Analysis of genetic polymorphisms may help identify putative prognostic markers and determine the biological basis of variable prognosis in patients. However, in contrast to other variables commonly used in the prognostic studies, there are special considerations when studying genetic polymorphisms. For example, variable inheritance patterns  need to be explored to identify the specific genotypes associated with the outcome. In addition, several characteristics of genetic polymorphisms, such as their minor allele frequency and linkage disequilibrium among multiple polymorphisms, and the population substructure of the cohort investigated need to be accounted for in the analyses. In addition, in cancer research due to the genomic differences between the tumor and non-tumor DNA, differences in the genetic information obtained using these tissues need to be carefully assessed in prognostic studies. In this article, we review these and other considerations specific to genetic polymorphism by focusing on genetic prognostic studies in cancer. KRAS mutations) have been extensively investigated as prognostic markers in human cancers. While several of these variables, such as the disease stage, have been used in predicting the prognosis and outcomes in cancer patients, there is nevertheless a considerable amount of variability in clinical outcomes of patients carrying similar baseline clinicopathological characteristics. Identification of additional variables, such as genetic variations, and their inclusion into prognostic prediction models may provide better prognostic predictions and help improve treatment decisions and clinical outcomes in cancer patients [Prognostic research aims to identify the factors that can predict the course of a disease in cohorts of patients . Traditipatients .In this article, we provide a review of the promises and special considerations arising from the unique features of genetic polymorphisms in prognostic research, particularly in relation to methodological and statistical applications, with an emphasis in cancer research.The human genome contains millions of sequence and structural variations. Among the most common variations are the single nucleotide polymorphisms (SNPs: estimated number >10 million), small insertions and deletions (indels), and copy number variations  ,3. BioloWhile due to the large number of variations in the human genome and their relatively poor biological characterization, the functional consequences and medical significance of a large portion of these variations are currently unknown, nevertheless, many genetic polymorphisms have been evaluated as potential prognostic markers in human diseases. In this article, for simplicity, we will use the term polymorphism to refer to any type of genetic variations that is commonly used in the contemporary research setting, regardless of their functional and phenotypic consequences. In addition, although, we will focus on SNPs, the concepts discussed in this manuscript are also applicable to other genetic variations (such as indels and CNVs).An extensive description of the statistical tests and interpretation of their results used in prognostic studies is beyond the scope of this article. Instead, a brief, non-mathematical prologue is provided below. Interested readers may refer to other articles for further information -10.t-test are commonly used to test statistical association of continuous outcomes . Logistic regression and χ2 tests are frequently used to test statistical association of binary outcomes . Log-rank test  and Cox regression analysis are two commonly used statistical methods to evaluate time-to-event outcomes . The result of these univariate analyses provides a P value and/or an estimated effect size  and hazard ratio (HR)) with confidence intervals that estimate whether a group of patients differs from another group of patients in terms of their prognostic characteristics. Specifically, in genetic prognostic research, these tests are used to determine whether a group of patients with a particular genotype (or genetic profiles combining multiple genotype data together) can be distinguished from patients with other genotypes or genetic profiles in terms of their outcomes.Initially, association of a polymorphism and clinical outcomes is assessed through univariate analyses. Linear regression and the If, after a univariate analysis, a significant association of a polymorphism with outcome is detected, then, the patients carrying a particular form of a polymorphism ) have a poorer or better outcome than the other group of patients carrying another genotype, allele or haplotype in that cohort. However, outcome in patients are affected by many different variables  and the compared patient groups in analyses may differ in these potentially confounding factors. Unfortunately, univariate analyses cannot adjust for these confounding factors. Thus, univariate analysis results are only the first step in analysis, helping us to understand our data and perhaps select the variables suitable for further studies, and need to be followed by multivariate analyses.In multivariate analyses, a number of selected variables are analyzed together in a single statistical model, typically a regression model such as a logistic or Cox proportional hazard regression model. In such analyses, several variables are analyzed simultaneously to test their contribution to the outcome independent of other variables in the model. For example, the individual predictive value of a genotype may be tested after adjustment for other variables entered in the model . Genotypes that show statistically significant results after this adjustment are concluded to be independent predictors of the outcome. Another benefit of the multivariable models is that if confounding factors are entered into the multivariable analysis, then this method also helps identify the confounding factors .P value in the univariate analyses (such as below 0.05 or other cut point at the discretion of the investigator) are entered into the model. If the number of variables is large, an automatic selection method  may also be performed by statistical programs to determine the variables to be entered into the final model. In another approach, all variables that cause the main association  to change greater than 10% are included in the final model. Last but not least, all variables may be analyzed in the multivariate analysis regardless of their P value in the univariate analyses . Utilization of these alternative approaches in multivariate analysis thus may result in different results.Selection, number and characteristics of the variables entered into a multivariable model are critical in statistical analyses. For instance, to ensure a proper model, variables in the model should be kept to minimum and have relatively common variable categories . Several approaches are available to select the variables entered into the multivariable models, with their own advantage and disadvantage . One of Assuming that a multivariable model is developed, its results can pinpoint those variables that are able to predict the outcome in a study cohort independent of other prognostic variables. Once such independent predictors of outcome are identified in a study, however, validation in additional studies is required to avoid false-positive statistical results. Several approaches, such as internal and external validations, may be used in validation studies . UsuallyIn addition to validation in other patient cohorts, meta-analyses can also be useful in identifying independent prognostic markers. Incorporation of independent prognostic markers in the clinical management of patients then requires clinical utility testing, followed by consensus guidelines for clinical adaption. Although there is a tremendous amount of research performed in this field, currently the number of genetic markers used in the clinic management of patients is surprisingly small, indicating the need to design better studies and the time required to validate prognostic models in a clinically meaningful way -13.In addition to the univariate and multivariate analyses that are commonly used in prognostic studies, we should also mention other statistical approaches that are relevant to genetic prognostic studies. For example, classification and regression tree (CART) analysis is a data partitioning method that can explore the relationship between the variables and the outcome in patient cohorts ,15. WhilIn many ways, genetic polymorphisms differ from other potential or established prognostic markers used in prognostic studies. While some of these characteristics ease our research and thus are advantageous, others still are challenging and may need to be debated within the scientific community to develop or establish ways to address them. In the following session, we discuss these characteristics and summarize the current challenges and solutions to some of these issues Table .The results of the HapMap project indicated that parts of the human genome are inherited as blocks  blocks); the polymorphisms located within these LD blocks are also inherited together with higher probability  Figure . UsuallyIn addition to reducing the redundancy among the study variables, analysis of tagSNPs alone may simplify the genotyping efforts and reduce the cost and resources required for genetic prognostic studies . This apThe LD among genetic polymorphisms in close proximity to each other also offers an additional advantage. For instance, once an association between an outcome and a polymorphism is detected and validated, this polymorphism may be considered as a prognostic marker. However, such a genetic marker may not be the direct biological modifier of the prognosis, but rather act as a proxy or surrogate for a nearby ‘true’ prognostic factor . Therefore, after this initial step, the genomic region around the validated polymorphisms should be investigated in detail to identify this prognostic factor. This critical information may then help elucidate the biological basis of variable prognosis in patients, but is unfortunately currently missing in the majority of the genetic prognostic studies. Therefore, future studies may also focus on this missing part of the genetic prognostic research.A confounder is a variable that is linked to both the variable and the outcome . ConfounIn the case of genetic variations, it is possible that another genetic variation in the same LD block  may be a confounder. Therefore, in contrast to many other epidemiological confounders, which are difficult to identify, at least some of the genetic confounders may be identified by examining the genomic region of interest and the other genetic variations located in it. Therefore, analysis of genetic variations as prognostic markers also offers unique benefits that do not currently exist in epidemiological research.In genetic research, as a quality control measure, the patient genotypes obtained for genetic polymorphisms are generally checked for genotyping or sampling errors using the Hardy-Weinberg equilibrium (HWE)  prior toHWE states that in an (infinitely) large population with random mating and absence of mutation, migration, or immigration, the allele and genotype frequencies of autosomal loci remain constant over time and follow specific genotype distributions. Therefore, any deviation of genotype distribution from HWE may indicate a population in flux, such as non-random mating and immigration. In genetic research, however, such deviations may also be caused by random fluctuations in samples included into the study, bias in samples collected or included into the research project, presence of samples from different ethnicities , and technical errors in genotyping . In The conventional approach, for example, in case-control studies is to check for such genotyping errors by applying the HWE test to genotype information in the healthy control population. If the test results do not indicate a deviation from the HWE, then the investigation of this polymorphism in the case-control cohorts proceeds forward. Usually, however, the polymorphisms whose genotype data do not satisfy the HWE are excluded from the analysis. Since, the deviations from HWE may also be due to the other factors (mentioned above), using this test to exclude some genotype data is a conservative approach. In addition, in contrast to the case-control association studies, prognostic research relies only on cases. In our opinion, how appropriate the HWE test in case-only design therefore needs to be debated.Genetic polymorphisms commonly consist of two different alleles (A and B). Thus, a person can carry two copies of the same allele  or one of each allele . While tri-allelic  and quadri-allelic polymorphisms  also exist in the human genome, albeit at a much lower frequency, in this article we develop our discussion around the bi-allelic polymorphisms.Usually, the genetic effects of each of the three genotypes  on prognosis are unknown and thus consideration of different genetic inheritance models may be required during statistical analysis. Most commonly used genetic models are the recessive, dominant, codominant and additive genetic models . In thesTypically, there is no, little, or conflicting biological or phenotype data to hint the right direction to use one inheritance model over another. Therefore, multiple models may need to be investigated in genetic prognostic studies. In our experience, the most common and robust genetic models investigated are the additive and the codominant models. In order to completely evaluate the role of a polymorphism with prognostic characteristics of patients, however, ideally all possible genetic models require investigation. Ironically, this also increases the number of statistical tests performed, which may require correction for multiple testing (see section entitled The multiple testing issues). In addition, the correction for multiple testing procedures almost always reduce the statistical power. Therefore, as a way of overcoming the multiple testing issue, many researchers opt for application of only one or a few of these inheritance models in their studies, rather than investigating multiple models for a comprehensive analysis. Such a practice, however, may lead to omission of potentially important findings.In addition to the correction for multiple testing, analysis of multiple genetic models in the same study presents another challenge. In univariate analysis, detection of a significant association of a polymorphism with an outcome in more than one genetic model is not uncommon. Especially if the models contain multiple polymorphisms, constructing a separate multivariate model for each inheritance model, for example, is not a logical solution as each polymorphism may affect the prognosis under different inheritance patterns.P values  [One solution to this issue is to determine the best pattern of genetic effect  for each polymorphism by inspecting the univariate Kaplan-Meier survival curves conducted for the codominant genetic model Figure . This wariation) .Genetic polymorphisms present in a range of minor allele frequencies (MAFs) in human (1% to 50%). The minor allele frequency of a polymorphism is critical information that helps determine the inclusion of the polymorphism into the statistical analysis, as rare variables may hamper the model construction . For exaFinally, in addition to the sample size and the effect size, we should also mention that the number of events per investigated variable in a multivariable model may impact the study power. A recommended rule for statistical power in multivariate analyses is the presence of at least ten events per independent variable . The eveUGT1A1 gene , which is more common in Caucasians than in Asians [Most of the genetic prognostic studies are based on population-based design with unrelated patients. However, these studies are prone to population stratification . Populatn Asians . In addin Asians . Therefon Asians .Since many cohorts investigated contain patients from different ethnicities and with potential population substructure, various methods have been developed to detect and control for population stratification in human genetic association studies, which may also be applied to genetic prognostic research; (a) the genomic control method  correctsP <0.0005; [When multiple hypotheses are tested in a study, using the significance level at the traditional value of 0.05 may lead to inflated false-positive results. In other words, the more comparisons we perform, the more likely we can obtain a significant result by chance. While for candidate gene studies, a modified significance threshold  was sugA variety of statistical correction methods have been developed  and may While the correction for multiple testing procedures aim to reduce the false-positive findings, there are other ways to help limit the number of spurious findings. For example, although not completely ideal, internal validation techniques such as cross-validation and bootstrap may be applied to the cohort data to reduce the false-positive discoveries ,11,12. TThe majority of prognostic studies have been conducted on retrospective cohorts collected for other purposes. Here the availability of genetic material and its efficiency in yielding the genotypes are not usually considered optimal. For example, in a significant portion of studies in cancer, formalin-fixed-paraffin-embedded (FFPE) tissue blocks (from both tumor and adjacent non-tumor tissue) collected during surgery are used to extract genomic DNA. The quality and the amount of this type of DNA may not be high and is susceptible to lower genotyping yields. This limitation in quantity and quality of the genomic DNA in retrospective cohorts usually restricts the options on suitable genotyping technology and the extent of the genotyping analysis . An increased proportion of failed genotyping may also create biased study results. Recently a few studies have shown that the limitations of FFPE-extracted DNA can be overcome by certain genotyping methods ,49. In aDue to the availability of the tumor tissue in many studies and the fact that disease progression and prognosis of cancer patients are affected by tumor behavior -54, analIn prognostic research in oncology, the optimal source of DNA depends on the study aims. If the association being tested is toxicity, then the optimal DNA may be the DNA in the target organ of the toxicity  or the organ that metabolizes, excretes, or activates the active drug . Surrogate DNA in this case may come from any germline  source, such as blood. In addition, if the mechanism influences the host stroma , the optimal source of DNA is from the host tissue . In contrast, if the association relates to efficacy and the polymorphism influences the tumor itself , then the most appropriate source of DNA is the tumor itself. Since it is impractical to obtain multiple sources of DNA to test different hypotheses in the same patient population, one can test tumor and non-tumor DNA for their correlation in genotype. High concordances  may suggest that the polymorphism itself is not a hotbed of somatic change in the tumor and thus allow tumor and non-tumor tissue to become surrogates for each other.Genetic prognostic research examining the relation and predictive value of genetic polymorphisms is a promising and rapidly developing research area. In contrast to other variables commonly studied, genetic polymorphisms have several unique characteristics that require special considerations in study design and data analysis. While some of these characteristics (such as linkage disequilibrium among genetic polymorphisms and tagSNPs) ease our efforts, other characteristics  may bias our results, if left unaccounted for. In addition, variability in study design and analysis can adversely affect advancement of the genetic prognostic research and translation of its results into the clinic. Recommendations modeled as guidelines  on how CNV: copy number variation; FDR: false discovery rate; FFPE: formalin-fixed paraffin-embedded; HWE: Hardy-Weinberg equilibrium; HR: hazard ratio; indel: insertion and deletion; LD: linkage disequilibrium; LOH: loss-of-heterozygosity; MAF: minor allele frequency; OR: odds ratio; SNP: single nucleotide polymorphism; tagSNP: tagging SNPThe authors declare that they have no conflicts of interest related to this work.SS initiated the idea, contributed to the design and organization of the manuscript, drafted and submitted the manuscript. GL contributed to the conception, organization and design of the manuscript, drafted and critically reviewed the manuscript. WX contributed to the conception, organization and design of the manuscript, drafted and critically reviewed the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1741-7015/11/149/prepub


Genetic variance in tolerance slopes is the genetic variance for tolerance. Variation in tolerance can induce genotype re-ranking and changes in genetic and phenotypic variation in host performance along the pathogen burden trajectory, contributing to environment-dependent genetic responses to selection. Such genotype-by-environment interactions can be quantified by combining random regressions and covariance functions. To apply random regressions, pathogen burden of individuals needs to be recorded. Secondly, when pathogen burden is not recorded, the cure model for time-until-death data allows separating two traits, susceptibility and endurance. Susceptibility is whether or not an individual was susceptible to an infection, whereas endurance denotes how long time it took until the infection killed a susceptible animal (influenced by tolerance). Thirdly, the normal mixture model can be used to classify continuously distributed host performance, such as growth rate, into different sub-classes , which allows estimation of host performance reduction specific to infected individuals. Moreover, genetics of host performance can be analyzed separately in healthy and affected animals, even in the absence of pathogen burden and survival data. These methods provide novel tools to increase our understanding on the impact of parasites, pathogens, and production diseases on host traits.Tolerance to infections is defined as the ability of a host to limit the impact of a given pathogen burden on host performance. Uncoupling resistance and tolerance is a challenge, and there is a need to be able to separate them using specific trait recording or statistical methods. We present three statistical methods that can be used to investigate genetics of tolerance-related traits. Firstly, using random regressions, tolerance can be analyzed as a reaction norm slope in which host performance ( Tolerance and resistance are two different defense mechanisms to defend against pathogens and parasites. Resistance is the ability of a host to prevent pathogen entry and to control pathogen life cycle in a way to reduce pathogen burden within a host individual. Tolerance to infections, in turn, is defined as the ability of the host to limit the impact of a given pathogen burden on host health, performance, and ultimately on host fitness  induced by infections Kause, . Secondly-axis) is regressed against pathogen burden of individuals (on x-axis)  Box . It is ixis) Box , and henxis) Box .Box 1An animal model random regression model is of the form:yi = b0 + b1PBurden + bi0 + bi1PBurdeni + ϵi, where yi is host performance of an individual i at its pathogen burden PBurden, b0 is the fixed population mean intercept, b1 PBurden is the fixed population mean tolerance slope, bi0 is the random genetic effect of intercept for an individual i, bi1PBurdeni is the random genetic effect of tolerance slope for an individual i, and ϵi is the random error term. Both bi0 and bi1 are modeled with a pedigree, allowing the estimation of their genetic variance.Covariance functions. Genetic variance of host performance as a function of pathogen burden can be calculated: as x'PBurdenGxPBurden, where 2b0 and σ2b1 are genetic variances for intercept and slope, respectively, and σb0b1 is covariance between the two terms  and infected individuals at a certain PBurden value can be calculated as: G is the genetic (co)variance matrix of slope and intercept, x0 is a vector of [1 0]′, and xPBurden is as described earlier  and abiotic factors in general . Qualitative data on burden  creates biased estimates of genetic variance for tolerance  performing individuals are infected, leading to biased estimates of genetic variation for tolerance  and endurance without a need for pathogen burden recording.Many studies, especially on aquaculture species, have analyzed survival or time-until-death in a challenge test in which individuals are experimentally exposed to a specific pathogen , either by resisting the infection, or by a successful recovery after the initial infection due to high tolerance Figure . FurtherA classical survival analysis of time-until-death assumes that all individuals are at risk and that all will eventually die given a sufficiently long follow-up period. When studying lifespan in general this is necessarily true, but may not hold when testing for mortality due to a specific pathogen. For non-susceptible animals time-until-death will necessarily be censored, irrespective of the follow-up time, and survival time may thus be a poor indicator for specific pathogen resistance. The endurance reflects the expected mortality per time-unit among susceptible individuals, but will have no effect on survival of the non-susceptible individuals  animals being still alive, and the true condition of each animal is unknown . In the cure model, probabilities of the alternative settings  can be estimated while simultaneously taking into account variation in endurance among the surviving animals  and non-susceptible (z = 0) animals the probability for an individual being still alive (censored) (c = 0) at time t is:Pr(z = 1) is the prior probability of being susceptible and Pr = 1. The probability of being still alive for susceptible animals, Pr, is a function of the endurance of the animal. Furthermore, if survival time is split into a series of binary survival scores , this probability is:Pr(sj = 0|z = 1) is the probability of surviving a period j, given that the animal is susceptible. Highly endure animals will have higher probabilities of surviving each sub-period and thus also higher probability of surviving until end of follow-up period. Putative non-susceptible animals will always survive.In a mixed population of susceptible (z = 1), while for surviving animals the true susceptibility status is not observable. Still, for these individuals the probability of being susceptible can be calculated as:For animals that die during the follow up period, susceptibility status is known . The low genetic variation for endurance and the genetic independency of endurance and susceptibility are in line with the results on other animal species  and healthy (z = 0) animals, the density of an observation y can be written as:In a mixed population of infected  sub-distributions with different means, and partly by variation within each sub-distribution Figure . Hence, Normal mixture models typically assume that an individual is either infected or not, and that infection has a certain effect on the phenotype Figure . HoweverRandom regression models are routinely applied in farm animal breeding programs, e.g., for milk test-day models in dairy cows and for growth curves (Schaeffer, The cure model has been applied to survival data in aquaculture species, leading to altered recommendations for routine disease-challenge testing (Ødegård et al., So far, only a limited number of breeding programs have considered selecting for tolerance. Some African cattle breeding programs are specifically selecting for trypanotolerance-related traits, the tolerance being a major breeding objective trait (Hanotte et al., The recent statistical developments provide tools to increase our understanding of genetics of alternative strategies to defend against parasites, pathogens, and production diseases. Most of the statistical methods can be applied in breeding value evaluations to breed for tolerance. Moreover, the methods presented here provide tools to quantify genotype-by-pathogen burden interactions that may explain a significant proportion of phenotypic variation in traits within populations that are exposed to various infections and production diseases. The traits whose variation is affected are typically production traits that are selected for in breeding programs. To be able to unambiguously select for the genetic potential of a production trait, the effects of resistance and tolerance should be separated from it. The methods presented in this paper provide potential to construct more effective breeding programs to increase both productivity and animal health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


FLT1 gene is significantly associated with both Q1 and the binary affection status phenotype. This association was detected by both the additive and dominant tests, although the additive phase-informed test resulted in a smaller p-value. No false-positive results were detected in the first phenotype replicate. Analysis of the average values of all phenotype replicates correctly identified five other genes important to the simulation, but with an increase in false-positive rates. The accuracy of our method is contingent on correct phase determination.To enable the assessment of compound heterozygosity, we propose a simple approach for incorporating genotype phase in a rare variant collapsing procedure for the analysis of DNA sequence data. When multiple variants are identified within a gene, knowing the phase of each variant may provide additional statistical power to detect associations with phenotypes that follow a recessive or additive inheritance pattern. We begin by phasing all marker data; then, we collapse nonsynonymous single-nucleotide polymorphisms within genes on each phased haplotype, resulting in a single diploid genotype for each gene, which represents whether one or both haplotypes carry a nonsynonymous variant allele. A recessive or additive association test can then be used to assess the relationship between the collapsed genotype and the phenotype of interest. We apply this approach to the unrelated individuals data from Genetic Analysis Workshop 17 and compare the results of the additive test with a dominant test in which phase is not informative. Analysis of the first phenotype replicate shows that the  Modern high-throughput DNA sequencing technology enables the detection of genetic variants at the most fundamental level. To analyze sequence data across large cohorts of individuals, investigators need to develop and implement methods that will intelligently combine the information from rare genetic variants that may share functional relationships. Genetic Analysis Workshop 17 (GAW17) provides an excellent opportunity to test such analysis methods in the context of an exome sequencing project [A common method for analyzing rare sequence variants is to pool or collapse all the variants that occur in a defined region  and to test for the presence or absence of any sequence variants within the region . This apThe condition of inheriting DNA sequence variants at two sites within a gene, with each variant coming from a different parent, is called compound heterozygosity. Each locus is heterozygous when considered individually, but the compound effect of having variants in each of the two copies of an autosomal gene may be functionally analogous to a homozygous mutation. In a family-based study, Pierce et al.  identifiThis finding leads us to ask whether it is possible to identify similar phenomena in a cohort of unrelated subjects. We propose extending the process of rare variant collapsing to combine the information from variants occurring together on phased haplotypes in order to utilize compound heterozygosity to inform association tests in a population-based cohort. We present this analysis of the GAW17 exome sequence data as a proof of concept. The major obstacle to successful detection of compound heterozygotes in unrelated individuals is the difficulty in determining genotype phase for rare genetic variants. The genetic variants found by sequencing may be rare or even unique to a single individual. Genotype phase for the unrelated subjects in the GAW17 data must be inferred from linkage disequilibrium patterns, which are difficult to assess accurately for rare variants. Algorithmic phase estimation might introduce artifacts in the analysis process. We therefore present results using two different phasing algorithms to assess the robustness of the proposed method. The results obtained from the phase-informed analyses are compared to the results obtained using a similar analysis method that does not consider phase.A to indicate the absence of a nonsynonymous variant and the letter B to indicate the presence of such a variant. The genotype was therefore homozygous for the variant allele (B/B) if at least one nonsynonymous variant was found on each haplotype in the gene, heterozygous (B/A) if nonsynonymous variants were identified on only one haplotype, and homozygous for the common or reference allele (A/A) if no nonsynonymous variants were found in the gene. Mutation type (synonymous or nonsynonymous) and the variant allele for each SNP were determined from the annotations distributed with the raw data [We determined genotype phase for all SNPs in the GAW17 exome sequence data using both fastPHASE  and BeagB/B), making a recessive test uninformative. We therefore chose to concentrate on an additive model, for which the limited number of B/B genotypes can still provide additional information over methods that do not consider phase. We used logistic regression to test for association of the primary phenotype (Affected) with the compound genotypes produced by each phasing method using an additive inheritance model. We ran the logistic regression again with a dominant inheritance model to assess the significance of collapsing rare variants within genes without phasing. The dominant model tests for the presence of one or more nonsynonymous variants in the gene, regardless of phase. We used linear regression to test for associations with the three quantitative traits , again using additive models for the compound genotypes from each phasing method as well as a dominant model. The analysis concentrated on the first phenotype simulation replicate using the entire cohort of 697 subjects.The compound genotype can be tested using any standard statistic for genotype associations. The collapsing procedure identified only a small number of subject-gene combinations with nonsynonymous variants on both haplotypes , version 7.4.0-Beta [All tests were adjusted for population stratification using principal components analysis (PCA). We calculated principal components using a subset of 4,360 SNPs with minor allele frequency (MAF) greater than 0.01 and maximum pairwise linkage disequilibrium of −5. A summary of results is shown in Table FLT1 gene on chromosome 13 was significantly associated in all three tests for the binary phenotype (affected status). Tests based on the Beagle phasing method identified four case subjects and two control subjects as FLT1 compound heterozygotes. The fastPHASE method identified three case subjects and no control subjects as FLT1 compound heterozygotes  than for the dominant test (p = 3.75 × 10−6). Association testing for Q1 also showed an extremely strong association at FLT1. Based on these results, we believe that FLT1 is an important factor for Q1 and the binary affection phenotype. No other genes reached the prescribed significance threshold for any phenotype based on the first phenotype simulation replicate. The strongest statistical association for Q2 was found at VNN1 . The strongest association for Q4 was found at GRIA4 .We performed tests on 2,196 genes. The Bonferroni significance threshold for this number of tests is 2.27 × 10KDR gene as associated with Q1, whereas the unphased approach did not find this gene.We repeated the additive association tests using the average of each phenotype across the 200 simulation replicates, with the assumption that the averaged phenotypes would give an accurate representation of the simulation parameters and the best estimate of each subject’s disease liability. Increased phenotypic accuracy should improve the power of the tests and reduce the stochastic noise inherent in analyzing a single simulation replicate. Tests were performed for the mean of the 200 simulated values for the quantitative traits. For the binary affection status, we counted the number of times each subject was affected in the 200 replicates and used this count as a quantitative response variable. A summary of the results from these tests is shown in Table p-values for some genes, but not all. The true phase of the SNPs in these data is not known, but if we assume that our phasing methods are accurate, our results show that additional power can be gained in some situations by incorporating compound heterozygosity in a large-scale analysis of sequence data.The intent of this analysis was to assess the feasibility of incorporating compound heterozygosity into an association test based on exome sequence data with unrelated subjects. Our approach used a simple method to collapse nonsynonymous variants on phased haplotypes within genes, and we compared results of an additive model incorporating compound heterozygosity with an analogous dominant model for which phase was not informative. We used two phasing methods to ensure that our approach was robust to phase estimation artifacts and found the results to be fairly consistent for the most significant genes. It is important to note that phase information was not included in the phenotype simulation model. This analysis is only a proof of concept. The additive tests using phase information resulted in lower FLT1.) As expected, analysis of the averaged phenotypes resulted in greater statistical power but also in an increase in false positives. No false associations were detected for Q4 or the binary affection trait, but there were numerous false positives for Q1 and Q2. The false-positive rates were similar for the phased and unphased approaches.Although it is discouraging that we detected only one true positive association in the first phenotypic replicate, it is also encouraging that there were no false-positive associations. (We should note that we found numerous false-positive associations in the preliminary results before the PCA correction was incorporated. For example, the uncorrected analysis for Q1 resulted in false-positive associations with five genes in addition to the one true-positive association with We believe that the major weakness of our method lies in the difficulty of inferring genotype phase for rare SNPs. Most of the nonsynonymous variants in the data were very rare. Out of 13,572 total nonsynonymous SNP variants in the data, 5,924 occurred only once and another 1,505 occurred only twice. The greatest certainty for phase determination is in the genes with numerous and relatively common variants. Genes with numerous variants also have additional statistical power to detect associations using the collapsing approach that we followed. As such, it is not surprising that most of the genes we identified as being associated harbor multiple variants with relatively high frequencies. Although the two phasing methods we used generally resulted in similar compound genotypes (Table In practice, we expect that our method will have the greatest value when applied to low-frequency variants. High-frequency variants might confound the collapsing process and hide the influence of rare deleterious variants. We expect sequencing data sets to remain small in the near future, which means that inferring phase from observed linkage disequilibrium patterns will be especially difficult for common and rare variants alike. We hope that future advances in sequencing technology will make it possible to assess phase more accurately or even to observe phase directly. This could be made possible by single-molecule analysis, extended read lengths, or advanced sequence capture technology. As DNA sequencing technology continues to improve, we anticipate that the methods we describe here will become useful for analysis of sequence data in large cohorts of unrelated subjects. Our method may also be adapted for use in analysis of rare SNPs assayed by modern array-based SNP genotyping platforms.The authors are employees of Golden Helix, Inc., a commercial software provider whose “SNP & Variation Suite” software was used extensively in this analysis.GBC conceived of the study, performed all analytical work and drafted the manuscript. CGL contributed to the study design and helped to draft the manuscript. All authors read and approved the final manuscript.


FLT1, PIK3C3, and KDR) were consistently selected using the proposed methods. In addition, compared to logistic regression models, the logistic kernel machine models were more powerful, presumably because they reduced the effective number of parameters through regularization. Our results also suggest that a screening step is effective in decreasing the number of false-positive findings, which is often a big concern for association studies.Rare variants are believed to play an important role in disease etiology. Recent advances in high-throughput sequencing technology enable investigators to systematically characterize the genetic effects of both common and rare variants. We introduce several approaches that simultaneously test the effects of common and rare variants within a single-nucleotide polymorphism (SNP) set based on logistic regression models and logistic kernel machine models. Gene-environment interactions and SNP-SNP interactions are also considered in some of these models. We illustrate the performance of these methods using the unrelated individuals data from Genetic Analysis Workshop 17. Three true disease genes ( High-throughput sequencing technologies have been evolving extraordinarily fast in the past few years. They have been recently applied to genome-wide association studies to study the effects of both common and rare variants. The different natures of these two types of variants call for distinct methods. For common variants, association tests based on individual SNPs are still widely used. However, such approaches suffer from multiple comparison problems and do not take into account possible interactions among variants. To overcome these limitations, analyses based on single-nucleotide polymorphism (SNP) sets have been developed to test the joint effect (either linear or nonlinear) of variants within a SNP set. For instance, Wu et al.  proposedAlthough many genome-wide association studies in the past focused on common variants, it is now widely believed that for complex diseases, rare variants are more likely to be functional than common variants . BecauseTo effectively detect association signals, investigators might find it beneficial to jointly model the common and rare variants and to account for correlations among both variants. For this purpose, in this paper we introduce several methods to jointly model the common and rare variants within a SNP set. Note that throughout this paper SNPs with minor allele frequency (MAF) less than 1% are treated as rare variants and all other SNPs are treated as common variants.We start with logistic regression models, including gene-environment interaction terms, and derive score statistics for testing the presence of any marginal or interaction effects. We then consider logistic kernel machine models, which can incorporate both interactions among SNPs and gene-environment interactions. This type of model is an extension of the method proposed by Wu et al.  and Liu ith individual , let response iy be 0 if the individual is unaffected and 1 if affected. Let iX be a q × 1 covariates vector (including an intercept term), iz be a p × 1 vector of SNP genotypes (or summary scores) for a given gene (SNP set) under testing, and is be the environment covariate that is also included in iX. We consider the following logistic regression model with gene-environment interactions:For the where:H0: a = b = 0, and we consider the corresponding score statistic. For a detailed derivation and expression of the score statistic, see Wang et al. [The goal is to test the null hypothesis g et al. .Following Wu et al.  and Liu h(·) and g(·) belong to reproducing kernel Hilbert spaces KH and K and h(·) and g(·) can be estimated by:where Following Liu et al. , the solih and ig are independent. Denote τ = 1/λ and H0: h(·) = g(·) = 0 in Eq. (3) can be reformulated as testing the absence of the variance components, H0: τ = where et al.’s  papers, Q* can be approximated by scaled chi-square distributions which is based on the score statistic of ariances .Finally, we construct a combined test statistic:p-value is then:The corresponding υ degrees of freedom. For detailed derivations and expressions of Q*, K and where g et al. . Note thp common variants, we introduce the summary score:For a gene with ikI is the number of times the kth variant is observed in the ith individual,where kth variant is observed among affected and unaffected individuals, respectively, and An and Un are the total numbers of affected and unaffected individuals, respectively. This summary score is derived based on the idea of principal fitted components for dimension reduction [and eduction .We propose a two-stage procedure to analyze the GAW17 data. In the screening stage, genes that do not show any statistical significance are filtered out. The main purpose of this stage is to achieve dimension reduction and at the same time to retain genes that are more likely to be associated with the disease. In the testing stage, we apply various methods to test the subset of genes that have passed the screening criteria.T2 test is used to test whether there is a mean difference between the affected and unaffected individuals [syn and WSnonsyn, respectively. Then a two-dimensional Hotelling T2 test is performed based on WSsyn and WSnonsyn. To test gene-environment interactions, we consider the null hypothesis Corr = Corr. We take the difference between Fisher’s z transformations of sample correlations for the affected and unaffected groups as the test statistic:In the screening stage, both genetic effects and gene-environment interaction effects are investigated, and common and rare variants are handled differently. Common variants are tested in the three subpopulations  separately, whereas rare variants are studied based on the whole population. For each gene, the genotypes of the common variants  are treated as a vector and the Hotelling ividuals . For rarividuals  are deriAgain, instead of testing each variant individually, we use combined scores for both common variants (Eq. (9)) and rare variants (the weighted-sum score) and test gene-environment interactions for each SNP set as a whole. In addition, for rare variants, we consider only the nonsynonymous variants.p-values are determined through permuting disease status . Finally, genes are deemed to pass the screening and become candidates for the testing stage if they have (unadjusted) p-values smaller than a prespecified threshold  for at least one of the tests.In all the tests, the In the testing stage, two kinds of models are considered: logistic regression models (Eq. (1)) and logistic kernel machine models (Eq. (5)). For all models, the covariates vector consists of Age, Sex, two principal component scores to account for population structure (see Results section for more details), and an environmental factor (Smoke status). For rare variants, we further introduce a combined weighted-sum score:combined = WSsyn + 2WSnonsyn, (13)WSwhere nonsynonymous variants receive more weight.combined is used for both scenarios. Finally, score statistics are calculated and the p-values are determined using theoretical chi-square distributions.For logistic regression models, we consider two different scenarios for the common variants, one using the original genotypes (referred to as logistic regression) and the other using the common score (Eq. (9)) with the weights calculated based on the corresponding screening data set (referred to as the logistic common score). In addition, WSK and K that models interactions among variants and a linear kernel for combined is used and the method is referred to as the quadratic rare WScombined method. For the linear kernel case, two scenarios are considered for combining rare variants, one using WScombined (referred to as the linear rare WScombined method) and the other using WSnonsyn (referred to as the linear rare WSnonsyn method). Moreover, for the kernel machine methods, the weighted-sum scores for rare variants and the genotypes of the common variants are both standardized (to have mean 0 and standard deviation 1) before model fitting.For logistic kernel machine models (Eq. (5)), the original genotypes are used for common variants. We consider two different schemes for the kernels. One uses linear kernels for both In total, we consider five different methods in the testing stage, which are summarized in Table The GAW17 data we analyzed in this paper have 200 replicates, each consisting of data for 697 unrelated individuals. The genotypes, age, and sex of these individuals are from real studies and are kept fixed across the 200 replicates. One environmental risk factor (smoking status) and a binary disease status were simulated for each replicate [The 697 individuals were from seven different sources: Denver Chinese, Han Chinese, Japanese, Luhya, Yoruba, CEPH (European-descended residents of Utah), and Tuscan. Through principal components analysis on about 1,000 common variants  with MAF larger than 10%, the first two principal components clearly divide the sample into three distinct clusters, corresponding to Africans (Luhya and Yoruba), Asians (Chinese and Japanese), and Europeans (CEPH and Tuscan).The genotype data consist of 24,487 SNPs from 3,205 genes on 22 autosomal chromosomes. The MAF for 74% of the SNPs is less than 1%. In our analysis, these are treated as rare variants, whereas all other SNPs are treated as common variants. Moreover, 2,208 genes contain at least one common variant, and the maximum number of common variants within a gene is 52. A total of 2,476 genes contain at least 1 rare variant and the maximum number is 179. One hundred sixty-two rare variants were removed from the analysis because they appeared in only one individual. Genes with a rare variant event occurring in less than 1% of individuals were removed, and 2,534 genes were left for subsequent analysis. Genotypes are coded as 0, 1, or 2, indicating the number of minor alleles at each locus.RUNX2, MUC3A, TMEM67, NIBP, AKAP2, GOLGA1, USP5, and FLT1) were selected at least 95 times. If a 0.05 threshold was used, the mean number of genes passing screening was 824 and 1 gene (FLT1) was selected 95 times. For each pair of screening and testing data sets, genes that passed the screening step were tested using the five methods described in the Methods section. P-values were adjusted using the Holm procedure [p-value was less than 0.1. Throughout the 100 pairs of screening and testing data sets, if a threshold of 0.1 was used in the screening step, then four genes  were selected more than 10 times by at least one of the five testing methods. In contrast, if no screening was used , nine genes were selected more than 10 times by at least one of the five testing methods. The selection frequencies of these genes are illustrated in Figure We randomly divided the 200 simulated replicates into 100 pairs. For each pair, one data set was used for screening and the other was used for testing. Across the 100 screening data sets, if a 0.1 threshold was used, the mean number of genes passing screening was 1,307 and 8 genes ) on the first replicate indicated that the (common) variant C13S523 was associated with disease status highly significantly . This variant was nonsynonymous with a MAF of 6.7%. The weighted-sum score of the rare variants in FLT1 also showed evidence of association . Gene KDR is on chromosome 4 with 14 rare variants and 2 common variants. Gene PIK3C3 has 7 variants (6 rare variants and 1 nonsynonymous common variant). It also seemed that this common variant was the reason that PIK3C3 was picked by the linear rare WScombined method about 20 times across the 100 replicates.Gene FLT1, PIK3C3, and KDR are true disease susceptible genes. However, other genes reported in Figure TAS2R48 was detected as a significant gene among 18 (out of 100) data pairs by the linear rare WScombined method when no screening was applied. However, for 15 out of 18 pairs, TAS2R48 would not pass the screening step if a 0.1 threshold was used.The results were obtained without knowledge of the underlying disease model. Afterward, we examined the GAW17 simulation model . It turnIn this paper, we considered SNP set analysis for detecting disease-susceptible variants using exon sequence data. In large-scale association studies, there is often a need to combine information across variants to improve detection power. This is especially the case for rare variants. Here, we adopted the weighted-sum score of Madsen and Browning  to summaThe authors declare that there is/are no competing interests.RW carried out data analysis and participated in the development of methods. JP and PW led the development of methods. All authors participated in drafting the manuscript. All authors read and approved the final manuscript.


Population-scale genome sequencing allows the characterization of functional effects of a broad spectrum of genetic variants underlying human phenotypic variation. Here, we investigate the influence of rare and common genetic variants on gene expression patterns, using variants identified from sequencing data from the 1000 genomes project in an African and European population sample and gene expression data from lymphoblastoid cell lines. We detect comparable numbers of expression quantitative trait loci (eQTLs) when compared to genotypes obtained from HapMap 3, but as many as 80% of the top expression quantitative trait variants (eQTVs) discovered from 1000 genomes data are novel. The properties of the newly discovered variants suggest that mapping common causal regulatory variants is challenging even with full resequencing data; however, we observe significant enrichment of regulatory effects in splice-site and nonsense variants. Using RNA sequencing data, we show that 46.2% of nonsynonymous variants are differentially expressed in at least one individual in our sample, creating widespread potential for interactions between functional protein-coding and regulatory variants. We also use allele-specific expression to identify putative rare causal regulatory variants. Furthermore, we demonstrate that outlier expression values can be due to rare variant effects, and we approximate the number of such effects harboured in an individual by effect size. Our results demonstrate that integration of genomic and RNA sequencing analyses allows for the joint assessment of genome sequence and genome function. The recent availability of almost fully sequenced human genomes by the 1000 genomes project allows the direct study of genetic variants that influence levels of gene expression in the cell. In this study, we explore the effect of rare and common variants on levels of gene expression. We show that the availability of a more comprehensive list of variants brings us closer to the likely causal variants, and we discuss their genomic and evolutionary properties. We also demonstrate the effects of variants that change splicing patterns or length of the protein product, the putative joint impacts of variants that affect gene expression, and those that affect protein structure. Finally, we show the impact of rare regulatory variants that cannot be detected by the conventional methodologies of association and require the interrogation of full genome sequencing and full transcriptome sequencing. These approaches bring us closer to the implementation of these data and methodologies to a direct clinical application. Deeper characterization of genetic variation is becoming increasingly available with advances in DNA sequencing technology cis-eQTLs), and compared the power of HapMap3 (HM3) against the much higher SNP density of the 1000 genomes project (1KG) genetic variants, using gene expression data from lymphoblastoid cells for matching individuals , we saw an enrichment in associations in RNA-Seq data for the respective donor and acceptor exon read count levels . In order to seek for such patterns from the ASE data, we calculated the proportion of heterozygous individuals that have significant ASE as a proxy for linkage disequilibrium between the coding ASE variant and the unknown regulatory variants. Furthermore, we used both HM3 and 1KG datasets to control for putative effects of genotyping error  on gene expression in the studied cell type. Across all the tissues and developmental stages, each individual is expected to have even hundreds of such rare, large effect regulatory variants. We have also demonstrated how studies integrating genomic or exome sequencing with RNA sequencing data from different tissues will also provide information of how the functional effects of protein-coding variation are modified by regulatory variation. Altogether, these approaches will bring us closer to a joint assessment of how genome sequence affects genome function, and how this relates to phenotypic diversity.www.1000genomes.org; REL-1003) and HapMap 3 release 3 genotypes (www.hapmap.org). For association analysis, we used 5 404 174 common (MAF≥0.05) SNPs for 60 RNA-sequenced CEU individuals (Utah residents with ancestry from northern and western Europe) and 5 329 982 and 6 976 232 SNPs for 57 and 56 expression-arrayed CEU and Yoruban individuals , respectively. For two individuals which were parents in a CEU trio which had variants independently called in the 1000 genomes (pilot 2), we intersected their genotype calls with pilot 1 calls; in cases where no genotypes were reported in the trio individuals, we added the reference homozygote state. Between pilot 1 and pilot 2, 3 398 517 sites were concordant and had genotypes reported . For indels, we used calls from the same release. In total, 592 081, 586 604 and 710 931 common indels were used in each population sample . For rare variant and ASE-based analyses we used only the pilot 1 1000 genomes genotype calls; this was to prevent biases due to the improved rare variant calling on the pilot 2 trio.We used 1000 genomes polymorphisms from the March 2010 pilot 1 and 2 release  at or below the 0.01 permutation threshold was fine-mapped from the HM3 into the 1KG data. Each of these eQTL variants from the HM3 was compared to D′ calculated by Haploview for all the 1KG variants with a Spearman association of p≤10e-3 with the same gene. The 1KG variants which were in LD (D′≥0.8) with the original HM3 variant were deemed to be underlying the same effect originally discovered in the HM3; the best association for that gene in the 1KG meeting this LD criterion was selected for comparison to the original HM3 variant. This methodology allowed us to survey new discoveries irrespective of whether they were the same variant, different variants at different frequencies or divergently-located with respect to the transcription start site.Functional variation was determined using the EnsEMBL 54 pipeline The PhyloP base-wise conservation scores were based on alignments of 46 vertebrate genomes, 33 mammalian genomes, and 10 primate genomes, and were downloaded from UCSC Haplotype homozygosity indicates the relative age of a haplotype by assessing the incidence (or lack thereof) of recombination or other mutation. This is achieved by comparing the extent of homozygosity between haplotypes by calculating the length of sequence from a target marker until a mismatch occurs. Phased data is required in non-haploid species to assess and compare individual haplotypes from a target marker position. Furthermore, since haplotype homozygosity is being assessed from a heterozygous target marker (required for assessing ASE), a decision needs to be made about what allele should be taken to represent the reference haplotype and in what direction haplotype homozygosity should be assessed (5′ or 3′). Here, we used phasing data as provided by the 1000 genomes project pilot release and compared all haplotypes carrying each allele for the heterozygous marker and in both directions to select the allele and direction with the average longest tract of haplotype homozygosity. Then, given this direction and reference haplotype, we take as a criterion for comparison that there must be at least 6 individuals where between 2 and 4 have ASE significant haplotypes in the same direction and at least 2 are non-significant for ASE. To compare the extent of haplotype homozygosity given the reference haplotype and the ASE status of each haplotype we compare significant ASE to significant ASE haplotypes and significant to non-significant ASE haplotypes and compute the average length of haplotype homozygosity for all pairwise individual comparisons within these categories. We then stratify the results for each ASE marker based on the number of significant ASE haplotypes were original discovered.To identify putative regulatory SNPs on rare regulatory haplotypes using ASE calculations we looked for all variants within 100 kb of the transcription start site which satisfied ASE sharing in 1, 2 or 3 individuals when at least 6 heterozygotes individuals could be tested for ASE. To satisfy sharing, the variant must be heterozygous with the same direction of effect (assessed through phasing) when an ASE effect is present in an individual and homozygous when the ASE effect is not present. To assess how well our putative causal regulatory variants discovery was performing we assessed the distribution of discoveries around the transcription start site by comparing counts of real predictions versus control predictions in 5 kb windows using the Fisher's exact test (Bonferroni-corrected for multiple testing). Control (null) predictions were obtained by matching each ASE test by reassigning significance in the opposite direction. For instance, if there were 6 heterozygotes, 2 of which show ASE, the control reassignment would assign ASE to the two least significant heterozygote individuals. To assign direction of effect, we matched the distribution of real directions determined with the phasing data to the control set.We compared the co-occurrence of expression outliers with rare variants by recomputing our expression files as Z-scores and binning at each allele frequency the distribution of expression measurements that were incident with the non-reference variant. To assess significance of divergence in this distribution, individual labels were permuted 200 times and 90 and 95% CI were obtained.Figure S1Comparison of HapMap 3 and 1000 genomes project eQTL discovery . The discovery of eQTLs from HapMap 3 (black) and 1000 genomes (red) project variants by expression platform (LCL expression interrogated on arrays from 56 Africans and 57 Europeans and LCL expression from RNA sequencing of 60 Europeans) was compared across p-value thresholds. Here, the equivalent permutation threshold for the 0.01 and 0.001 levels are highlighted. This highlights that the addition of 5–7× as many markers through whole genome sequencing is requiring a higher threshold for association significance providing the potential to remove many weaker but previously significant regulatory haplotypes.(EPS)Click here for additional data file.Figure S2Fold changes for significant allele specific expression variants. We plotted the fold change distribution for significant ASE variants (p≤0.05). The median fold was 1.39. The mean inter-individual fold change was 1.41 with a standard deviation of 0.16.(EPS)Click here for additional data file.Figure S3Comparison of distance to transcription start site of fine mapped variants from HapMap3 (HM3) into 1000 genomes (1KG). An estimate of whether newly discovered associations in the 1KG are more likely to be causal mutations is to assess their position relative to the transcription start site (TSS) with the expectation that causal mutations may be more proximal to the TSS. We assessed this for array-based and RNA-seq eQTLs and for each platform we observed that there was no significant trend towards to the TSS for improved associations. However, we observed that many of the newly discovered variants are proximal to the previous reported association from HM3. This supports that measured eQTLs are not strictly limited to a class that is influencing the proximal promoter machinery.(EPS)Click here for additional data file.Figure S4Comparison of distance to exon midpoint of fine mapped variants from HapMap3 (HM3) into 1000 genomes (1KG). Considering that many newly discovered associations for the 1KG may be marking genetic differences in splicing or transcript termination, we interrogated if these associations were more likely to be discovered proximal to the mid-point of the exon compared to the original associated variant discovered in HM3. We assessed the distance to the exon midpoint for exon-eQTLs discovered in HM3 that were also discovered and in the 1000 genomes. We did not observe a pattern which preferentially indicated that the new associations were closer to the exon midpoint. This indicates that in general the improvement in association is not a function of a particular component of gene regulatory architecture but is likely a heterogeneous mix of different genetic effects which influence the transcriptome.(EPS)Click here for additional data file.Figure S5Comparison of distance to transcription start site of fine mapped variants from HapMap3 (HM3) into 1000 genomes (1KG) for CpG versus non-CpG promoters. We hypothesized that the genetic effects on gene expression would be manifested differently depending on whether a gene's proximal promoter contained a CpG island. Our rationale was that CpG promoters might have broader effect locations while non-CpG and likely more binding-sequence dependent promoters might be constrained closer to the TSS. We did not observe that either had preferential discovery closer to the transcription start site. A prevailing observation was that discovered effects were close to the original tagging variant in HM3.(EPS)Click here for additional data file.Figure S6Comparison of conservation difference between best association and second best association within platforms. Given that the average conservation for HM3 variants was higher than in 1KG , we tested if we saw a larger improvement in conservation score between the best association and the second best association (conditioned on them being in D′≥0.8). This was matched for the same eQTL as the HM3 and 1KG best associations were also in D′≥0.8. In general, this was not observed except for YRI . This suggests that many of these newly discovered associations in 1KG are not more highly constrained at the base level then the HM3 associated-variants.(EPS)Click here for additional data file.Figure S7Enrichment of 1000 genomes splicing associations. To assess the impact of splicing variation identified through annotation, we assessed the enrichment of associations that we observed for acceptor versus donor exons stratified by acceptor (left-panel) and donor snps (right-panel). We plotted this relative enrichment as a function of mean number of reads per individual to assess if difference in gene quantities were responsible for differential discovery of splicing effects.We further also plotted as the background the same relative enrichment for synonymous variants which were greater than 15 bp away from the splice junction and had not be identified as splicing SNPs. We observed that we could detect enrichment of splicing variation in the RNA-seq data when sufficient reads were present. This was consistent specifically for better quantified exons (with sufficient read depth). This pattern of enrichment for splicing variants is reinforced by both tests having consistent deviation from the background despite some observable stochasticity. In this plot, for each binned measure of enrichment at least 30 variants were available. Enrichment was measured as the log ratio of the 1-pi0 statistic for acceptor versus donor exon associations .(EPS)Click here for additional data file.Figure S8Enrichment of 1000 genomes splicing associations without mapping filter. To address if the enrichment of splicing variants we observed was due to threshold effects due to mapping we removed mapping quality filters and repeated the same analysis. For both acceptor and donor variants we observed the same enrichment relative to synonymous variants.(EPS)Click here for additional data file.Figure S9Enrichment of exon-eQTLs for overlapping exons of synonymous, nonsynonymous and gained stop variants. We observed significant enrichment for exon-eQTLs for gained stop variants when compared to enrichment we observed for synonymous and nonsynonymous variants. This enrichment suggests that nonsense mediated decay is detectable in RNA-Seq data. The level of enrichment was estimated using the 1-pi0 statistic.(EPS)Click here for additional data file.Figure S10Proportion of ASE of all heterozygotes per variant with respect to derived allele frequency. To investigate the relationship between regulatory and coding variation we looked at the proportion of heterozygotes which had allele specific expression (ASE). This proportion is expected to be high when the coding variant is in strong linkage with a cis-regulatory variant causing the allelic imbalance. We calculated the proportion of ASE with respect to DAF for cSNPs with ASE and at least 3 heterozygotes in the full 1KG data (a), in 1KG SNPs with both allele observed at least once (b), and in SNPs with genotypes concordant with HM3 data (c). The dark blue lines are means in sliding windows of 600, 400 and 150 SNPs respectively, proportionally to the number of SNPs, and the cyan lines denote the 10% upper and lower quantiles. The analysis was performed for these three datasets to control for possible effects of false heterozygote genotype calls in the 1KG data. However, it is frequent that only one allele is observed in the RNA-Seq data even when analyzing the high-quality heterozygotes from the HM3 dataset (2042 of 6859 (30%) of ASE heterozygotes). This proportion was higher in the full 1KG data , which could be caused by genotyping error. However, false ASE calls are likely to create single random ASE observations per SNP and decrease the mean ASE proportion; thus, our analysis looking of an increase is likely to be conservative in this respect. Altogether, the results show increased ASE proportion in the end of the DAF distribution. This can arise by chance through new regulatory mutations landing on haplotypes carrying a rare coding allele, or a new coding mutations landing on haplotypes carrying a rare regulatory allele. Additionally, even co-evolutionary pressure on a haplotypes containing both variants can increase the proportion of ASE.(EPS)Click here for additional data file.Figure S11Proportion of ASE of all heterozygotes per variant for synonymous and nonsynonymous variants. For sSNPs and nsSNPs with ASE and at least 3 heterozygotes in the full 1KG data (a), in 1KG SNPs with both allele observed at least once (b), and in SNPs with genotypes concordant with HM3 (c), we observed that that the proportion of ASE is higher for nonsynonymous than synonymous variants. This may suggest increased LD between regulatory variants and nsSNPs through favoring of functional coding variants on a particular regulatory haplotype over other haplotype combinations. Significance was assessed using Mann-Whitney.(EPS)Click here for additional data file.Figure S12Mean proportion of ASE of all heterozygotes with respect to derived allele frequency for synonymous and nonsynonymous variants where the derived allele is higher or lower expressed. The proportion of heterozygote individuals with ASE was separated by derived allele's expression (either “loss” or “gain”) for sSNPs and nsSNPs with ASE and at least 3 heterozygotes in the full 1KG data (a), in 1KG SNPs with both allele observed at least once (b), and in SNPs with genotypes concordant with HM3 (c). The lines are means in sliding windows of 300, 200 and 75 SNPs respectively, proportionally to the number of SNPs, and the Mann-Whitney p-values for sSNPs versus nsSNPs have been calculated in DAF bins of 0.05 with an overlap of 0.025. The proportion of ASE is high especially in rare nonsynonymous variants where the derived allele is lower expressed, potentially suggesting evolutionary preference of rare derived nsSNP alleles to be on the lower expressed haplotype. This could be a signal of rare deleterious coding variants that are tolerated in the population due to being on a lower expressed haplotype, which lowers their penetrance. The trend can be seen also in the 1KG data with both alleles observed but is expectedly weaker, since this phenomenon would be strongest when the rare derived allele is hardly expressed at all. The lack of signal in HM3 may be explained by these variants being underrepresented in the SNP genotyping arrays.(EPS)Click here for additional data file.Figure S13Extent of haplotype homozygosity for shared ASE effects. To identify signals of recent and rare eQTLs in the 1000 genomes data, we assessed the extent of haplotype homozygosity for shared ASE individuals versus ASE and non-ASE individuals . We observed that when ASE was shared among 2 or 3 individuals, there were longer tracks of haplotype homozygosity indicating shared genealogy for an underlying causal mutation. This result supports previous findings we observed using HapMap3 phasing data .(EPS)Click here for additional data file.Figure S14Number of prSNPs detected for real ASE and non-ASE effects. We plotted the distribution of the number of real prSNPs from control prSNPs detected for each ASE effect. Here, at each testable marker, the number of real prSNPs is subtracted from the number of control prSNPs identified and plotted as a histogram. The shift to the left shows that many more ASE effects have more real prSNPs than control prSNPs.(EPS)Click here for additional data file.Figure S15χ2 p-value<2e-16). Bins where there were significant prediction differences using a Bonferroni-corrected Fisher's exact test are highlight by stars. This result indicates that many real prSNPs are located within the gene or near promoters and suggests that the predictions are preferentially targeting regions with a higher prior of functionality.prSNPs distributed around the transcription start site. We assessed the location of prSNPs discovered for the real versus non-ASE predictions. We found many of the real predictions where centred around the transcription start site and downstream within the respective gene. The difference in this distribution was highly significant ((EPS)Click here for additional data file.Figure S16Real prSNPs are more conserved and have lower derived allele frequency. We compared evolutionary constraint and derived allele frequency for prSNPs versus control SNPs and found that the prSNPs trended towards being slightly more conserved  and were more likely to have lower derived allele frequency (p = 3.437e-12 MW). This suggests that the prSNPs are more likely targeting functional sequence and are more likely to be slightly deleterious.(EPS)Click here for additional data file.Figure S17th of these variants are expected by chance. However, large differences can also be observed and are candidates for bonafide rare variants which have important effects. Of note, the CEU variants also appear enriched for expression differences relative to YRI.log2 difference from mean for Z>2 conserved singleton variants. The distribution of expression differences for conserved singleton Z>2 variants was plotted to observe the average effect size of these variants. Approximately 4/5(EPS)Click here for additional data file.Figure S18Observed coincident array expression outliers by expected number for singletons. For singletons, we plot the number of expected coincident outliers at a particular Z-score  versus the observed number (black line). Where the difference is greater than 5% of all permuted observations, a red dot is marked and the number of effects per individual is tabulated. Here, we observe, consistent with results summarized in (EPS)Click here for additional data file.Figure S19Observed coincident RNA-seq expression outliers by expected number for singletons. For CEU RNA-Seq data we observe a similar pattern of enrichment for conserved singletons being outliers as we observe in the array data . Here we(EPS)Click here for additional data file.Table S1Associated variant discovery from 1000 genomes (1KG) to HapMap 3 (HM3). EQTL variants discovered in the 1KG (best associated variant at 0.01 permutation threshold per gene) were compared to their equivalent discovery in HM3. Only 1/5 of these 1KG eQTLs were found in the HM3 and passed its equivalent significance threshold. Approximately 2% were in the HM3 but fell below this threshold indicating either genotype error in the HM3 or, less likely, stochastic improvement in association due to genotyping error in the 1KG. The remaining 4/5 of the association are for new markers that were not assayed in the HM3. This indicates that if these new variants are bonafide causal variants, whole genome sequencing is uncovering a large number that had previously not been identified. *Independent eQTLs defined by recombination interval and LD filtering as previously reported in (DOCX)Click here for additional data file.Table S2Associated variant discovery from HapMap 3 (HM3) to 1000 genomes (1KG). EQTL variants discovered in HM3 (best associated variant at 0.01 permutation threshold per gene) were compared to their equivalent discovery in 1KG. Approximately 2/5 of the associations genotyped in both passed the equivalent discovery thresholds. Approximately 3/5 of the associations did not pass the discovery threshold in 1KG indicating that the extra multiple testing correction of the many extra variants gained through whole genome sequencing is masking some eQTLs. Only a marginal fraction of the associated SNPs were not found in the 1KG. *Independent eQTLs defined by recombination interval and LD filtering as previously reported in Nica et al. (DOCX)Click here for additional data file.