P-values down to 4.4 × 10−58. Integrative analysis attributes as much as 56.3% of the observed variance to non-disease factors. We propose that information on the biomarker-specific profile of major genetic, clinical and lifestyle factors should be used to establish personalized clinical cutoffs, and that this would increase the sensitivity of using biomarkers for prediction of clinical end points.Ideal biomarkers used for disease diagnosis should display deviating levels in affected individuals only and be robust to factors unrelated to the disease. Here we show the impact of genetic, clinical and lifestyle factors on circulating levels of 92 protein biomarkers for cancer and inflammation, using a population-based cohort of 1,005 individuals. For 75% of the biomarkers, the levels are significantly heritable and genome-wide association studies identifies 16 novel loci and replicate 2 previously known loci with strong effects on one or several of the biomarkers with  Protein biomarkers could play an important role in the diagnosis and management of diseases. Here the authors investigate the impact of genetic, clinical and lifestyle factors on 92 protein biomarkers for cancer and inflammation and suggest that personalized biomarker thresholds should be used in cancer management. A number of protein biomarkers are used for diagnosis and management of cancers and other diseases. Examples include prostate-specific antigenet al.Here we aim to understand the factors that influence normal variation in plasma levels of established and potential biomarkers for cancer, autoimmune diseases and inflammation with the specific goal to facilitate the establishment of individualized clinical cutoffs. To this end, we use the highly sensitive and specific proximity extension assay (PEA)The abundance of 92 proteins , represeP-value<0.05, Bonferroni adjusted) on one or more of 52 of the 77 proteins  of ABO blood group on three proteins; E-selectin, PECAM-1  and TIE2 (angiopoietin-1 receptor). The connection between E-selectin and blood groups is known1416To study the effect of clinical and lifestyle factors, we selected 158 phenotypic covariates, including age, sex, blood pressure, BMI, tobacco use, medication, lifestyle (occupation) and sample collection round (2006 or 2009) from the comprehensive set of clinical data available for NSPHS. A multiple linear regression model showed a total of 18 phenotypic covariates to have a significant effect (proteins . Factorsproteins . NotablyR2 greater than 0.5 (R2=0.85). CASP-3 was also highly correlated with epidermal growth factor (R2=0.81), which in turn was highly correlated with CD69 (R2=0.78). The strong correlation between some of the biomarkers does not appear to be reflected at the transcription levels. For instance, the Illumina Body Map1920R2=0.13), suggesting that the high correlation observed at the protein level is either because of post transcriptional regulation, for example, epigenetic regulation, or owing to expression patterns in distinct cell types. Several of the 12 pairs that were highly correlated were proteins with similar functions, such as C-X-C motif chemokine (CXCL)-9, -10, -11, and TNF-R1 and TNF-R2, whereas in other cases apparently unrelated proteins were highly correlated. These correlations may reflect as yet unknown patterns of co-regulation, and bring into question their value as independent biomarkers.Inter-biomarker correlation was investigated using abundance levels adjusted for significant clinical and lifestyle covariates. These was then rank-transformed into normally distributed values and used to identify 12 pairs with a Spearman’s Rho than 0.5 . The higP-value <0.05), with heritability ranging from 0.19 to 0.78 and the highest heritability for CCL24  and CCL24 (one SNP) in liverABO-gene, determining the blood group  were used to estimate the heritability for the 77 proteins with measurable levels by evaluating the co-segregation of the protein levels with the relatedness among individuals using a polygenic model (see Methods for details). In 75% (58 out of 77) of the proteins, the levels were found to be heritable  for HGF and 4  remained significant after correction for multiple testing. These covariates accounted for 3.3%, 2.9%, 14.1% and 19.3%, respectively, of the percentage-of-variance-explained. In addition, the use of platelet aggregation inhibitors (ATC: B01AC) and loop-diuretics (ATC: C03CA) explained 5.7% and 7.1% of the variance observed in the unadjusted ddCq-values, respectively, whereas the top ranking SNP only accounted for 1.6%. In the middle part of the distribution in The relative importance of individual genetic, clinical and lifestyle factors on the abundance differed dramatically between the 77 biomarkers . Some biInformation on the set of important variables for each biomarker can be used to reduce the non-disease-related variation. For instance, soluble CXCL10, which shows elevated levels in patients with a number of autoimmune-related diseasesdoi:10.5879/BILS/g000001.Full summary statistics of the combined results from the 14 GWAS’s with genome-wide significant hits are available from R2=0.29, We have shown that for 72 of the 77 biomarkers studied, the circulating plasma levels are strongly associated with genetic, clinical or lifestyle factors. Most biomarkers are highly heritable, and for 14 biomarkers, we identified strong genetic associations, with the top SNP explaining as much as 36% of the variability in protein abundance between individuals. For these biomarkers, stratifying patients based on their genotype may dramatically enhance the ability to detect deviations from normal circulating levels. A number of non-genetic factors also show a strong effect on biomarker levels, with age, systolic blood pressure and weight affecting a large number of the biomarkers. As cancer incidences increase with age−1 in patients diagnosed with Graves’ Disease as compared with 72±32 pg ml−1 in controls35In a clinical context, circulating levels of CXCL10 have been estimated to 120±83 pg mlWe also find biomarkers that are not significantly affected by any of the variables examined, rendering them less susceptible to variability induced by non-disease-related factors. Although we have investigated a large number of genetic, clinical and lifestyle factors, they altogether explain at most 56% of the variation in biomarker levels between individuals. The remaining variance must reflect other factors, or non-additive interaction between some of the factors studied, and their identification could further increase the utility of biomarkers by reducing sources of variation unrelated to disease state. For example, CCL24 had a heritability of 0.78, indicating that additional genetic loci might affect protein levels. For 15 of the biomarkers, the vast majority of abundances were below the detection limits in our cohort. Several of these could represent ideal biomarkers without major presence in normal plasma and thus with no influencing genetic or lifestyle factors. Among these was for instance mucin-16 (or CA125) that is used clinically as a test for ovarian cancerN=1005) for genetic association studies. Despite this fact, we identify and replicate 12 novel associations of large effect on the disease biomarkers. Although large GWAS consortia have identified hundreds of genetic variants associated with variation in disease-related phenotypes40This study identifies several previously unknown genetic and lifestyle factors influencing the circulating plasma levels of disease biomarkers in a population-based cohort, but has its limitations. First, we have a relatively small sample size , of which 719 individuals participated in the study (KA06 cohort). As a second phase of the NSPHS, another 350 individuals from a neighbouring village (Soppero) were recruited in 2009 (KA09 cohort). For each participant in the NSPHS, blood samples were taken (serum and plasma) and stored at −70 °C on site. Both the 2006 and 2009 samples used in this study have undergone two freeze–thaw cycles before the measurements carried out here. DNA has been extracted for genetic analyses and detailed descriptions of this study have been published elsewhere4445The NSPHS study was approved by the local ethics committee at the University of Uppsala  in compliance with the Declaration of Helsinki96 × 96 kit and quantified by real-time PCR using the Fluidigm BioMark HD real-time PCR platform as described earlierProtein levels in plasma were analysed using the Olink Proseek Multiplex Oncology I wheren=35) or where too many (>75%) measurements were below detection limits in any PEA (n=1) were also excluded from further analyses . We wanted at least 200 observations per protein above detection limit in order to conduct the downstream statistical analyses and therefore proteins with fewer observations were excluded from further analyses. After individual and protein quality control, 77 proteins measured in 970 individuals remained. Out of the removed proteins, seven proteins  had 100% of measurements below detection limit. Uniprot recommended short names have been used throughout when these are available otherwise; the assay manufacturers’ abbreviations have been used. All assay characteristics including detection limits and measurements of assay performance and validations are available from the manufacturer’s webpage (http://www.olink.com/products/proseek-multiplex/downloads/data-packages).and n=1059) on the Illumina Human Exome Beadchip containing 247,901 SNPs, insertions and deletions primarily selected to have coding changes. The genotype calling was done with the software GenomeStudio 2011.1 (Illumina Inc.) using a Project Sample generated Cluster File as recommended by the manufacturer. The Exomechip data were quality controlled requiring 95% and 98% genotyping rate on marker and individual levels, respectively, and a Bonferroni-corrected Hardy–Weinberg cutoff of 0.05 leaving 242,519 markers at a total genotyping rate of 99.94% in the 1,033 unique individuals previously genotyped. This analysis was carried out using custom R-scripts and PLINK (v1.07)The KA06 and KA09 cohorts have previously been genotyped on the Illumina Infinium HapMap300v2 BeadChip  and Illumina Human OmniExpress BeadChip  arrays, respectively, as described earliern>1.5 M) where any individual so far sequenced at the Uppsala Genome Centre had called SNP or INDEL, we then checked our 100 individuals for coverage in order to differentiate between missing and reference calls. These positions were included to maximize the overlap with the 1,000 genomes reference panels to ensure proper imputation using two reference panels. Reference calls for SNPs were made if there were at least three reference sequence reads with unique start points and a maximum of 5% reads with non-reference at that position. Reference calls for INDELs were made if there was no reads at all without the reference call. All other calls were set to missing. We then required at most 5% missing call rate per SNP or INDEL. This resulted in 83,568 SNPs with non-zero MAF at 98.74% total genotyping rate and 38,290 INDELs with a total genotyping rate at 99.45% and an additional 350k positions with reference calls only. We then required a genotyping rate of 95% in both individual and marker level and a Bonferroni-corrected Hardy–Weinberg cutoff at 0.05, which resulted in 468,630 markers at total genotyping rate of 98.79%.We selected 100 individuals, 68 from KA06 and 32 from KA09, for Whole-Exome Sequencing using Agilent’s SureSelect system (Agilent) for exome capture and the SOLiD 5500xl instrumentation for sequencing. Each sample was sequenced to at least 30X coverage. The individuals were selected to represent as much genetic variation of the cohort as possiblen=182,916, and all the markers from the Illumina Human Exome chip. In this step, there was no additional filtering done on minor allele frequency in order to maximize the overlap with the SNPs in the 1,000 genome panel. The total number of markers in the in-house reference panel was 847,855. The reference haplotypes were created using in-house R-scripts, PLINK (v1.07) and phased using SHAPEIT (v2.r)We created an in-house reference panel to be used simultaneously with the 1,000 genomesABO gene. These four SNPs allow for accurate assignment of both the A/B/O groups and subtyping of A into A1 and A2 and subtyping of O into O01 and O02  in the region of the O02 ref. . Using tP-values were below 0.05 . Each PEA measurement was individually adjusted for significant covariates  and performing genetic association analysesn=182,916) between the two types of genotyping arrays used in the KA06 and KA09 cohorts. Contribution of single SNP’s to phenotypic variation on the unadjusted ddCq values was calculated in R by fitting a linear model (using ‘lm’) with ddCq values as response and the posterior genotypic probabilities as terms and fraction of variance explained was determined from the resulting model using ‘summary.lm’. Fraction of variance explained by a single SNP in the adjusted phenotypes including effects of relatedness was estimated by dividing the resulting chi-square test score (from ProbABEL) with the number of samples used.The NSPHS is a population-based study and includes many relatives and special care has to be attributed to avoid relational biases. Therefore, all genetic association calculations were carried out using the GenABEL or ProbABELP-values  were used to report significance in the discovery cohort and the replication cohort . We also ran a combined analysis with the same cutoff used as in the discovery phase. For all proteins with replicated hits, a conditional analysis was carried out in which the genetic associations were re-calculated using the dosage values of the top-ranking SNP as covariate. This analysis was only run in the combined material and on chromosomes that had hits in that replicated in the discovery-replication phase and P-value <5 × 10−8 was used as cutoff.The KA06 cohort was used as discovery cohort in the GWAS and KA09 as replication cohort. As we cannot rule out protein degradation effects due to differences in storage time between the two cohorts, this split is favourable to a random split where degradation effects could affect the association analysis. Strict Bonferroni-adjusted S.E. designed experiments, developed analysis tools, analysed data and wrote the paper. Å.J. contributed to the design of the experiments. S.B.E. interpreted results. U.G. conceived of the study, designed the experiments and wrote the paper.How to cite this article: Enroth, S. et al. Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat. Commun. 5:4684 doi: 10.1038/ncomms5684 (2014).Supplementary Figures 1-2Information on the biomarkers.Information on which covariates that significantly influence the individual proteins.Detailed information on the hits from the GWAS.


Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects . Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve. Understanding the genetic basis of cognitive traits could aid the development of numeracy and literacy skills in children. Here the authors show that reading and mathematics have a large overlapping genetic component and suggest that a child's learning environment has a key role in creating differences between them. Understanding the aetiology of complex cognitive traits such as reading and mathematics ability is essential for helping children achieve their potential24It has been shown61112Dyslexia was one of the first traits studied using QTL sib-pair linkage analysis1416118Here we conduct a GWAS of Reading and Mathematics abilities in a sample of ~3,000 twin pairs. We find no replicable loci with convincing levels of evidence for association, consistent with a substantially polygenic contribution of genetics to these traits. Using bivariate twin- and population-level models, we estimate the heritability and genetic correlation between the two traits. We find a high genetic correlation (around 70%), indicating substantial pleiotropy, and accounting for a large proportion of phenotypic correlation.PGWAS<5 × 10−5; reading, N=2,243; mathematics, N=2,772) in a further 2,153 individuals, some of whom were co-twins of individuals in the discovery data. One region on chromosome 19 (rs349045) achieved a P-value of 9.63 × 10−9  for reading ability in the joint analysis of discovery and replication data. However, this association failed to replicate using a related phenotype (the Test of Word-Reading Efficiency (TOWRE)—one of four reading tests from the TEDS analysis) in the Avon Longitudinal Study of Parents and Children . The results for the GWAS are shown in As part of the Wellcome Trust Case-Control Consortium 2 (WTCCC2), in collaboration with the Twins Early Development Study (TEDS), we performed a GWAS using 2,794 unrelated members of monozygotic (MZ) and dizygotic (DZ) twin pairs, measured for their reading and mathematics ability using a combination of web- and phone-based tests at age twelve. The scores were combined across tests and adjusted for age, while gender was used as a covariate in the analyses. Using genotype imputation we performed association analysis for 1,588,650 autosomal markers with reading and mathematics scores separately (see Methods). We followed up the strongest signals of association  will impact on both reading and mathematics ability. Twin studies have also identified a genetic contribution to the correlationOne explanation for the lack of compelling evidence for association at individual single-nucleotide polymorphisms (SNPs), despite large sample sizes and high heritability estimates, is that the traits studied here are substantially polygenic, with each variant having a small effect. Recent studies have demonstrated that the genetic variants that determine measures of intelligence early and late in life overlapρA) between reading and mathematics is 0.64 (0.56–0.72), with shared and non-shared environmental correlations of 0.90 (0.67–1.00) and 0.30 (0.24–0.37), respectively.To investigate the genetic contribution to the correlation, we first fit a bivariate version of the classical twin model using both MZ and DZ twin pairs for whom the reading and mathematics scores were available (Methods and ρG) of 0.74 (0.32–1.00)  that bases inference on small differences in allele sharing between individuals who are not closely related32–1.00) . By bothAs discussed elsewhere23This observation can be interpreted in at least two ways. First, as a decomposition of the correlation in reading and mathematics ability see , where tPGWAS=0.0084, N=2,243) in the DCDC2 gene, which has been implicated in neuronal development2627Our results support previous evidence that common learning abilities and their associated disabilities are unlikely to be affected by common genetic variants of large effect; even with a sample of thousands of individuals and 1,588,650 genetic variants, our most convincing signal of association failed to replicate in an independent sample . However, we do find suggestive evidence in favour of some previously reported associations see  for readImportantly, our analyses show that a substantial proportion of the observed correlation in reading and mathematics abilities is due to genetics. If a large proportion of the genetic factors that affect these traits are pleiotropic, then the factors that lead to differences in an individual’s abilities (or disabilities) are relatively more likely to be environmental. Understanding the aetiology of these patterns increases our chances of developing effective learning environments that will help individuals attain the highest level of literacy and numeracy, increasingly important skills in the modern world.TEDS recruited over 15,000 families of twins born in England and WalesAt age 12, the TEDS twins participated in web- and telephone-based testing, as described previouslyN=2,432) included N=2,153 individuals with at least one phenotypic measurement, of which N=1,388 were DZ co-twins of individuals in the discovery sample. For analyses, taking into account family structure, we additionally included untyped MZ and DZ co-twins of individuals typed in the discovery or Immunochip phases  for a combined sample of N=7,323. For twin analyses, to parallel the population-based variance estimates as closely as possible, we used a sub-sample of the full TEDS cohort: the 2,794 informative samples with genome-wide data plus their co-twins, for a sample size of N=2,794 twin pairs.Phenotypic measurements were available for 2,243 (reading) and 2,772 (maths) of these, with 2,794 samples having at least one measurement and 2,221 samples having both. The sample genotyped on the Immunochip . The sample used for replication here is a population-representative group of participants who were tested for word-reading efficiency (TOWRE) at the age of 12.5 years. After combining TOWRE scores across two subtests , with estimated dates of delivery between April 1991 and December 1992 ref. . Ethicalests see  a total http://sourceforge.net/projects/outmodedbonsai/files/CelQuantileNorm/). These normalized intensities were used to call genotypes with an updated version of the Chiamo softwareSamples were genotyped at the Affymetrix’s service laboratory on the Genome-Wide Human SNP Array 6.0. For all samples passing Affymetrix’s laboratory quality control, raw intensities (from the.CEL files) were renormalized within collections using CelQuantileNorm ( https://www.ebi.ac.uk/ega/).As is the standard practice for GWAS studies, we excluded sets of individuals whose genome-wide patterns of diversity are outliers compared with the majority of those in the study34To assess relatedness among study individuals, we compared each individual with the 100 individuals they were most closely related to  and used a hidden Markov model (HMM) to decide, at each position in their genome, whether the two individuals shared 0, 1 or 2 chromosomes identical by descent. We obtained a set of ‘unrelated’ individuals with identity by descent <5% by iteratively removing the member of each pair of putatively related individuals with more missing genotypes. A total of 3,154 individuals were included in subsequent analyses.In addition to standard SNP filters , we consWe imputed additional genotypes from a combined reference panel of the 120 CEU trios in HapMap2 and HapMap3 and the common control group of WTCCC2. As an additional quality control step, prior to imputation we re-imputed each typed SNP using IMPUTE version 1 and removed any SNP where the concordance between typed and imputed genotypes was <0.965. We used this high-confidence subset of 736,939 SNPs from the array to impute additional genotypes using IMPUTE2 refs .IMPUTE2 adopts a two-stage approach using both a haploid reference panel and a diploid reference panel. For the haploid reference panel, we used HapMap2 and HapMap3 SNP data on the 120 unrelated CEU trios; and for the diploid reference, we used a merged set of genotype calls from Affymetrix 6.0 and Illumina 1.2 M genotyping chip typed on 5000 1958 Birth Cohort (58C) and National Blood Service (NBS) individuals forming the common control group of WTCCC2. For association testing we included SNPs with info measure of at least 0.98 (if imputed from HapMap) or 0.9 (if imputed from the WTCCC2 controls) and having an estimated minor allele frequency of at least 1%.Replication samples were typed on the Illumina ‘Immunochip’, a custom chip designed by the Immunochip Consortium and WTCCC2, at the Wellcome Trust Sanger Institute. Bead intensity data were processed and normalized for each sample in BeadStudio. Data for successfully genotyped samples were extracted and genotypes were called using the Illuminus algorithmN=63 individuals who were recorded as having scored zero on either part of the test were removed, leaving a total of N=2,077 individuals for analysis.We obtained data for 194 SNPs from the region 48891732-49091732 (NCBI build 36) on chromosome 19 for ALSPAC participants by application to the ALSPAC executive. Details on ALSPAC genotyping and imputation are described elsewherehttps://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html), fitting an additive linear model to the data, with sex as a covariate. We used the missing data likelihood score test implemented in SNPTEST to compute P-values, and refer to these as PGWAS above. SNPs with a P-value<5 × 10−5 were further analyzed in a sample combining the GWAS and Immunochip participants, along with informative co-twins with phenotype data but no genotypes available . To take account of the relatedness in the combined sample, we fitted the association model using Merlin softwareAfter quality control, 1,588,650 SNPs were analyzed in the GWAS using SNPTEST ( Y (either reading or mathematics ability in our study) into five components Y=A+D+I+C+E, where A, D and I correspond to additive, dominance and interaction genetic effects over the whole genome, respectively, and C and E are within-family and individual environmental effects, respectively. We assume that these components are defined to be uncorrelated with each other and thus the phenotypic variance is also partitioned into five components VY=VA+VD+VI+VC+VE.For twin and population-level analyses, we consider a general partitioning of a quantitative phenotype D=I=0). To extend the model to bivariate phenotype, we introduce three parameters ρA, ρC and ρE to describe the correlation between additive genetic, shared environmental and individual environmental effects, respectively, between reading and mathematics abilities  that is due to the available panel of SNPs, and the residual component which in principle includes D, I, C and E as defined previously, together with the part of the additive component A that is not captured by G. Thus it can be used for estimating a lower bound for the variance and covariance (or correlation) between the additive genetic components of the two phenotypes  before the variance component analysis. We checked that the programme GCTAρG=0, and used both permutations and simulations to obtain a P-value for this model comparison . If S is correlated with genetics—for example, through parental genotypes—its effect in the twin model, where S is unmodelled, will be to increase the estimated proportion of CS potentially contributes to the estimated proportion of G. In principle, including PCs in the model may help control for this effect. We find little difference in the estimates between the model including and not including PCs  environmental effects that are more similar between MZ twins than DZ twins444344The population-level model assumes a particular dependency between the minor allele frequency at a SNP and the size of the effect of that SNP on the phenotype .Supplementary Figures 1-5, Supplementary Tables 1-6, Supplementary Notes 1-2, Supplementary Methods and Supplementary References


Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice. In genetic association studies, phenotypes are often measured in counts. For such count data, the standard methods for modeling the genotype–phenotype relationship include the Poisson and Negative Binomial regression models. The standard Poisson model assumes that the conditional variance of the phenotype is equal to the conditional mean. The Negative Binomial model generalizes the Poisson model by providing greater flexibility in explaining the relationship between the conditional variance and the conditional mean as compared to the Poisson model. However, in practice, it is not uncommon to observe excess zero counts as compared to what is expected based on either the Poisson or Negative Binomial model. As an example, in arthritis research, data are often collected in multiple joints within a person  or multiple sub-regions of a joint  model Lambert,  and the In addition to the above, there is also a relative paucity of literature on their implications in genetic association studies underlying zero-inflated count traits. In recent years, many Genome-wide association (GWA) studies have identified multiple genetic loci as being associated with rheumatoid arthritis ′. The GLM  can take various forms, including Normal, Gamma, Binomial, Negative Binomial (for a known dispersion parameter), and Poisson distributions and ϕ is a dispersion parameter. The standard algorithm for fitting GLMs is the iterative weighted least squares (IWLS). Given the current estimates of the parameters zi and the pseudo-weight wi for each data point yi aswhere the distribution p by the weighted normal likelihood asand approximates the likelihood where The GLM includes the Poisson regression (PR), for which the corresponding probability mass function is given byE(y) = Var(y) = μ and g(μ) = log(μ). An alternative to the PR is the NB regression, which is a flexible generalization of the PR, and is known to better handle the overdispersion present in a count data. The probability mass function for the two-parameter NB distribution is given bywhere g(μ) = log(μ). Note that, we do not have a dispersion parameter for the PR; that is, ϕ is fixed at 1. On the other hand, the dispersion parameter for the NB regression is given by ϕ = θ. The parameter θ can be estimated from the data; however, the EM algorithm can be slow to converge. For simplicity, we assume θ = 1 in this article.Where Following Lambert , a ZIP myi can be written as followsThe probability mass function of i = 0. Also, when πi > 0, It can be observed that Equation (8) reduces to the general PR when πSimilarly, following Greene , a ZINB yi can be written as followsThe probability mass function of i = 0. Also, when πi > 0, i and μi can be modeled  reduces to the general NB regression when πLambert,  by a logXi contains all variables corresponding to the ith observation, i = 1, …, n and β and γ are the vectors of all genetic and non-genetic coefficients and the intercept corresponding to the count and zero components respectively. In practice, we may have a different set of predictors corresponding to each of these two components, for which Equation (11) can be adapted accordingly. The ZINB model is more appropriate to incorporate extra over dispersion not accounted for through zero-inflation by the ZIP model  estimator by modifying the EM algorithm developed by Wang et al.  for theith Wang, . With thi = exp(Xiβ), and where μFor variable selection, we consider a penalized ZINB model with the adaptive LASSO penalty, which results from the following regularization problemwj1 = 1, wj2 = 1, j = 1, …, p, it reduces to the LASSO estimator  state, i = 1, …, n. Since, zi's are not observable, we treat them as the “missing data”. With these missing variables, the complete-data log-likelihood function is given byWe formulate Equation (13) as a missing data problem and solve the problem by EM algorithm. To this end, we assume that there are two distinct processes driving the zeros in the count phenotype of interest: one is the result of a zero process, and the other one is a part of a counting process (NB). We consider the following latent variables: The penalized ZINB model with the complete data log-likelihood is given byzi's by their conditional expectation given the observed data and current estimates, and in the M step, we estimate the coefficients by minimizing the expected penalized complete-data log-likelihood Equation (15). Starting with an initial guess of the coefficients, the algorithm proceeds as follows:where at the E step of the algorithm, we compute the expectation of the complete-data log-likelihood by replacing 1) E Step2) M Step: Minimize the following objective functiont = 1, 2….3) Repeat 1 and 2 until convergence, Note that, Equation (16) can be written as a sum of two components as followswhere R package mpath. Here we further improve the algorithm by allowing different penalties for each of the variables. The variable-specific parameters provide a way to pool the information among the variables. Naturally, for the unimportant variables, we should put larger penalty parameters on their corresponding coefficients. Although the general LASSO estimator may not be consistent  to the zipath function in the R package mpath through the argument penalty.factor. In situations when the maximum likelihood estimates are not available, we make use of the reciprocal of the ridge-penalized estimates as weights, available from the R package mpath.To implement our algorithm, we make use of the In this section, we conduct extensive simulation experiments to evaluate the finite sample performance of the proposed method, and compare the results with several existing methods with respect to model misspecification and variable selection.k SNPs) is generated as follows:We generate the simulated data as in Wang and Elston . The gen1) First, we generate a latent vector Z into a haplotype, say hj1 = I(Zj < MAF), where the MAF is fixed at 5%.2) Second, we dichotomize H2. Combining the two haplotypes (H1 + H2) we obtain an individual's genotype.3) Similarly, we generate another independent haplotype X assuming an additive model and generate the phenotype of interest according to the model in Equation (11), where the phenotype follows a ZINB distribution. For the null case, we assign β = 0; for the non-null case with varying association strengths, the effect sizes (for the count model) are determined so that all causal SNPs contribute the same marginal variance hj2 to the linear predictor, where hj2 is defined as hj2 = 2 *MAF* (1–MAF) * βj2. We assume that there are k = 50 SNPs among which 5 are assumed to be causal . To ensure that the overall effect of all variants is reasonably low, we fix the sum of their marginal variances h = ∑ hj2 at 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30 under the alternative hypothesis, assuming equal marginal variance for each causal SNP. The simulation scenarios are also presented in Table n = 200), moderate (n = 500), and large (n = 1000).Based on the genotype data, we construct our design matrix Equation 1, where Xi's) from a real data to generate a zero-inflated NB outcome, and performed additional simulation studies by taking advantage of the real genotypes of variants in a large real dataset.As one anonymous reviewer suggested, to mimic real data and to be as practical as possible, the best way to validate and study the properties of the proposed method will be to take real sequence data obtained from many individuals and simulate phenotypes based on variants in those sequences, making assumptions only about genetic effects of variants. To this end, we used the genotypes  is fixed at 5, 10, 15, 20, 25, and 30% under the alternative hypothesis, assuming equal marginal variance for each causal SNP. For each situation, 1000 replicated datasets are simulated.Kaklamani et al.  investigEach generated data is analyzed using six methods, viz. the Poisson regression (PR), ZIP regression, Negative Binomial (NB) regression, ZINB regression, the LASSO method of Wang et al. , and then = 200, the type I error rate is out of control for the ZINB for all values of ρ, although not necessarily so for other scenarios. For moderate to large sample sizes, the ZINB performs better than the NB regression, as expected. Third, the proposed method is the most conservative among the methods considered, as it consistently maintains better control of Type I error rate as compared to other methods across all scenarios. Also, among the methods having well controlled type I error rates, the proposed AL procedure generally has higher power as compared to their non-penalized counterparts. Fourth, for high LD scenarios (ρ = 0.9), there is a systematic trend in the empirical powers for the methods considered. In particular, it follows the order: AL > ZINB > NB. Overall, the zero-inflated count models perform better than their regular counterparts in most of the scenarios. Fifth, we also evaluated sensitivity and specificity of the penalized regression methods for all the simulation scenarios described above  of its parameter estimates in the linear predictor scale. That is, for a method, suppose βAs shown in Tables In this paper, we have proposed a novel variable selection method for detecting SNPs associated with zero-inflated count phenotypes in a negative binomial regression framework. We have considered a computationally efficient EM algorithm, which can simultaneously include a large number of genetic and environmental variables in the model. We have shown the superior performance of the proposed method through extensive simulation studies. Despite their wide applications in other scientific disciplines , the estimation process for θ becomes more computationally intensive for the proposed method as compared to others. However, since in practice, we anticipate to estimate θ for better prediction accuracy, following the suggestion of one anonymous reviewer, we replicated our simulation studies to learn the impact of estimating the dispersion parameter on the overall conclusion. The detailed results are presented in the Supplementary File (Supplementary Figures With the recent technological advances, and the upcoming sequencing experiments, we have the potential to identify additional causal variants across the entire genome, which will give us a better understanding of the genetic basis of complex diseases, such as rheumatoid arthritis (RA; Viatte et al., HM conceived the idea, performed the analysis, and wrote the manuscript; HT conceived the idea, designed the model, and wrote the manuscript. All authors read and approved the final manuscript.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.


Major advances in genotyping technology have generated high-density maps of single nucleotide polymorphism (SNP) markers that provide an unprecedented opportunity to identify genes underlying complex traits. Several family-based statistical methods showing robust population stratification have been developed to test the association between multiple markers and disease-susceptibility genes. Only a few methods focus on testing for maternally mediated genetic effects, which is a critical risk for birth defects. The present study focuses on testing for association and maternally mediated genetic effects with family-based methods.PC integrating principal component analysis, to test association or maternally mediated genetic effects with case-parent data. The proposed method only uses the genotypes of case-parents triads and accommodates missing SNP data. Our results demonstrated that this method is powerful to test association or maternally mediated genetic effects and attractive because it provides a tool for testing the null hypothesis of no association and no maternally mediated genetic effects. Simulations with the permutation procedure as well as an application in the Crohn’s disease study showed that the type I error rates of the proposed statistic were nominal with slightly higher power as compared to those of the max_Z2 test.In the present study, we proposed a new method, max_PC is a good approach to test association or maternally mediated genetic effects.We conclude that the max_ The rapid advancement of genotyping technologies and the availability of enormous quantities of genotype or haplotype data provide an unprecedented opportunity for identifying genes involved in susceptibility to complex diseases. The utilization of information from individual markers as well as linkage disequilibrium (LD) structure between the markers has resulted in the use of haplotype-based association studies to determine the relationship between complex traits and a series of possibly linked markers. However, haplotype-based methods are challenging as the large number of distinct haplotypes will in turn produce a large number of degrees of freedom. In addition, some haplotype-based methods need the estimation of haplotype phases when haplotypes are not directly observed. A potential strategy to avoid the estimation of haplotype phases and frequencies is to develop genotype-based statistical approaches that directly use genotype data.PCTL2. Based on the same difference vector, Fan et al. [2 test, while Shi et al. [2 test. The max_Z2 test is superior to other methods as it is able to study the maternally mediated genetic effects while only using the genotypes of affected individuals and their parents. It could also accommodate missing SNP data. Maternally mediated genetic effects arise when the phenotype of an offspring is influenced by maternal alleles via the intrauterine environment [Population-based case–control studies and family-based studies are two common strategies for association analysis. Compared to case–control studies, family-based studies are more attractive due to their robustness to population stratification. Several genotype-based methods with case-parent data have been proposed for association test. McIntyre et al.  proposedn et al.  proposedi et al.  developeironment . This efironment . The matironment , 6. So iPC, to test association and maternally mediated genetic effects with case-parent data. Here, the null hypothesis is that neither association nor maternally mediated genetic effects exist. The max_PC test has three characteristics: (1) only using the genotypes of case-parent triads while accommodating missing SNP data, (2) utilizing the principal component analysis, and (3) evaluating the association and the maternally mediated genetic effects at the same time. Simulations analysis using a permutation procedure and an application in Crohn’s disease study indicated that this max_PC statistic has good performance.In this report, we proposed a new statistic, max_PC, we performed a simulation study using a wide range of parameters. The simulations were implemented as described by Lee WC [D and d was uniformly located within the candidate region. The frequency of disease allele D was set to be 0.1. Let R1 denote the relative risk with one copy of the disease allele D and R2 denote the relative risk with two copies compared with no copies. We considered a simple mutational process for the disease allele D where the allele D appears in an individual with the “ancestral haplotype” and it broke up because of meiotic recombination. We used the first-order Markov model [To assess the performance of the statistic, max_y Lee WC . A candiy Lee WC , 7. Assuov model  to gener1 = R2 = 1. For the alternative hypothesis that there exists association or maternally mediated genetic effects, we considered the following three scenarios. First, there exists association where the state of disease phenotype of the offspring was determined by the offspring genotype; the second, there exists maternally mediated genetic effects where the state of disease phenotype of the offspring was determined by the mother genotype; and third, there exist both association and maternally mediated genetic effects where the state of disease phenotype of the offspring was determined by the genotypes of both mother and offspring. For the third scenario, we considered a simple model in which the relative risk was the product of the relative risks of the offspring and the mother. For the above three scenarios, we let the baseline disease risk (the penetrance of genotype dd) be 0.01, and R1 = R2 = 2 and R1 = 1, R2 = 2, which represented a dominant and a recessive model, respectively. The number of case-parent triads, N, was chosen as 100, 200, or 400. We assumed that each individual had an available marker that could be utilized as a genotype in the analysis. Upon simulation of the familial data, we first calculated the value of max_PC, and then recalculated it by using the permutation procedure 1,000 times. The α, the power/type I error rate is then estimated as the proportion of rejecting the null hypothesis when For the null hypothesis that neither association nor maternally mediated genetic effects exist, we let RPC in the presence of population admixture. We assumed that there were two subpopulations that differed in disease allele frequency and baseline disease risk. Disease allele D was introduced 1,000 and 500 generations ago through two different ancestral haplotypes in the two subpopulations, respectively. The frequencies of the D allele were set as 0.1 and 0.2, and the baseline disease risks were set as 0.01 and 0.05 in the first and second subpopulation, respectively. We used the previously described approach to generate the markers and haplotypes in each subpopulation and let the two subpopulations admix at the last generation, with 30 percent for the first subpopulation and 70 percent for the second subpopulation.We also investigated the performance of max_2 was also investigated so that we could compare the performance of max_PC with that of max_Z2. It should be noted that max_Z2 uses the difference vector 2C − F − M and M − F under the first scenario and the second scenario, respectively. However, we perform two tests under the third scenario: one uses the difference vector 2C − F − M to calculate max_Z2 for testing association and the other uses the difference vector M − F to calculate max_Z2 for testing maternally genetic effects. It is observed from Table PC for testing association and maternally mediated genetic effects is highest among three scenarios. The powers for testing association and maternally mediated genetic effects reach 80 % and 90 %, respectively, when the sample size is 200 or 400. The power of max_PC under the recessive model is slightly lower than those under the dominant mode. The second to the fifth columns of Table PC are similar to those of max_Z2. Under the third scenario of testing association and maternally mediated genetic effects at the same time, the powers of max_PC are slightly higher than those of max_Z2 with the difference vector 2C − F − M and those of max_Z2 with M − F. Table As shown in Table PC on more realistic scenarios, we applied our method to a real data set of Crohn’s disease. Crohn’s disease is one of the two major forms of inflammatory bowel diseases [PC and the max_Z2 to the publically available subset of 129 trios genotyped at 103 common SNPs. We assessed P value using the permutation procedure 10,000 times. The P value using the max_PC is 3.62 × 10−4, indicating that this region is associated with Crohn’s disease or there is a possible maternal effects. When the max_Z2 with 2C − F − M and M − F was used, the P values are 3.78 × 10−4 and 4.05 × 10−3, respectively, indicating that there may exist association and maternal effects. In order to further compare the two methods, we also considered risk-haplotype-tagging alleles which are highly predictive of disease susceptibility as descripted by Shi et al. [P value being smaller than a preset threshold to be related to risk and designate the corresponding overtransmitted allele as a risk-haplotype-tagging allele. It can be seen from Fig. PC  the max_PC test uses the principal component analysis, and (2) the max_PC can be used to test association and maternally mediated genetic effects at the same time, whereas two tests with max_Z2 should be performed for this case. The performance of max_PC was assessed by simulations analysis as well as the application in Crohn’s disease study.Based on the principal component analysis, we proposed a statistic max_PC value for each locus, indicating that it is capable of handling missing SNP data. Moreover, our approach is not biased toward admixture population and Hardy-Weinberg equilibrium was not essential when using our method. The null hypothesis for the max_PC test is that there is no association and no maternally mediated genetic effects, whereas the alternative hypothesis is that there is an association or maternally mediated genetic effects. One attractive feature of max_PC is that it is able to test association and maternally mediated genetic effects exist at the same time. Also, it provides a method for testing the null hypothesis of no association and no maternally mediated genetic effects. When the null hypothesis is rejected we will use max_Z2 with the difference vector, 2C − F − M, for testing association, and use max_Z2 with the difference vector, M − F, for testing maternally mediated genetic effects. In this way, we will be able to determine whether only maternally mediated genetic effects or only association exists. It is worth noting that testing for maternally mediated genetic effects rely on the assumption of mating symmetry when max_PC was used according to M − F. However, when an offspring effect is present, parent-of-origin effects can also cause the effect of symmetry [In practice, when multiple markers are studied, individuals may have incomplete information of individual marker data. Our method calculates the symmetry . In thisWe have proposed a statistical approach for detecting association or maternally mediated genetic effects with case-parent data. The approach is powerful to test association and the maternally mediated genetic effects at the same time. It also provides a valid tool for testing no association and no maternally mediated genetic effects. The proposed method for association analysis incorporating maternally mediated genetic effects is particularly useful in studying diseases that happened during fetal life.In this study, all datasets were publically available and no research requiring ethics approval was conducted.M, F, and C be the number of copies of allele “A” carried by the mother, father, and affected offspring, respectively. 2C − F − M is the paired differences in genotypes between the affected offspring and the complement, which carries the non-transmitted genotypes in the case-parent data. M − F is the SNP-count difference between the mother and the father, which can be used to measure the maternally mediated genetic effects with the assumption of mating symmetry. Let X = 2C − F − M and Y = M − F. X = 0 when there is no association, and Y = 0 when there is no maternally mediated genetic effects. Define a two-dimensional random variable Z = T. Under the null hypothesis of no association and no maternally mediated genetic effects, Z = 0. We assume that there are n case-parent triads. Let S be the sample covariance matrix of Z for the observed case-parent trios data, Zi = T . Denote the two sample eigenvalue-eigenvector pairs for S by kth  sample principal component is ζk = êkTZ, with variance of ζ1, ζ2 are uncorrelated, i.e., Cov = 0. The kth sample principal component for the ith case-parent trio is ζik = êkTZi . Let k = 1, 2). Under the null hypothesis of no association and no maternally mediated genetic effects, k = 1, 2. Define a statistic, denoted by PC, as follow:We consider a biallelic marker with alleles “A” and “a”. Assume that case-parent triads are sampled with the genotypes known for each member of the triads. Let PC is asymptotically a central χ(1)2 distribution under the null hypothesis of no association and no maternally mediated genetic effects.The statistic q biallelic markers each with alleles “A” and “a”. The markers are indexed by l . Let PCl)( across all q marker loci, i.e., PC approach is able to accommodate missing individual marker genotypes and does not require any haplotype information. Here, the statistical significance is assessed by a permutation procedure. We first calculate the data-based statistic. Then we permute the “case” and “complement” labels with equal probability and recalculate the statistic. The estimated P value (denoted by Now, we consider http://www.broadinstitute.org/archive/humgen/IBD5/raw_data.txt and http://www.broadinstitute.org/archive/humgen/IBD5/haplodata.html.The data set used in this article is shown in Tables


Previously identified single nucleotide polymorphisms (SNPs) in genome wide association studies (GWAS) of cardiovascular disease (CVD) in participants of mostly European descent were tested for association with subclinical cardiovascular disease (sCVD), coronary artery calcium score (CAC) and carotid intima media thickness (CIMT) in the Multi-Ethnic Study of Atherosclerosis (MESA). The data in this data in brief article correspond to the article Common Genetic Variants and Subclinical Atherosclerosis: The Multi-Ethnic Study of Atherosclerosis  Specifications TableValue of the data•Genetic variations play an important role in the atherosclerotic process.•The data shows novel associations between genetic variations and atherosclerosis.•The data also shows that previously described genetic associations with atherosclerosis vary considerably depending on ethnicity.•More research is needed to further elucidate the effect of ethnic-specific genetic variation in cardiovascular disease.1Previously identified single nucleotide polymorphisms (SNPs) in genome wide association studies (GWAS) of cardiovascular disease (CVD) in participants of mostly European descent were tested for association with subclinical cardiovascular disease (sCVD), coronary artery calcium score (CAC) and carotid intima media thickness (CIMT) in the Multi-Ethnic Study of Atherosclerosis (MESA).22.1The MESA study has been previously described and it was designed to investigate the impact of sCVD and CVD risk factors on the development of clinically overt CVD 2.2P-value≥10−5.The 66 single nucleotide polymorphisms (SNPs) included in this study  were obt2.3The imaging outcomes in the present study are coronary artery calcium  and carotid artery intima-media thickness .CAC was measured by either electron-beam tomography or multi-detector computed tomography, as described previously CIMT measurements were performed by B-mode ultrasonography of the right and left, near and far walls, and images were recorded using a Logiq 700 ultrasound device . Maximal CIMT-i and CIMT-c was measured as the mean of the maximum values of the near and far wall of the right and left sides at a central ultrasound reading center  as described previously 2.4Given skewed distributions, the common (CIMT-c) and internal (CIMT-i) IMT values were log normalized. CAC was analyzed as a continuous variable by obtaining the log of the raw CAC score plus one (CAC-c) or as a dichotomous variable (CAC-d) with CAC>0. Analyses were first performed stratified within each racial/ethnic group. For analysis involving EUA and CHN, an unrelated subset of individuals was constructed by selecting at most one individual from each pedigree. For analysis of phenotypes with a substantial familial component, among AFA and HIS, the analysis was performed using a linear mixed-effects model (continuous variables) and by generalized estimating equations (dichotomous variables). Associations between each SNP and each individual phenotype was determined using separate multiple linear regressions (continuous variables) or logistic regressions (dichotomous variables) assuming an additive model. Two models were used to analyze the data. Model 1 accounted for age, sex, site of ascertainment, and principal components. Model 2 included Model 1 plus HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides, body mass index (BMI), hypertension status , diabetes status (fasting blood glucose was 126 mg/dL or greater or use of diabetes medications), and current smoking use (self-reported current smoking use within the past 30 days). Fixed effect meta-analysis was used to combine results across all four race/ethnic groups, as implemented in METAL. Fine mapping of the 9p21 region  was performed for each ethnic group by selecting all SNPs on the chromosome 9 imputation set (NCBI Build 37) between positions 21997022–22225503. A total of 3282 SNPs were identified . This list of SNPs was supplemented by adding novel SNPs identified by deep sequencing efforts in this region p<7.6×10−4 given 66 SNPs tested (0.05/66) for the initial analysis, with greater number of SNPs used for the correction for the fine mapping effort). To assess genetic heterogeneity seen in stratified analyses of the four MESA race/ethnic groups, we used the I2 heterogeneity metric to quantify the proportion of total variation across studies attributable to heterogeneity rather than chance Significance was defined by Bonferroni correction by dividing an alpha of 0.05 by the number of SNPs tested (http://www.nhlbi.nih.gov). MESA Family is conducted and supported in collaboration with MESA investigators; support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071252, R01HL071258, R01HL071259, M01-RR00425, UL1RR033176, and UL1TR000124. Funding for MESA SHARe genotyping was provided by NHLBI Contract N02‐HL‐6‐4278. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. This manuscript was approved for submission by the Presentations and Publications Committee.MESA and the MESA SHARe project are conducted and supported by contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-001079 and UL1-TR-000040 from the National Heart, Lung, and Blood Institute (NHLBI,


Hirundo rustica erythrogaster) – is influenced by both genetics and aspects of the developmental environment, including variation due to the maternal phenotype and the nest environment. Within individuals, nestling color is predictive of adult color. Accordingly, these early environmental influences are relevant to the sexually selected plumage color variation in adults. Early environmental conditions appear to have important lifelong implications for individual reproductive performance through sexual signal development in barn swallows. Our results indicate that feather color variation conveys information about developmental conditions and maternal care alleles to potential mates in North American barn swallows. Melanin-based colors are used for sexual signaling in many organisms, and our study suggests that these signals may be more sensitive to environmental variation than previously thought.Phenotypic differences among individuals are often linked to differential survival and mating success. Quantifying the relative influence of genetic and environmental variation on phenotype allows evolutionary biologists to make predictions about the potential for a given trait to respond to selection and various aspects of environmental variation. In particular, the environment individuals experience during early development can have lasting effects on phenotype later in life. Here, we used a natural full-sib/half-sib design as well as within-individual longitudinal analyses to examine genetic and various environmental influences on plumage color. We find that variation in melanin-based plumage color – a trait known to influence mating success in adult North American barn swallows ( Morphological signals including horns, antlers, and plumage ornaments are important aspects of an individual's phenotype used to attract mates and defend territories and resources necessary for reproduction (Andersson Parus major), nestlings with greater mass are more likely to acquire high-quality breeding habitat as an adult  , detectable even after multiple molts (Evans and Sheldon Notymystis cincta) nestlings influenced the color of adult white ear tufts, but did not impact juvenile or adult carotenoid- or melanin-based colors  and carotenoid-based (yellow) plumage has been shown to be associated with nestling body condition and nest environment  can significantly impact an individual's phenotype. For example, recent medical research suggests that, in humans, maternal diet during pregnancy can influence an offspring's predisposition to diabetes and obesity during childhood and adolescence as well as their predisposition to associated diseases in adulthood  the relative influence of different sources of phenotypic variation, (2) how a phenotype changes and develops throughout an individual's lifetime, and (3) how different sources of phenotypic variation might interact in the production of a phenotype Danchin . Miller Hirundo rustica)  what is the relative influence of genes and the environment – including the prelaying maternal environment – on the expression of phenotypic variation in plumage color, and (2) how does early environment  influence the expression of this trait into adulthood. To address these questions, we quantified the contributions of genetic and environmental variance to the development and expression of a highly variable, continuously distributed melanin-based plumage color trait known to be a target of sexual selection in adult barn swallows (H. r. erythrogaster) do not attend to long tail streamers in males, but rather to darker melanin-based ventral plumage color . We collected morphological measurements , a blood sample from the brachial vein, and a plumage sample from the breast of the bird. As nests were initiated, we identified the social male and female associated with the nest, and documented the clutch initiation date, the clutch size, the hatch date, and the brood size. On day 12 of the nestling period, we banded and measured nestlings (wing length and mass) and took blood and plumage samples. Finally, we estimated when and how many nestlings fledged from successful nests. Despite their socially monogamous mating system, high rates of extra-pair paternity (∼30%) have been reported in many populations of barn swallows  aluminum bands and given a unique color combination that consists of randomly chosen plastic color band and/or colored tail spots that allowed for individual identification during behavioral observations. These color combinations do not have an effect on an individual's reproductive success . Theta and phi are measures of angular displacement that describe where in the three-dimensional color space a particular sample lies. Variation in these hue metrics is due to differences in pigment type and/or nanostructure of the feather; for melanin-based colors, the relative proportion of eumelanin to pheomelanin pigment will influence the values of theta and phi. The third metric, r, describes the distance from the achromatic center of the color space ; therefore, here we use r achieved (rA), which is a measure of r relative to the maximum value of r (rmax) possible for that location. We also quantified average brightness of each plumage sample as a measure of how much light, regardless of wavelength, is reflected off the feather surface . All plumage color metrics were quantified using the R package pavo  as variation in these metrics is likely driven by differing genetic and physiological mechanisms relating to melanogenesis  , and were amplified with the following protocol: initial denaturation step of 94°C for 1 min, followed by 10 cycles of 94°C for 30 sec, 55°C for 30 sec, and 72°C for 45 sec, with an additional 25 cycles starting at 87°C for 30 sec instead of 94°C, and completed with a final extension at 72°C for 3 min. The Pocc6 reaction was modified from the above conditions using 1.25 mmol/L MgCl2, and the above conditions were altered for the Hir19 reaction with 3 mmol/L MgCl2, and 0.2 mmol/L each forward and reverse primer. The PCR amplification protocol for Pocc6, Hru6, and Hir19 was similar to the previously described protocol for the pooled reaction with the exception that 60°C was used for the annealing temperature. Amplified PCR products containing the fluorescently labeled forward primer were detected using an ABI3730 DNA analyzer .DNA samples were extracted from blood taken in the field using Qiagen DNeasy Blood & Tissue Extraction kits . Polymerase chain reaction (PCR) was utilized to amplify seven previously developed microsatellite loci – Escu6: . Genotypes from adults and offspring were incorporated into a paternity analysis using CERVUS software (v2.0) to calculate exclusion probabilities and assign paternities. Paternity exclusion was conducted using similar parameters described in Neuman et al. . BrieflyTo examine the quantitative genetics of plumage color, we used the animal model, a mixed-effects model that partitions phenotypic variance into different components, such as environmental, genetic, and maternal effects, from which heritability and other parameters can be estimated  of each color metric was calculated as the sum of the variance components: VP = VA + VCE + VR, where VA is the additive genetic variance, VCE is the nest environmental variance, and VR is the residual variance (see Table h2 = VA/VP) and the effect of nest environment (ce2 = VCE/VP). An advantage of the Bayesian framework used here is that the uncertainty associated with each component carries over into the subsequent variance ratio estimates allowing for Bayesian credible intervals (BCI) to be estimated for heritability and nest environmental effects for each color metric. Because there are significant phenotypic correlations among color metrics within individual nestlings  and maternal effects would likely be confounded by environmental effects in the larger dataset. This pedigree consisted of 246 offspring, 32 mothers, and 45 fathers for a total of 323 identities. As with the complete dataset, we first included year, sampling date (nested within year), nestling mass, and nestling sex as fixed effects; however, these did not have a statistical effect on the model and were therefore not included in the final model (Table 2 = VME/VP), where VP = VA + VCE + VME + VR.We also used a subset of our data for which females had multiple broods within or across breeding seasons allowing us to assess maternal effects separate from nest environment. We used this subset as only a small sample of females had multiple broods n = 2 and matWe specified the priors for variance–covariance matrix as an inverse Wishart matrix distribution ; these individuals are representative of all nestlings banded in our study population in terms of ventral color. Using these individuals, we modeled the linear relationship between color measured within the same individual at two different stages (standardized to the mean): as a nestling and as an adult in their first breeding season to determine if juvenile plumage color predicted adult color within individuals. We included sex as a covariate in this model as color is dimorphic in this subspecies  and comparisons of adult color between individuals of known relatedness are therefore extremely difficult. However, nestlings begin to grow juvenile feathers with qualitatively similar coloration to that of adults, while they are still in the nest  and 209 40.8%) as extra-pair young . Of those 209 extra-pair young, we were able to determine the identity of the extra-pair father for 63 nestlings. Based on all families genotyped between 2008 and 2012, we found a similarly high rate of extra-pair paternity in our study population, with, on average, 41.1% of nestlings sired by extra-pair males. On average, 66.8% of nests contained at least one extra-pair young (EPY), with some nests having 100% EPY (see Table 0.8% as e2) and shared nest environment (ce2) for each color descriptor  explains approximately 28% of the phenotypic variation in melanin-based plumage coloration  also explains a significant proportion (approximately 25%) of phenotypic variation in coloration (Table 2) and nest environment (ce2) explained 47% of phenotypic variation.While most related individuals also shared the same nest environment, our dataset consisted of several maternal and paternal half siblings that experienced different environments. We determined that nest environment (ceon Table . Combine2), nest environment (ce2), and maternal effects (me2) for each color descriptor  and nest environment (ce2) decreased ; phenotypic variation in coloration explained by prelaying maternal environment was approximately 13% (Table 2), nest environment (ce2), and maternal effects (me2) explained 47% of phenotypic variation.When maternal identity is included in the model, the phenotypic variation explained by both additive genetics . In each model, the interaction between sex and age was significant , and we used a Tukey's post hoc analysis to assess the pairwise comparisons of interest. In adults, we found significant sexual dichromatism in all four color metrics; on average, males are darker with more saturated color (higher rA) and significant differences in theta and phi suggest that males and females occupy different color space  relative to nestlings , we explored age and sex differences in plumage color using two-way ANOVAs for each color metric  leading to unequal variances for each pairwise comparison. We found no statistically significant difference in plumage coloration between returning nestlings compared to nonreturning nestlings from the same years ; therefore, we infer that the returning nestlings are a representative subset of the nestlings hatched in our population. As adults, individuals hatched in our study area and recruited into the breeding population generally did not differ in plumage color compared to first-time breeding adults recruited from a different population . While there was a statistically significant difference in one of the metrics of hue, phi ; FigureFor the color comparisons between recruited and nonrecruited individuals, we used Welch's two-sample rA  was partitioned into three variance components: additive genetics, nest environment, and prelaying maternal environment (Table h2) is relatively low for a melanin-based color trait that varies from light to dark on a continuous scale . In a different population of barn swallows Saino et al. (h2 ≅ 0.80) using parent–offspring regressions. However, analyses such as parent–offspring regression that do not control for other sources of nonindependence such as shared environment may overestimate heritability. Consequently, the animal model approach used here allows for a more accurate partitioning of phenotypic variance , and brightness  has lasting effects on adult phenotype, a pattern that has not been well documented for melanin-based plumage color (but see Griffith et al.


SORT1, HMGCR, MLXIPL, FADS2, APOE and MAFB) on early development of dyslipidemia independent of the degree of obesity in children, we investigated their association with total (TC), low density lipoprotein (LDL-C), high density lipoprotein (HDL-C) cholesterol and triglyceride (TG) levels in 594 children. Furthermore, we evaluated the expression profile of the candidate genes during human adipocyte differentiation.To assess potential effects of variants in six lipid modulating genes (1 to >104 fold during human adipocyte differentiation, suggesting a potential link with adipogenesis. In genetic association studies adjusted for age, BMI SDS and sex, we identified significant associations for rs599839 near SORT1 with TC and LDL-C and for rs4420638 near APOE with TC and LDL-C. We performed Bayesian modelling of the combined lipid phenotype of HDL-C, LDL-C and TG to identify potentially causal polygenic effects on this multi-dimensional phenotype and considering obesity, age and sex as a-priori modulating factors. This analysis confirmed that rs599839 and rs4420638 affect LDL-C.Expression of selected genes increased 10SORT1 and APOE influence lipid levels independent of obesity in children. Bayesian modelling suggests causal effects of these variants.We show that lipid modulating genes are dynamically regulated during adipogenesis and that variants near  Alterations in blood lipid phenotypes culminating in dyslipidemia are important risk factors for the development of cardiovascular disease . ElevateMeta-analyses of genome-wide association (GWA) studies revealed many genetic loci influencing blood lipid levels underlying the polygenic cause of dyslipidemia and thereby identified suspected as well as unsuspected new candidate genes –10. HoweInvestigation of childhood cohorts has several advantages though. They are much less biased by chronic disease and treatments but already show considerable heterogeneity regarding blood lipid levels. Also, considering future prediction of developing dyslipidemia, it is important to assess whether associations between genetic variants and blood lipid phenotypes observed in adults are already evident in children and adolescents . Due to SORT1 (sortilin 1), HMGCR (3-hydroxy-3-methylglutaryl-Coenzyme A reductase), MLXIPL (MLX interacting protein), FADS2 (fatty acid desaturase 2), APOE (apolipoprotein E) and MAFB (V-maf musculoaponeurotic fibrosarcoma oncogene homolog B) for which high effect-sizes regarding lipid phenotypes were reported. Going beyond classical association analysis, we additionally performed a Bayesian modelling approach to identify unconfounded relationships between genetic and non-genetic covariables and lipid phenotypes. Considering that obesity is a risk factor for dyslipidemia per se and that adipose tissue is an important tissue for lipid metabolism, we also assessed a potential relationship of the candidate gene expression for adipogenesis by studying time-series of gene-expression during human adipocyte differentiation.In the present study, we aimed at assessing associations of six variants with lipid traits in a sample of mainly obese children. Selected variants are located in or near the genes MAFB), rs4420638 (APOE), rs599839 (SORT1), rs3846663 (HMGCR), rs174570 (FADS2) and rs3812316 (MLXIPL). We excluded well-known SNPs in genes like LDL-receptor because we were interested in new candidate genes influencing blood lipid levels.Genes were selected according to evidence of genotype-phenotype-associations established in meta-analyses of adult cohorts , 16, 17.SORT1  primers . Primer sequences are provided in RNA extraction was performed using the RNeasy MiniKit  including DNase digestion according to the manufacturer’s instructions. Reverse transcription of 50 ng/μl RNA was carried out using the M-MLV Reverse Transcriptase Kit  with random hexamer [p(dN)TBP, HPRT and USF2.Experiments were performed in three distinct experiments each in triplicates. Target gene-expression was normalized to the averaged expression of three housekeeping genes 4-lysis, and centrifugation, we extracted DNA using QIAmp DNA Blood MiniKit (Qiagen) according to the manufacturer’s manual.Fasting venous EDTA blood samples were stored at -80°C. After washing with phosphate buffered saline, erythrocyte depletion by NHGenotyping probes and primers were obtained from Applied Biosystems . Primer sequences are listed in In classical analysis of genetic data, every combination of a single SNP and a single phenotype is tested for association. Prior to analysis, lipid phenotypes TC, LDL-C, HDL-C and TG were log transformed to approximate normal distribution. Continuous phenotypes TC, LDL-C, HDL-C, TG, age and BMI SDS were standardised to zero mean and unit variance before analysis in order to obtain dimensionless effect estimates which are better comparable between different predictors and studies.SNP associations with BMI SDS were tested with a linear model assuming three different genetic models, an additive effect of both alleles, and a dominant and recessive effect of the major allele, respectively. All models were adjusted for age and sex. Similarly, lipid SNP associations were tested with a linear model and three different genetic models. All models were adjusted for age, BMI SDS and sex. Adjustment for pubertal state instead of age is also reasonable. Due to the high correlation of age and pubertal state (Spearman r = 0.84), the genetic results are essentially the same (not shown). Also, pubertal stage is assessed by two parameters (pubic hair and breast development or testicular volume), which are not necessarily coherent. Furthermore, pubertal timing differs between boys and girls. Since dyslipidemia would be more related to age as an indicator of duration of obesity and dyslipidemia it is not necessarily influenced by pubertal development per se, this was another reason to adjust for age. We, therefore, decided to use the continuous and less ambiguously measurable trait age instead of pubertal state.-4 controls the family-wise error rate at 5% and was therefore used to correct for multiple testing in our study.Since we tested five phenotypes, six SNPs and three genetic models, it is necessary to correct for multiple testing. However, due to multiple correlations between phenotypes and effects of genetic models, it was necessary to simulate the null-distribution. In our situation, a significance level of 6.7x10Statistical analysis was performed using R 2.10.1.The major drawbacks of the classical analysis mentioned above are the large number of tests to be performed due to the large number of possible combinations of SNPs and phenotypes and the assumption of a specific model of genetic and non-genetic effects. To overcome these limitations, we performed Bayesian model analysis in addition to our classical association analysis. By this approach, we can estimate plausibilities of different models and corresponding effect sizes. Bayesian modelling also allows some kind of causal inference by analysing all lipid phenotypes and possible influencing factors in parallel considering their overall correlation structure. To some extent, this avoids spurious associations.The method is well conceived with application in analysing complex genotype-phenotype associations in medical research –30. AddiSimilar to the univariate analysis, transformed and standardised data were used. Lipid phenotypes were modelled with the Bayesian variable selection approach described in , 31 usinIn our analysis, the set of co-variables consists of age, BMI SDS, sex and a recessive and a dominant part for each of the six SNPs defined by indicator variables “genotype” = 0 and “genotype” = 2 respectively. If both indicator variables are included, different levels of co-dominance can be expressed by corresponding effect estimates. Hence, 15 co-variables were available for selection for each of the 3 lipid phenotypes.Each different subset of these co-variables forms a model. Prior to analysis, one assumes that all models are equally likely. We calculated Bayesian posterior probabilities representing the plausibilities of the models given our data. Details of Bayesian modelling and fitting can be found in Bayes factors  are usedThere was no significant association between BMI SDS and any of the selected SNPs indicating that the variants are not related to the degree of obesity in our data. Results for the additive model are presented in SORT1 rs599839 with TC  and LDL-C  as well as for APOE rs4420638 with TC  and LDL-C , whereas the variants were not associated with other lipid phenotypes .We performed Bayesian modelling of the multi-phenotype of HDL-C, LDL-C and TG. TC was not included into the model due to its strong correlation with LDL-C. Analysed relations are illustrated in MLXIPL).The top models of TG contain age and BMI SDS, too. Additionally, the second best model includes the dominant part of rs3812316 (SORT1) and rs4420638 (APOE) and BMI SDS contribute to the top 5 models of LDL-C. In less probable models for LDL-C, combinations of the recessive and dominant parts of rs599839 and rs4420638, age and BMI SDS occur. Further, the dominant part of rs6102059 (MAFB) is included once.Various different models are plausible for LDL-C: The recessive parts of SNP rs599839 (SORT1) and rs4420638 (APOE) have a high certainty of affecting LDL-C independent of the degree of obesity. Conversely, the following effects cannot be ruled out (i.e. no decisive evidence against the effect was found): rs4420638 (APOE) on HDL-C, rs3846663 (HMGCR) and rs6102059 (MAFB) on LDL-C, rs3812316 (MLXIPL) on TG.The impact of each co-variable independent of a certain model can be assessed by interpreting the inclusion probabilities for co-variables . In addiEffect estimates of co-variables with inclusion probability greater than 0.5% are listed in The estimated covariance of the model is shown in Polygenic effects for LDL-C are illustrated in SORT1, HMGCR, MLXIPL, FADS2, APOE and MAFB during differentiation of human preadipocytes into adipocytes to assess a potential physiological relevance in lipid metabolism. We observed an up-regulation of these genes by magnitudes of 10 to 104 , MAFB (rs6102059), HMGCR (rs3846663), MLXIPL (rs3812316), APOE/C1/C4/C2 (rs4420638) and CELSR2/PSRC1/SORT1 (rs599839), we observed a strong impact of rs599839 (SORT1) and rs4420638 (APOE) on circulating LDL-C levels independent of the degree of obesity in conventional linear regression analyses adjusting for age, sex and BMI SDS. This was confirmed by our Bayesian model analysis suggesting causality of these two variants on LDL-C. Bayesian analysis also revealed that effects of rs4420638 (APOE) on HDL-C, rs3846663 (HMGCR) and rs6102059 (MAFB) on LDL-C as well as rs3812316 (MLXIPL) on TG cannot be ruled out. Still, these variants should be considered as candidates requiring further investigations.Of the six selected SNPs located in or near the genes APOE-SNP rs4420638 is located on chromosome 19 in a cluster with APOC1, APOC4 and APOC2. The SNP rs599839 is located on chromosome 1, close to the genes CELSR2, PSRC1 and SORT1. Multiple other studies investigated SNPs in or near the APOE and SORT1 genes. Rs4420638 and rs599839 showed replicable associations with lipid levels (mostly LDL-C) in Caucasian and non-Caucasian population cohorts and meta-analyses [p = 6.5x10-4, β = -0.28, Males: p = 4.5x10-3, β = -0.32).The analyses , 35. Theanalyses . In a smanalyses . Sex-strFADS2) with TC, LDL-C and HDL-C were observed in a meta-analysis of 16 European studies [FADS2) is the most implausible of the considered candidates, since, in contrast to the other variants, it is dismissed for all three lipid phenotypes analysed.Associations of the other variants/candidates could not be confirmed in our sample. Correlations for rs174570  with LDL-C was observed by some [For other variants, even large sample-sized and high-powered adult studies gave controversial results. While a significant association of rs6102059  was most strongly associated with TG in adults [Rs3812316  risk factors that accumulate or increase with life time (double/multiple hit theory). Such a relationship has been discussed for the manifestation of coronary artery disease in patient with genetic risk factors . It alsoFor adults it is common practise to combine diverse cohorts  and rs4420638 (APOE) are strongly associated with alterations in blood lipid levels independent of the presence and degree of obesity. Our integrative Bayesian model analysis provided further candidate associations requiring further investigation of the candidates. Therefore, we conclude that this novel approach can improve the detection of weaker associations in genotype-phenotype data sets.We could show for the first time in children that rs599839  and 3 refer to missing values. Lipid phenotype levels are provided as hdl_c (high density lipoprotein cholesterol), ldl_c  and tg (triglyceride). Accordingly, the covariables age, sex and bmi_sds are provided.(XLSX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 FigEffects of identified genetic risk variants on LDL-C.(DOCX)Click here for additional data file.S1 Methods(DOCX)Click here for additional data file.S2 Methods(DOCX)Click here for additional data file.S3 Methods(DOCX)Click here for additional data file.S1 Results(DOCX)Click here for additional data file.S2 Results(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Xieyou9308 is a certified super hybrid rice cultivar with a high grain yield. To investigate its underlying genetic basis of high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) was constructed for identification of quantitative trait SNPs (QTSs) associated with two important agronomic traits, plant height (PH) and heading date (HD). By re-sequencing of 138 recombinant inbred lines (RILs), a total of ~0.7 million SNPs were identified for the association studies on the PH and HD. Three association mapping strategies  were adopted for data analysis. Using a saturated mixed linear model including epistasis and environmental interaction, we identified a total of 31 QTSs associated with either the PH or the HD. The total estimated heritability across three analyses ranged from 37.22% to 45.63% and from 37.53% to 55.96% for the PH and HD, respectively. In this study we examined the feasibility of association studies in an experimental population (RIL) and identified several common loci through multiple strategies which could be preferred candidates for further research. Oryza sativa L.) plays a pivotal role in food security in most rice growing countries especially in AsiaAs an important staple crop that provides food source for over half of the world population, rice (http://archive.gramene.org/qtl/)458910121315et al.Oryza sativa indica subspecies and identified a total of 37 loci associated with 14 agronomic traits, including 7 loci for HD. Moreover, a GWAS based on 413 diverse accessions of O. sativa from 82 countries, identified 234 loci associated with 34 agronomic traits using 44,100 identified SNP variantset al.Oryza sativa indica and Oryza sativa japonica subspecies.In general, the PH and HD are regarded as complex traits which are quantitatively inherited. Based on the conventional molecular markers and experimental population, many studies on QTL linkage mapping have been conducted to explore the casual loci for the phenotypic variation of the PH and HD in various rice populations as recorded in the Gramene QTL Database  population derived from the Xieyou9308 was sequenced for the identification of quantitative trait SNPs (QTSs) associated with the PH and HD by using a newly developed association mapping software, r2) of SNPs drops to the half of its maximum value r2 every 1 kb distance. The LD patterns were analyzed both on whole-genome wide scale and each separate chromosome and the results are presented in 12r2 for every specific SNP only with its 10 adjacent SNPs (the default setting for − −ld− window of PLINK).Linkage disequilibrium (LD) is the basis of association mapping which is also called as linkage disequilibrium mapping (LDM). The extent of LD highly varies across species, populations, even chromosome regions, and thus it is more reliable to estimate the empirical LD according to the investigated population. Here the LD decay rate is measured at the chromosomal distance when the average squared pairwise correlation coefficient  were adopted to identify candidate variants responsible for the phenotypic variation of the PH and HD; as a result, a total of 31 QTSs were detected significantly associated with the target traits. As shown in r2) between each other  with the other three SNPs (ranging from 0.006 to 0.02), indicating it should be another genetic variant although it is quite physically near the other three SNPs and also exhibits similar magnitude additive effect on the PH as rs9040677 in the QBA (r2) reaching up to 0.78, indicating these two SNPs might still represent one same causal locus. These common loci detected in multiple strategies could be regarded as high priority candidates for follow-up.As shown in  the QBA . In addi the QBA . For ther2) of 0.40 (see r2) ranging from 0.34 to 0.53. In addition, there are other two loci located on chromosome 1, one of which was captured by rs12807091 in the GWA and by rs14583697 in the QBA (with LD (r2) of 0.53), and the other was captured by rs22665688 in the GWA and by rs22139712 in the GBA (with LD (r2) of 0.46). As described above, these common or approximately common loci detected by at least two strategies could be regarded as preferred candidates for further research.For the HD , we tota0.40 see . The afor2 ≈ 0.52, see r2 ≈ 0.003), indicating that the SNPs on chromosome 7 represent actually two different genetic variants though they are quite close in terms of physical distance. For the locus on chromosome 12, the high LD between the representative SNPs and the strong phenotypic correlation between PH and HD , of 31 QTSs associated with the PH and the HD, 15 QTSs are located within annotated genes as presented in 22E. coli26According to the latest version of the rice genome annotation  has become a powerful tool for mapping causal gene with fast advances in high-throughput genotyping and sequencing technology. Generally, natural population is the first choice for GWAS because of its convenience to collect, abundant accumulated historical recombinant events which is important to achieve much higher mapping resolution. However, the unknown and complex population structure among the collected samples which would lead to the increased false positives, has always been haunting. For most plant species, many designed experiment mapping populations have been constructed for QTL linkage mapping of complex traits, and some of these resources may be also good materials to conduct association mapping when genome-wide high density markers are available because these controlled populations are well-praised for their explicit population structure so that the haunting issue on population heterogeneity could be avoided. However, a widely supposed weakness of controlled populations would be the limited recombinant events which might result in lower mapping resolution compared with natural population. In the present study, as we showed on LD decay rate, the resolution of the experiment population (RIL) is not greatly inferior to that of the natural population (~1/10 to ~1/5). Compared with DH and F2, RIL is more desirable to reduce LD because RIL accumulates more recombinant events than DH and F2 during producingQTXNetworkMost phenotypic traits of economic importance in plants, such as the PH and HD, are complex traits which are final performance of jointly interacting networks of multiple genes and environmental factorsAs shown in μ + G) of superior line which is an optimized pure line with homozygous genotype of QQ or qq at each SNP loci, we found that they were 95.80 cm, 93.67 cm and 93.59 cm for the PH through three strategies, respectively. These predicted values are far lower than the average performance of the Xieyou9308 (~120 cm for the PH) which is the F1 hybrid of bi-parents with genotype consisting of many heterozygous loci. It is suggested that the allelic interactions (dominance) or their related epistasis interactions might play a crucial role in the genetic basis of these two target traits; therefore, for further investigation of this genetic architecture, a recombinant inbred intercross (RIX) population which is generated from random mating of recombinant inbred (RI) strains would be ideal since it contains more heterogeneous loci as well as more kinds of combination of two genes in different positions on genome, which are basic requirements for analyzing dominance, dominance-related epistasis effects and their interaction effects with environment, and then we might observe more epistasis or environmental interactions.Even though we used a saturated model including epistasis and gene by environmental interaction to dissect the phenotypic variation, no interactions were detected. It is consistent with the previous QTL studies13 lines, which are derived from the super hybrid rice cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) through single seed descent method. Now these RILs are maintained in China National Rice Research Institute (CNRRI), which can be accessed with the permission of CNRRI.The RIL mapping population used in this study consists of 138 F−1, 127.5 kg P (phosphorus) ha−1 and 330 kg K (potassium) ha−1 (N:P:K ≈ 2:1:3), and with normal pest control focusing on Rice Scirpophaga incertulas  and Nilaparvata lugens . The average monthly temperatures were 22.9–29.6 °C in Hangzhou during cultivation (May to September 2009), and 20.7–26.1 °C in Hainan (December 2009 to April 2010), respectively. The photoperiod from seeding to all heading were ranged from 12.6 hL (light)/11.4 hD (dark) to 14.1 hL/9.9 hD in Hangzhou, and it was 11.0 hL/13.0 hD to 12.3 hL/11.7 hD in Hainan, respectively.The phenotypic experiments were conducted at two experimental farms of CNRRI: One is located at Hangzhou city , Zhejiang province, China, and the other is located at Lingshui county , Hainan province, China see . Due to Six plants in middle of each plot were sampled for phenotype investigation. The PH was measured by the distance from the soil surface to the top of the panicle of the tallest tiller (in centimeters) and the HD was measured by the duration from the seeding to the showing of the main panicle (in days). The phenotypic data at each location were summarized in DNA re-sequencing was conducted by the Beijing Genome Institute (BGI) for two parents with 10X coverage and 138 lines with 2X coverage see . The minhttp://rice.plantbiology.msu.edu/index.shtml)For the GWA, all the originally genotyped SNPs  were used for association analysis, but the subsets of SNPs in the annotated genes, and in the QTLs regions previously identified for the target traits in this population, were used for the GBA, and the QBA, respectively. For GBA, the gene annotation information was downloaded from the FTP site of Rice Genome Annotation Project  can be expressed by the following mixed linear model,A saturated genetic model was adopted for modeling the genetic architecture of complex traits from multi-environment trials, which includes additive effect (a), additive by additive epistasis effect (aa) as the fixed effects and the corresponding environment interaction effects  as the random effects. The genetic model for phenotypic value of the μ is the population mean; ai is the additive effect of the i-th QTS with coefficient xik, fixed effect; aaij is the additive-additive epistasis effect of the i-th QTS and the j-th QTS with coefficient xik · xjk, fixed effect; eh is the main effect of the h-th environment, random effect; aehi is the additive by environment interaction effect of the i-th QTS and the h-th environment with coefficient uhik (=xhik), random effect; aaehij is the interaction effect of the aaij and the h-th environment with coefficient uhijk (=xhik · xhjk), random effect; and εhk is the random residual effect of the k-th line in the h-th environment.where, In the present study, three association strategies including the GWA, QBA, and GBA, were adopted for data analysis. In the GWA and GBA, in order to alleviate heavy computation burden resulting from detection of the epistatic interactions for a large amount of SNPs , the efficient parallel computation software GMDR-GPUQTXNetworkF-test. The locus with F-statistic exceeding the threshold value was considered as a candidate individual SNP. 2) Epistasis loci detection. Using the significant SNPs detected in step 1 as covariates, a two dimension (2D) interaction scan was conducted for all possible SNP pairs by using F-test. The pair of loci with F-statistic larger than the threshold value is considered as candidate epistasis interacting loci. For the above two steps, the permutation testing was employed to determine the empirical threshold value of the F-statistic at the significance level of 0.05, being aimed to control the experiment-wise type I error rate. Then, model selection was performed using stepwise method based on F-test with each candidate locus and each candidate epistasis loci as selection unit; as a result, a final QTS full model like the et al.The software How to cite this article: Zhou, L. et al. Dissection of genetic architecture of rice plant height and heading date by multiple-strategy-based association studies. Sci. Rep.6, 29718; doi: 10.1038/srep29718 (2016).