Osteoporosis is characterized by low bone mass and compromised bone structure, heritable traits that contribute to fracture risk. There have been no genome-wide association and linkage studies for these traits using high-density genotyping platforms.We used the Affymetrix 100K SNP GeneChip marker set in the Framingham Heart Study (FHS) to examine genetic associations with ten primary quantitative traits: bone mineral density (BMD), calcaneal ultrasound, and geometric indices of the hip. To test associations with multivariable-adjusted residual trait values, we used additive generalized estimating equation (GEE) and family-based association tests (FBAT) models within each sex as well as sexes combined. We evaluated 70,987 autosomal SNPs with genotypic call rates ≥80%, HWE p ≥ 0.001, and MAF ≥10% in up to 1141 phenotyped individuals . Variance component linkage analysis was performed using 11,200 markers.-6 and 2.5 × 10-5, respectively. Of the 40 top SNPs with the greatest numbers of significantly associated BMD traits , one half to two-thirds were in or near genes that have not previously been studied for osteoporosis. Notably, pleiotropic associations between BMD and bone geometric traits were uncommon. Evidence for association (FBAT or GEE p < 0.05) was observed for several SNPs in candidate genes for osteoporosis, such as rs1801133 in MTHFR; rs1884052 and rs3778099 in ESR1; rs4988300 in LRP5; rs2189480 in VDR; rs2075555 in COLIA1; rs10519297 and rs2008691 in CYP19, as well as SNPs in PPARG (rs10510418 and rs2938392) and ANKH (rs2454873 and rs379016). All GEE, FBAT and linkage results are provided as an open-access results resource at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.Heritability estimates for all bone phenotypes were 30–66%. LOD scores ≥3.0 were found on chromosomes 15  and 22  for femoral shaft section modulus. The ten primary phenotypes had 12 associations with 100K SNPs in GEE models at p < 0.000001 and 2 associations in FBAT models at p < 0.000001. The 25 most significant p-values for GEE and FBAT were all less than 3.5 × 10The FHS 100K SNP project offers an unbiased genome-wide strategy to identify new candidate loci and to replicate previously suggested candidate genes for osteoporosis. Whereas low BMD is among the strongest risk factors for fracture [Osteoporosis is a skeletal disorder characterized by compromised bone strength leading to an increased risk of fracture . In the fracture ,4, a numfracture -7. Thus,fracture ,9. A grofracture ,11.BMD, QUS, and femoral bone geometry are approximately normally distributed, complex traits. A wealth of studies have documented BMD to be under strong genetic control with 50–70% heritability ,13. SimiNumerous biological candidate genes for bone phenotypes have also been proposed, but few have been validated with large-scale evidence . Recent ® bone sonometer , in members of the Offspring Cohort at their examination cycle 6/7 and the Original Cohort during their exam 24 assessment. There were 1141 phenotyped individuals, 245 from the Original Cohort  and 896 Offspring Cohort participants , belonging to 241 families.The Framingham Osteoporosis Study is an ancillary study of the Framingham Heart Study (see description of the FHS sample in the Overview paper ). BeginnOriginal Cohort participants had bone measures performed at either exam 22 or 24. Offspring Cohort participants had these measures obtained during a period that included part of the official Framingham exams 6 and 7. The following traits were studied: Bone mineral density ; quantitative ultrasound of the calcaneus , and geometric indices of the hip from DXA . In total, 10 traits are reported here. Details of the measurements and phenotype definition ,23,31 asMultivariate regression analysis was performed in each sex (men and women) and Cohort  in order to obtain normalized or ranked residual phenotypes, adjusted for covariates. Table Overview paper [We used the Affymetrix 100K SNP GeneChip marker set in the Framingham Heart Study to examine genetic associations with the above phenotypes (see description of the FHS 100K and Marshfield STRs in the ew paper ). We evaWe performed genome-wide association (GWA) analyses by two approaches, using both family-based and population-based methods. . Both sIn order to prioritize SNPs potentially associated with multiple phenotypes, we evaluated several phenotypic subgroups: (1) clinically important bone mineral density traits , (2) a combination of BMDs with BUA, (3) hip geometry , and (4) hip BMDs with hip geometry. We also focused on BMD in sex-specific subgroups. For each SNP we calculated the number of traits significantly associated with this SNP at alpha <0.01 in both GEE and FBAT, assuring that the FBAT and GEE effects were in the same direction. We then selected top SNPs with highest numbers of nominally significantly associated traits. For identical proportions of nominally significant traits, SNPs were additionally ranked by the mean of GEE p-values across traits .Entrez Gene and also queried PubMed using the gene name and "osteoporosis", "fracture" or "bone mass" terms, in order to identify biological plausibility or evidence relating these genes specifically to bone disease.For SNPs that were selected by more than one strategy and that pertained to a specific gene (according to the NCBI Build 35), we perused http://www.sfbr.org/solar/doc/00.contents.html. Heritability (h2) of each phenotype was estimated as the proportion of the total phenotypic variance attributable to the additive effects of genes. Subsequent linkage analyses were also conducted using SOLAR. This method, described in detail elsewhere [Variance component analyses (VCA) for all phenotypes were performed on normalized or rank-normalized residuals using the computer package Sequential Oligogenic Linkage Analysis Routines  version 2.0, available online lsewhere  (see deslsewhere ), entail2 only. Among these, the following ten multivariable-adjusted residuals are the primary phenotypes presented here: BMD of the femoral neck , trochanter  and the second to fourth vertebral bodies of the lumbar spine ; a quantitative ultrasound measure of the calcaneus, broadband ultrasound attenuation ; femoral geometry measures at the "narrow neck" region of the hip and the femoral shaft including narrow neck section modulus  and width , femoral shaft section modulus  and width , femoral neck-shaft angle , and femoral neck length . Additional bone-related quantitative phenotypes are available in the FHS 100K SNP resource (web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007) including calcaneal speed of sound; several hip geometry measures including neck cross-sectional moment of inertia; neck, shaft and intertrochanteric buckling ratio, and shaft cross sectional moment of inertia from these same examinations.Bone-related quantitative traits and derived phenotypes analyzed in the FHS 100K SNP resource are listed in Table -6) and FBAT models (p < 2.5 × 10-5), ordered by chromosomal position, are shown in Tables 2 ≥ 0.8). When this occurred we displayed results for the higher ranking SNP based on a combination of FBAT and GEE p-values. Of note, some SNPs were associated with more than one trait: thus, rs10514345 on chromosome 5 was associated (GEE) with NeckZ1 (combined sample) and LSBMD  and rs1209921 on chromosome 21 was associated (FBAT) with TRBMD (combined sample) and FNBMD  , the second was BMD + bone ultrasound (BUA), the third was bone geometry , and the fourth, hip BMDs with hip geometry. For each SNP we calculated the number of traits in the subgroup that were significantly associated with the SNP at p < 0.01 in both GEE and FBAT, and then ranked SNPs with highest numbers of nominally significantly associated traits in which the direction of association was the same. In cases where SNPs had the same proportions of nominally significant traits, SNPs were additionally ranked by the mean of GEE p-values across traits.http://genome.ucsc.edu/, of the top 20 SNPs for the BMD subgroup, 9 (45%) were identified in genes or within 60 kb of a known gene, and none were coding (Table CDH9) on 5p14 and gene deleted in colorectal carcinoma (DCC) on 18q21. It is also noteworthy that the association of TRBMD with rs1209926 on chr. 21  occurred in the region identified in our original genome-wide linkage study in the FHS [PPARG) was also associated with the BMD traits in GEE tests and less strongly in FBAT analyses , and rs768207 , all in women , rs1479679 (in en Table . There wIL1RL1) on 2q12 (not shown). Of the top SNPs for the Hip Geometry subgroup , 50% were identified in genes or within 60 kb of a known gene; none were coding. The nominally significant intragenic SNPs were found in several genes, including rs7151976 in heat shock 70 kD protein 2 (HSPA2) on 14q24 and rs2834719 in runt-related transcription factor 1  on 21q22. Significant associations of SNPs with BMD phenotypes did not overlap with geometric phenotypes, and within the hip geometry subgroup, SNPs significant for one geometric phenotype did not overlap with other phenotypes within this subgroup (data not shown).We also looked for evidence of pleiotropy by evaluating association results within the other three pre-specified phenotype subgroups, BMD + BUA and hip geometry, as well as combinations of hip BMD with hip geometry (fourth subgroup). Notably, pleiotropic associations between BMD and other traits were uncommon (not shown in a table). When considering BMD and BUA together (phenotype subgroup 2), there were nominally significant associations (p < 0.0005) with a SNP rs953934 in the interleukin 1 receptor-like 1 gene  that was associated with femoral shaft section modulus and neck-shaft angle by GEE and FBAT, respectively. We [ESR1 gene  with nominally significant associations with bone phenotypes were in LD with widely published SNPs (rs2234693 and rs9340799). A single intronic 100K SNP from LRP5 (rs4988300) was associated with shaft section modulus in males and with femoral neck BMD in females. This 100K SNP was not in LD with published rs3736228 in LRP5 [HapMap website for the SNPs widely studied by others with respect to BMD (rs4988321 and rs627174). For the VDR gene there was a single 100K SNP (rs2189480) nominally significantly associated with femoral neck section modulus and spine BMD; however, there were no data available on LD between this 100K SNP and the 3 out of 5 SNPs reported in a recent meta-analysis of association studies for osteoporosis [CYP19 gene (rs10519297 and rs2008691) associated with femoral neck-shaft angle that were in LD with at least one of the previously reported SNPs studied with respect to bone phenotypes . Also, there was a single 100K SNP in the COL1A1 gene (rs2075555) that was associated with femoral neck width in women and shaft width in men; however, there were no data at the HapMap website regarding LD with the three other SNPs in this gene reported by other osteoporosis research groups .We further queried the FHS 100K array results, to verify, vely. We  have prevely. We ,39. Noneoporosis . Two SNPTo our knowledge, this is the first genome-wide association study of many of the most commonly measured osteoporosis-related phenotypes. This unbiased approach allowed simultaneous exploration of multiple genetic loci rather than focusing on a single-gene association. Using a strategy that combined GEE and FBAT association tests within the subgroup of BMD traits, 40 SNPs were each found to be associated with several bone phenotypes. More than half of these were in or near several genes, including cadherin 9 type 2 (CDH9) on 5p14, nuclear receptor subfamily 5, group A gene (NR5A2) at 1q32, gene deleted in colorectal carcinoma (DCC) on 18q21, and PPARG on 3p25. Some of the genes found to be associated with one or several bone density phenotypes have not previously been studied for osteoporosis. Analyses stratified by sex confirmed association of BMD phenotypes with CDH9 and DCC in women. The sex specificity of our findings is fully in line with the previous observations of both sex-specific linkage [ linkage ,21 and s linkage .IL1RL1) gene on 2q12 was associated with all bone mass traits. In contrast there was a notable absence of pleiotropic associations between BMD and hip geometry . This is an important observation in light of the current debates about the role of femoral bone geometry in fracture prediction above and beyond BMD, as well as a criticism of DXA-derived bone geometric traits, which are perceived as partially captured in the BMD measurement. We have clearly shown that BMD traits  share associations with SNPs in several genes, whereas there are no such SNPs that share associations with both hip BMD and hip geometric traits. Despite substantial shared genetic determinants between hip BMD and geometric traits (preliminary unpublished results from our group), these phenotypes are at least in part governed by distinct gene variants. Since genes contributing to variation in BMD do not always contribute to osteoporotic fractures [Our phenotypic subgroup combining BMD and the ultrasound trait, BUA, demonstrated that the interleukin 1 receptor-like 1  that were associated with BMD and ultrasound and SNPs in ANKH (rs2454873 and rs379016) that were associated with femoral neck section modulus.When we queried the FHS 100K association results, several SNPs in previously well-studied candidate genes for osteoporosis were identified (FBAT or GEE p < 0.05), such as rs1801133 in We observed limited overlap between the results of the association and linkage analyses, which may, in part, be attributed to a low power of the linkage analysis. The resolution of linkage peaks depends not only upon the extracted information content of the genotyped markers, but also upon the useful recombination information contained in the sample (which is related to family structure and sample size). Despite low power for detecting modest genetic effects in linkage analyses, we identified several loci with a substantial indication of linkage, such as LOD scores ≥3.0 on chromosomes 15  and 22  for bone geometry phenotypes. Also, LOD scores for BMD phenotypes generally increased after restricting our sample to the members of the Offspring Cohort only, despite potential loss of power due to smaller sample size. This observation indirectly supports our previous finding of refined LOD scores in subgroups of age  and may http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007, which makes it a unique resource contributing to ongoing efforts of other groups.To our knowledge, this is a first genome-wide association study of many of the most commonly measured osteoporosis-related phenotypes. It is distinct in many respects. First, this is a population-based sample rather than a study of affected families. Second, our study included adults from two generations of the same families. The longitudinal nature of the FHS and ongoing recruitment of the third generation  will enable us to validate the results of this study with an even larger sample of 3 generations. Third, results of this collaborative effort (FHS 100K SNP study) are publicly available on the web One of the strengths of this study lies in the unbiased approach that allowed simultaneous exploration of multiple genetic loci rather than focusing on a single-gene association. However osteoporosis is likely to be a polygenic condition with a strong environmental component. Therefore, an obvious limitation of this study is the absence of analyses testing gene-environment and gene-by-gene interactions , which iA second potential limitation of this study is that there may be unmeasured factors that would affect the association or impact on generalizability of our results. Sampling of homogenous individuals is needed to minimize these effects. We attempted to approach this problem in part by stratifying our sample by gender; however, a more involved stratification, such as younger men or postmenopausal women, was not performed due to sample size restrictions. A third potential shortcoming of this study is the limited coverage of the genome by the 100K SNP array. These SNPs are undersampled from coding regions (both non-synonymous and synonymous) and oversampled from regions outside genes, relative to SNPs in the overall HapMap database . CurrentFourth, at the present there is no clear-cut strategy for prioritization of the regions identified by our approach to whole genome analyses. We implemented one possible SNP prioritization strategy based on a somewhat arbitrary grouping of multiple phenotypes. Pleiotropy across different (but probably related) phenotypes may be an indication of genes that influence more than one biological pathway and thus may affect overall risk of osteoporosis.Finally, we are hesitant to regard any result reported in this paper as genome-wide significant. Although observing evidence for association at a significance level of .0001 in both the GEE and FBAT test is very encouraging, it does not meet the standard of genome-wide significance. Not only do we have a large number of statistical tests for each phenotype, but we also have numerous phenotypes . None ofAn unbiased genome-wide strategy to detect genetic associations with skeletal traits provides an opportunity to identify novel pathways for replication in other human populations. This study is thus hypothesis-generating and awaits replication in comparable samples. On one hand, ample data on genetic markers and associated traits may form the basis of new phenotype definitions, aimed at defining phenotypic groupings that are characterized by the sharing of genetic linkage and association. Such data may uncover biological mechanisms not obvious at the phenotypic level . On the BMD = bone mineral density; Chr = chromosome; DXA = dual-energy X-ray absorptiometry; FBAT = family based association testing; GEE = generalized estimating equations; GWA = Genome wide association; HWE = Hardy-Weinberg equilibrium; IBD = identity-by-descent; LD = linkage disequilibrium; LOD = logarithm of the odds; MAF = Minor allele frequency; QUS = bone quantitative ultrasound; SNP = single nucleotide polymorphism; VCA = variance component analysis.The authors declare that they have no competing interests.All authors have made substantial contributions to conception and design or acquisition of phenotypic data. JD participated in the design of the study and performed statistical analyses; DPK, DEK, SD, JMM, and KLL contributed to the analysis and interpretation of data. DK and DPK drafted the manuscript and revised it critically for important intellectual content. All authors read and approved the final manuscript.


Atherogenic dyslipidemia (AD) is a common feature in persons with premature coronary heart disease. While several linkage studies have been carried out to dissect the genetic etiology of lipid levels, few have investigated the AD lipid triad comprising elevated serum triglyceride, small low density lipoprotein (LDL) particles, and reduced high density lipoprotein (HDL) cholesterol levels. Here we report the results of a whole-genome screen for AD using the Framingham Heart Study population.Our analyses provide some evidence for linkage to AD on chromosomes 1q31, 3q29, 10q26, 14p12, 14q13, 16q24, 18p11, and 19q13.AD susceptibility is modulated by multiple genes in different chromosomes. Our study confirms results from other populations and suggests new areas of potential importance. Atherogenic dyslipidemia (AD) is characterized by three lipid abnormalities: elevated serum triglyceride, small low density lipoprotein (LDL) particles, and reduced high density lipoprotein (HDL) cholesterol levels . This lith percentile for age and sex; and 2) serum HDL cholesterol at or below the 25th percentile for age and sex – as given in NHANES III. Individuals with both high triglycerides and low HDL as defined above were considered "affected", while those with neither were considered "unaffected". Affection status was "unknown" in the rest . LDL values were not included in the data set.In this study, we used the Framingham Heart Study population included in Problem 1 distributed to Genetic Analysis Workshop 13 participants. AD was operationally defined as follows: 1) serum tryglyceride level at or above the 90n = 394), and then only for 12 individuals. One individual was found to be affected with the AD, 9 were unaffected, and 384 had unknown status. Triglycerides and HDL data are available on all five occasions (spanning two decades) in the offspring cohort (n = 1308). Individuals were classified as having AD if they fulfilled the operational criteria at any occasion (n = 119); conversely only individuals who were unaffected on every occasion when data were available were categorized as unaffected (n = 598) – the rest had unknown status (n = 591).Triglycerides and HDL data are available for only one year (1971) in the original cohort  and AFM031XA5 (16q24)  and of Vionnet et al. [Several chromosomal areas showed some evidence of linkage by more than one statistical method: 1p (parametric and MLS), 1q (parametric and Haseman-Elston), 14q (NPL and MLS), 16q (parametric and Haseman-Elston), and 19q (parametric and NPL). The latter, 19q13, is particularly important given that this is the area of the ApoC2/ApoE/ApoC1/ApoC4 gene cluster. Several studies have reported significant LOD scores for components of the metabolic syndrome in this region. For example, Elbein and Hasstedt  reportedt et al.  in FinniAtherogenic dyslipidemia susceptibility is modulated by multiple genes on different chromosomes. Our analyses provide some evidence for linkage to atherogenic dyslipidemia on chromosomes 1q31, 3q29, 10q26, 14p12, 14q13, 16q24, 18p11, and 19q13. Our study confirms results from other populations and suggests new areas of potential importance.


Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait.Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes. Use of linear models for the estimation of genetic parameters for categorical traits violates basic assumptions of mixed linear model methodology. Algorithms have been developed to transform linear model estimates to the underlying liability scale in order to compensate for the estimation bias caused by analysis of non-linear traits in linear models, but transformed genetic parameter estimates might still be significantly biased e.g., -4]. Use . Use 4]]Markov chain Monte Carlo (MCMC) methods such as Gibbs sampling (GS) make it feasible to implement multivariate threshold models or multivariate mixed linear-threshold models. Animal models fully use the available pedigree information, but accuracy of genetic variance and covariance estimates and convergence of GS chains might be a problem in the case of low trait prevalences and few observations per individual e.g., ,8-10]. T-10. T8-1In animal breeding, health data are often recorded using discrete categories, whilst most performance traits are continuous. In the horse, binary coding has been used for investigations on radiographic findings in the equine limbs, and high prevalences of radiologically visible alterations, mostly in the range of 10–25%, have been determined in the limbs of young horses e.g. -17]. Thi. Thi17]]Quantitative trait loci (QTL), i.e. genome regions which include genes that influence the phenotype of an individual with respect to a particular trait, have been identified for production and health traits in different species . IncreasThe aim of this study was to characterize the properties of multivariate estimation of genetic parameters for categorical, continuous and molecular genetic data using linear-threshold animal models and Gibbs sampling. Impact of data structure and quality of molecular genetic data on the accuracy of genetic parameter estimates was studied in the context of important radiological health traits in Warmblood horses.The number of rounds of Gibbs sampling that had to be discarded as burn-in ranged between 5000 and 53000. Mean lengths of burn-in ranged between 8100 and 13866 rounds in the analyses of the different datasets. Mean, minimum and maximum length of burn-in were lowest in the analyses of dataset A2 and highest in the analyses of dataset B1.g12 in analyses of datasets B2 and C2.Mean, minimum and maximum ESS of heritabilities and additive genetic correlations by dataset and quality of genotype information on T2 are given in Tables Mean MCE was 0.016 to 0.017 for heritability of T1 in analyses of datasets B1 and C1, and 0.002 to 0.010 for heritability of T1 in analyses of datasets A1, A2, B2 and C2 and heritabilities of the binary traits (T2 to T5) in analyses of all datasets. Mean MCE for additive genetic correlations ranged between 0.001 and 0.004 in all analyses.2 = 0.487–0.667 for T1, h2 = 0.058–0.171 for T2, h2 = 0.193–0.343 for T3, h2 = 0.081–0.170 for T4, and h2 = 0.210–0.354 for T5. Ranges of estimates for the simulated additive genetic correlations were rg12 = 0.009–0.327, rg13 = 0.102–0.316, rg14 = -0.295 to -0.034, and rg45 = -0.506 to 0.150.Genetic parameter estimates differed little between scenarios r0p9, r1p9 and r0p7. In the analyses of the six datasets ranges of heritability estimates were hMean, minimum and maximum bias of heritabilities and additive genetic correlations by dataset and quality of genotype information on T2 are given in Tables Simulated categorical, continuous and molecular genetic data were used for multivariate estimation of genetic parameters in mixed linear-threshold animal models via Gibbs sampling. General statements on the properties of multivariate analyses in mixed linear-threshold models require validation using independent sample datasets. For this study, simulation of multiple populations per study scenario and random sampling of one set of datasets per population was straightforward, but computationally expensive. In a preliminary study we therefore tested an alternative approach, with simulation of one population per study scenario and repeated random sampling of sets of datasets from one population. Because results regarding effective sample sizes and biases were not statistically different under these two approaches (results not shown), the computationally less expensive approach was chosen for the main study, i.e. replicate datasets were generated by repeated random sampling from single multi-generation populations.Datasets used for the analyses differed in the number of animals with trait information, the distribution of animals with trait information and the availability of marker genotype information. The simulated pedigree structure resembled that of the Hanoverian Warmblood horse, and simulated data resembled the distribution of radiographic findings in the limbs of young Warmblood riding horses . The tra2 = 0.25) were over- or underestimated by maximally 10 percent. If heritability of the binary trait was lower (h2 = 0.10) and only phenotype information was considered, mean bias increased to up to 25 percent over- or underestimation. These values are in agreement with literature . Heri. Heri2]]terature  and cleaterature  reported2 = 0.10) resulted in downward bias of continuous-binary correlation estimates by up to 50 percent, i.e. estimates for both positive and negative additive genetic correlations were closer to zero than the simulated values. Estimates for the positive additive genetic correlation between the continuous and the moderately heritable binary trait (h2 = 0.25) were on average less biased, with upward bias of up to 18 percent in the small datasets and downward bias of up to 8 percent in the larger datasets. Ranges of biases were similar for all continuous-binary correlation estimates. Estimates for the positive additive genetic correlation between two of the binary traits showed considerable variation of bias and were on average overestimated by 13 to 38 percent. Variation of bias was larger if information on animals from only one generation was considered, and overestimation was larger if information on animals from two subsequent generations was considered. Underestimation of positive additive genetic correlation between one continuous trait of moderate heritability (h2 = 0.25) and binary traits of low heritability (h2 = 0.05 or 0.10) and low prevalence (0.05 or 0.15) has been reported previously [Bias of additive genetic correlations between continuous and binary traits was dependent on heritability of the binary trait, but largely independent of the sign of the simulated correlation. Low heritability of the binary trait , and negative between T1 and T4 and between T4 and T5 (rg = -0.20). For T2 two QTL and two flanking markers per QTL with five randomly distributed and equally prevalent alleles each were simulated. Linkage between markers and corresponding QTL was assumed, with one of the marker alleles being associated with the unfavorable QTL allele, therewith indicating increased probability of affection with regard to T2. We assumed that the closely linked markers and the unfavorable allele of each of the markers had been identified in previous association studies, allowing the distinction between individuals homozygous positive, heterozygous or homozygous negative for the unfavorable allele of the respective marker. Only additive effects and no dominance effects were simulated, and total QTL variance was set equal to the polygenic variance. In order to study the effects of different quality of QTL information on the estimation of genetic parameters, three scenarios with respect to recombination rate between markers and QTL (r) and polymorphism information content (PIC) of markers were simulated: r = 0.00 and PIC = 0.9 for all markers (r0p9); r = 0.01 and PIC = 0.9 for all markers (r1p9); r = 0.00 and PIC = 0.7 for all markers (r0p7). In this context, a recombination rate of 0.01 between a marker and the QTL will mean that given the unfavorable marker allele, the QTL allele will be unfavorable with probability p = 0.99. A recombination rate of 0.00 between a marker and the QTL will mean that given the unfavorable marker allele, the QTL allele will be unfavorable with probability p = 1.00. This case is identical with the situation in which the causal mutation is definitely known. For the categorical traits, simulation of liabilities on the linear scale was followed by dichotomization in order to obtain trait prevalences of 25%  or 10% . Details on the simulation procedure are summarized in Table Simulated data, which resembled the situation encountered in the population of the Hanoverian Warmblood horse, were used for this study. Characteristics of this population include a high percentage of artificial insemination and coexistence of many small breeding farms and few large studs. The number of horses used for breeding is rather low when compared to the total number of horses, but data collection for genetic analyses is not limited to broodmares and sires. We simulated fixed effects, residual and additive genetic variances for one continuous trait (T1) and liabilities of four categorical traits T2 to T5), and QTL effects for the liability of one of the categorical traits (T2). Heritabilities were set to 0.50 (T1), 0.25  and 0.10 . Simulated additive genetic correlations were positive between T1 and T2 and between T1 and T3  was adopted in order to ensure posterior propriety. The fixed effects of sex and contemporary group were considered in all analyses. The fixed effect of marker genotype was considered in the analyses of datasets C1 and C2 only, distinguishing between individuals homozygous negative for the unfavorable alleles of all genetic markers, individuals heterozygous for the unfavorable allele of at least one of the genetic markers, and individuals homozygous for the unfavorable allele of at least one of the four genetic markers.Genetic parameters were estimated using Gibbs sampling with the threshold version of the Multiple Trait Gibbs Sampler for Animal Models (MTGSAM) , a softwyijlm = μ + SEXi + CONTj + al + eijlm, andyijklm = μ + SEXi + CONTj + QTLk + al + eijklm(datasets C1 and C2),yijlm(yijklm) = observation on trait T1 (continuous) or on trait T2, T3, T4 or T5 (binary) for the lth animal,with μ = model constant,SEXi = fixed effect of the sex of the animal (i = 1–2),CONTj = fixed effect of the contemporary group ,QTLk = fixed effect of the QTL marker genotype (n = 1–3),al = random additive genetic effect of the lth animal , andeijlm(eijklm) = random residual.est) from the true, i.e. simulated, values (partrue).The total length of the Gibbs chain was set to 205000 in all analyses, and all samples after 5000 rounds of burn-in were saved. Convergence of the Gibbs chain and the need for additional rounds of burn-in to be discarded was checked by visual inspection of sample plots. Effective sample size (ESS) and Monte Carlo error (MCE) was calculated for all (co)variance estimates by the times series method implemented in the post-Gibbs analysis program POSTGIBBSF90  with a tThe influence of data structure and quality of genetic marker information on ESS and bias was tested via analysis of variance using the procedure GLM of Statistical Analysis Systems, (SAS), version 9.1.3 . Effective sample size or bias were considered as dependent variable, and dataset  and quality of genetic marker information  were considered as fixed effect.KFS designed and carried out the simulation study, and drafted the manuscript. OD conceived of the study, participated in its design, and revised the manuscript. IH participated in conception and design of the study, coordinated it, and revised the manuscript. All authors read and approved the final manuscript.


Subclinical atherosclerosis (SCA) measures in multiple arterial beds are heritable phenotypes that are associated with increased incidence of cardiovascular disease. We conducted a genome-wide association study (GWAS) for SCA measurements in the community-based Framingham Heart Study.Over 100,000 single nucleotide polymorphisms (SNPs) were genotyped  in 1345 subjects from 310 families. We calculated sex-specific age-adjusted and multivariable-adjusted residuals in subjects tested for quantitative SCA phenotypes, including ankle-brachial index, coronary artery calcification and abdominal aortic calcification using multi-detector computed tomography, and carotid intimal medial thickness (IMT) using carotid ultrasonography. We evaluated associations of these phenotypes with 70,987 autosomal SNPs with minor allele frequency ≥ 0.10, call rate ≥ 80%, and Hardy-Weinberg p-value ≥ 0.001 in samples ranging from 673 to 984 subjects, using linear regression with generalized estimating equations (GEE) methodology and family-based association testing (FBAT). Variance components LOD scores were also calculated.-5 by GEE and five SNPs with p < 10-5 by FBAT for multivariable-adjusted phenotypes. Among the associated variants were SNPs in or near genes that may be considered candidates for further study, such as rs1376877  for maximum internal carotid artery IMT and rs4814615  for maximum common carotid artery IMT. Modest significant associations were noted with various SCA phenotypes for variants in previously reported atherosclerosis candidate genes, including NOS3 and ESR1. Associations were also noted of a region on chromosome 9p21 with CAC phenotypes that confirm associations with coronary heart disease and CAC in two recently reported genome-wide association studies. In linkage analyses, several regions of genome-wide linkage were noted, confirming previously reported linkage of internal carotid artery IMT on chromosome 12. All GEE, FBAT and linkage results are provided as an open-access results resource at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.There was no association result meeting criteria for genome-wide significance, but our methods identified 11 SNPs with p < 10The results from this GWAS generate hypotheses regarding several SNPs that may be associated with SCA phenotypes in multiple arterial beds. Given the number of tests conducted, subsequent independent replication in a staged approach is essential to identify genetic variants that may be implicated in atherosclerosis. Myocardial infarction, stroke and other atherosclerotic cardiovascular diseases comprise the leading cause of death for men and women in the U.S. , and wilAPOE and ACE [CFH and age-related macular degeneration [INSIG2 and obesity [NOS1AP with QT interval variation [TCF7L2 [IGF2BP2 and CDKA1 with diabetes mellitus [While SCA measures are heritable, relatively little is known regarding the role of genetic variants in the interindividual variability in these quantitative measures of atherosclerosis. To date, results from candidate gene association studies for clinically apparent cardiovascular disease or subclinical disease have been inconsistent, though overviews of multiple studies seem to provide evidence for modest associations for variants in a number of candidate genes, such as  and ACE ,15. Genoneration , INSIG2  obesity , NOS1AP ariation , and sev [TCF7L2  as well mellitus -22. Addimellitus ,24 that mellitus -22. Of nmellitus ,24.With the availability of high resolution imaging of atherosclerosis phenotypes in specific arterial beds in community-based cohorts such as the Framingham Heart Study, GWAS analysis is now possible for quantitative SCA phenotypes in major arterial beds. Recently, over 100,000 single nucleotide polymorphisms (SNPs) were genotyped  in 1,345 subjects from 310 pedigrees in the Framingham Heart Study. The primary objective of this report is to present a brief summary of the results of this GWAS for SCA detected in the lower extremity arteries, carotid arteries, aorta and coronary arteries, by conduct of genetic association analyses and genetic linkage mapping. This report is one of a series of manuscripts from a collaborative project conducted by Framingham Heart Study investigators; the overall approach to this project's statistical genetic methods is summarized in an Overview manuscript that summarizes the 100K genome-wide association study .Participants from the Offspring Cohort of the Framingham Heart Study who underwent one or more SCA measurements and genotyping with the Affymetrix 100K GeneChip are included in the study sample; genotyping was completed in a total of 1,345 subjects . Original cohort subjects did not undergo the recently conducted MDCT or carotid IMT testing and as such were not included in this study. Of the 1,084 Offspring cohort subjects with genotyping, up to 984 participants with SCA phenotype information were analyzed. Details regarding selection of participants and genotyping are provided in the Overview .Determination of the clinical characteristics in Offspring participants reported here was obtained at study entry (baseline) and at each follow-up examination, including Offspring examinations 6 and 7. The methods of measurement of the covariates  used in these analyses have been previously described . For imaParticipants underwent carotid ultrasonography according to a previously reported standardized protocol at Offspring examination 6 (1995 to 1998) . ImagingAll studies were recorded on optical disk and read according to a standardized protocol . To quanMeasures of CAC and AAC were obtained between 2002 and 2005 in Offspring participants, between examinations 7 and 8. All eligible participants were imaged with an eight-slice multidetector computed tomography  of the chest, as previously described , as wellCalcium measurements were performed on an offline workstation  by a trained technician. Scoring of coronary calcification has been previously described, and we reported excellent intra and inter reader reproducibility for the CAC measurements . A calciAnkle-brachial systolic blood pressure measurements were obtained at Offspring examinations 6 and 7 according to a standard protocol by trained technicians, as previously described [The ABI is defined as the ratio of the average systolic blood pressure in the ankle divided by the average systolic blood pressure in the arm. The higher arm mean was used to calculate the ankle-brachial index for each leg. The lower of the two ankle-brachial index measurements was used for analysis.Details of the genotyping methods are available in the Overview . BrieflyIn total, of the 1,084 Offspring cohort subjects with genotyping, 673 to 984 participants with analyzable phenotype information were available for analysis. Residuals were created from multiple linear regression models in all subjects with the SCA measures, regardless of whether they were genotyped or not, to adjust phenotypes for covariates; these residuals were created in women and men separately. Covariate adjustment for blood pressure and/or hypertension was performed is several different ways. For ABI, a dichotomous measure for hypertension (systolic blood pressure > 140 or diastolic blood pressure > 90 or on treatment) was used. For CAC and AAC, systolic blood pressure and use of anti-hypertensive treatment were used as covariates. For carotid IMT, treatment-adjusted systolic blood pressure was used. As previously described, systolic blood pressures for those on treatment are imputed to estimate what these values would be if the subject were not on treatment . Ranked All association analyses were performed using either generalized estimating equations (GEE) or family-based association testing (FBAT). We evaluated associations of the SCA phenotypes using an additive genetic model with 70,987 SNPs meeting criteria above. Details regarding these analytic methods are provided in the Overview . Only reP < 0.01 in both GEE and FBAT. For instance, for ABI, we evaluated GEE and FBAT results for both age-sex-adjusted and multivariable-adjusted measures of ankle-brachial index from both examination cycle 6 and 7 (in Table P < 0.01) associated phenotypes. For identical proportions of significant phenotypes, SNPs were additionally ranked by the logarithm of the mean of GEE p-values.We further sought to identify SNPs with consistent associations with phenotypes within measurement groups using GEE and FBAT analyses. SNPs were selected based on associations with multiple correlated traits in both population- and family-based tests. We evaluated the following five phenotypic subgroups using sex-specific age-adjusted and multivariable-adjusted residuals Table : ABI, coTen SCA atherosclerosis measures were studied Table . For eachttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. The top 25 most statistically significant associated SNPs in GEE analyses are presented in Table -5 in GEE analyses. The top three associated SNPs are rs1376877  associated with maximum internal carotid IMT, rs2390582  associated with maximum CAC score, and rs3849150  associated with AAC score. The top 25 most significantly associated SNPs in FBAT analyses are shown in Table -5 by FBAT. The top three associated SNPs by FBAT are rs4814615  associated with maximum common carotid IMT, rs6053733  associated with mean common carotid IMT, and rs10499903  associated with ABI.Tables -4, 8.8 × 10-5, and 1.7 × 10-4, respectively) lying within a 15 kB region implicated in recent GWAS's for coronary heart disease. Similar associations were noted for age-and sex-adjusted residuals for CAC.When we examined below the top 25 associations on our list of nominally significant GEE results for various SCA phenotypes, there were significant associations with multivariable-adjusted CAC for several SNPs on chromosome 9, including three SNPs  for AAC, adrenergic, beta-2-, receptor (ADRB2) for CAC, myocyte enhancer factor 2C (MEF2C) and thrombospondin 2 (THBS2) for common carotid artery IMT, and cAMP-specific phosphodiesterase 4D (PDE4D) for ankle brachial index.Results for all multipoint LOD scores > 2.0 are displayed in Table In Table CCR5, FGB, ESR1, IL6, NOS3, TNFRSF11, ADM, CD44, and CCL2  the gene. In Table -8, and none of our association findings meet these criteria for genome-wide association., However, we did note a number of associations with p < 10-5 after rank-ordering SNP associations using GEE and/or FBAT analysis. While we expect that many of these genotype-phenotype associations may be false positives, the SNPs identified in this manner are nonetheless hypothesized to include SNPs that merit further follow-up. To our knowledge, this is the first report of a GWAS using SCA phenotypes.In this manuscript, we report the principle findings for a GWAS of SNPs from a 100K Affymetrix scan with SCA phenotypes in multiple major arterial beds. A conservative Bonferroni correction using this number of tests  would yield an approximate threshold of genome-wide significance to be 5*10The first replicated GWAS findings for clinically apparent coronary heart disease were recently reported by two independent groups, identifying an association with SNPs in a region of chromosome 9p21 ,24. InteBecause there may be distinct genetic determination for atherosclerosis occurring in individual vascular beds , we further examined for consistency of association of specific SNPs within five trait groups – ABI, common carotid artery IMT, internal carotid artery IMT, AAC and CAC. By this method of ranking associations, we identified distinct sets of SNPs, for which there was consistency of overlap with top SNP results by both GEE and FBAT Table . There iAPOE, ACE, and NOS3 [ALOX5AP [LGALS2 [An extensive literature exists for prior studies of candidate gene variation associated with clinical atherosclerotic cardiovascular disease, in particular coronary heart disease , as welland NOS3 . More re[ALOX5AP ,34 and L [LGALS2 . When weOur study was conducted in a moderate-sized, well-characterized community-based sample, and the conduct of SCA imaging was conducted without ascertainment for prior cardiovascular disease. Strengths and limitations of the GWAS in this sample using a 100K screen are discussed in the Overview . A strenhttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007., we provide a full disclosure of the totality of our results, including all "non-significant" associations. In light of the multiple association tests that result from a GWAS, a powerful and efficient follow-up approach to increase the statistical confidence for an association of common variants with complex traits is a staged design, in which a modest number of results from the first stage are then tested in a second independent sample and the combined statistical evidence considered [in silico replication of our initial findings in other GWAS studies. The latter approach will be feasible with completion of the planned dense GWAS  in over 9,000 men and women from the Framingham Heart Study [In conclusion, using a GWAS unconstrained by existing knowledge, we have identified new candidate SNP association hypotheses and further confirm some existing candidate gene and candidate SNP hypotheses. In particular, the evidence for association of several SNPs with CAC in the region of chromosome 9 that was recently reported to be associated with CHD or CAC in tens of thousands of subjects ,24 provinsidered . A furthrt Study .AAC = abdominal aortic calcium; ABI = ankle-brachial index; CAC = coronary artery calcium; FBAT = family-based association test; GEE = generalized estimating equations; GWAS = genome-wide association study; IMT = intimal-media thickness; LOD = logarithm of the odds; MDCT = multidetector computed tomography; SCA = subclinical atherosclerosis; SNP = single-nucleotide polymorphism.The authors declare that they have no competing interests.CJO conceived of the MDCT project, planned the analyses, and drafted and critically revised the manuscript. LAC assisted in planning andconducting the analyses, and inwriting and critically revising the manuscript. RBD assisted in planning the carotid measurements and contributed to critically revised the manuscript. CSF assisted in planning andconducting the analyses, and inwriting and critically revising the manuscript. UH planned the MDCT project and assisted in writing and critically revising the manuscript. SJH planned and conducted the analyses. EI critically revised the manuscript. CYL assisted in the analysis and critically revised the manuscript. JMM conceived of the ABI measurement project and contributed to drafting and critically revising the manuscript. JFP contributed to conceiving the carotid measurement project and contributed to critically revising the manuscript. PAW contributed to conceiving the carotid measurement project and contributed to critically revising the manuscript. SD planned and conducted the analyses, and drafted and critically revised the manuscript.


The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided. The Collaborative Study on the Genetics of Alcoholism (COGA) has utilized a multidisciplinary approach, bringing together expertise in many domains to study this complex and important health problem. COGA has been committed to sharing data with researchers in this field to expedite progress in understanding alcoholism and related phenotypes. COGA has also provided data to Genetic Analysis Workshop 11 (GAW11) , alon, alon6]]GABRA2 , GABRG3  . The median call rate over all 1,381 samples was over 95% with an estimated accuracy of greater than 99%. Two forms of quality control were performed on the samples before the final submission of the dataset to GAW14: calls on the X chromosome were checked against the labeled sex of each sample and families within the study were checked for Mendelian inheritance errors. These quality controls revealed 10 problematic samples (sex or pedigree inconsistencies)  in the samples supplied by COGA.® Mapping 10k Array data set to CIDR. SNPs were mapped to unique physical positions on NCBI genome build 34 and interpolated onto one of two framework genetic maps: deCode [Genetic maps were supplied with the Affymetrix GeneChip: deCode  and Mars: deCode . Because: deCode . These m: deCode .Production genotyping began on January 16, 2004 and the genotyping report files were delivered on March 5, 2004, a time span of 56 days from the receipt of DNA to data delivery. The files contained the DNA barcode ID, the locus ID, the genotypes, and the GenCall score for each genotype. As genotypes are designated by alleles A and B, an allele key file was provided with context sequence for each SNP and the designation for the nucleotides that represent alleles A and B.Genotypes were reported for a total of 1,376 DNA samples. Of the 4,763 SNP markers, Illumina reported genotypes for 4,752 resulting in a locus conversion rate of 99.77%. In addition, genetic map positions for each SNP were provided from observed meiotic recombination in 28 CEPH reference pedigrees as described in Murray et al. .Currently the laboratory methods used for the 10k Affymetrix assay include multiple manual steps involving the movement of DNA and reagents via a single-channel or multi-channel pipettes. As a result, one sample swap occurred in the CIDR lab. This sample problem was detected when checking for Mendelian inconsistencies, sex, and replicate errors. Identity by state sharing was calculated within families and across all samples in the dataset as an additional method to screen for problematic samples. Suspect samples were re-genotyped. Confirmation that the problems were resolved was achieved by checking lab-to-lab and within-lab replicates as well as confirming genotype concordance for 26 SNPs in common between the Affymetrix and Illumina datasets.Two versions of the data were provided to GAW Tables  and 2: r1) If a parent or two parents are inconsistent with a child, the genotype of the child will be zeroed out.2) If a specific parent is inconsistent with more than 1 child, the genotype of that specific parent will be zeroed out.3) If two parents are inconsistent with more than 2 children, the genotypes of the nuclear family will be zeroed out.After removing Mendelian inconsistencies, files in the format of MERLIN and Linkage PRE-MAKEPED were generated for each chromosome with 250 SNPs per file. SNPs within all chromosome data files were ordered according to the genetic map.Because the families and individuals selected for genotyping as part of GAW14 were a subset of families from both the initial and replication datasets used for COGA's published analyses, we do not expect that results will be identical to those previously published. This Genetic Analysis Workshop provides a remarkable opportunity to compare genome surveys using microsatellites to those using SNPs, in a very rich dataset that has both qualitative  and quantitative  phenotypes reflecting a common, complex disease, alcoholism. We hope that the data we have provided will serve as a stimulus for progress in the genetic analysis of complex diseases.ASO: Allele-specific oligonucleotideCIDR: Center for Inherited Disease ResearchCOGA: Collaborative Study on the Genetics of AlcoholismEEG: ElectroencephalogramERP: Event-related potentionalERO: Event-related oscillationGAW: Genetic Analysis WorkshopIRB: Institutional Review BoardsLIMS: Laboratory Information Management SystemLSO: Locus-specific oligonucleotideNIAAA: National Institute of Alcohol and AlcoholismNIDA: National Institute of Drug AbuseSNP: Single-nucleotide polymorphismSSAGA: Semi-Structured Assessment for the Genetics of AlcoholismSTR: Short tandem repeatCOGA authors  were responsible for the ascertainment, assessment, initial DNA preparation, and microsatellite genotyping. CIDR authors  were responsible for all DNA sample formatting and distribution, genotyping of replication sample subset on each platform at CIDR, quality control of all SNP data, and formatting of all SNP data released to GAW 14 participants. Affymetrix authors  and Illumina authors  were responsible for the SNP genotyping done with their platforms.


With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 . The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the  Quantitative geneticists attempt to understand variation between individuals within a population for traits such as height in humans and the number of bristles in fruit flies. This has been traditionally done by partitioning the variation in underlying sources due to genetic and environmental factors, using the observed amount of variation between and within families. A problem with this approach is that one can never be sure that the estimates are correct, because nature and nurture can be confounded without one knowing it. The authors got around this problem by comparing the similarity between relatives as a function of the exact proportion of genes that they have in common, looking only within families. Using this approach, the authors estimated the amount of total variation for height in humans that is due to genetic factors from 3,375 sibling pairs. For each pair, the authors estimated the proportion of genes that they share from DNA markers. It was found that about 80% of the total variation can be explained by genetic factors, close to results that are obtained from classical studies. This study provides the first validation of an estimate of genetic variation by using a source of information that is free from nature–nurture assumptions. The theoretical basis for the resemblance between relatives due to genetic factors was developed by R.A. Fisher in a now famous and classic paper that reconciled Mendelian and biometrical genetics . FollowiIn human populations, the interplay of genetic, environmental, and cultural factors that cause family resemblance is complex; and crucially, the ultimate separation of nature and nurture effects can generally not be tested empirically through controlled experiments. If the true (unknown) effects causing between-family variance deviate from the assumed model of family resemblance, then the resulting estimates of genetic parameters, and their estimated standard errors (SE), will be biased. This bias could be severe if strong assumptions are necessary to estimate genetic parameters. In the classical twin design, only three underlying parameters are estimated, and strong assumptions regarding the causes of familial resemblance are necessary. For example, the assumption that twin resemblance due to common environmental effects is the same for MZ and DZ pairs is often made. Although some of these assumptions can and have been tested empirically ,9, the uUntil now, it has been impossible to exclude a possible confounding between genetic and non-genetic causes of family resemblance. We propose an alternative approach to estimate genetic variance that is based upon the observed proportion of the genome that is shared by relatives and does not make any assumptions about the variation between families.The actual genome-wide relationship, defined as the proportion of the genome that two relatives share IBD, varies around its expectation because of Mendelian segregation –14, exceIn this study, we estimated heritability for height in humans without making any assumptions regarding the causes of resemblance between relatives. We present the relevant theory and estimate the heritability of height from collections of 3,375 full-sib pairs, using genome-wide estimates of actual additive genetic relationships. Bias and accuracy of our estimation approach was explored analytically and by computer simulation. Ours is the first example of an estimate of heritability in humans for which a possible confounding between nature and nurture can be excluded.2). However, the SE is under-estimated because not all pair-wise sib comparisons are independent, so that the departure from expectation is less significant than it appears from the reported p-values. The SD of the mean (additive) IBD and mean IBD2 (dominance) sharing proportions were 0.036 and 0.040, respectively. One quality control measure of our IBD calculations is to test for independence of chromosome-specific additive and dominance relationships. For the combined dataset, 8/231 and 2/231 Spearman rank correlations of the mean IBD sharing between chromosomes were significant at the 0.05 and 0.01 level, respectively, when 12 and 2 were expected under the assumption of independent segregation. For IBD2 sharing the corresponding numbers were 9/231 and 1/231. The observed numbers are not significantly different from expectation under the null hypothesis of independent segregation of chromosomes .There were a total of 4,401 quasi-independent sibling pairs with estimates of genome-wide IBD sharing statistics. The average proportion of the genome-shared IBD between the sib pairs (the coefficient of additive genetic variance) was 0.498 , with a range of 0.374–0.617. The distribution of the genome-wide additive coefficients is shown in n = 3,375 pairs), the 95% confidence interval (CI) was from 0.46 to 0.85 (a SE of approximately 0.1) with strong statistical support (p = 0.0003) for a variance associated with genome-wide IBD. The SE of the estimated proportion of variance due to additive genetic variance 2) is more accurate. For the combined dataset, the ML estimate of this proportion is 0.80, with a 95% CI of 0.62–0.85. Hence, we have estimated the equivalent of an MZ correlation without having such pairs in our data.ML estimators of heritability are shown in The estimates from the FE model reflect the sibling correlation of 0.40 and 0.39 for the adolescent and adult datasets. Estimates of the proportion of variance due to additive genetic effects from the AE model . However, there is unlikely to be sufficient power to distinguish these components with our sample size, consistent with our observed correlation coefficient of 0.89 between the additive genetic and dominance coefficients .We have shown that it is feasible to estimate genetic parameters solely from segregation within families, without making any assumptions regarding an underlying model for between-family effects. In fact, our only assumption in the analysis is that the additive genetic covariance between relatives is proportional to the actual proportion of the genome that is shared IBD. The resulting estimates of the heritability for height  and residual family effects  are very close to estimates from twin studies , where tPreviously, methods have been proposed to estimate kinship and genetic parameters from marker data when pedigree data are not available, for example, in natural populations –18. RelaWe do not suggest that all estimation of genetic (co)variance from classical designs that utilize between-family comparisons should be abandoned. On the contrary, such designs, for example, those employing twin families, are in principle powerful enough to separate genetic and non-genetic causes of family resemblance if the statistical models are correct or at least a good approximation of the true underlying causes of variation. With sufficient data, our approach allows the testing of hitherto untestable underlying assumptions in other models and, for large samples, allows the estimation of non-additive genetic variation for disease susceptibility and quantitative traits. Therefore, the two methods should be seen as complementary.1/8 2 + r4 = [(1 − r)2 + r2]2. Hence the covariance of the indicator variable (δ) at loci i and j is:Dominance variance is a function of the probability that two siblings share both alleles IBD = IBD2). In a non-inbred population, this probability is also called the coefficient of fraternity )f2/σ(h2/σ(ĥ2), respectively. This was validated using simulations (unpublished data).Hence, the SE of the estimate of the non-genetic familial resemblance is approximately half of the SE of the estimate of the genome-wide heritability. The above SE of the estimates can be used to calculate the probability that the ML estimate is zero, using standard normal distribution truncation theory  with tru2f = 0 then the estimate of the heritability is approximately twice the intra-class correlation of the sibs. Hence, asymptotically,When the true residual familial component is zero, the ML estimate is zero with a probability of ½, and > 0 with a probability of ½ ,28. When2f > 0, the mean estimate of the familial component is, approximately,When the estimate of i the mean value of a truncated standard normal distribution. For a truncation value of 0, as is the case here, i = 0.798 [with  = 0.798 . The var2f gives the mean and variances as:Taking the whole of the distribution of the estimate of andSimilarly for the estimate of the heritability,andEquations 23, 30, and 31 were used to predict the mean and SE of the estimate of the heritability and were found to be close to simulation results for large samples . For smaij = μ + Fi + Aij + Eij, with μ the fixed effects of the mean and F, A, and E the random effects of non-genetic family, additive genetic, and residual factors, respectively. The covariance between the phenotypes of two siblings is modeled as cov = var(Fi) + cov = σF2 + πa(i)σA2, and cov = 0 if i ≠ j. Extensions to non-additive models are straightforward, in principle. For example, the covariance for a model containing dominance (D) and additive-by-additive (AA) effects is: cov = σF2 + πa(i)σA2 + πd(i)σD2 + πa(i)πa(i)σAA2.The basic additive genetic model, fitted in both the simulation study and data application, is YSimulations were performed to validate the predictions of the sampling variance of the heritability and statistical power. Genome-wide IBD sharing between pairs of sibs and their phenotypes were simulated from a simple model,with μ, F, A, and E defined as before, with distributionsn) in the simulation was either 2,500 or 10,000; heritability values were 0.4, 0.6, and 0.8; and the proportion of variance due to non-genetic family effects was either 0.0 or 0.2. For each set of population parameters, 1,000 replicates were run. Power was calculated using Web-based software for power of QTL analysis [Regression and ML analyses were performed . The nuanalysis  at a typWe estimated the mean and variance of genome-wide IBD sharing from 4,401 quasi-independent full-sib pairs, and applied the ML estimation method to 3,375 quasi-independent full-sib pairs with both marker data and phenotypic measurements on height. These data were collected from two cohorts of Australian twins and their siblings. Phenotypes for the adolescent cohort were collected in the context of continuing longitudinal studies examining risk factors for melanoma  and cognGenotypic information was available for a subset of the adolescent and adult participants. For the adolescent cohort, genotypic information was available for 1,201 individuals from 500 families, yielding 950 quasi-independent full-sib pairs. Genotypic information was available for up to 791 autosomal markers. The number of markers per participant in the current study ranged from 211 to 791, with a mean and SD of 588 and 194, respectively, giving an average marker spacing of 6 cM per genotyped individual. The genotyping, error checking, and cleaning of these data have been described in detail elsewhere . For thePhenotypes for height were missing on 481 individuals, eight in the adolescent cohort and 473 in the adult cohort. The number of sib pairs for which both individuals had a measured phenotype for the adolescent cohort, the adult cohort, and the combined cohort was 931, 2,444, and 3,375, respectively.IBD probabilities at 1 cM intervals were calculated using Merlin , and theEach dataset was first adjusted for fixed effects, using a general linear model in which sex was fitted as a fixed factor and age at measurement as a linear covariate. Residuals from this analysis were standardized by the residual variance for each dataset because there was some evidence of heterogeneity of variance: the residual SD for the adolescent and adult dataset was 7.71 cm and 6.89 cm, respectively.i was modeled as cov = σF2 + πa(i)σA2, with πa(i) the estimate of the genome-wide actual additive relationship of the sibling pair. Reduced models FE and AE were subsequently fitted. A likelihood-ratio-test was performed to test the null hypothesis that A was zero, by comparing the MLs of models FAE and FE. A p-value was calculated assuming that the test statistic has an asymptotic distribution that is 0 with a probability of ½ and a one degree of freedom χ2 with a probability of ½ [ML analysis was performed using Mx . The fulity of ½ ,28. CIs


Anopheles gambiae: Mbita strain from western Kenya and Ifakara strain from Tanzania.The use of transgenic mosquitoes with parasite inhibiting genes has been proposed as an integral strategy to control malaria transmission. However, release of exotic transgenic mosquitoes will bring in novel alleles along with parasite-inhibiting genes that may have unknown effects on native populations. Thus it is necessary to study the effects and dynamics of fitness traits in native mosquito populations in response to the introduction of novel genes. This study was designed to evaluate the dynamics of fitness traits in a simulation of introduction of novel alleles under laboratory conditions using two strains of An. gambiae strains. These two geographically different strains were cross-bred and monitored for 20 generations to score fecundity, body size, blood-meal size, larval survival, and adult longevity, all of which are important determinants of the vector's potential in malaria transmission. Traits were analysed using pair-wise analysis of variance (ANOVA) for fecundity, body size, and blood-meal size while survival analysis was performed for larval survival and adult longevity.The dynamics of fitness traits were evaluated under laboratory conditions using the two th generation compared to founder strains. Adult longevity had a significantly higher mean up to the 10th generation and average blood-meal size was significantly larger up to the 5th generation, indicating that hybrids fitness is enhanced over that of the founder strains.Fecundity and body size were significantly higher in the progeny up to the 20Hybridization of the two mosquito populations used in this study led to increased performance in the fitness traits studied. Given that the studied traits are important determinants of the vector's potential to transmit malaria, these results suggest the need to release genetically modified mosquitoes that have the same or very similar backgrounds to the native populations. Anopheles gambiae cell lines have been successfully transformed with the Hermes element [Minos transposable element, bearing an exogenous gene, has been efficiently integrated into the genome of Anopheles stephensi [An. gambiae provides an unprecedented opportunity to study the genetic aspects of malaria transmission and to identify new targets for transmission blocking [Malaria is one of the most fatal infectious diseases in the tropics despite continued efforts to contain it -3. Manip element ,9 and thtephensi . A numbetephensi , salivartephensi , phosphotephensi  and mosqtephensi ,15. It htephensi . The avablocking .The success of the transgenic mosquito approach will depend on the spread and even the fixation of parasite-inhibiting genes into natural vector populations ,6. DespiAn. gambiae Mbita strain from western Kenya and Ifakara strain from Tanzania. These two strains were crossbred and monitored up to the 20th generation to determine: a) if the dynamics of gene frequency from either strain can be predicted based on selected fitness traits and b) at what generation the fitness traits stabilize. Since the two founder populations are geographically different, it is likely that the hybrids' fitness will be enhanced in the initial crosses. The fitness traits studied were blood-meal size, adult body size (measured by wing length), fecundity, larval survivorship, and adult longevity. These traits are important indicators of vector fitness as discussed in Yan et al [This paper reports the results of an experiment designed to evaluate the dynamics of genes under laboratory conditions using an et al .An. gambiae mosquitoes were used in this study. The Mbita strain was originally collected at Mbita Point  in west Kenya and has been maintained in the laboratory since 1999. The Ifakara strain was originally collected in Njage village, 70 km from Ifakara, southeastern Tanzania, and has been maintained under laboratory conditions since 1996. The colonized populations did not exhibit a significant reduction in the observed heterozygosity in six microsatellite markers, including AGXH1D1 and AGXH131 of chromosome X, AG2H46 and AG2H79 of chromosome 2, and AG3H29C and AG3H33C of chromosome 3 .Two strains (Mbita and Ifakara) of laboratory-reared The populations of the two parent strains used in this study were raised from mosquito eggs obtained from the existing colonies maintained at the International Centre for Insect Physiology and Ecology (ICIPE), Mbita Point Field Station in the mosquito-rearing insectary. Post- mated females were allowed to engorge from a volunteer's arm for 15 minutes to facilitate oviposition. Fully engorged females were isolated from non-engorged ones and placed in single oviposition cups. Each oviposition cup contained a wet Whatman filter paper disc at the bottom and a strip of the same Whatman filter paper leaning against the walls of the oviposition cup for the mosquito to perch on. During the oviposition period, the mosquitoes were maintained on a diet of glucose solution (6%) provided by a wick of cotton placed on top of the mesh covering the oviposition cup.® fish food and were subjected to natural ambient light. On pupation, the pupae were moved to standard 30 × 30 × 30 cm netting cages. After emergence, adult mosquitoes were held in cages and were offered 6% glucose and distilled water by means of moistened cotton wick placed on top of the mesh on the cages. The cages were kept in the insectary at ambient tropical temperature and under artificial light provided by fluorescent tubes. Relative humidity was maintained at ambient by basins of water placed in the insectary.The oviposited eggs were collected from the wet filter paper disks in the oviposition cups and transferred to plastic containers of distilled water. Upon hatching, the first in-star larvae were transferred to plastic trays measuring 21 cm in diameter. Larval densities were maintained at 300 larvae per tray in distilled water maintained at a depth of 8 cm. Trays containing larvae were fed daily on 30 mg of Tetramin1 generation from the two strains were obtained mixing 100 Ifakara females, 100 Ifakara males, 100 Mbita females, and 100 Mbita males in a single 30 × 30 × 30 cm cage. The F1 hybrid larvae were reared to adults under similar conditions. Subsequent generations from F2 to F20 were obtained by mass mating and reared under similar conditions with a population size maintained at approximately 1,000 individuals.Hybrids of the F0), and the hybrids between the two strains at the F1, F5, F10, F15, and F20 generations, was measured. In order to confirm the direction and effect of heterosis and also to control any assortative mating of mixed populations, reciprocal matings of Mbita male and Ifakara female and vice versa were was carried out, and their F1 and F5 progeny were subjected to measurement of fitness traits. The fitness components studied included mosquito fecundity, body size (measured by wing length), blood-meal size, larval survival, and adult longevity. All of these fitness traits were measured in two replicate experiments. Because the traits measured in this study could be sensitive to environmental parameters, the populations and filial generations used were reared in the same manner as previously described. The differences observed among the two founder populations and their different filial generations are therefore likely to be due to their genetic differences.The fitness of mosquitoes sampled from cage populations of the two single strains, Mbita and Ifakara  and measuring the corresponding absorbance. The absorbance of unfed female mosquitoes (n = 5) was also measured, and the average of their absorbance reading was used as a correction factor for the absorbance obtained from blood-fed females.® fish food, and were subjected to natural ambient light, and monitored daily. Dead larvae were recorded and removed; larvae that pupated were also recorded, removed, and then transferred to emergence cages. Mosquito adult survivorship of the two founder populations and their respective filial generations were examined. In each case, 50 individuals were put in a cage in the insectary and maintained on a glucose diet, as previously described. Mosquitoes were examined daily and dead individuals were counted and removed until the day the last individual died.Larval survivorship has a significant influence on the number of pupae and adult mosquitoes that emerge. Adult longevity, on the other hand, influences the number of gonotrophic cycles and the potential for pathogen transmission. For determining larval survivorship, 50 first in-star larvae of the founder populations and their filial generations were transferred into distilled water maintained at a depth of 8 cm in plastic trays measuring 21 cm in diameter. Trays containing larvae were fed daily with 30 mg of Tetramint+1) = 1 - p(At+1) - p(Bt+1), where p(ABt+1) is the proportion of hybrids between the two strains at generation t, p(At+1) is the proportion of Mbita homozygotes at generation t, and p(Bt+1) is the proportion of Ifakara homozygotes at generation t.The original fitness traits data were analysed using analysis of variance (ANOVA) for fecundity, body size, and blood-meal size. Larval and adult survival were subjected to survival analysis to compare the performance of phenotype traits between the founder parents, reciprocal crosses and each filial generation. Comparisons were also made between successive filial generations to infer the level at which maximum genotype hybridization was achieved. The significance of all statistical tests was set at 0.05. Statistical analyses were performed in MINITAB (Minitab Inc.) and SPSS (SPSS Inc.) computer software. The dynamics of population hybridization were also simulated in random-mating generations founded by Mbita strain (A) and Ifakara strain (B), using p. There was a significant difference in the fecundity of F1 and F10 (p < 0.05), but no significant difference between F1 and F15 or F15 and F20 (results not shown). Likewise, there was no significant difference in fecundity between F5 and F20 or F10 and F20.There was a significant difference in fecundity p < 0.001) between the Mbita and Ifakara strains, with the Ifakara strain having at least 20% more eggs than the Mbita strain in the two replicate measurements . This can be attributed to heterosis for this trait. The inter-progeny difference was significant between F1 and F5 (p < 0.05) but not between F5 and F10, F10 and F15, and F15 and F20(results not shown).No significant difference in wing length was found between the Mbita and Ifakara strains Table . However1 progeny (p < 0.05). All of the other filial generations had a significantly larger blood-meal size than the Ifakara strain (p < 0.001), whereas the F5, F10, F15, and F20 generations' blood-meal size did not differ significantly from the Mbita strain (Table 1 and F5 (p < 0.001), but not between F5 and F10, F10 and F15, and F15 and F20. Mean blood-meal size showed significant difference between F1 and F10, F1 and F15, and F1 and F20, whereas there was no significant difference between the means of F5 and F15, and F5 and F20. The mean blood-meal sizes of F10 and F20 were also not significantly different.An analysis of variance showed a significant difference in blood-meal size between the Mbita and Ifakara strains  and should, therefore, be considered in strategies aimed at controlling malaria transmission through the introduction of exotic and genetically modified mosquitoes. The results of this study show that fitness traits are subject to the effects of heterosis, i.e., hybrids have increased values for fitness traits than do the parent populations. Mosquito body size (measured as wing length) and fecundity showed heterosis up to the 20th generation. In both replicates, the 20th generation had significantly higher values (at least 5% longer wing length and 20% more eggs) than either parent. Blood-meal size also showed heterosis, with the F1 and F5 generations imbibing at least 50% more blood than the Ifakara strain. Subsequent generations were not significantly different from the Mbita strain, but showed significantly greater blood-meal size than the Ifakara strain. Adult longevity showed equally robust heterosis in the hybrid generations. The F1, F5, and F10 generations had, respectively, 40%, 66% and 100% higher mean longevity than the founding Ikafara strain; the 15th and 20th generations had mean longevity that was between the two founder strains but was higher than the Ifakara strain. Further analysis indicated that the maximum heterosis for body size, fecundity, and adult longevity occurred at the 10th generation, after which there was no significant pair-wise difference.In this study, the fitness consequences of population hybridization were examined in 20 filial generations from crosses between two geographically different These results can be attributed to increasing hybridization of the Ifakara and Mbita strains. In the experimental design used in this study, 100 adult mosquitoes of each sex from the two strains were put together to mate. In the first generation, there were more (Mbita × Mbita) and (Ifakara × Ifakara) matings. In subsequent generations, the genotypes mating were Mbita, Ifakara, and intercrosses of the founder populations, and therefore the probability of within-strain mating  reduced from generation to generation. The increasing trends of heterosis in the studied traits were due to increasing hybridization of the Mbita and Ifakara strains, as demonstrated in Figure Anopheles gambiae populations lead to enhanced fitness than the parental populations. In the proposed use of transgenic mosquitoes as a strategy to control malaria transmission, it is expected that transgenic mosquitoes with the inherent ability to resist malaria parasite development will be released into the field [th generation. If exotic genetically modified mosquitoes are introduced, it is, therefore, probable that the hybrids may live longer, exhibit higher fecundity, have larger body size and will likely feed more. Increased hybrid fitness would lead to stabilizing selection, rendering the refractory genes more difficult to be fixed. Given that greater mosquito density, fecundity, human biting habits and longevity of anopheline mosquito vectors are positively correlated with increased vectorial capacity [The results of this study show that hybridisation of different he field -6. By crhe field . If the capacity ,29, thesDM and TG conducted fitness studies and prepared the manuscript. DZ, AP and GZ analysed the data and assisted with manuscript preparation. GY, LG, and JCB conceived the design of the study. All authors read and approved the final manuscript.


Genetic and environmental variation are both known to influence development. Evolution of a developmental response that is optimized to the environment (adaptive plasticity) requires the existence of genetic variation for that developmental response. In complex traits composed of integrated sets of subsidiary traits, the adaptive process may be slowed by the existence of multiple possible integrated responses. This study tests for family (sibship) specific differences in plastic response to hypoxia in an integrated set of cardiovascular traits in zebrafish.Cardiac output, which is the integrated product of several subsidiary traits, varied highly significantly between families, and families differed significantly in the degree and direction of response to developmental oxygen level. The cardiac output response to oxygen environment was entirely family specific with no significant overall trend due to oxygen level. Constituent physiological variables that contribute to cardiac output all showed significant family specific response to hypoxia. Traits that were not directly related to cardiac output, such as arterial and venous diameter, and red blood cell velocities did not respond to hypoxia in a family specific manner.Zebrafish families vary in their plastic response to hypoxia. Genetic variation in plastic response to hypoxia may therefore provide the basic ingredient for adaptation to a variable environment. Considerable variation in the degree of familial response to hypoxia exists between different cardiovascular traits that may contribute to cardiac output. It is possible that the integration of several subsidiary traits into cardiac output allows the maintenance of genetic variance in cardiac response. What processes constrain and redirect the outcome of the adult phenotype? Many developmental studies at all biological levels attempt to answer this question. While genetic instructions provide the basic template for development, the ultimate phenotype also depends on environmental influences -3. RecenDevelopmental plasticity, in which juvenile environment shapes adult phenotype, is common  and may 2] < 5 mg l-1) to severe ([O2] < 1 mg l-1) hypoxia that triggers a suite of organismal, cellular, and genetic responses. In many cases, these responses are attenuated as the hypoxic exposure becomes chronic. Chronic hypoxia has a profound effect on fish behaviour [Detection of developmental plasticity is evolutionarily important when the environmental parameters are within the range of normal environmental variation and of a magnitude that they provide significant stress to the organism. In these cases, a developmental response may have evolved that buffers the effects of environmental variation. For example, many species of fish routinely experience mild . This species can commonly be found in fast moving river tributaries, slow moving and stagnant bodies of water [We chose to use a simple quantitative genetic design to investigate family specific response to developmental stress in zebrafish , end diastolic volume (EDV), and stroke volume (SV) Table . While dWhat processes constrain and redirect the outcome of the adult phenotype? We found that for cardiac output in larval zebrafish there is family specific developmental response to the environment Table . While pPlastic response to environmental variation is a major topic in evolutionary biology . Evolution of adaptive responses to variable environments requires heritable variation for plastic response. Given the proper selective environment, the variation in plastic response we find in cardiac output could be the raw material of future adaptation. To understand the dynamics of adaptation of a complex trait however, it is important to understand something about the way in which subsidiary traits contribute to that complex trait. If there are interacting traits that can combine in multiple ways to create optimal or near optimal response, then a rugged adaptive landscape will be produced ,24 that Cardiac output is the product of heart rate and stroke volume. Stroke volume is, in turn, the difference between end systolic and end diastolic volume. The family specific response to hypoxia for cardiac output could either be created by complex family specific interactions of these underlying traits, by family specific response in a single trait, or by a unified family specific response seen across all traits. Interestingly, all of the cardiac performance measures Table  showed sWhile all cardiac performance traits show family specific responses to the environment, only heart rate and ESV showed a significant trend in the response to hypoxia Table . When heVariation between families in cardiac performance in this study may be the product of both genetic and confounding maternal effects. While maternal effects on development can be important ,27, therin vivo measurement of blood pressure, it remains unknown how pressure is altered by either environmental or family differences.One curious finding is that, despite having the statistical power to detect small family differences in RBC velocity, oxygen environment did not influence RBC velocity either generally or in a family specific manner Table . Given tFamily-specific developmental responses to the oxygen environment play a major role in cardiac performance in zebrafish larvae. Genetic variation in plastic response to hypoxia may therefore provide the basic ingredient for adaptation to a variable oxygen environment via altered cardiac output. The complexity of physiological interactions that create that genetic variation may, however, slow the rate at which adaptation of plastic response to hypoxia should occur. While integrated cardiovascular response to hypoxia is family specific, red blood cell velocities were not altered by developmental environment at all, and arterial diameter changes in response to hypoxia were not family specific. It is therefore unlikely that these traits will evolve an adaptively plastic response to variable oxygen levels.Danio rerio) were obtained from Scientific Hatcheries Inc.  and populations of wild type and long fin gold strains were maintained as breeding stocks using standard husbandry procedures [Zebrafish were chosen for this study because breeding pairs are easily manipulated, clutch sizes are large, fertilization and development is external allowing manipulation of the developmental environment and optical measurements through the transparent body wall can be made throughout early development . Adult zocedures . A 14L:1Males and female were paired randomly both within and between strains to produce larvae. The mating pairs were placed in 2 L containers supplied with a common water source  to minimize variation in the maternal environment. All larvae used were from the first clutch of eggs produced by a given female. By using only the first clutch for each female, we removed inter-clutch variation which could otherwise be a significant source of clutch level variation that is attributable to maternal, non-genetic environment . We use 2 l-1) and the other group was placed into hypoxic water (dissolved oxygen 1.0 ± 0.2 mg O2 l-1). The system for housing the fish in both normoxic and hypoxic conditions is described in Marks et al. (2005). Briefly, the system is comprised of a partially split sump system  with O2 stripped from the hypoxic side of the sump by sparging with N2 in 2 m columns. Active transfer and diffusion allowed equilibration of water quality between treatments. The oxygen level and temperature were controlled by a YSI 5200 monitoring system.Eggs from each clutch were removed before the eight-cell stage and randomly and equally divided into two groups. One group was placed into normoxic water . All measurements were made with the larvae in their own developmental oxygen and temperature environment.At 96 hours post-fertilization, high-speed digital video recording was used for a series of morphological and physiological measurements. Larvae were placed in the treatment water in a temperature-controlled stage (Harvard Apparatus) under an inverted microscope (Leica DMIRB). Digital video was captured at 125 frames sec® software . Each diameter was measured six times per larvae per location within one frame and the mean of these measures was used as the vessel diameter.Videos of trunk vessels were used to measure the diameter both of the dorsal and caudal vein and artery at the cross section of the anus (cloaca) as described in . The act-1. The frame closest to the middle of a systolic cycle was used. Six distances were taken of six different red blood cells within one frame, and this procedure was repeated at six different frames in different beats to reduce sampling and measurement error.The velocity of red blood cells within the artery and vein was calculated from the high speed video files as described in . DistancHeart rate was measured by recording the time elapsed over 15 heart beats and extrapolating to beats per minute. This was measure was repeated six times per larvae and the mean heart rate was used as the individuals heart rate.To measure end systole volume (ESV) and end diastole volume (EDV), a single frame for each was chosen during the cardiac cycle, and the perimeter of the ventricle was outlined manually ,31-33. S2)/L) = Volume (nl)(((8/3Π)*Awith A representing area and L representing the length across the long axis. The calculation was performed on the mean values of each larva. To calculate stroke volume, ESV was subtracted from EDV.To obtain values for cardiac output, the values for mean stroke volume and mean heart rate for a given larvae were multiplied together. Thus,Cardiac Output (nl/min) = Stroke Volume (nl) * Heart Rate (beats/min).The purpose of this study was to determine if there is variation between families in the plastic response of the cardiovascular system to developmental oxygen levels. ANalysis Of VAriance (ANOVA) utilizing the general linear models procedure in the SAS software package  was used to test which, of several, sources of variation were significant contributors to variation in cardiovascular development. In these analyses, variance between family means would be indicated if Family was a significant source of variation. If developmental oxygen treatment (Environment) is a significant source of variation, it indicates that there is plasticity across environments. Testing for significant Family by Environment interaction (Family × Environment) allows detection of differences in the plastic response between families. In the absence of non-genetic maternal effects this term would comprise a portion of the Genotype × Environment term that represents the genetic variation in plastic response. Since examination of ANOVA residuals for violations of parametric assumptions did not indicate the necessity of any data transformations analysis was performed on untransformed data.MH participated in the design of the study, executed the experiments, collected and organized the data and produced a rough draft of the manuscript. FBGM participated in the design of the study, helped with its coordination, performed the statistical analyses and made a significant contribution to both the rough and the final draft of the manuscript. BB conceived of the study, participated in its design and coordination and helped in the drafting of both the rough and final copies of the manuscript. Manuscript revisions after reviewer comments were primarily completed by FBGM. All authors read and approved the final manuscript. FBGM and BB designed and constructed the environmental control systems.