When the definition of a positive finding is extended to those SNPs that are in LD with the DSL, the power estimates are higher than the independence scenario. This is a significant finding, given that some array platforms for genome-wide genotyping do not employ LD-tagging methods, and as chip density increases , linkage disequilibrium will have a greater impact on the analysis of genome-wide association studies.−8 for significance . For simplicity, we ran these simulations for 2000 trios.Since in practice the underlying mode of inheritance is unknown, we ran a second set of simulations to reflect this reality and assess the impact on the power of the proposed method and the standard approach. In the data analysis step of the following simulation, the true genetic model was considered to be “unknown.” We simulated three scenarios, where the true (but unknown) generating model was either additive, dominant, or recessive, and conducted separate FBAT analyses under all three genetic models. To evaluate the power for the weighted Bonferroni method The results of the second set of simulations are displayed in For the additive model, in comparison to the simulations where the genetic model is known, the power estimates tend be slightly lower. In the independence scenario, for an odds ratio of 1.5 and MAF of 0.2, when the genetic model is known, the weighted Bonferroni method has 91% power versus 85% for the standard, whereas, when the genetic model is unknown, the power estimates are 80% and 57%, respectively. However, our new method seems much more robust to analysis under multiple models in comparison to the standard correction. For an effect size of 1.5, the power loss in the unknown model ranges from 7 to 15%, depending on MAF, while power loss under the standard method ranges from 15 to 63%. Similar observations are made for the power comparisons between the weighted and standard methods for the LD scenarios. The overall power is reduced relative to the situation where the generating genetic model is known, but the difference in power between the weighted and standard methods is more striking. In comparing the independence scenario to the LD scenarios, the patterns observed when the genetic model is known hold here as well: when LD is present and the DSL or SNPs in LD with the DSL are considered, the power is highest, followed by the independence scenario. The lowest overall power is noted when LD is present and only the DSL is examined for significant association. In summary, while the overall power drops, the benefits of our methodology versus the standard are more pronounced when the genetic model is unknown and multiple analyses are conducted.In comparing our method with weighted Bonferroni significance levels to the standard under dominant and recessive models, our procedure consistently demonstrates greater power, regardless of the degree of LD, effect size, or MAF. However, under a recessive model, a MAF of 0.3 or greater is required to achieve adequate power for the range of effect sizes that we examined (OR = 2–2.5).Overall, our new methodology has the greatest impact for the low to moderately powered markers. For SNPs with standard Bonferroni power estimates ranging between 40% and 70%, the new method generally boosts power by an absolute difference of 10–15%, potentially providing marginally powered SNPs with a better chance of detection.Our simulation studies illustrate that the application of the proposed testing strategy is not limited by the number of trios analyzed, the degree of correlation among SNPs, the genetic model, or the size of the genetic effect. When standard approaches fail to provide sufficient power, the proposed testing strategy maintains acceptable power levels for small to moderate effect sizes (n = 2000) for the additive generating models, and moderate effect sizes under the dominant or recessive models or designs with fewer trios (n = 500–1000). As a general rule of thumb, our simulation experiments suggest that the testing strategy achieves optimal power levels for partitioning parameters of K = 7 and r = 2 for 500,000 markers, though power estimates were similar for K = 5–10 and r = 2–3. A comparison of the achieved power levels for differing number of trios and various genetic models illustrates that the impact of the multiple testing problem on a genome-wide association study can be minimized by the use of the proposed testing strategy.Asthma is a complex respiratory disorder, likely due to both genetic and environmental influences that affect the developing respiratory system. Asthma has been shown to have substantial heritability Thus, we applied our methodology to a family-based genome-wide association study of asthma. The families were originally recruited through the Childhood Asthma Management Program (CAMP) SNP genotyping was performed using Illumina HumanHap 550v3 arrays. Of 547,645 SNPs, 2.5% were removed during data cleaning due to genotype completion rates <95%, parent-offspring Mendelian errors, or because the assay sequence could not be aligned to one genomic locus, which resulted in 534,290 autosomal markers for analysis. Genotyping was conducted on 1215 subjects in 422 families. After removing 43 subjects with inadequate data, 1172 subjects comprising 403 families were analyzed. We applied the new power rank-weighting methodology, under an additive genetic model, to all 534,290 SNP, using equation R4 (Equation 6) to estimate genetic effect sizes, which had consistently had the highest power in the simulation studies. The power rankings were used to individually weight the family-based association test  for each marker, using the method of Ionita-Laza et al. From the analysis, two SNPs were identified as genome-wide significant with a global alpha level of 0.05. These SNP were also the top two by power. Thus, the Top K Method by Van Steen et al. With the current genotyping capabilities, genome-wide association studies have become a reality. In order to utilize the wealth of SNP data obtained in such studies to identify genes for complex diseases, new statistical approaches are needed that can handle the multiple comparisons problem on an increasingly large scale. For population-based studies, multi-stage designs have been suggested. In each stage of the design, the “most promising” SNPs  are pushed forward to the next level in which they are genotyped in another sample. Overall significance is established by combining the evidence from all stages into a single analysis. While this is a cost-effective approach, it is not as powerful as genotyping all subjects Testing strategies that use the same data set for genomic screening  and testing In this manuscript, we have developed an approach that makes such testing strategies available for the commonly used TDT design. Our simulation studies show that our method outperforms standard methodology substantially. The effect size estimators that we suggest allow for the assessment of the genotype relative risk at a population level in ascertained family samples. In contrast to association tests for affected-only designs in population-based studies PBAT, which is freely available at http://www.biostat.harvard.edu/˜clange/default.htmThe testing strategy as well as the corresponding power and sample size calculations has been fully implemented in the software package