The synthetic association hypothesis proposes that common genetic variants detectable in genome-wide association studies may reflect the net phenotypic effect of multiple rare polymorphisms distributed broadly within the focal gene rather than, as often assumed, the effect of common functional variants in high linkage disequilibrium with the focal marker. In a recent study, Dickson and colleagues demonstrated synthetic association in simulations and in two well-characterized, highly polymorphic human disease genes. The converse of this hypothesis is that rare variant genotypes must be correlated with common variant genotypes often enough to make the phenomenon of synthetic association possible. Here we used the exome genotype data provided for Genetic Analysis Workshop 17 to ask how often, how well, and under what conditions rare variant genotypes predict the genotypes of common variants within the same gene. We found nominal evidence of correlation between rare and common variants in 21-30% of cases examined for unrelated individuals; this rate increased to 38-44% for related individuals, underscoring the segregation that underlies synthetic association. A key assumption in genome-wide association studies (GWAS) is that multiple common genetic variants of relatively small effect should make up most of the functional variation responsible for common complex human diseases ,2. The gDickson et al.  raised thttp://www.1000genomes.org] to characterize the extent of correlation between rare and common variants within the same gene.An important aspect of synthetic association, as noted by Dickson et al. , is thatWe used the real exome genotype data from 697 unrelated individuals as provided by the 1000 Genomes Project as well as the simulated genotypes for 697 related individuals from Genetic Analysis Workshop 17 (GAW17) [P < 0.05) regressions was recorded. Because our interest in this study is to gain a sense of how often rare alleles appear in coupling with common variant minor alleles, our rather crude SRVGS ignores the possibility that rare variants may act to either increase or decrease a phenotype of interest. In a proper genome-wide association study this would be dealt with by using, for example, a more sophisticated collapsing method [All analyses were performed in SOLAR . Differeg method .In the family data, the regression models included the random effect of kinship and the fixed effect of the SRVGS. Results were compared for the unrelated individuals and family data sets; to account for the possibility of genotypic correlations introduced by selection, we also classified the results by type of polymorphism (synonymous vs. nonsynonymous changes in coding for amino acids).Principal components correction for population stratification was performed for one series of tests in unrelated individuals. Principal components were computed in R using the 10,113 synonymous SNPs; individual scores on the first four principal components were used as covariates in the stratification-corrected analyses.There was modest evidence of correlation between common and rare variant genotypes in the unrelated individuals, with 21–30% of regression tests showing nominally significant evidence  Table . This reAs noted in the Methods section, we classified regression results according to functional type (synonymous vs. nonsynonymous), expecting that selection might induce correlation of similar functional types. However, we saw no evidence of this in either the unrelated individuals or the family members Tables , 2.Both LD and synthetic association are based on correlations between SNP genotypes, although, as noted, synthetic association is observed to act over much larger genomic distances than LD. In our analyses of nonsynonymous rare and common SNPs in unrelated individuals, we found generally low levels of LD overall . There was little evidence of association between the average LD in the set and the regression results Figure , althougThe comparison between unrelated individuals and family members is complicated by the substantial number of rare variants that became monomorphic in the families as a result of founder effects; for direct comparisons of common variants, the mean (SD) number of rare variants was 10.28 (6.73) in the unrelated individuals and 4.58 (5.42) in the families. It is well known that familial segregation may stochastically amplify the copy number of rare variants . This may be reflected in the similarity in distribution of the SRVGS per common variant: mean (SD) for SRVGS = 0.0192 (0.013) in unrelated individuals and 0.011 (0.013) in families despite the difference in the number of rare variants.An interesting alternative explanation for the generally greater degree of genotypic correlation in the families is that the process of segregation induces gametic phase disequilibrium between rare and common variants. Although direct evidence of this is limited in our data, Table The phenomenon of synthetic association—association of common variants with disease because of accidental genotypic correlations between common marker variants and functional rare variants—implies that such correlations should occur fairly often. We have used the exome data provided by GAW17 to characterize the extent of such correlation without reference to any phenotype . In a random draw of common variants, we found evidence of nominally significant prediction of the CVGS by the SRVGS in about 20–30% of cases in unrelated individuals and in about 40% of cases in families. Thus stochastic correlation between rare and common variant genotypes appears to occur frequently enough that synthetic association need not be a rare occurrence. We found no evidence that this correlation is due to selection, at least by comparison of synonymous and nonsynonymous coding SNPs.Although the increase in evidence for genotypic correlation in families may be due to several factors, we find some evidence to support a role for gametic phase disequilibrium induced by familial segregation. Interestingly, Sun et al.  found a The authors declare that there are no competing interests.JB and RD conceived of the study. JWK designed the study, performed the analyses, and co-wrote the manuscript. VF co-wrote the manuscript. HHHG, LA, and TDD provided helpful commentary and technical advice. All authors read and approved the final manuscript.


For the family data from Genetic Analysis Workshop 17, we obtained heritability estimates of quantitative traits Q1 and Q4 using the ASSOC program in the S.A.G.E. software package. ASSOC is a family-based method that estimates heritability through the estimation of variance components. The covariate-adjusted mean heritability was 0.650 for Q1 and 0.745 for Q4. For the unrelated individuals data, we estimated the heritability of Q1 as the proportion of total variance that can be accounted for by all single-nucleotide polymorphisms under an additive model. We examined a novel ordinary least-squares method, a naïve restricted maximum-likelihood method, and a calibrated restricted maximum-likelihood method. We applied the different methods to all 200 replicates for Q1. We observed that the ordinary least-squares method yielded many estimates outside the interval . The restricted maximum-likelihood estimates were more stable than the ordinary least-squares estimates. The naïve restricted maximum-likelihood method yielded an average estimate of 0.462 ± 0.1, and the calibrated restricted maximum-likelihood method yielded an average of 0.535 ± 0.121. Our results demonstrate discrepancies in heritability estimates using the family data and the unrelated individuals data. The heritability of a trait is usually calculated using family data. The identified genetic variants found through genome-wide association studies account for only a small portion of heritability for most complex traits  comparedFor the family data, the heritability is the narrow sense heritability, estimated with the polygenetic effect model; we conducted a George-Elston transformation  to estimFor the family data, we chose to use quantitative traits Q1 and Q4 of four randomly selected data set replicates  consists of the sex, age, and smoking status of the ith individual, respectively, m unknown causal variants such that iz = , and ith individual at the jth causal variant is aa, aA, or AA, respectively, jf is the frequency of allele A and where m causal variants be:Let the effects of where nI is the identity matrix of order n,where y is:Then the variance-covariance matrix of X have the rank r , and let where where:andNote thatThus the slope and intercept of the regression of:ip − jp)′G(ip − jp) are G is unknown, it is replaced with an estimate. One naïve estimate is A, the genetic relationship of genome-wide SNPs. Yang et al. [A* for G by calibrating the prediction error of genetic relationship G of unobserved causal SNPs. Replacing G with A* in the regression, we can estimate the heritability as:on  form eight pedigrees. The pedigrees all have four generations of family members and a mean size of 87.13 individuals . The pedigrees include 194 sibships with a mean size of 2.55 . In the four randomly selected replicates, the heritability estimates for Q1 ranged from 0.608 to 0.698 with an average of 0.650; the heritability estimates for Q4 ranged from 0.687 to 0.773 with an average of 0.745 , which was 0.188 less than the mean for the family data. One possible reason is that the polygenic component (0.440) in Q1 is not due to any SNPs in the GAW17 sequence data set. We should not be able to uncover the polygenic effect using unrelated samples. However, the mean naïve REML estimate (0.462) is much larger than the heritability because of the causal SNPs (0.135). The reason is that we used all 24,487 SNPs to estimate the relationships among individuals. There might be other sources contributing to the heritability estimates.Finally, we failed to estimate the heritability for Q4 using the unrelated samples because of the convergence problem, which was the result of no genotyped exonic SNPs in the data contributing to the phenotypic variation.The authors declare that there are no competing interests.PBS performed the statistical analysis of family data and HQ performed the statistical analysis of the unrelated individuals data. PBS , HQ, JN and XZ drafted and revised the manuscript. XZ conceived the project, RCE criticized and edited the manuscript. All authors read and approved the final manuscript.


Molecular footprints of phenotypic variation are usually explored by two different approaches in trees. On the one hand, association genetics is seeking for statistical correlation between SNPs and traits owing to the significant heritability that is usually observed in progeny tests. On the other hand, detection of outlier Fst values of single SNPs has also been implemented to account for the very large differentiation of traits that are observed in provenance tests. The rationale of both approaches is based on the extensive within or between population genetic variability that has been widely recorded in fields tests. While the cataloguing of candidate genes has steadily increased in trees, so has the inventory of their diversity in natural or breeding populations. There is now a rapidly growing body of experimental results showing a very large discrepancy between the expectations of both approaches and the SNP variation that is monitored in candidate genes. Most association studies show that single SNPs explain usually less than 5% of the phenotypic variation of the trait, while Fst values are at best of the same value than neutral markers. In my presentation I will explore the reasons of the decoupling between trait and gene variation, by focusing on the multilocus structure of traits, as compared to the monolocus SNP information. Indeed, association studies and Fst outlier detection are essentially based on single locus approaches, while traits are multidimensional structures. There are at least three properties of multilocus structures that will be investigated: cumulativity, interactions and covariation of gene effects. I will show that cumulativity and interactions may explain the discrepancy in the case of association studies, while covariation of gene effects (at the between population level) explain the missing Fst of genes underlying adaptive traits. These conclusions are supported by experimental results, theoretical background and simulation predictions.


However, it is essential to recognize that the objectives of geneticists remain the same, although the technology allows the answer to questions that some years ago were unthinkable. With the myriad of statistical methods, experimental designs and phenotypic and molecular data that are now available a lucid and comprehensive vision of the objectives, findings and challenges facing nowadays by geneticists is more needed than ever. There is a renewed interest for Quantitative genetics since the genomic revolution. Quantitative or complex traits are those controlled by multiple genes so that the inheritance of such complex traits is non-Mendelian, although each individual gene shows Mendelian inheritance. With the advent of genomic tools, for example Next Generation Sequencing (NGS) and deep NGS sequencing of RNA transcript  with genes with an effect on the trait. Jack Dekkers, one of the pioneers of the application of genomic selection, indicates in his chapter that genomic selection increases the rate of genetic improvement not only by increasing the accuracy of breeding values, but also by reducing the generation interval. The author classifies the statistical methodologies into two main groups: Bayesian with a more or less arbitrary choice of prior distribution of gene effects and BLUP mixed models (GBLUP) which typically assume that genetic variance is distributed equally across all SNPs. For the SNP density used nowadays experimental results show similar prediction ability for Bayesian and for GBLUP; however, the SNP density is increasing as genotyping costs are dropping, and at higher SNP densities the Bayesian methods are expected to have higher prediction ability than GBLUP methods. Genomic selection only recently received attention from plant breeders, although, as discussed by Perez de Castro The study of complex traits in humans has a different final objective than in animals or plants: prediction of genetic risks to diseases rather than genetic improvement of the populations. However, the genetic approach is very similar and the main relevant question remains to predict the genotypic value of individuals. For the development of risk predictors for complex diseases the area under the receiver operator characteristics (ROC) curve can be used to assess the discriminative ability of a prediction model. Suzanne Rowe and Albert Tenesa, as explained in their chapter about human genetics, simulated data for different diseases and found that the discriminative ability is higher when all markers all included in the analysis, although the accuracy of prediction is very dependent on the underlying genetic architecture of the trait. This is in agreement with the principles of genomic selection, although the success of prediction methods for complex diseases will be limited by the disease prevalence and the heritability. Regarding the elucidation of the genetic architecture of quantitative traits, according to Hill, one of the more precise designs to analysis quantitative trait loci (QTL) is “association mapping” because it uses populations with low LD which increases the resolution for detecting QTLs. If animal breeders were the first to successfully apply genomic selection, human geneticists have carried out the more complete association mapping experiments until now. Rowe and Tenesa indicate that, by means of genome wide association studies (GWAS), there are more than 90 cancer loci susceptibility loci identified, 39 for type 2 diabetes and 71 for Crohn disease. According to the authors, most large common variants have now been discovered, although a huge amount of the genetic variability remains hidden; therefore the crucial question is how to capture the remaining variation. Dekkers performs an interesting analysis in layer chickens in which the genome was divided into 1000 windows of 1 Mb and the genetic variance of each window was estimated. The main conclusion is that over 50 % of the genetic variance resides in a large number of genomic regions spread across the genome approaching the infinitesimal model of quantitative genetics. The complexity of a huge number of genes together with pleotropic effects, which also could play a significant role according to some experiments discussed by Hill and by Rowe and Tenesa, make the study of the quantitative traits a very challenging task. For that reason, as pointed out for most of the authors of the issue, the underlying biological mechanisms remain elusive for most traits.If things look quite complicated with only genetic effects averaged over environments, genotype × environment (GE) interaction adds further complexity to our understanding of genetic variation. The main GE models with their advantages and disadvantages are reviewed and discussed in great detail by Jose Crossa who himself has developed several GE models. The author presents, for example, the factorial regression (FR) model which allows incorporating external environmental and genotypic variables into the model and is useful for finding the climatic causes of GE or for estimating effects and locations of Quantitative traits loci (QTL) and QTL × environment interaction (QEI). A multi-trait multi-environment (MTME) mixed model is also presented by Crossa with an example that illustrates the importance of studying jointly, not only different environments, but also different traits. The author indicates that most genomic selection methods are environment-specific and points out the need for the development of new multi-environment and multi-trait models for genomic selection. The author himself described the use of biplots and a LASSO Bayesian model to studied GE at the level of estimated marker effects.It is clear from the chapters of this issue that Quantitative genetics continue to be an active area of research with many groups working on the development of better human risk predictors or breeding values estimators using genomic information. Nowadays, one of the main challenges of quantitative geneticists is the integration and interpretation of the huge amount of information that is generated by new technologies. There is a great expectation for the possibilities of systems genetic for elucidating the genetic architecture of Quantitative traits, although time will tell if systems genetic meet the expectations. Anyway, as pointed out by one the author of this issue, the true is that much of the work it is still to be realised.I acknowledge Bill Hill for his invaluable help in the preparation of this issue. I would like also to acknowledge Bruce Walsh, Daniel Gianola and other anonymous referees that have improved its quality.


Mus musculus crosses, e.g., heterogeneous stock (HS), provide increased resolution for quantitative trait loci detection. However, increased genetic complexity challenges detection methods, with discordant results due to low data quality or complex genetic architecture. We quantified the impact of theses factors across three mouse crosses and two different detection methods, identifying procedures that greatly improve detection quality. Importantly, HS populations have complex genetic architectures not fully captured by the whole genome kinship matrix, calling for incorporating chromosome specific relatedness information. We analyze three increasingly complex crosses, using gene expression levels as quantitative traits. The three crosses were an F2 intercross, a HS formed by crossing four inbred strains (HS4), and a HS (HS-CC) derived from the eight lines found in the collaborative cross. Brain (striatum) gene expression and genotype data were obtained using the Illumina platform. We found large disparities between methods, with concordance varying as genetic complexity increased; this problem was more acute for probes with distant regulatory elements (trans). A suite of data filtering steps resulted in substantial increases in reproducibility. Genetic relatedness between samples generated overabundance of detected eQTLs; an adjustment procedure that includes the kinship matrix attenuates this problem. However, we find that relatedness between individuals is not evenly distributed across the genome; information from distinct chromosomes results in relatedness structure different from the whole genome kinship matrix. Shared polymorphisms from distinct chromosomes collectively affect expression levels, confounding eQTL detection. We suggest that considering chromosome specific relatedness can result in improved eQTL detection.Complex  The design and implementation of such experiments is straightforward; further, over time there have been marked improvements in data analysis  and DBA/2J (D2) inbred mouse strains; (2) the HS4 derived by crossing the B6 and D2 strains with the BALB/cJ and LP/J strains; (3) the HS-CC.FThe phenotypes used in this study are brain gene expression data described previously  analysis]. A publicly available method that implements a variant of SM analysis and is suitable for use in complex mouse crosses is the efficient mixed-model association  were obtained from Jackson Laboratory. The strains were randomly assigned a letter from A to H; the order of assignment is the order noted above. The goal of the breeding strategy described below was to create a small panel (32 families) of the HS-CC mice; for such a small panel, a completely balanced breeding design is not possible. At G1 the following reciprocal F1 were hybrids were formed, followed at G2 by forming the 48 possible reciprocal four-way crosses. The four-way crosses were then outbred following a similar design to that noted for the HS-CC. At G19 one male and one female from each family was randomly chosen for striatal gene expression analysis. Details of the sample preparation are found in  were obtained from the Jackson Laboratory. At G1 the 12 possible reciprocal F1 hybrids were formed, followed by the formation of the four possible reciprocal F2 hybrids. Eight males and females were randomly selected from each of the reciprocal crosses. From the 64 samples, high quality expression data were obtained for 56 samples.Male and female B6 and D2 mice were obtained from the Jackson Laboratory. The reciprocal F2, and 54 to 47 samples in the HS4.Gene expression data were obtained from the striatum using the Illumina WG 6.1 array exactly as described by the manufacturer. Data were imported into RStrip-level quantile normalization  and “true positives” (TP), based on the proportion of EMMA results recovered. The results span a curve within the unit square between the points  and , with chance overlap results falling on the main diagonal and perfect overlap including the  point; better overlap is denoted by the ROC curve “bending” more toward the  point and away from the main diagonal. Intuitively, more of a “bend” signifies that the TP are increasing faster than the false negatives as the threshold is increased.In order to quantify the level of agreement between the methods, we employed an adapted version of Receiver Operator Characteristic (ROC) analysis , and 54 to 47 samples (HS4).In an attempt to improve the concordance between the two methods, a series of additional data pre-processing steps were performed. First, the gene expression samples were systematically examined for array outliers. The approach closely followed the strategy outlined in , 13758 probes (HS4), and 10233 probes (HS-CC).Recent work has demonstrated that the physical configuration of the Illumina WG 6.1 BeadChip array has an effect on array performance  with the full genome kinship matrix, signifying that genetic relatedness is relatively uniformly distributed across the genome. However, for the HS4 and HS-CC data, only about half of the genomic intervals delineated by the available markers were correlated with the kinship matrix, implying that genetic relatedness is more varying across the genome.As previously demonstrated for CD-1 mice  while other pairs have correlation as low as 0.01 (chromosomes 2 and 17). We conclude that kinship information is unevenly distributed amongst the chromosomes; for example while some samples are related according to the chromosome 2, they could appear unrelated according to chromosome 17. The situation is very similar in HS4, where correlations between chromosomal specific kinship matrices and the full genome kinship matrix range between 0.3 and 0.5, while the lowest correlation between chromosomal kinship matrices is −0.02.p < 2 × 10−16, odds ratio 6.6), where odds ratios above 1 denote overlap above chance. We report odds ratios as a more informative value since, for all comparisons, the Fisher test p-values were extremely low. We also verified that the number of eQTLs detected by HAPPY in each interval did not correlate with the length of the interval between the markers. For the EMMA and the JM procedures, the overabundance was still significant but at much lower levels . This decrease in overabundance of eQTLs can be explained by the incorporation of the kinship information by the latter procedures. We find similar situation in HS4, where HAPPY results showed eQTL overabundance for selected interval/probe pairs (odds ratio 2.6), while overabundance was alleviated for EMMA and JM . This analysis was not applicable in the F2, where almost all of genomic intervals were correlated with the kinship matrix.In a similar manner to computing correlations between genetic relatedness matrices, a correlation value between the kinship matrix and an individual probe expression level can be computed. The absolute value difference in probe expression value between samples results in a square matrix; this matrix can then be correlated with the kinship matrix. In the HS-CC filtered dataset, of the 10,233 probes 279 probes displayed a correlation value >0.1 with the kinship matrix; slightly different thresholds did not substantially alter the results. We hypothesized that the 279 by 608 probe/interval pairs correlated with the kinship matrix will have an overabundance of eQTLs due to the kinship structure effects. In order to test this hypothesis, we computed the overlap between the selected marker/probe pairs and the total eQTLs detected by each method. Using the Fisher exact test, we found that the HAPPY results showed very significant overabundance of eQTLs for the selected probe/intervals  and we found 188 such probes. Additionally, we found 156 genomic intervals also correlated with the chromosome 1 kinship matrix. These combinations probe/intervals resulted in significant overabundance of detected eQTLs returned by all three methods, with odds ratios of 9.19, 3.89, and 3.02 for HAPPY, EMMA, and JM, respectively.Importantly, we note that in spite of incorporating the full kinship matrix information, EMMA and JM did not fully eliminate the overabundance of eQTLs due to the shared genetic information on chromosome 1. The situation is similar for almost all 19 chromosomes: HAPPY results are highly overabundant for interval/probe pairs that are correlated with the chromosomal kinship matrix . EMMA and JM are still affected, albeit with less severity: for EMMA the odds ratios varied between 1.89 and 3.89 (mean 2.56), while for JM the odds ratios varied between 1.16 and 3.02 (mean 1.82). These values are highly significant, but they are smaller than the odds ratios for HAPPY, signifying that correction by the full genome kinship matrix has a beneficial effect. However, in order to control for the effects of shared genotypes on chromosome 1, the analysis has to include the chromosome 1 kinship matrix. We accomplished this adjustment using the chromosome 1 kinship matrix, incorporated into the EMMA analysis. As expected, this procedure significantly reduced the amount of overabundance of eQTLs: the odds ratio dropped from 3.89 to 2.01.We performed two complementary comparisons of eQTL detection results: comparison across methods and a comparison across genetic backgrounds. The first comparison resulted in several steps that greatly improved detection reproducibility. The second comparison resulted in biological insights about the nature of genetic modulation of gene expression. We find that distributed genetic control of expression is detectable using genome-wide or chromosome-wide genetic relatedness.Our results indicate that a naïve search of eQTLs in HS populations can result in large number of discordant results. We find that including low reliability data in the analysis results in low reproducibility across methods. We believe that results not reproducible across methods are more likely to be artifactual, and the fact that we can substantially increase reproducibility across methods by appropriately filtering the data strengthens this argument.The eight founder inbred strains used in the development of the CC differ widely across a large set of phenotypes. When the same phenotypes are mapped in CC-derived populations, it is of interest to elucidate whether the effects of an ancient parental allele, now acting on a different genetic background, are concordant with the phenotype differences between the founder strains. Therefore, determining the likely origin of a genomic interval within CC-derived populations is desirable and HAPPY accomplishes this inference using an HMM approach. On the other hand, correcting for population kinship structure is also important, and EMMA accomplishes this in a computationally efficient manner, suitable even for the large datasets resulting from high throughput expression and genotyping studies. We retain both of these features in the JM procedure. As expected, JM preferentially detects eQTLs that were found by both HAPPY and EMMA. These results suggest that JM could an optimal approach for eQTL detection in complex crosses.The full genome kinship structure of our HS populations is complex due to the specifics of the breeding process. A possible factor contributing to this uneven relatedness between samples is the relatively small number of breeding generations for our HS-CC population. The large differences in relatedness across chromosomes are surprising and their origin is unclear. We hypothesize that a possible explanation lies in the chromosome specific recombination dynamics. Recombination frequency is uneven across the genome, some of the inbred lines display differences in recombination rates and the rate of recombination is heritable (Koehler et al., Regardless of the causes, the variability of the relatedness structure across the genome strongly affects eQTL detection. We observed significant overabundance of eQTLs for specific interval/probe pairs, which we were able to predict using the correlation structure between probes, genomic intervals, and kinship matrices. While the confounding effects of the full kinship matrix are attenuated by EMMA and JM procedures, the overabundance due to chromosomal specific kinship structure remains. A possible solution for this problem is the inclusion of additional kinship matrices in the analysis pipeline, as demonstrated by the HS-CC EMMA results on chromosome 1. However, the chromosome specific kinship information could be incorporated into any other methods that adjust for the full genome kinship structure.These results have potential biological implications. First, we show that large numbers of probes are correlated with either the full genome kinship matrix or with specific chromosomal differences between the samples. This implies that significant biological modulation of gene expression is due to correlated genetic polymorphisms distributed across large genomic regions. This “distributed” modulation of biological activity likely coexists and interacts with the one-to-one relationship between genotype and phenotype that is the focus of QTL studies. One approach of uncovering this distributed genetic control is the matrix correlation technique employed in the current study. A second biological implication regards the effect of population substructure on genetic associations. While we show that chromosomal specific effects have the potential to confound interval/marker associations, we also note the converse: it is possible for individuals that are not closely related according to the full genome kinship matrix to have similar transcription profiles, provided that they are related according to one of the chromosomal kinship matrices.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Quantitative trait loci (QTLs) associated with androgenic responsiveness in triticale were analyzed using a population of 90 DH lines derived from the F1 cross between inbred line ‘Saka 3006’ and cv. ‘Modus’, which was used in a number of earlier studies on molecular mapping in this crop. Using Windows QTL Cartographer and MapQTL 5.0, composite interval mapping (CIM) and association studies  for five androgenesis parameters  were conducted. For the studied components of androgenic response, CIM detected in total 28 QTLs which were localized on 5 chromosomes from A and R genomes. Effects of all QTLs that were identified at 2.0 or above of the LOD score explained 5.1–21.7 % of the phenotypic variation. Androgenesis induction was associated with seven QTLs (LOD between 2.0 and 5.8) detected on chromosomes 5A, 4R, 5R and 7R, all of them confirmed by K–W test as regions containing the markers significantly linked to the studied trait. What is more, K–W test revealed additional markers on chromosomes: 5A, 2BL, 7B and 5R. Both total and green regeneration ability were controlled by genes localized on chromosome 4A. Some of the QTLs that affected final androgenesis efficiency were identical with those associated with androgenic embryo induction efficiency, suggesting that the observed correlation may be either due to tight linkage or to pleiotropy.Key message Five regions of the triticale genome were indicated as revealing significant marker/trait association. Markers located in these regions are potentially useful for triticale breeding through marker-assisted selection. Very fast—within one generation—production of totally homozygous lines makes the DH technology a very valuable tool for breeding practise, as well as for biotechnology and molecular studies.One of the features that distinguishes plants from animals is the fact that the majority of differentiated plant cells do not lose their developmental potentialities and under specific conditions can return to a totipotent embryogenic state. One of such processes called ‘microspore embryogenesis’/‘androgenesis’ starts with the reprogramming of male gametophytic cells (microspores). As a result, instead of mature pollen grains, the microspore forms an androgenic structure (AS) that closely resembles a zygotic embryo or can sometimes be similar to callus tissue. In both cases ASs can regenerate into haploid  through interval mapping technology can indicate genome regions associated with androgenic responsiveness. Determination of markers linked to this trait and estimation of their phenotypic effects could bring significant progress to DHs utilization. Identification of molecular markers linked with high effectiveness of the process can be used to determine the value of a genotype in a breeding program and in marker-assisted breeding aimed at transferring genes from highly responsive genotypes to the recalcitrant ones  is an intergeneric hybrid derived from a cross between wheat (Triticum e.g. T. turgidum) and rye . It has been developed into a commercially valuable feed grain crop due to a combination of several advantageous features: high yield potential, good grain quality and relatively high tolerance to unfavorable environmental conditions. As a consequence of still growing economical importance of triticale, numerous research papers focused on its genetics and genome organization have been published in the last decade  derived via wide crossing with maize was used as the model for anther culture response evaluation. Then, a recently produced well-saturated genetic linkage map  photoperiod. The seedlings were then planted into pots containing a mixture of soil, de-acidified substrate peat and sand  and grown in a greenhouse  until the flowering. Additional illumination (Philips 400 W Son-T Agro High Pressure Sodium Lamp) was applied to prolong the light period or as supplementary light during unfavorable weather conditions.The experiment was carried out in three independent replications in various vegetation seasons  to evaluate the effect of uncontrolled endogenous and environmental factors.Tillers were collected when the majority of microspores were at the mid- to the late-uninucleate stage of development. The tillers were then wrapped in plastic bags, placed in jars containing Hoagland’s salt solution and stored at 4 °C, in the dark for 3 weeks. Subsequently, the spikes were sterilized with 96 % ethanol. Aseptically excised anthers were placed in 60 × 15 mm Petri dishes (100 anthers from one spike per dish) with modified C17 induction medium  with 16/8 h (day/night) photoperiod.Androgenic structures (AS) of size >1 mm were transferred successively starting from the sixth week of cultivation at 3-week intervals. The structures were put into 90 × 20 mm Petri dishes (30 AS per a dish) with regeneration medium modified 190-2  were collected from 4 to 6 plants; from each spike 100 anthers were placed on medium and cultured.The effectiveness of androgenesis induction was based on the number of androgenic structures (AS) produced per 100 anthers of the donor plant (AS/100A). Each dish containing 100 anthers collected from one spike was assumed to be a replicate. For the majority of DH lines, mean values were calculated from six replicates for each experiment. The effectiveness of regeneration phase was expressed in the total number of regenerants and in the number of green regenerants per 100 androgenic structures transferred to regeneration medium . The final androgenic responsiveness was expressed in the total number of regenerants and in the number of green regenerants per 100 isolated anthers .p ≤ 0.05).Statistical analysis of results was performed using Statistica version 8.0. For each parameter , the normal distribution of scores has been verified by Shapiro–Wilk test to validate the use of the parametric tests. The effect of tested variable/variables was examined by one-way or multi-factor analysis of variance (ANOVA). Post-hoc comparison was conducted with the use of Duncan’s multiple range test , the coefficient of variation (CV) for each DH line was calculated according to the formula: 100 × [(standard deviation of array)/(average of array)] = CV (%).R2). The CIM and Kruskal–Wallis test analyses were performed separately for each experimental replication.The relationship between segregations of single marker and trait was analyzed with the Kruskal–Wallis test (K–W) using MapQTL 5.0 package (Van Ooijen p < 0.001) between the replications.The analysis of androgenic responsiveness among studied DH lines showed a great variation in all components of this feature: androgenic structure induction, total plant regeneration and green plant regeneration ability. Moreover, even though all three experiments were carried out under the same greenhouse conditions, ANOVAs showed highly significant differences . Average total regeneration ability was similar for parental genotypes  but the number of regenerated green plants was almost two times higher in the case of DH line ‘Modus’ (8 vs. 4.5 GR/100AS). It resulted in almost three times higher final androgenesis efficiency for this genotype in comparison with DH line ‘Saka 3006’ both in respect of total and green regenerants production (12 vs. 4 R/100A and 2 vs. 6 GR/100A).The DH progeny population presented a much wider spectrum of responses: the mean AS production per 100 anthers ranging from 8 to 169, and mean regeneration ability from 4 to 30 for R/100AS and from 1 to 18 for GR/100AS.Table To compare the degree of variation from one replication to another, CV was calculated separately for each DH line and each androgenesis parameter. The results summarized in Table p < 0.005) located on chromosomes 2BL and 7B  were localized on four chromosomes: 5A, 4R, 5R and 7R. None of them was significant across all three replications Table ; Fig. 1.00A were p < 0.01  describe the final efficiency of the androgenesis process. Genomic regions associated with these traits were localized on four chromosomes 5A, 4R, 5R and 7R. The majority of them were located closely to the QTLs involved in androgenic structure production  on chromosomes 5A, 7B and 5R significantly associated with final efficiency of androgenesis  constituting final androgenesis effectiveness are polygenically controlled and independently inherited ; arcsin√x) did not improve the normality of the analyzed data distribution, the non-parametric Kruskal–Wallis test (MapQTL 5.0) was used for association studies.Almost normal frequency of distribution of the majority of tested traits allowed for the utilization of CIM method (Windows QTLCartographer 2.5). The problem of skewed distribution observed in some cases could be theoretically overcome by transformation of the data. However, as the transformations usually applied  revealed the variation in androgenic responsiveness induced by some uncontrolled environmental  or endogenous  factors or their interaction effects. On the one hand, it seems to be inconvenient for molecular analysis but on the other it makes it possible to identify only the most significant QTLs associated with the studied traits. It also revealed the range of variation induced by other than genomic factors and helped identify highly influenced parameters. In this case, it was the green regeneration ability which confirmed earlier suggestions that the regeneration process is under strong environmental effect Ryőppy .In the studied triticale population, the regions involved in androgenic embryo induction detected on chromosomes 5A, 5R and 7R seem to be the most important in androgenic responsiveness. QTLs responsible for AS production were identified on chromosome 5A in all three experimental replications. QTLs indicated on 5R and 7R chromosomes were significantly associated with this trait in two out of three replications. The last two QTLs associated with androgenic embryo induction were detected on the 4R chromosome in only one experimental replication. Almost all detected QTLs on 5A, 5R and 7R explained over 10 % of the phenotypic variance. Kruskal–Wallis test confirmed significance of all detected QTLs and additionally revealed other markers associated with this trait localized on two chromosomes of the B genome .rfg1) whereas other authors  method. They can be also applied for evaluation of other genotypes in breeding programs. Nonetheless, in some cases our results need further confirmation, as some detected QTLs were expressed only in a particular environment/experiment, or were expressed differently in different replications, whereas QTL consistency across different environments and backgrounds is an important prerequisite for application of MAS in breeding programs.


Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive.We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR . Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity.http://www2.imperial.ac.uk/~gmontana.The Java codes are freely available at  In the map phase of our application, each input key corresponds to a unique tree ID and value is NULL since we load the full data set to build trees. A user-defined number of mappers, nmap, are executed whereby each mapper function learns one or more decision trees from bootstrapped versions of the data set. The output of the map phase consists of three types of information: (1) Sample identifier (key) and predictive value, which is then used to estimate the OOB error rate at the reduce phase; (2) SNP identifier (key) and the decrease in sum-of-squares , which is used to obtain the SNP importance scores at the reduce phase; (3) Sample pair identifier (key) and its proximity , which is used to produce the final proximity matrix extracted from RF. All these outputs from mappers are sorted, shuffled, and copied to reducers by Hadoop. An illustration of this initial process is given in Figure DistributedCache mechanism, which copies the read-only files on to the slave nodes before launching a job. In our implementation, each map task loads the full version of the data set, which is then bootstrapped and used to learn each tree. In this illustration, Ntree = 3, and each one of the three mappers builds one tree. When the number of required trees is larger than the total number of mappers, each mapper builds more than one tree.The MapReduce model involves three phases: the rmation:  Sample ireduce tasks, which receive their input in the form of ] pairs, where the first element can be SNP or sample identifier and the second element is a list of values associated with that SNP or sample. The reduce tasks run a user-defined reducer function, and generate an output again in the form of key-value pairs to be saved on file. The reduce tasks compute information gain importance score by summing up all the ϕ(j) evaluations obtained by the individual trees in the map phase. Again, the computations are equally distributed across reducers. For instance, in the bottom part of Figure The output from all the mapper functions, consisting of key-value pairs, is then sorted, shuffled and copied to the This parallel RF regression algorithm has been implemented in Java. It can be run in standalone, pseudo-distributed, and fully distributed mode. The current version, which supports biallelic markers, gives users the options to calculate OOB, variable importance scores, and the sample proximity matrix. Both standard and distance-based splitting criteria are available, and the Euclidean distance featuring in (3) could be easily replaced by other distances. For the standard RF version, currently both the Euclidean and Mahalanobis distances are available. An installation and user guide is available from the web site.7, and a minor allele frequency < 0.1. Missing genotypes were imputed as in [This work was motivated by experimental data produced by the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimer's Disease (AD) is a moderate to highly heritable condition, and a growing list of genetic variants have been associated with greater susceptibility to develop early- and late-onset AD . We haveed as in .yi consists of 148, 023 slope coefficients, one for each voxel, quantifying the temporal rate of linear brain tissue loss over time, and therefore providing a localised imaging signature of the disease. A more detailed description of the preprocessing steps and the procedure used to extract this imaging signatures can be found in [For each subject in this study, longitudinal brain scans at 6, 12 and 24 months after the initial screening were available. Our multivariate quantitative trait provides a measure of structural change observed in the brain relative to baseline over the three time points. More specifically, each individual phenotype vector found in .yi was generated by randomly drawing from a multivariate normal distribution with covariance matrix Monte Carlo simulation studies were designed to compare the performance of our PaRFR software against other implementations of RF regression. In order to reduce the computational burden incurred in extensive and repeated simulations, we used a multivariate phenotype consisting of 100 voxels that were randomly selected from the 148, 023 available, and a set of 1, 000 SNPs randomly selected from the 434, 271 available markers. Using the 100 observed voxels, we estimated the phenotype sample covariance matrix, ribed in . The userandomForest. Since the R implementation only handles univariate responses, for this specific evaluation we take the average across phenotypes, We first report on simulation experiments aimed to test and validate our RF implementation. We compare the OOB error obtained by PaRFR against the analogous OOB error obtained by using a publicly available implementation of RF in R package map and reduce task to have 800 MB memory, and the whole cluster capacity to run 400 map tasks and 100 reduce task.To assess the speedups that can be obtained by our implementation using the distance-based splitting function, we simulate a large dataset containing 1, 000 independent samples, 1 million SNPs and a 10, 000-dimensional phenotype. This analysis was run on a private cluster with 20 nodes. Each node had 24 GB memory and 16 processors with Intel(R) Xeon(R) CPU 2.27 GHz. We configured each We then compare the running time for different values of the Mtry parameter, ranging from 10 to 10, 000. Figure To test the scalability of our implementation as the number of available nodes increases, we analyze the same size dataset with parameters Mtry = 1,000 and Ntree = 2,400. This analysis was run on our local cluster with 10, 20, 30 and 40 nodes, respectively. Figure In order to illustrate the benefits of the proposed PaRFR algorithm, we carried out the analysis of the ADNI data set. A handful of genetic variants with very large effect size are known to be related to AD, and we aimed at detecting those genes despite the relatively low sample size as a demonstration that PaRFR provides a useful toolbox for imaging genetic studies. Only the observed quantitative traits, and not the actual clinical status, were used by the algorithm. It is widely believed that such endophenotypes carry more signal than the cruder case-control status thus requiring much smaller sample sizes.The analysis was run on our local cluster using 20 nodes. Different Mtry and Ntree parameter values were initially tested to determine how sensitive the results were to changes in these values, and in order to identify optimal values for which a stable SNP ranking can be produced. In particular, we explored various combinations of 3 different Mtry values and Ntree values varying from 10 up to 50, 000. The performance of PaRFR was monitored as more trees were added to the RF, in blocks of 500; once an additional block of trees was added to the ensemble, a measure of SNP ranking agreement with the previous ranking, the Jaccard coefficient, was calculated. Figure k bases of any of top 10 SNPs was considered mapped and, according to this criterion, we found 12 mapped genes. Several genes that have been reported to be linked to increased AD susceptibility are found in this list, including APOE4, TOMM40, PICALM and PVRL2 [g, we first obtained an observed score, gs. A p-value was then computed using this null distribution. As an example, the null distribution of 2 important genes, TOMM40 and TNK1, is shown in Figure In Table nd PVRL2 ,27,28. Thttp://www.geneontology.org (dated 23 April 2011). As GO uses UniProt IDs while our genes were annotated using ENSEMBL IDs, we used Biomart to map UniProtKB accessions to Ensembl Gene IDs. This allows proper cross-comparisons. The through-path rule was applied for annotations to each gene, i.e. for a given GO term G annotated to some gene p, all the ancestral terms of G is also applied to p. This step was necessary because GO only maps a gene and/or its corresponding protein to the most specific term, which corresponds to some branch on the GO graph. To reduce redundancy and dependencies amongst the reported functional terms, informative biological process terms filtering was performed from the GO OBO file [To understand the functional annotations of our top 12 mapped genes we used Gene Ontology (GO). GO provides a controlled vocabulary for evaluating complex function. The annotation files and GO tree (ver. 1.2) files for Homo Sapiens were downloaded from OBO file . SignifiP of size N × N without any additional computations. Each element ij pof this matrix indicates the genetic similarity between a pair of samples, which is obtained by considering the fraction of trees where the two samples appeared in the same leaf. A measure of genetic distance between any two samples is then simply obtained by taking ij d= 1 − ij p. Further insights into the heritability of AD can be gained by analysing such estimated genetic distances with regards to the observed phenotypic distances. Specifically, we explored whether there was an association between phenotypic diversity, obtained from the neuroimaging measurements, and the inferred genetic distances. When any of these distances is used for clustering samples, we would expect to be able to identify at least two well-separated clusters of points corresponding to two the clinical conditions represented in the ADNI cohort, i.e. AD patients and healthy controls, despite the fact that the clinical status was not used by the PaRFR algorithm. In order to verify this, we applied multidimensional scaling (MDS) with the objective to visualise both genetic and phenotypic distances on a two-dimensional Euclidean space.Our PaRFR algorithm also estimates pair-wise genetic proximities in the form of a symmetric proximity matrix Figure An alternative view of the phenotypic diversity characterising the samples is provided by the three-dimensional MDS plot of Figure reference state and the minor allele be the mutated state. In order to precisely define the severity of the disease for a given sample, we initially apply a hierarchical clustering algorithm to segment the samples into four non-overlapping clusters (from C1 to C4) as shown in Figure For this analysis we focused on the top 1, 000 SNPs ranked in decreasing order of importance by the PaRFR algorithm. For each one of these SNPs, we let the major allele be the mutation patterns, in the genotypes containing mutated states that are frequently present in samples near the C4 centroid. For this analysis, we considered the top ranked 100 SNPs. All homozygous two-major-allele genotypes were removed in the data because disease-causing mutation patterns are expected to occur in the homozygous two-minor-allele genotypes and heterozygous genotypes. Only mutation patterns that were more frequently found in the C4 cluster were included in this analysis. Specifically, for each pattern we tested the null hypothesis that the proportions of that pattern in the C4 cluster and in the entire sample are equal, and only retained those patterns for which the null hypothesis was rejected at a 5% nominal level. We also required the frequency of each pattern to be at least 0.05 as a criterion for inclusion in the analysis. This procedure generated approximately 17, 000 mutation patterns. Figure As a further refinement of this approach, we performed a second analysis in which we explored SNP/SNP combinations, or In this paper we have introduced four contributions. First, we have proposed Random Forest regression with multivariate responses as a suitable approach to genetic association studies involving quantitative, and possibly high-dimensional, endophenotypes. This algorithm, which has been previously employed only in case-control studies, provides a natural framework for obtaining a ranked list of SNPs according to their predictive power. Second, we have proposed a parallel version of Random Forest regression, called PaRFR, which enable large-scale genetic association studies to be carried out in a computationally efficient way. The corresponding software, which has been made freely available, takes advantage of the Hadoop framework for distributed computing to build very large tree ensembles on large commodity clusters. We have also reported on extensive simulation results and comparisons of our implementation to a traditional  implementation in terms of scalability and speed. Third, we have described an application of the proposed methodology to an imaging genetics study of Alzheimer's disease using quantitative traits extracted from brain scans by means of neuroimaging techniques. To the best of our knowledge, this is the first time that such a regression modelling methodology is used in imaging genetics. Lastly, we have demonstrated how the Random Forest regression algorithm can also be used to infer a measure of genetic similarity that is predictive of a quantitative phenotype, and have discussed this particular feature of the algorithm using the ADNI data set.Mahout (http://mahout.apache.org). COMET is particularly suited for large data sets as it splits the input files into blocks of fixed size. However, this implementation only supports classification problems, and therefore is not suitable for handling quantitative traits, and is not open source. Mahout is an open-source machine learning library for large-scale data processing using Hadoop. Its latest version includes regression problems, but these are limited to univariate traits. Currently, other features are also missing in those implementations that are needed for performing genetic association studies as described in this paper, including mechanisms to generate SNP rankings and estimating sample proximities based on the genetic data. In future work we plan to improve further on the current implementation of PaRFR by building forests using partial data (or splits) generated by Hadoop for applications to much larger data sets. We also plan to build a multi-pass MapReduce implementation which can monitor changes in OOB error as the forest grows, and incorporate a sequential hypothesis testing procedure [Other Hadoop-based implementations of the Random Forest regression algorithm currently exist, including COMET  and Mahorocedure  to automOur proposed distance-based node splitting criterion can potentially allow for more general phenotypic distances to be used within the same framework. Alternative distances would be able to better capture the phenotypic variability observed in the populations, and may be appropriately selected depending on how the multivariate quantitative traits are measured. For instance, PaRFR can be used to detect SNPs that are highly predictive of variability in more complex neuroimaging phenotypes, such as brain connectivity networks, which are commonly represented as graphs (i.e. collections of nodes and links between pairs of nodes). Such networks describe the set of connections in the neural system, or connectome, in which nodes could be neurons or cortical areas, and edges could be axons or fibre tracts . PaRFR oAlthough we were mostly motivated by applications in neuroimaging genetics, the algorithm proposed here has wider scope and can be used for any QTL mapping study involving a very large number of genetic markers and high-dimensional responses. For instance, there is recent interest in detecting genetic variability associated with facial shape, which can be quantified using 3D phenotypes obtained from statistical shape analysis . Other mThe authors declare that they have no competing interests.http://adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.ucla.edu/study-design/ongoing-investigations. All authors read and approved the final manuscript.YW implemented the PaRFR algorithm, and co-wrote this manuscript. WG discussed and analysed the GWA study results to find overlapping informative GO terms. LW participated in discussion of the method, and proposed the approach based on frequent mutation patterns. GM proposed the idea for the study and RF algorithm, and guided the ADNI data analysis. Both LW and GM edited this manuscript. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (GO terms mapped by 47 AD linked genes. The file contain three columns, the first column is the id of GO terms, the second column is the name of GO terms and the third column is the p-value of hypergeometric test.Click here for fileGO terms mapped by 12 top-ranked genes ranked PaRFR. The file contain three columns, the first column is the id of GO terms, the second column is the name of GO terms and the third column is the p-value of hypergeometric test.Click here for file


PLoS Genetics with regard to these guidelines.Genome-wide association studies (GWAS) have revolutionized human genetics. They have led to the identification of thousands of loci that affect both normal variation and susceptibility to disease, and have clarified our understanding of the genetic architecture of complex traits. In just five years, the methodology has moved from extraordinary to commonplace, and with the advent of affordable genome-scale data collection, GWAS and other genomic technologies are being adopted by model organism researchers. As GWAS applications have evolved so too have the community standards that pertain to them. Here we clarify the editorial policy of Our overall goal—that is, the goal of our editorial board—is to emphasize work in which genetic approaches and genetic logic help us learn more about biology. As such, we are committed to publishing work in which the conclusions are both broadly significant and analytically rigorous. For studies in which a GWA approach is central, “broadly significant” means that the basic science, biomedical, agricultural, and/or social impact will be of substantial and interdisciplinary interest. For example, in well-studied diseases, the identification of a small number of additional loci whether in a novel sample or by meta-analysis will usually be considered more appropriate for a specialized audience. Regardless of the trait, we will be most enthusiastic about work that includes experiments or genetic analyses that address the mechanism by which a GWAS-based variant gives rise to phenotypic differences.p-value < 5×10̂-8 for a conventional GWAS) will be required, ideally accompanied by independent replication and analyses that include consideration of the joint as well as the individual discovery and replication datasets. For work focused on genetic architecture , controlling for both type I and type II error is important, with the overall goal of ensuring that a robust statistical approach leads to an advance that is broadly significant.By “analytically rigorous”, we mean that controlling for multiple comparisons, population stratification, relatedness, and technical quality is critical. For work focused on gene discovery, minimizing the false positive rate is a more important consideration than controlling the false negative rate. In most of these cases, genome-wide significance thresholds  as work that can add to the strength of advance but not substitute for rigor of the quantitative genetic conclusions.Finally, a central tenet of the Public Library of Science (PLoS) is open access, not only to the work being described, but also to the data being analyzed. Unless there are compelling—usually ethical—reasons to the contrary, complete datasets of both genotype and phenotype should be immediately available without restrictions. For human research, some restrictions on data availability may be necessary in order to respect privacy, or to prevent identification of participants; in these situations, and in meta-analyses, we expect policies to evolve in such a way that the benefit to the scientific community of data availability is balanced with the need to maintain high ethical standards.PLoS Genetics are the autonomy of judgment and wisdom of consensus that stem from our editorial board structure. But just as the community benefits from consensus it will also benefit from consistency and rigor, and we hope that these guidelines will further the positive impact that GWAS can have on our understanding of the natural world and human health.We intend for these considerations to serve as guidelines rather than dogmatic requirements; indeed, hallmarks of


We examine the performance of various methods for combining family- and population-based genetic association data. Several approaches have been proposed for situations in which information is collected from both a subset of unrelated subjects and a subset of family members. Analyzing these samples separately is known to be inefficient, and it is important to determine the scenarios for which differing methods perform well. Others have investigated this question; however, no extensive simulations have been conducted, nor have these methods been applied to mini-exome-style data such as that provided by Genetic Analysis Workshop 17. We quantify the empirical power and false-positive rates for three existing methods applied to the Genetic Analysis Workshop 17 mini-exome data and compare relative performance. We use knowledge of the underlying data simulation model to make these assessments. Study designs for genetic association studies fall into two broad categories: (1) population-based studies that recruit unrelated individuals and (2) family-based studies that collect some number of related pedigrees. Often, both study designs are used for a particular investigation. For example, when a linkage study has been performed and family data are collected, follow-up analysis can include association using a new unrelated study population. The analytic methods appropriate for either design differ, thus making difficult the aggregation of the association metrics across the study designs. Heuristically, population-based metrics attempt to quantify a measure of correlation or association between some function of genotype at a given marker and the disease phenotype, whereas family-based association measures use properties of Mendelian transmissions from parents to offspring and are inherently conditional.Because analyzing the disparate types of data in isolation most often results in nonoptimal statistical power, investigators have proposed several methods for efficiently combining these data. We briefly summarize three methods to be applied to the Genetic Analysis Workshop 17 (GAW17) data in the Methods section. Each approach is distinguished by the study designs for which it is appropriate, the assumptions necessary for valid inference, and the handling of population stratification . Operationally, these methods are distinguishable by computation and implementation considerations and by empirical performance. We assess the performance in this paper. Other researchers have investigated the question of relative performance [An important consideration to keep in mind throughout this investigation is the underlying causal model that was used to generate the GAW17 data . First, The first attempts to combine population- and family-based association data were developed by Nagelkerke et al. , who useEpstein et al.  generaliEpstein et al.’s and Chen and Lin’s methods rely on two strong assumptions: a rare disease and the absence of population stratification. Later work has been targeted at both relaxing the rare disease assumption and adjusting for population stratification. Zhu et al.  used a pBecause the approach by Chen and Lin  is not iChen and Lin’s  approachW1 and W2 are weights derived from linear model theory assuming the parameter estimates follow a multivariate normal distribution  in each of the 200 simulation replicates, with affected status as the phenotype. Although an adjustment for multiple testing would be appropriate for this study design, we chose to use a 5% nominal level of significance throughout in order to better compare the methods. Although these methods readily generalize to handling other genetic models, we assumed an additive mode of inheritance throughout.Table Although the power averaged over causal SNPs was low, some of the SNPs were detectable at high rates. Figure Several methods address the problem of combining population- and family-based genetic association data. These methods differ fundamentally in whether they incorporate within-family transmissions and rely on tests for population stratification to justify effect estimate aggregation or perform between-individual analyses using family data. Performance related to population stratification cannot be assessed here because no stratification was simulated in the GAW17 data.Although the Zhang et al.  method pThe authors declare that there are no competing interests.The study was conceived by DWF, LM and PB. DWF, ARD and PB ran the analyses. DWF and PB summarized the results and created figures. DWF and PB drafted the manuscript, which was revised by JL, LM and CIR. All authors read and approved the final manuscript.


Diabetic nephropathy (DN) is associated with excess morbidity and mortality, in both type 1 (T1D) and type 2 (T2D) diabetic patients. Despite intensification of treatment, DN remains a growing problem worldwide New management and treatment approaches are desperately needed and defining the genetic architecture regulating DN would accelerate their development. The landmark study by Seaquist et al. in 1989 PLOS Genetics, the GENIE consortium presents results of the largest DN GWAS meta-analysis performed to date. The discovery phase included 6,691 T1D patients from three cohorts, and SNPs with p<10−5 were moved forward into a replication analysis that included an additional 5,156 T1D patients in nine cohorts ascertained for nephropathy phenotypes ERBB4 (chromosome 2) showed a consistent protective effect across cohorts . Intriguingly, ERBB4 encodes a member of the EGF receptor tyrosine kinase family and modulates kidney tubule proliferation and polarity during nephrogenesis In this issue of AFF3 gene, and an intergenic locus between RGMA and MCTP2 on chromosome 15. However, as the authors correctly point out, enthusiasm for AFF3, a transcriptional activator, should be tempered. This locus appears driven by two cohorts and technically did not replicate (p = 0.25 in stage 2 replication), although the effect direction was consistent across studies. The authors argue that power of the replication sample was limited for the alternative case definition due to the low number of ESRD cases . The authors further support the association of AFF3 with diabetic ESRD by providing experimental evidence that AFF3 expression levels mediate TGF-β-1–driven fibrosis in an epithelial cell culture model. TGF-β-1 has consistently been implicated in the pathogenesis of fibrosis in DN, and these data provide a plausible function for AFF3 in profibrotic pathways that characterize progressive diabetic kidney disease. However, the lack of significant association in replication analysis calls for independent confirmation of this locus in other studies before its implications for DN mechanisms can be drawn.However, the DN definition essentially mixes two traits, each with distinct underlying pathomechanisms: ESRD as the extreme form of reduced kidney function , and macroalbuminuria reflecting severe glomerular filtration barrier dysfunction. Since these two traits have distinct genetic underpinnings the definitive “DN gene(s)”—or is this merely wishful thinking? It is sobering that this largest and long-awaited GWAS of T1D DN fails to provide unassailable statistical genetic evidence for associated variants, especially when compared to the success of GWAS in identifying convincing loci associated with other kidney diseases such as idiopathic membranous and IgA nephropathy, ANCA-associated nephropathy, or nondiabetic ESRD in African Americans So—does this publication really let the “GENIE” for DN gene discovery out of the bottle, discovering at last We believe that the definition of DN may lie at the crux of the overall disappointing reproducibility of genetic DN studies. In contrast to kidney diseases where diagnosis is based on a kidney biopsy  or imaging studies , the diagnosis of DN is almost always made using clinical criteria and not by histology. The clinical diagnosis uses phenotypic parameters derived from the typical course of DN: after many years of diabetes duration with normal GFR and absent albuminuria, DN onset is marked by mildly elevated albuminuria , frequently with increased GFR . SubsequThe present study consists primarily of cross-sectional studies and cannot capture the definitive DN outcome on an individual level. Using the most severe forms of DN to define cases reduces some potential misclassification but definitely does not overcome the critical inaccuracy of case definition not based on histology . Further, the diabetic patient is exposed to many nonspecific kidney-damaging events in the course of disease , which in their sum also contribute to progression to ESRD. Overall, the sum of potential misclassification involved in using an exclusively clinical DN definition in cross-sectional studies reduces statistical power to detect underlying genetic variants.With this meta-analysis of DN in T1D patients, Sandholm et al. have taken an important step towards defining the genetic architecture of DN. Strengths of the study include its large sample size, consideration of alternative DN phenotypes based on reproducible epidemiological and genetic data reported by other groups studying kidney diseases, and experimental support for the associated loci. Now the challenge will be building on these results. Two consortia, FIND and SUMMIT, should be reporting GWAS results for type 2 DN, and it will be interesting to see if common or unique genetic loci are identified of DN in T1D and T2D patients. Similarities in the clinical phenotypes and treatment responses of patients with T1D and T2D DN suggest a shared pathogenesis. Finally, additional meta-analyses of T1D and T2D DN cohorts with larger sample sizes, application of sequencing technologies, and use of more precise DN phenotypes from longitudinal studies should further define the genetic architecture of this most common cause of chronic kidney disease. These data will provide the foundation needed to advance understanding of diabetic nephropathy and impact patient outcomes.


Genome-wide association studies (GWAS) have become a major strategy for genetic dissection of human complex diseases. Analysing multiple phenotypes jointly may improve both our ability to detect genetic variants with multiple effects and our understanding of their common features. Allelic associations for multiple biochemical traits , C-reactive protein (CRP), ferritin, gamma glutamyltransferase (GGT), glucose, high-density lipoprotein cholesterol (HDL), insulin, low-density lipoprotein cholesterol (LDL), triglycerides and uric acid), and body-mass index, were examined.We aimed to identify common genetic variants affecting more than one of these traits using genome-wide association analysis in 2548 adolescents and 9145 adults from 4986 Australian twin families. Multivariate and univariate associations were performed.-8. These are located on chromosome 8 (LPL gene affecting HDL and triglycerides) and chromosome 19 (TOMM40/APOE-C1-C2-C4 gene cluster affecting LDL and CRP). A locus on chromosome 12 (OASL gene) showed effects on GGT, LDL and CRP. The loci on chromosomes 12 and 19 unexpectedly affected LDL cholesterol and CRP in opposite directions.Multivariate analyses identified eight loci, and univariate association analyses confirmed two loci influencing more than one trait at p < 5 × 10We identified three possible loci that may affect multiple traits and validated 17 previously-reported loci. Our study demonstrated the usefulness of examining multiple phenotypes jointly and highlights an anomalous effect on CRP, which is increasingly recognised as a marker of cardiovascular risk as well as of inflammation. Genome-wide association studies (GWAS) have become a major strategy for genetic dissection of human complex diseases. There is substantial overlap, both phenotypically and in allelic associations, between biomarkers and/or risk factors and between related diseases, and it is becoming important to understand the ways in which polymorphisms affect multiple phenotypes. Many phenotypes may be available from a single study population but current GWAS approaches usually examine them separately within a univariate framework. This strategy ignores potential genetic correlation between different traits.From the perspective of maximising power for a given size of dataset, it has been shown that joint analyses of correlated traits in linkage analysis have substantially improved power in localizing genes -4. SimilGenetically complex  diseases such as cardiovascular disease and type 2 diabetes often have common risk factors. A number of biochemical markers are known to be associated with obesity, pre-diabetic states, or risk of cardiovascular disease. Lipid traits such as triglycerides, and the low-density lipoprotein (LDL) and high-density lipoprotein (HDL) components of cholesterol, are well-known risk factors for cardiovascular disease. Other biochemical markers such as C-reactive protein (CRP) , the enzThe importance of genetic variation has been shown previously through univariate analyses of serum lipids , uric acThe aim of our study is to identify genes and regions associated with multiple biochemical traits related to cardiovascular risk, type 2 diabetes or metabolic syndrome. We used a recently described multivariate association test  to perfoBiochemical traits were measured in serum samples from twins and their families, and genome-wide SNP markers were genotyped. The study participants comprise:(1) Adolescent twins and their non-twin siblings living in south-east Queensland  who had participated in the Brisbane Longitudinal Twin study -32. Full(2) Adult twins consisting of twins and their family members who participated in studies of: (i) alcohol and nicotine dependence and metabolic risk for alcoholic liver disease ; (ii) anCombining these studies, 20,230 individuals had biochemical measurements and 11,683 (from 4986 families) had both genotype and phenotype data. Out of the 11,683, there are only 1483 (from 1015 families) who had data for all the 13 traits. Where multiple measurements of the same trait in an individual were available, the average of the values was used.For each of these studies, participants  gave informed consent to the questionnaire, interview, and blood collection, and all studies were approved by the QIMR Human Research Ethics Committee.Serum was separated from the blood samples and stored at -70°C until analyzed. Serum cholesterol, HDL cholesterol, triglycerides, BCHE, glucose, uric acid, ferritin, CRP, AST, ALT and GGT were measured using Roche methods on a Roche 917 or Modular P analyzer . LDL cholesterol was calculated using the Friedewald equation. Insulin was measured on an Abbott Architect. BMI was calculated from measured or self-reported weight and height for the adults and from measured weight and height for the adolescents.MACH1.0 (http://www.sph.umich.edu/csg/abecasis/mach/index.html) [r2 ≥ 0.3 was included in the genotype data.DNA was extracted from blood samples using standard methods and genotyped with Illumina 610K, 317K or 370K chips at CIDR or deCODE Genetics. Data cleaning for SNP genotypes included checking the expected relationships between individual family members and resolving Mendelian errors [ex.html) -40 progr2), sex, sex × age and sex × age2. Standardized residuals were obtained and used in the association analysis. All data pre-processing and descriptive analyses were done using STATA version 7.0 [SPSS version 17.0.2 . Multivariate association analysis was performed using the PLINK (v1.07) implementation of the multivariate test described by Ferreira and Purcell [-8, we identified the traits that showed evidence for association with that locus  and confirmed that specific trait-SNP association with a univariate association test using all relatives for each family. The univariate association test was performed using "fastassoc" in MERLIN 1.1.2 [Distributions of all biochemical variables were examined. Serum AST, ALT and GGT, CRP, triglycerides and BMI were log-transformed. For each trait, individuals who were more than five standard deviations from the mean of that trait were excluded. Results for glucose and insulin in adults were adjusted for fasting time based on the reported time of last meal and time of blood collection. Prior to genetic association analysis, the variables were also adjusted for the effects of age, squared age  loci associated with biochemical traits with a p-value of less than 5 × 10-8 . HDL, CRP, triglycerides and BCHE had trait loadings of greater than |0.2|. To confirm the multivariate result, we individually tested each of these traits using a univariate test in the full sample of 11,683 individuals. The univariate tests confirmed the association with HDL (p = 5.7 × 10-12) and triglycerides (p = 5.1 × 10-15) but not at genome-wide significance for CRP (p = 0.008) and non-significant (p = 0.069) for BCHE. This variant is in strong or partial LD with previously-reported variants for HDL or triglycerides [The most strongly associated SNP at the chromosome 8p21.3 locus was rs17091905 (multivariate p = 2.8 × 10ycerides -45 (Addi-14) which is located on chromosome 12q24.2 (OASL) was confirmed to be significantly associated with GGT (p = 3.6 × 10-15) [-5) and CRP (p = 8.8 × 10-5)   and also-10) located on chromosome 19q13.32 (TOMM40/APOE-C1-C2-C4 gene cluster) where HDL, LDL, CRP and triglycerides had trait loadings of greater than |0.2|. Significant univariate associations were observed for LDL (p = 1.6 × 10-14) and CRP (p = 4.2 × 10-8) and close to genome-wide significant univariate associations were seen for HDL (p = 8.1 × 10-8) and triglycerides (p = 9.6 × 10-7) (Table 2 = 0.4) with this SNP  in univariate analyses were removed Figure  from theExamination of the directions of the allelic effects on the different phenotypes showed unexpected results. At LPL on chromosome 8, the minor allele A at rs17091905 increased HDL-C and decreased triglycerides, but the direction of the nominally significant effect on CRP was to increase it. At the chromosome 12 locus the minor allele A at rs3213545 tended to increase LDL-C but it significantly decreased GGT and tended to decrease CRP. Similarly at the chromosome 19 locus, the effect of the minor allele (G for rs2075650) was to increase LDL-C and triglycerides and to decrease HDL-C, consistent with an increase in cardiovascular risk, but to decrease CRP, again suggesting opposite allelic effects on the markers of different aspects of cardiovascular risk.We have applied a multivariate approach to identify variants associated with more than one trait, initially using 4986 unrelated individuals across 13 biochemical traits. Univariate testing of the significant or near-significant loci, on the full sample of 11,683 individuals, was then used to confirm these findings. We are interested firstly in the usefulness of multivariate analysis as a substitute for the more laborious and potentially less powerful approach of conducting multiple univariate analyses and comparing the results, and secondly in the details of the loci which are found to have effects on multiple variables in our data.-8 for single variables (Table -5) was used, a further six published loci were also identified ; with GGT and possibly LDL and CRP on chromosome 12 (OASL gene MIM 603281); and with HDL and LDL and possibly CRP and triglycerides on chromosome 19 (TOMM40 (MIM 608061) /APOE (MIM 107741)-C1 (MIM 107710)-C2 (MIM 608083)-C4 (MIM 600745) gene cluster). Each gene has been previously recognised in genome-wide association studies concentrating on a few of these variables [LPL plays a key role in lipid metabolism and is responsible for hydrolysis of triglyceride molecules present in circulating lipoprotein. APOE and APOC genes also play a key role in lipid metabolism and cholesterol transport by helping to stabilise and solubilize lipoproteins as they circulate in the blood [LPL and APOE polymorphisms have been found to be significantly associated with increases in LDL and decreases in HDL [OASL gene  and these phenotypes is unclear. However, nearby genes in linkage disequilibrium with the lead SNP in OASL include HNF1A and c12orf43. HNF1A is expressed in liver, kidney and endocrine pancreas and regulates a number of genes involved in lipoprotein metabolism including apolipoproteins, cholesterol synthesis enzymes and bile acid transporters [HNF1A also has allelic associations with type 2 diabetes [We have identified polymorphisms showing strong evidence of allelic associations with HDL and triglycerides on chromosome 8  with rs2075650 has allelic effects in the opposite direction on LDL [The multivariate approach helps us to understand the connections between variables. For example, for rs2075650 on chromosome 19, the multivariate approach suggested LDL, CRP, HDL and triglycerides might be associated with this particular SNP. Although the effects on LDL, HDL and triglycerides are consistent with what was expected , the effects on CRP are contrary to expectation. The effect direction is opposite to those for LDL and triglycerides, and the same as that for HDL. This suggests that the alleles or haplotype which have risk-increasing effects on lipids have a potentially protective effect on CRP and (so far as the effect on CRP is reflecting the degree of inflammation) on the inflammatory process. The effect estimates of LDL for rs2075650 obtained in our study were similar to obtained by Aulchenko et al. . The effn on LDL ,59-61 ann on LDL . SimilarOur study differs from previous investigations as it examines a large number of correlated biochemical traits, initially using unrelated individuals and following up the findings in other members of the families. It confirms some published associations and identifies new ones. As our cohort consisted of adolescents and adults, results from adults, adolescents and combined (adults and adolescents) cohorts were examined and compared. Because of the larger number of adults studied, results from adults were similar to the combined data. Results from the adolescents were not notably different from the combined data.One main limitation of our approach is that only a subset of the data (from unrelated subjects) can be used for the initial multivariate analysis. Although it would add power, it is too computationally intensive to use all the available data  in genome-wide multivariate analysis. Although a subset of data was used, the method applied in our study was very efficient and easy to perform. A more specific limitation in our data is that the glucose and insulin measurements were not made on fasting blood samples. In adults, we made adjustments for the time since the last meal but in adolescents we had to rely on the fact they were seen at the same time of day and blood was taken around three hours after the expected time of breakfast.Another set of limitations is related to the use of biomarkers of risk or, for CRP, of systemic inflammation. It seems that some loci may affect HDL-C or triglycerides without affecting cardiovascular risk , and it Our study demonstrated that it is useful to examine multiple phenotypes jointly in order to better understand the connections between them and to make the distinction between common and unique genetic effects. Our efficient approach  was able to identify three possible loci that might affect multiple traits, and validated 17 loci that have previously been reported. It highlighted anomalous effects on CRP, which is increasingly recognised as a marker of cardiovascular risk as well as of inflammation. Confirmation and extension of our findings will require studies which measure multiple phenotypes in each genotyped subject, and will benefit from combination of data from multiple studies to achieve sufficient power.The authors declare that they have no competing interests.RPSM analysed data, interpreted and prepared the manuscript. MARF and JBW contributed to data interpretation and manuscript preparation. AKH and GWM coordinated genotyping studies. ACH, PAFM and NGM coordinated the phenotype studies. All authors read and approved this manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/12/123/prepubAdditional tables and references. Additional Tables S1-S4 and references cited in these tables. Table S1 gives a descriptive statistics for males and females in the two genotyped cohorts. Table S2 gives the phenotypic correlations between each pair of age-corrected traits separately for males and females in combined sample. Table S3 compares the multivariate and univariates results from our study with published results. Table S4 gives a summary of borderline significant associations from multivariate analysis.Click here for file