With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits. Oryza sativa L.), a crop species of economic importance, provides the staple food for more than half of the population in the world. In China, the super-hybrid rice plays a pivotal role in the country's food security. There are almost eighty super-rice varieties, such as Xieyou9308, that have been successfully bred and commercially released to rice farmers since the super-rice breeding program was initiated by the Chinese government in 1996 [Rice  is positively correlated with resistant starch (RS) content of granular starches –10, whicAs high-throughput technologies producing dense single nucleotide polymorphisms (SNPs) across the whole genome, the genome-wide association study (GWAS) provides us with insightful information into genetic architecture of complex traits and is a common approach to uncover genetic components of agronomic traits. Association mapping is a high-resolution method to map quantitative trait SNPs (QTSs) based on linkage disequilibrium (LD). Association analytical methods can evaluate whether certain alleles within a population are correlated with the phenotypes of interest more frequently than the expected ones under the null hypothesis. Thus, the limitations in the traditional linkage mapping due to the statistical ambiguity with insufficient molecular markers can be alleviated. It has been widely applied in plant resource populations such as rice, maize, barley, and wheat recently –19.However, the current GWAS analysis fails to detect epistatic and gene-environment interactions in most studies such as maize. But phenotypes of all living organisms represent the consequence of several genetic components including epistatic effects and their interactions with environment; therefore, to estimate genetic merit relevant to the epistases and their interactions with environment certainly plays a crucial role in planning an effective breeding regime , 21. SeaOn the other hand, due to the prohibitively intensive computation required for a GWAS, the available methods are unpractical and difficult to extend for detection of gene-gene, gene-environment, and gene-gene-environment interactions in an experimental data with multiple environments with enormous SNPs. Currently, a workable solution is provided with the availability of generalized multifactor dimensionality reduction (GMDR) algorithm on a computing system with graphics processing units (GPUs), a type of hardware implementation of parallel computation that can be adapted for many scientific tasks . The preA RIL mapping population consisting of 138 lines, derived from super-hybrid rice Xieyou9308, was planted in Linshui City, Hainan Province, and Hangzhou City, Zhejiang Province, in 2009, respectively. Three quality traits, GT, AC, and GC, were investigated.DNA resequencing was conducted in Beijing Genomics Institute (BGI) for two parents with 10X coverage and 138 lines with 2X coverage. The latest version of Nipponbare sequence  was useds genes. An experiment under multiple environments is conducted for gene mapping. The phenotypic value of the kth individual in the hth environment (yhk) can be expressed by the following mixed linear model:μ is the population mean; ai is the additive effect of the ith SNP with coefficient xik, fixed effect; aaij is the additive × additive interaction effect between the ith SNP and the jth SNP with coefficient xikxjk, fixed effect; eh is the random effect of the hth environment; aehi is the additive × environment interaction effect of the ith SNP and the hth environment effect with coefficient xik, random effect; aaehij is the interaction effect of aaij with the hth environment with coefficient xikxjk; and εhk is the random residual effect of the kth individual in the hth environment. The coefficient xik can be determined according to the genotype of the SNP, taking values of 1 and −1 for the homozygotes of high and low frequency alleles, respectively, and of 0 for the heterozygote.Association mapping was performed using the mixed linear model approach. Suppose that the genetic variation of one quantitative trait is controlled by F-statistic and the threshold specified by the permutation method at the experiment-wise error rate of 0.05. Then, all the significant SNPs were used to build a full model and the MCMC method was employed to generate the distribution of each effect in the full model. On the basis of the distribution, each effect was estimated by the mean and its significance is tested by t-statistic [http://ibi.zju.edu.cn/software/QTXNetwork/).A two-step mapping strategy for GWAS was employed to dissect genetic architecture of AC, GC, and GT. First, we used GMDR method  to screetatistic . The datTo investigate the efficiency and accuracy of the proposed methods, we performed a series of Monte Carlo simulations to verify the unbiasedness and robustness as well as statistical power. Because our real experiment data is based on a rice RIL population, we conducted the simulations based on this kind of population to examine our methods. Two environments were considered. 525 SNPs were scattered across 3 chromosomes, with 175 markers evenly distributed on each chromosome. Three individual QTSs controlling the quantitative trait were assigned on 3 chromosomes and two-paired epistatic QTSs were also set with gene-gene-environment interaction effects. Our simulations would also investigate the influences of heritability and population size on estimation of QTS parameters. Different heritabilities and population sizes were used in simulations. Each simulation generated the experimental samples according to the parameter setting for analysis and the results from 200 simulations are summarized in Tables Under a heritability of 70%, we conducted simulations to investigate the effectiveness and robustness of our proposed method in estimating QTS parameters, according to the results in Tables In order to understand the robustness of our method, we conducted simulations under two heritabilities of 70% and 50%, respectively, under the population size of 150 individuals. The simulation results Tables  and 4 clIn conclusion, according to the increase of population size, the power values of QTS with median effects became higher and the estimated effects were closer to the true parameter values. Therefore, a population consisting of around 150 or more RILs is a reasonable size to maintain the estimation efficiency and power for a trait controlled by modest QTS effects.Based on the above simulation consequences, an association study was conducted to analyze the genetic architecture for three quality traits of super-hybrid rice Xieyou9308 RIL population with 138 lines in two environments and the results were listed in For AC, two QTSs were detected and involved only in additive effects with very high significance and both of effects were negative. Within them, the heritability of rs1644460 was as high as 51.78% whose proportion was up to 75.4% of total heritability.There were also two QTSs discovered for GC. Positive additive effect of rs1644460 and negative additive effect of rs919289 were detected, similar to AC; the former, with an extremely high significance, accounted for more than 89% of the genetic variance of GC. The special QTS, rs1644460, was significant in both environments but showed varying effects in different environments, indicating an unstable expression of this QTS across different environments.For GT, two significant QTSs were identified, which were involved not only in additive effects and additive × environment interaction effects but also in epistasis and interaction of epistasis with environments. The QTS, rs1289107, with highest additive effect reached the highest heritability (24%) of all the effects. Similar to GC, there were two QTSs, rs1289107 and rs1644460, which were expressed in distinct patterns under different environments and they reached high heritability in interaction effects of additive with environments, indicating that the expression of these two QTSs depended substantially on environments. It should be noted that one-paired epistatic QTSs were detected for GT with different pattern epistasis × environment interaction effects.Throughout the three traits, rs1644460 was detected for all the three quality traits with high significance and heritability, suggesting a pleiotropic role of the QTS for the three traits. Generally, there were diverse patterns in genetic effects of QTSs among three quality traits. The QTSs controlling all the three traits expressed mainly in genetic main effects and subtle environment-specific additive or additive-additive epistasis effects were detected. Furthermore, only two highly significant QTSs (rs1644460 and rs1289107) were expressed both in genetic main effects and genetic-environment interaction effects. On the contrary, two QTSs (rs1610021 and rs919289) were expressed mainly in additive effects and modestly in genetic-environment interaction effects for AC and GC. Conclusively, environment is a very crucial factor to affect gene expression for rice quality traits. Most QTSs interact with environment and the environment can enhance or weaken the expression of most QTSs for the three quality traits.Epistasis and its interaction with environment have been recognized as important components of cultivar performance and have received more attention in rice breeding programs. Nevertheless, these effects have not yet effectively been analyzed by the current GWASs for the reason of absence of appropriate statistical methodology and heavy burden of computation –34. If aTraditional plant breeding is based on phenotypic selection of superior genotypes among segregating progenies, and its effectiveness is often affected by environment and genotype-environment interaction. Therefore, it sometimes leads to unreliable selection of some traits –40. AlthAccording to the association analysis, two QTSs (rs1610021 and rs1289107) of the four QTSs, which are involved in the genetic variation of three quality traits of rice, are located in the region of the known genes (Os06g0130100 and Os06g0124300), and the other two, rs1644460 and rs919289, lie in the upstream or the downstream of known genes (Os06g0130800 and Os07g0116900). Some of these genes have been well defined; for example, there is one QTS, rs919289, near the gene of Os07g0116900 that is described as NADH ubiquinone oxidoreductase, 20 kDa subunit domain containing protein. NADH plays essential roles in metabolism, which emerges as an adenine nucleotide that can be released from cells spontaneously and by regulated mechanisms , 42. ButAlthough the three rice quality traits exhibit different genetic architecture in the pattern of genetic effects, we believe that all of these are crucial genetic resources for improvement of the traits by selection of genetic effects. However, more validation is needed if these QTSs will be extended to other populations with different genetic background. In addition, more detailed whole-genome scanning, more powerful bioinformatics tools, and larger size of mapping populations are required since only causative polymorphisms with large effects can be detected given the size of the used RIL population .


Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost.Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared , singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3.The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.The online version of this article (doi:10.1186/s12864-015-1597-y) contains supplementary material, which is available to authorized users. Tree improvement programs are long-term and resource demanding endeavors requiring repeated cycles of selection, breeding and testing. Most of conventional tree breeding programs face major challenges; including, long breeding cycles, large field experiments planted over vast territory, late expression of economic traits , and low to medium heritability of traits . The pheMeuwissen et al.  introducThe development of Next-Generation-Sequencing (NGS) technologies and the implementation of genetic markers from sequence data in quantitative genetics related to GS, the Genomic Best Linear Unbiased Predictor (GBLUP) , and thePicea glauca (Moench) Voss), Engelmann spruce (Picea engelmannii Parry), and their hybrids and, because of their similar growing habitats and silvicultural requirements, they are often collectively treated as one complex . The sites were established in 1972/73 and consisted of 181 open-pollinated families using 3-year-old seedlings planted at 2.5×2.5 m spacing in a complete randomized block design with five or ten blocks and ten or fifteen tree-row-plots, respectively. Twenty-five families were selected based on their superior growth traits and four trees per family from four blocks per site were randomly sampled (maximum of 32 trees per family). Evidence of similar genetic diversity between selected and unselected populations have been reported for spruces, including white spruce ), (3) traditional k nearest neighbor (kNN:[28]), (4) expectation maximization imputation (EM:[31]), and (5) k-nearest neighbor imputation but newly derived for half-sib family structure (kNN-Fam).k most significant eigenvectors of the SNP markers. The k eigenvectors were then used as predictors for linear regression estimation of the missing data. SVD was implemented in R [x) were further classified into three separate genotype classes, −1, 0, and 1. The classification algorithm was taken as a modified k-means algorithm [k1), 0 (k2), and 1 (k3). The assignment of genotypes was done by satisfying:For SVD, the original SNP matrix was used to obtain a set of the ted in R  using thted in R . The reslgorithm , with thFor traditional kNN, the missing values were replaced with the weighted average of SNP values at the k closest SNP markers. The distances between all possible pairs of markers were computed by Euclidean distance. We selected five families  to test the imputation accuracy, as well as the efficiency of iterations for convergence . K = 10 and 30 were selected for accuracy estimates for kNN imputation. All iterations reached convergence criteria that were used in , howeverK1 is the number of neighbors within the half-sib family based on the genomic similarity, K2 is the number of neighbors from outside the family based on the Euclidean distance, and y is the original locus mean. We conducted exhaustive search for the optimal values of K1 and K2, by permutating K1 through 1 to 30 , and K2 from 1 to 250, as the total sample size of the panel is 1,126. The accuracy of kNN-Fam imputation was conducted for each permutation by randomly masking one million known data points from the filtered SNP table of the 5 selected families, and calculating the percentages of markers being imputed back to the correct SNP values.The kNN-Fam algorithm is derived from the kNN method of Troyanskaya et al. . Missing3 (VOL) following Millman’s formula)  and (b) three wood quality attributes (wood density in kg/m3 using X-ray densitometry (WDX-ray), resistance to drilling (WDRes), and acoustic velocity in km/s (VDir)) [X-ray and VDir were used to derive the dynamic modulus of elasticity (MoEd) [X-ray is commonly used to estimate wood density using increment cores extracted from the sampled trees, while WDRes and VDir represent indirect  methods that rely on wood density for either creating resistance during drilling or the speed of transmitting sound though the wood, respectively [The studied trees were phenotyped for (a) two growth traits (height in m (HT) and diameter at breast height in cm (DBH) which were subsequently used to estimate stem volume in m (VDir)) . Furthery (MoEd) . WDX-rayectively .The breeding value for each tree was estimated using ASReml v.3 using two different mixed linear models . The firy is the phenotypic measurement of the analyzed trait, β is a vector of fixed effect , a is a vector of random additive effect of individual trees ~ N, sb is a vector of the random effect of block within site ~ N, sa is a vector of random site x genotype interaction ~ N, e is a vector of random residual effect ~ N, and X and Z1-Z3 are incidence matrices assigning fixed and random effects to each observation and I and A are the identity and average numerator relationship matrices, respectively. Narrow-sense heritability was calculated as h2 = σa2/(σa2 + σsa2 + σe2) for the multi-site model.Multi-site model:Single-site model:h2 = σa2/(σa2 + σe2). Additionally, Genomic Best Linear Unbiased Predictor (GBLUP) [This model is identical to the multi-site mixed linear model but without all terms related to site . Narrow-sense heritability was calculated as  (GBLUP)  was used (GBLUP)  using oby is the vector of EBV, β is the vector of fixed effect which is the overall mean, b is the vector of random SNP effects, X and Z are incidence matrices for β and b, respectively, X is a vector of 1 while Z was built from  for aa, Aa and AA, respectively. The codes for Z were standardized according to the allele frequency using VanRaden’s method [β and b are estimated simultaneously using Henderson’s mixed model equation (MME) [th SNP and is estimated as:bj is the SNP effect, and hjj is the (n + j)th diagonal element of the matrix H = T (T’T)−1T’, whereThe SNP effects were estimated on the basis of two different methods: 1) Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) implemented in R package rrBLUP  and 2) Gs method . β and bon (MME) :7\documeZ of the individuals in the validation population and summing over the estimated general mean:u is intercept, Z is genotype at the ith locus of the jth individual and m is the marker effect. The accuracy of GS to predict the breeding value (BV) was estimated as the correlation of the vector of GEBV for all individuals  with their estimated BV . As we used 20 replicates, we obtained 20 estimates for prediction accuracy and we estimated means and standard errors for these estimates. The developed models were validated under the following four scenarios, namely, (1) within site, (2) in all 6 possible combinations for cross-validation comparisons across sites, (3) as a multi-site population, where training and validation populations were derived from the combined population for cross-validation and (4) again as a multi-site population, but where the entire multi-site population was used as training population and the individual site as validation population.The predictive accuracy was estimated using a 10-fold cross-validation approach with 20 replications. In each replication, the data were randomly divided into 10 subsets (folds) and each one was used as validation population (representing 10% of the data set), while the remaining 9-folds were used as the training population (90% of the data set) to fit the GS model. This process was repeated 20 times with random assignment of the data to the 10 folds -69. One Moreover, we estimated the type-b genetic correlation across sites, which is the additive genetic correlation between the traits measured on different individuals from the same genetic group but present in different environments, using a method described by Burdon .Dir, WDRes, and WDX-ray EBVs as variables to determine the PCs that best express these phenotypes and used their score as a new phenotype in subsequent RR-BLUP GS model for the multi-site scenario using the kNN-Fam imputation.We applied Principle Component Analysis (PCA) to distil the correlated variables (EBV) into a set of linearly independent variables ). We used HT, DBH, VNs)” [Ne) . In this method, the co-ancestry coefficients are estimated from the pedigree values of the selected individuals (ABLUP) while in the GBLUP case, we used the marker-based relationship matrix [Nge: [Notwithstanding the relatively small number of 25 open-pollinated families under investigation, to illustrate the benefits of incorporating genomic information in selection, we conducted a selection exercise of 40 elite genotypes for inclusion into a hypothetical production population (seed orchard) following the group merit selection scheme of Lindgren and Mullin . Group mNs)”  which isp matrix  to approix [Nge: ).Not required.http://doi.org/10.5061/dryad.8kb37.The data sets supporting the results of this article are available in the Dryad Digital Repository,


Belgian Blue cattle are famous for their exceptional muscular development or “double-muscling”. This defining feature emerged following the fixation of a loss-of-function variant in the myostatin gene in the eighties. Since then, sustained selection has further increased muscle mass of Belgian Blue animals to a comparable extent. In the present paper, we study the genetic determinants of this second wave of muscle growth.MRC2 mutation causes crooked tail syndrome in homozygotes. Genomic prediction models indicate that the residual additive variance is largely polygenic.A scan for selective sweeps did not reveal the recent fixation of another allele with major effect on muscularity. However, a genome-wide association study identified two genome-wide significant and three suggestive quantitative trait loci (QTL) affecting specific muscle groups and jointly explaining 8-21% of the heritability. The top two QTL are caused by presumably recent mutations on unique haplotypes that have rapidly risen in frequency in the population. While one appears on its way to fixation, the ascent of the other is compromised as the likely underlying Contrary to complex traits in humans which have a near-exclusive polygenic architecture, muscle mass in beef cattle , appears to be controlled by (i) a handful of recent mutations with large effect that rapidly sweep through the population, and (ii) a large number of presumably older variants with very small effects that rise slowly in the population (polygenic adaptation).The online version of this article (doi:10.1186/1471-2164-15-796) contains supplementary material, which is available to authorized users. Race de Moyenne et Haute Belgique” still exists (now referred to as “Blancs Bleus Mixtes” or BBM), intense selection for increased muscle mass, driven by premiums paid for double-muscled carcasses and enabled by the systematization of artificial insemination (AI) in the sixties, led to the fixation - in less than 20 years (i.e. ~5 generations) - of what has become the defining feature of the new BBC breed. It was demonstrated (f.i. [p.D273RfsX13) in the myostatin (MSTN) gene. MSTN encodes a “chalone” (i.e. a hormone inhibiting the growth of the tissue by which it is produced) controlling the growth of skeletal muscle in wild-type individuals (f.i. [Belgian Blue Cattle (BBC) are famous for their exceptional muscular development referred to as “double-muscling” Figure . BBC dered (f.i. ) that thp.D273RfsX13 MSTN allele, and (ii) that this residual genetic variation has been exploited to further increase muscle mass of BBC. That homozygosity for the p.D273RfsX13 MSTN variant only accounts for part of the spectacular muscular hypertrophy of modern BBC is also obvious from the comparison of their carcass scores with that of present-day BBM animals that are homozygous for the p.D273RfsX13 MSTN mutation  the heritability of muscular development remained as high as 30-45% in BBC after fixation of the p.D273RfsX13 MSTN mutation? Are Quantitative Trait Loci (QTL) with large effects on muscle mass segregating in present-day BBC? Or is the residual heritability for muscle mass in present-day BBC largely polygenic or “quasi-infinitesimal”?In this paper, we aimed to define the nature of the genetic determinants underpinning the second wave (after 1985) of muscle growth witnessed in BBC. Did it involve the rapid fixation of one or more mutations with effects of a magnitude similar to the did the second wave of muscle growth that occurred in BBC after 1985, involve the rapid fixation of variants with major effect on muscle mass, hence mimicking the fixation of the p.D273RfsX13 MSTN mutation prior to 1985?) we aimed at identifying genomic regions characterized by (i) reduced genetic variability (as a result of a selective sweep) in “modern” BBC (born in 2000 or later), and (ii) high genetic differentiation  with “old” BBC (born in 1985 or before), BBM, and Holstein-Friesian (HF) animals. HF were selected as controls because they are of dairy type, yet are phylogenetically closely related to BBC , (iii) 52 animals from the dual-purpose BBM subpopulation, and (iv) 191 Dutch HF sires, with the Illumina BovineHD array interrogating >700 K single nucleotide polymorphisms (SNP) dispersed across the genome. We first identified regions of low heterozygosity using a previously reported Hidden Markov Model (HMM) implemented with the “SWEEPY” program [Extension locus (MC1R) affecting coat color [PLAG1 known to affect stature in cattle [MSTN . The corresponding signatures are assumed to be genuine and to result from selection for black-based coat color and increased stature. The MSTN region exhibited a signature of reduced variability in old BBC, but not in BBM and HF. The region was highly differentiated between BBC on the one hand, and BBM and HF on the other hand. This was exactly as expected for a selective sweep accompanying the dissemination of double-muscling in BBC as a result of the fixation of the p.D273RfsX13 MSTN variant. Of the 88 remaining  regions, only three were differentiated with respect to BBM and HF, and two with respect to HF only , we performed a genome-wide association study (GWAS) for muscularity in modern BBC. In addition to the 28 old and 301 modern BBC sires used to detect signatures of selection (vide supra), we genotyped 264 sires born between 1986 and 1999 with the Illumina BovineHD array, for a total of 593 genotyped BBC sires. All were AI sires and 555 of them had recorded offspring (304 on average and ranging from 1 - 4675). The phenotypes used for GWAS were twice the “Daughter Trait Deviations” (DTD) for five categorical traits pertaining to muscular development: general muscularity (GM), muscularity of the back (BM), muscularity of the shoulder (SM), muscularity of the rump (rear view) (RMR), and muscularity of the rump (side view) (RMS). Association analyses were conducted using a previously described haplotype-based method [To provide a response to the second question (i.e. 10p > 5.60) at a single genomic location  for GM and BM. The same chromosomal region clearly affected SM, RMR and RMS as well, although the corresponding effects were not genome-wide significant. The observed signal was primarily driven by one haplotype (AHAP17) increasing muscle mass. The QTL explained between 4.5% (RMS) and 7.8% (BM) of the total additive genetic variance (estimated as the variance of the polygenic effects in a model without haplotype effects) . We previously reported that loss-of-function mutations  in the MRC2 gene cause Crooked-Tail Syndrome (CTS) in homozygotes/compound heterozygotes and are associated with increased muscle mass and decreased height in heterozygotes in BBC [MRC2 mutations. Twenty four and 0.2 percent of the bulls were carriers for the c.2904-2905delAG and c.1906 T > C mutations, respectively, in agreement with previous estimates. Linkage disequilibrium between AHAP17 and the c.2904-2905delAG variant was 0.905 (r2) or 0.992 (D’). This indicates that the more common c.2904-2905delAG mutation is virtually exclusively encountered on the AHAP17 haplotype, but that mutation-free versions of the latter also exist. We added MRC2 genotype to the association model and showed that it had a major effect on all muscularity phenotypes as expected  of the residual haplotype effects in the BTA19 46.6-48.0 Mb interval dropped severely for GM, BM, and SM, but remained as high as 3.53 and 2.79 for RMR and RMS. This suggests that the MRC2 variants accounts for most if not the entire QTL effect on GM, BM and SM, but that other variants with more modest effects on RMR and RMS may exist in this chromosomal region  exceeded the genome-wide significance threshold -log0p > 5.60ype AHAP1 increasiype AHAP1 increasitype AHAP increasi10p > 4.73), respectively on chromosomes BTA2 (52.6 Mb), BTA9 (92.5 MB), BTA14 (62.6 Mb) and BTA19 (7.8 Mb)  became genome-wide significant for GM, RMR and RMS. The effect was largely driven by a single haplotype (AHAP9) increasing muscle mass. It explained between 4.5% (BM) and 7.2% (GM) of the additive genetic variance . For the proximal BTA19 QTL (7.8 Mb), the frequency of the AHAP9 haplotype increased from ~0.17 for bulls born before 1985 to ~0.42 for bulls born during the 2000-2004 interval , hence suggesting that they were driven by selection.To provide additional evidence that the two genome-wide significant QTL on BTA19 contributed to the increase in muscle mass experienced by BBC since 1985, we examined the evolution of their allelic frequency during the corresponding time lapse. The frequency of the of the importance of the polygenic or quasi-infinitesimal component of muscularity in BBC. When adding the MRC2 genotype as fixed effects and the four significant or suggestive QTLs as random effects in a mixed model including a random animal effect, we estimated that the residual polygenic variation accounted for 76% (BM) to 92% (RMR and RMS) of the additive genetic variation. To gain additional insight in the molecular architecture of this polygenic component, we evaluated the performances of “genomic selection” models  as fixed effects) differing in their prior assumptions regarding the distribution of polygenic effects. While GBLUP [We next addressed the last question, i.e. le GBLUP  assumes le GBLUP  and Bayele GBLUP  assume tle GBLUP  and Daetle GBLUP  showed tp.D273RfsX13 MSTN variant around 1985. We have not obtained convincing evidence of the fixation of alleles with major effect on muscle mass (other than at the MSTN locus) in modern BBC between 1985 and the present. This is unlikely due to a lack of power as three convincing signatures of selection, corresponding to previously described major gene effects, were successfully identified. These involve respectively (i) the fixation of the MSTN p.D273RfsX13 mutation in (old and new) BBC marked by a ~500 Kb segment of reduced variation and high differentiation, (ii) the fixation of the MC1R p.L99P mutation in (old and new) BBC, BBM and HF, underlying the shared, breed-defining dominant black color of the pigmented hairs, and marked by a ~700 Kb segment of reduced variation, (iii) the fixation of a previously characterized haplotype spanning PLAG1 in (old and new) BBC, BBM and HF, associated with a major positive effect on stature [MSTN p.D273RfsX13 mutation in (old and new) BBC and the MC1R p.L99P mutation in (old and new) BBC, BBM and HF is obvious. Selection is also very likely to underlie the segment of reduced genetic variation encompassing PLAG1. The present-day standard shoulder height is > 140 cms for several breeds, including HF and BBC, while it was only ~110 cms in the 11th century [PLAG1 allele in these breeds [We detected two segregating QTL by GWAS, which explain up to 8.5% and 7.2% of genetic variation for muscularity traits, respectively. Both QTL are located on BTA19 and appear each to involve one “Q allele” embedded in a unique haplotype . The distal BTA19 QTL seems to be largely driven by the MRC2 c.2904-2905delAG variant shown previously to cause CTS in homozygotes. Indeed, the c.2904-2905delAG variant is in complete LD (D’ ~ 1) with AHAP17, and fitting c.2904-2905delAG as a covariate in the model completely annihilates the QTL signal for GM, BM and SM. Independent evidence supporting the causality of the MRC2 gene are (i) the allelic heterogeneity of CTS in BBC, and (ii) the known role of MRC2 in bone formation. We detected two independent MRC2 loss-of-function mutations causing CTS: c.2904-2905delAG and c.1906 T > C[MRC2 mutations because of their visible and desirable effect on muscularity and stature in heterozygotes [MRC2 is an endocytic collagen receptor that plays an essential role in remodeling the extracellular matrix required during bone growth. Accordingly, the length of long bones, including tibia and femur, is reduced in MRC2 knock-out mice [MRC2 c.2904-2905delAG and c.1906 T > C directly affect stature is thus very likely. Their apparent effect on muscularity could therefore just reflect a change in the ratio of muscle mass (unchanged) to skeleton size (diminished). Despite the strong evidence supporting the causality of the MRC2 loss-of-function variant, it is worth noting that adding the c.2904-2905delAG in the model did not completely eliminate the QTL effects on RMR and RMS. This suggests that other, as of yet unidentified variants affecting muscularity might exist in this genomic region.We herein analyze the genetic underpinnings of the muscular hypertrophy that has continued to augment in BBC after fixation of the  stature , and mar century . Increase breeds . It is we breeds . These pOur molecular understanding of the proximal BTA19 QTL is much more rudimentary. Mining available genome-wide resequencing data for 50 BBC sires (data not shown), did not reveal any striking mutation associated with the AHAP9 haplotype that might underlie the QTL. It is worthwhile noting, however, that the two BTA19 QTL appear to affect different body segments. The distal QTL (47.7 Mb) primarily affects BM and height, while the proximal QTL (7.8 MB) primarily affects RM. This suggests that different molecular pathways are recruited to control the growth of distinct muscle groups.MRC2 c.2904-2905delAG variant in the BBC population) is much too large to be explained by Mendelian segregation in the corresponding pedigree alone. Based on these data, we estimated that carriers of the MRC2 c.2904-2905delAG had nearly twice as much chance to be selected as elite sires than their non-carrier sibs [MRC2 c.2904-2905delAG variant and the AHAP9 haplotype, with that of control variants/haplotypes matched for population frequency in 1985. During this period, the frequency of the MRC2 c.2904-2905delAG variant increased by 16% and that of the AHAP9 haplotype by 26%. Both values exceeded the corresponding 99th percentile, hence supporting the selective sweep hypothesis. The 16% increase of the c.2904-2905delAG variant is even more remarkable as it is lethal in homozygotes which would have caused counterselection. Further increase of the frequency of the MRC2 c.2904-2905delAG variant would probably have stalled, as the proportion of affected homozygotes would have augmented. Such balancing selection now appears to be relatively common in livestock [Assuming that the two BTA19 QTL are genuine, the corresponding “Q” alleles should undergo a selective sweep in the BBC population as a result of the intense selection for increased muscularity. We previously demonstrated that the proportion of present-day offspring of Précieux  but of multiple haplotypes. In such case, multiple haplotype states would have been associated with increased muscle mass, rather than one as observed Figures  and 4. AOne might a priori expect that the suggestive selective sweeps undergone by the AHAP17 and AHAP9 haplotypes would be accompanied by detectable signatures of selection specific for segregating sites (whereas the method implemented in SWEEPY identifies signatures of selection associated with complete sweeps - where the selected variant has reached fixation). To that end, we evaluated the integrated haplotype score (iHS)  that seaWe herein provide evidence that the remaining 78.5-91.5% of the additive genetic variation is largely quasi-infinitesimal. Indeed, GBLUP (which assumes a uniform distribution of genetic effects across the genome) performed as well as Bayesian models  in predicting muscularity in cross-validation experiments. We attempted to characterize the polygenic architecture of muscularity from the direct output of the Bayesian models . As thep.D273RfsX13 MSTN variant has an even larger effect; it has been fixed in a few generations and does not contribute any longer to the genetic variation in modern BBC), and (ii) a quasi-infinitesimal polygenic tail of what are likely to be a very large number of variants with very small effects that would require immense sample size to be individualized. Moreover, the data suggests that the detectable QTL might correspond to novel variants that appeared only recently in the population. Evidence from QTL analyses of other production traits in livestock, including milk production and composition in cattle [Thus, the genetic architecture of muscularity in modern BBC – a typical production trait subject to intense directional selection – involves (i) a handful of detectable QTL that individually explain of the order of 5%, and jointly of the order of 8.5 to 21.5% of the additive genetic variance . QTn cattle  appears Contrary to complex traits in humans which have a near-exclusively polygenic architecture, muscle mass in beef cattle , appears to be controlled by (i) a handful of recent mutations with large effect that rapidly sweep through the population, and (ii) a large number of presumably older variants with very small effects that rise slowly in the population (polygenic adaptation).A set of 593 Belgian-Blue beef (BBC), 52 Belgian-Blue dual-purpose (BBM) and 191 Dutch Holstein (HF) bulls were genotyped with the BovineHD genotyping array  containing 735,293 SNPs mapping to autosomal chromosomes (on the UMD3.1 built). Individuals were required to have a call rate above 0.90.For identification of selective sweeps, a subset of 301 BBC sires born in 2000 or later (after 30-40 years of intense selection for muscular development) were representing ‘modern’ BBC whereas 28 BBC sires born in 1985 or earlier represented ‘old’ BBC. Markers with call rates below 95% in any of the three breeds or significantly deviating from HWE (p < 0.001) in at least one breed were excluded from the study. As a result, 672,754 SNPs were conserved.For association studies and genomic prediction, we used the genetic evaluations of Belgian-Blue sires for five muscularity traits (back muscularity (BM), shoulder muscularity (SM), rump muscularity - rear view (RMR), rump muscularity - side view (RMS) and a synthetic note of muscularity (GM) obtained by a linear combination of the previous traits (GM = 1 × SM + 1 × BM + 2 × RMR + 2 × RMS)). For individual traits, the animals were scored from 1 to 50 by a technician. These conformation traits were measured on their daughters between 15 and 56 months of age. The phenotypic values were equivalent to daughter-trait deviations (DTD) as proposed by VanRaden and Wiggans . For each within a given hidden state). For “sweep” regions, the distribution of heterozygosity maximizes probability of low heterozygosity and penalizes markers with moderate or high levels of heterozygosity. For the intermediate and the neutral state, the emission probabilities were estimated from the data  with that population was higher than the 95th percentile of the average FST values (with the same population) for a set of 10,000 randomly sampled regions containing the same number of SNPs.Only regions presenting differentiation with HF, BBM or ‘old’ BBC were conserved since we focus on recent selective sweeps associated with muscular development, which should be specific to the modern BBC breed. To that end we computed Fmeasures  between Association analyses were conducted using a previously described haplotype-based method that corrects for stratification by means of a random polygenic effect . Haplotyy is a vector of DTD, h is the vector of random QTL effects corresponding to the K defined ancestral haplotypes, Zh is an incidence matrix relating ancestral haplotype effect to records of sires, u is the vector of random individual polygenic effects  and individual error terms with corresponding variance i is the weight corresponding to DTD of individual i and corresponds to the number of records equivalents as estimated in VanRaden and Wiggans [where s , Zu is  Wiggans  or Garri Wiggans  were est Wiggans . EvidencMarkers with call rates below 95% in the 593 BBC bulls or significantly deviating from HWE (p < 0.001) were not used for association studies or genomic prediction. As a result, a subset of 708,579 SNPs was used.N, by simple linear regression, realizing that:Genome-wide significance thresholds at p < 0.05 were defined based on Bonferroni corrections for multiple-testing with N independent tests. Suggestive QTL correspond to level of significance which are expected to be reached once per genome scan and correspond to a threshold of p <0.37 (e.g. ). The nuth and 99th percentiles for this analysis.We only used corrected p-values ranging between the 1Using this approach, we estimated that the number of independent tests was equal to 20,130  and the resulting genome-wide significance threshold was fixed at 5.60 (4.73 for suggestive QTLs).We applied three models to perform genomic predictions that fit all the SNP simultaneously. The first model is the so-called GBLUP :y is a vector of records (DTD), X is an incidence matrix relating fixed effects to records  mutation and another variable equal to the number of copies of haplotype AHAP9 (see Results) carried by the individual), b is a vector of fixed effects including the mean, the effect of the CTS mutation and of one copy of haplotype AHAP9, g is a vector of individual genomic (or polygenic) effects with variance G being the genomic relationship matrix), Zg is an incidence matrix relating individuals to respective records and e is the vector of individual error terms with variance where s e.g., , 12)::y is a fk being the frequency of allele 1 for marker k and W is an NIND x NSNP matrix where element wik is the number of alleles 1 for SNP k for individual i minus twice fk .With g) for animals with and without phenotypes and variance parameters Individual genomic effects  for SNP k, vk is the allelic substitution effect for SNP k, Zv is a NIND × NSNP matrix with column k equal to zk and v is the NIND × 1 vector of allelic substitution effects. Individual genomic effects can be obtained as Zvv.Where The prior for the allelic substitution effects are :Where π is the probability of SNP having a non-zero effect and is a parameter related to the number of QTL . As suggested by Garrick and Fernando , the parThe BayesB model was solved by MCMC techniques using GenselR.The third model is BayesR and includes fixed effects, polygenic effects  and SNP effects , 41:ith SNP effect has four possible values SNP effects are normally distributed e et al.  and Kempe et al.  with Bay2 value above 0.95 with at least one SNP from the cluster. Only one SNP per cluster was conserved to reduce the number of highly correlated variables and reduce confounding effects. As a result, only 289,707 SNPs were selected.SNPs used in the association study were further filtered. First, SNPs with a MAF below 0.01 were discarded. Then, SNPs were clustered based on linkage disequilibrium: SNP were added to a cluster if they presented a rst 2005. The phenotypes from these 63 more recent bulls were erased and not used in the genomic prediction model. Then, the accuracy of genomic prediction was estimated as the squared weighted correlation between predicted genomic values (including fixed effects of the MRC2 genotype and the AHAP9) and DTD (using the number of effective records as weights).The efficiency of genomic prediction was assessed by splitting the data in two groups: a set of 492 animals born prior to 2005 and 63 bulls born after January 1No animal experiments were performed specifically for this manuscript. Where data were obtained from existing sources, references for these experiments are provided.mh mutation. Carcass scores of BBC ), and dual-purpose BBM cows (which can be either mh/mh (orange), mh/+ (yellow), or +/+ (white). The effect of the partial recessive mh allele can be seen from the superiority of mh/mh over mh/+ and +/+ BBM animals. The additional superiority of mh/mh BBC over mh/mh BBM animals highlights the effects of other muscle-enhancing genetic variants in BBC animals. (TIFF 950 KB)Additional file 1: Figure S1: Comparison of carcass scores in function of the Additional file 2: Figure S2: Measures of genetic distances between different bovine breed samples genotyped with a Bovine 50 K chip. Each breed is represented with a different color . BBB = Belgian-Blue beef, BBM = Belgian -Blue dual-purpose, HOL = Holstein, ANG = Angus, CHL = Charolais, SIM = Simmental, JER = Jersey, HRF = Hereford, WAG = Wagyu, SEP = Senepol, SGT = Santa-Gertrudis, BRM = Brahman, NEL = Nelore. A. Coordinates (first two coordinates) obtained from a mutli-dimensional scaling analysis. B. Neighbourgh joining tree. Distance was measured based on number of identical-by-state allele at each marker allele. (ZIP 10 KB)Additional file 3: Table S1: Table reporting the 91 candidate regions exhibiting reduced variability in modern BBC and identified with SWEEPY. (XLSX 15 KB)MSTN. The top panel represents the sweep probability estimated by Sweepy (red curve), the SNP heterozygosity in BBC (grey curve), the differentiation (measured as FST) with BBM (orange points) and HF (blue points). The lower panel represents the local Ensembl annotation. (TIFF 3 MB)Additional file 4: Figure S3: Description of the sweep encompassing PLAG1. The top panel represents the sweep probability estimated by Sweepy (red curve), the SNP heterozygosity in BBC (grey curve), the differentiation (measured as FST) with BBM (orange points) and HF (blue points). The lower panel represents the local Ensembl annotation. (TIFF 2 MB)Additional file 5: Figure S4: Description of the sweep encompassing MC1R. The top panel represents the sweep probability estimated by Sweepy (red curve), the SNP heterozygosity in BBC (grey curve), the differentiation (measured as FST) with BBM (orange points) and HF (blue points). The lower panel represents the local Ensembl annotation. (TIFF 2 MB)Additional file 6: Figure S5: Description of the sweep encompassing MRC2 genotype in the model. B. Manhattan plot with the MRC2 genotype in the model. (TIFF 193 KB)Additional file 7: Figure S6: Manhattan plots for general muscularity (GM). Alternating gray and black symbols mark the limits between successive chromosomes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. A. Manhattan plot without the MRC2 genotype in the model. B. Manhattan plot with the MRC2 genotype in the model. (TIFF 192 KB)Additional file 8: Figure S7: Manhattan plots for muscularity of the back (BM). Alternating gray and black symbols mark the limits between successive chromosomes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. A. Manhattan plot without the MRC2 genotype in the model. B. Manhattan plot with the MRC2 genotype in the model. (TIFF 190 KB)Additional file 9: Figure S8: Manhattan plots for muscularity of the shoulder (SM). Alternating gray and black symbols mark the limits between successive chromosomes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. A. Manhattan plot without the MRC2 genotype in the model. B. Manhattan plot with the MRC2 genotype in the model. (TIFF 194 KB)Additional file 10: Figure S9: Manhattan plots for muscularity of the rump - rear view (RMR). Alternating gray and black symbols mark the limits between successive chromosomes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. A. Manhattan plot without the MRC2 genotype in the model. B. Manhattan plot with the MRC2 genotype in the model. (TIFF 195 KB)Additional file 11: Figure S10: Manhattan plots for muscularity of the rump - side view (RMS). Alternating gray and black symbols mark the limits between successive chromosomes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. A. Manhattan plot without the Additional file 12: Table S2: Estimated effect (and associated variance) of the Crooked-Tail Syndrome variant for the five muscularity traits. (DOCX 14 KB)MRC2 genotypes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. (ZIP 38 KB)Additional file 13: Figure S11: Association on BTA19 for general muscularity (GM). The association was performed with a mixed model including a polygenic effect , haplotype effects and without (blue) or with (green) correction for the MRC2 genotypes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. (ZIP 38 KB)Additional file 14: Figure S12: Association on BTA19 for muscularity of the back (BM). The association was performed with a mixed model including a polygenic effect , haplotype effects and without (blue) or with (green) correction for the MRC2 genotypes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. (ZIP 38 KB)Additional file 15: Figure S13: Association on BTA19 for muscularity of the shoulder (SM). The association was performed with a mixed model including a polygenic effect , haplotype effects and without (blue) or with (green) correction for the MRC2 genotypes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. (ZIP 38 KB)Additional file 16: Figure S14: Association on BTA19 for muscularity of the rump - rear view (RMR). The association was performed with a mixed model including a polygenic effect , haplotype effects and without (blue) or with (green) correction for the MRC2 genotypes. The two red horizontal lines correspond to the thresholds for genome-wide significant and suggestive association, respectively. (ZIP 37 KB)Additional file 17: Figure S15: Association on BTA19 for muscularity of the rump - side view (RMS). The association was performed with a mixed model including a polygenic effect , haplotype effects and without (blue) or with (green) correction for the


Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear.This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach.uncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with Puncorrected = 6.95E-5 with odds ratio (OR estimated from β3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15).Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e.g. additive dominant model PWe conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously unsuspected effects that could provide novel insights into MDD risk, but much larger sample sizes are needed before this strategy can be powerfully applied. Major depressive disorder (MDD) is a common  complex A recent mega-analysis shows that it is unlikely that MDD is caused by a few genetic risk factors with large effect . The expNumerous methods are available for detecting gene-gene interactions -26, eachHistorically, two statistical principles have evolved: one represents genetic model-based methods and one model-free method ,28. The For this study we stated two hypotheses. Our first hypothesis was that common genetic risk factors in a set of candidate genes, act through a variety of interactions in the susceptibility for MDD. Our second hypothesis was that no single available statistical method would completely capture all possible forms of interactions. We therefore chose three different methods for modeling potential genetic interactions and examined to what extent these different statistical methods report synergy effects with relevance for susceptibility to MDD.To address these questions we designed an incremental approach  study  and the The 68 candidate genes we used in this study were derived from in the STAR*D study and were prioritized on the basis of previous results of association to MDD, for association to response to antidepressant treatment   assuming an additive model, using the top 4 principal components as covariates. For chromosome X markers the Unphased software  was used. Fixed effects (Mantel-Haenzel) or random effects (DerSimonian-Laird) meta-analyses were performed after Woolf’s test for heterogeneity with an applied threshold of P < 0.05, performed in R (rmeta-package). The single-marker analysis and meta-analysis was performed in order to assess a shared genetic background to MDD between the two study populations. We used nominal P value as our cutoff for defining homogeneity in association between the two study populations. The threshold for reporting association to MDD in the single marker analyses was estimated by a family-wise error rate (FWER), using Bonferroni’s method.We considered three different algorithms for detecting SNP-SNP interactions. Interaction effects in the additive and multiplicative methods were tested using logistic regression implemented in a JAVA coded software designed to handle large datasets (coded in our own lab and available upon request) which is a modified version of the GEIRA software . TestingTest for correlation between identified pair-wise set of markers between different statistical interaction methods was estimated by calculating Pearson’s r (carried out in R).We considered a nominal threshold of association (P < 0.05) for evaluating a shared genetic background in the 68 candidate genes to MDD between the two study populations.Of the 173 SNPs that were genotyped in both STAR*D and GAIN none were found to be nominally associated in both study populations. Of the 34 SNPs found at P < 0.05 in STAR*D 10 markers were genotyped in GAIN.2 ≥ 0.8).Of the 57 SNPs with P < 0.05 in GAIN 3 markers were genotyped in STAR*D . However, only 10 markers had the same risk allele and only one marker was nominally associated in the meta-analysis, rs2137330 in the . HoweverA shared genetic risk between the two study-populations could not be confirmed. Therefore only the GAIN sample, which was the sample with more complete genotyping in the candidate genes, was used for the subsequent gene-gene interaction analyses.Genotypes from 3,704 imputed or genotyped markers in the 63 tested candidate genes  were tested for interaction effects. None of the tests generated P values that survived correction for multiple testing with permutation in either the logistic regression or MDR methods. Additional file The large number of tests performed penalized our approach with regard to report a significant interaction, and none of the tests generated a P value that survived correction for multiple testing.2 ≤ 0.2) in each one of the different interaction methods as a source of information for better understanding of the genetic interacting landscape in these candidate genes for MDD-susceptibility.In spite of the non-significant results there are interesting observations worth noting. Among the model-based methods, a large number of interactions were observed with P values of interaction below a nominal significance level . This observation prompted us to address the question if the negative AP values are valid estimates for the reduction of risk  (Table ARHGAP10 gene. These 3 markers are in a weak LD (r2 ≤ 0.2) with each other. This gene is involved in intracellular signaling and has a role in regulation of apoptosis. In the recessive model .Among the ten most significant interactions in the dominant additive model, the effect measures of interaction (AP) were relatively strong . The most significant interaction  consisted of two markers located in the ARHGAP10 and GRIK4.For the additive model the strongest interaction effect, AP value of 0.72 , was for markers in the genes rec = 4.99, uncorrected P = 6.95E-5) was for markers in GRIN2A and GRIN2B genes and for the dominant model  for markers in GRIK1 and GRIK4 genes. Additionally, there was a marked interaction (ORrec = 3.70) from the ARHGAP10 gene in combination with the SLC6A2 gene, and from the HTR2A gene in combination with the COMT gene (ORdom = 3.05).In the multiplicative interaction method Table C-D the sUsing the model-free approach, there were no interaction events with large effect, OR < 1.51  to test the covariance of interactions among the 0.5% region in one method against the entire dataset of another method. This test was used for combinations of all methods  scores, sensitivity and specificity scores or AUC values were calculated , the additive method identified pairs of markers with large effects. An important note is however that estimates of AP do not make any inference for how many exposures are present in the identified sufficient cause. Nor do they describe the number of sufficient causes that they are involved in. Hence, the AP value should not be interpreted as the proportion of risk to MDD that is explained by this interaction but the excess risk for MDD among individuals with both risk factors which are due to their interaction.In the analysis using the model-free approach, the interaction with the best classifier value  was not repeatedly validated. The poor consistency of identified interactions is mirrored by the modest OR estimates and indicates the presence of many genetic factors that interact, each with only marginal risk contribution to MDD. Since we observed that the same markers were involved in several different 2-way models Table  we emploFrom the results of the analysis of sensitivity and specificity scores, we conclude that none of the top interactions are on their own sufficient to explain why people develop MDD Table A-E. Prevhttp://geneticassociationdb.nih.gov/) and found relatively few gene-gene interaction analyses have been conducted for MDD. There are reports of interaction between BDNF and NTRK2 and the 5-HTTLRP region in the SLC6A4 gene [BDNF Val66Met polymorphism) were among the 0.5% most significant interactions in any of the methods. Thirdly, for the model-based approaches we had expected to find the largest effects in groups with few individuals. However, our preliminary results indicate that the genetic risk, in these candidate genes, is shared by many and confers risk with small effect.Secondly, to our knowledge none of the present top interactions have previously been reported to interact in risk for MDD. We performed a literature search on PubMed and the Genetic Association Database . Additionally, only a proportion of the top 0.5% interactions were found among the top 0.5% interactions in another method  calculations and statistical analyses (ii) analysis plan and study design (iii) development of logistic regression model (iv) interpretation and conclusion of results and (v) writing of manuscript. PA & JHM; responsible for (i) the data mining machine learning (DMM) approach (ii) and interpretation and conclusions of results. HK; responsible for development of the GEIRA software. DU; responsible for coding the software calculating interaction using a logistic regression model. DC, HM, JAR & FJM; responsible for (i) selection of candidate genes, marker selection, SNP-genotyping and collection of the STAR*D sample and (ii) interpretation and conclusion of results. All authors read and approved the final manuscript.Flow chart of analytic strategy and summary findings. A brief overview of our incremental strategy for finding shared common genetic risk factors using an exploratory study population (STAR*D) and a replication sample (GAIN) and finally to explore the genetic interacting landscape in MDD. Number of SNPs, analysis method and the main findings are presented at each stage. All P values related to single marker analysis in the figure are uncorrected for multiple comparisons.Click here for fileSupplementary Materials.Click here for filehttp://www.genome.ucsc.edu/, http://www.genome.jp/kegg/). Genes are listed according to proposed hypotheses for involvement in regulation of MDD-susceptibility. Numbers of genotyped SNPs that have passed the quality control filtering for each gene are also presented. Markers maps within 10 kb of each candidate gene according to the NCBI36/hg18.List of 68 candidate genes. Gene names, genomic size and functions were generated using the UCSC and KEGG databases  indicates the power estimates using Bonferroni’s corrected P value for significance in each study population; 0.05/654 (STAR*D) and 0.05/2,896  (GAIN). B: Output trend statistics under each separate parameter value settings (corresponding to a significance level of P < 0.05).Click here for file10 P values plotted for the single markers tested in the STAR*D and GAIN samples. The 68 candidate genes are arranged in a relative scale along chromosome 1–22 and chromosome X. We performed a bioinformatic search and found no overlap between nominally associated markers in the STAR*D or GAIN samples with literature findings in the NHGRI GWAS catalog for risk to MDD. Nor were marker replicated in either the discovery or replication analysis in the mega-analysis of genome-wide association studies for major depressive disorder (Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Mol Psychiatry. 2013. doi:10.1038/mp.2012.21). However, two nominally associated markers in the GAIN sample were located in two genes (GRIA1 and GRIK3) which were found to be reported for risk to schizophrenia in NHGRI GWAS catalog. A: −Log10 of the P values are plotted for 654 genotyped markers in the STAR*D sample. The 10 nominally associated markers that were identical with markers genotyped in the GAIN dataset and included in the meta-analysis are highlighted in red. Threshold values for the nominal P value of 0.05 and Bonferroni’s corrected P value (7.6E-5) are illustrated. B: −Log10 of the P values are plotted for 1,516 genotyped (black) and 1,380 imputed (blue) markers in the GAIN sample. The 10 nominally associated markers that were identical with nominally associated markers in the STAR*D dataset and included in the meta-analysis are highlighted in red. Threshold values for the nominal P value of 0.05 and Bonferroni’s corrected P value (1.7E-5) are illustrated. C: −Log10 of the P values are plotted for the 10 markers tested in the meta-analysis.-LogClick here for fileNominally associated markers identified from single marker analysis in the STAR*D and GAIN samples. A: Results of single marker association in STAR*D. B: Results of single marker association in GAIN. C: Results of single marker association in GAIN (imputed markers).Click here for file10 P or balanced accuracy values of the interaction tests are plotted against the rank of the test in each method. Column charts depicts (i) number of interactions at P < 0.05 (ii) number of interactions in top 0.5% region and (iii) the number of interactions with predicted effects  in the total dataset. Number of unique markers for each method in top 0.5% region is also illustrated. A and B: P values are plotted, which are derived from significance testing from estimates of the attributable proportion due to interaction (AP) in the additive method and from the interaction term for the multiplicative method (dominant model in black and recessive model in blue). Threshold for significance after a 1,000-fold permutation analysis accounting for number of comparisons (5% significance level) are illustrated as well as the cut-off values representing nominal P value and the top 0.5% of all interactions that were tested in each method. QQ-plots (with 95% C.I.) are illustrated for additive and multiplicative method. In the QQ-plot of the additive dominant and recessive models negative estimates of AP values illustrated with green dots. C: Interaction results of ~6.1 million ranked interactions in the MDR analysis plotted against balanced accuracy value. Threshold after a 1,000-fold permutation analysis is illustrated as well as the threshold line to define the top 0.5% of all interactions. In a subsequent analysis we observed that all 63 candidate genes are represented in the top 0.5% region in all tested methods (data not shown). Moreover, the interactions present among the 0.5% most significant ones were not restricted to a small group of markers.Results of SNP-SNP interaction analyses. -LogClick here for fileEvaluation of AP values. A: Histogram of AP values. B: The lowest number of individuals in any exposure group, i.e. A0B0, A0B1, A1B0 or A1B1 (see Methods for definition of exposure groups), plotted against AP values. C: The lower of the two OR values from single markers (in any of the A0B1 or A1B0 groups) plotted against AP values. D: The higher of the two OR values from single markers (in any of the A0B1 or A1B0 groups) plotted against AP values.Click here for file2 < 0.2). Abbreviations: AP, attributable proportion due to interaction; Permuted P, permuted P value, corrected for multiple comparisons; 00, 01, 10 and 11 denotes risk (1) or no risk (0) genotype for marker 1 and marker 2; OR, odds ratio estimates for the interaction term in the multiplicative method; P values of the multiplicative method are derived for the interaction term; AUC, area under the curve metrics derived from the ROC analysis (metrics for the diagnose classifier for a particular set of par-wise markers); For the MDR method OR's are calculated using the number of cases/controls in high and low risk groups as defined in the MDR analysis; Balanced accuracy, statistical metrics denotes how accurately individuals are classified into high versus low risk groups; The MDR P values are derived from test for significance of the OR estimates; TPR (true positive rate) and FPR  is the sensitivity and specificity metrics to test the diagnose classifier for a particular set of pair-wise markers; PAF, population attributable fraction, proportion of disease burden in the population due to exposure to genetic risk. For each MDR risk group (high risk and low risk) are the number of cases/controls and genotype combinations displayed (-2 denote missing genotype). Notes: Asterisk (*) denotes that recoding of the preventive factors was made before measures of interaction in the additive scale were calculated. Recoding was done as follows: * 00 to 10, 01 to11, 10 to 00 and 11 to 01. ** 00 to 01, 01 to 11, 10 to 00 and 11 to 10. *** 00 to 11, 01 to 10, 10 to 01 and 11 to 00.Detailed results for the 10 most nominally significant interactions. Linear models are presented in A-D and results from MDR analysis in E. All top 10 interactions of each method are LD filtered (rClick here for fileOverlap of interaction results between different methods. A-L: Comparison of shared interactions between the top 0.5% regions (red dots) of one particular interaction method with the complete dataset (black dotted line) of another. The insert figures illustrate the correlation for shared interactions between two analysis methods. The gray dashed lines depict the highest value of the two methods and the 0.5% threshold respectively. Pearson’s correlation coefficient (r) was computed for the shared interactions in the top 0.5% regions. The r statistics and P values for each correlation analysis are depicted. Of note, as shown in the insert figures there was an absence of observations for shared interactions of the absolute top regions . Thus, the remaining shared interactions yield a marked correlation statistics masking the low correlation of the interactions in the absolute top regions. Of note, as the interaction analysis in the MDR approach allowed to test all possible pair-wise SNP combinations which was not practically feasible in the additive or multiplicative models the comparison between the MDR versus logistic regression models are not fully illustrative.Click here for file


Finally, we describe recent rare variant association findings, highlighting the unexpected conclusion that most rare variants have modest-to-small effect sizes on phenotypic variation. This observation has major implications for our understanding of the genetic architecture of complex traits in the context of the unexplained heritability challenge.Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. Despite these successes, much of the heritability of these traits remains unexplained. Because low-frequency and rare variants are not tagged by conventional genome-wide genotyping arrays, they may represent an important and understudied component of complex trait genetics. In contrast to common variant GWASs, there are many different types of study designs, assays and analytic techniques that can be utilized for rare variant association studies (RVASs). In this review, we briefly present the different technologies available to identify rare genetic variants, including novel exome arrays. We also compare the different study designs for RVASs and argue that the best design will likely be phenotype-dependent. We discuss the main analytical issues relevant to RVASs, including the different statistical methods that can be used to test genetic associations with rare variants and the various bioinformatic approaches to predicting  Simple (or Mendelian) diseases such as sickle cell anemia and cystic fibrosis are caused by mutations in single genes. These mutations, which may have variable penetrance and expressivity, are causal. On the other hand, complex (or common) human diseases such as myocardial infarction or schizophrenia result from the combined effect of multiple genetic variants and environmental stresses. Genetic variants associated with complex human diseases do not cause diseases but rather influence the risk of developing them. The focus of this review is on the role of rare genetic variants in complex human diseases. Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. As GWAS-based consortia have expanded to include hundreds of thousands of samples ,2, the rABCA1 and PCSK9, associated with high-density lipoprotein (HDL)- and low-density lipoprotein (LDL)-cholesterol levels, respectively [PCSK9 gene association [The original excitement over RVASs sparked from targeted gene sequencing experiments, which identified rare coding variants with strong effects on phenotypic variation. These included genetic variation in ectively -8. In paociation  raised hRVASs are now possible because of next-generation DNA sequencing (NGS) technologies and the development of software to process, control the quality of, and call DNA variation from the vast amount of sequence reads generated . The 100Because 98% of the human genome is non-coding and therefore more difficult to interpret, enrichment methods were developed to capture only a fraction of the genome before building sequencing libraries. Whole-exome sequencing (WES) relies on the solution-based capture of exons, and several companies , Roche’s Nimblegen  and Agilent’s SureSelect ) offer well-designed exome-wide capture reagents. In the last few years, WES has been instrumental in defining the culprit gene(s) for dozens of Mendelian diseases . RecentlIn many applications - for instance, to follow up candidate genes from a GWAS or to screen clinically important genes in a genetic clinic - sequencing 50 to 1,000 kilobases is often desirable , it is possible to interrogate these variants using standard genotyping arrays. After an important contribution from the human genetics community, exome-wide genotyping arrays (exome chips) are now available  that test hundreds of thousands of exonic variants at modest costs  [PNPLA5 gene that were associated with LDL-cholesterol. Similarly, Emond and co-workers [Pseudomonas infection in individuals with cystic fibrosis. This approach yielded a novel genetic association between rare coding variants in the DCTN4 gene and time to first Pseudomonas infection, a surrogate measure of cystic fibrosis severity. More recently, Flannick and colleagues [SLC30A8 that was strongly protective against T2D. Additional genotyping of over 44,000 cases and controls confirmed a 53% reduction in T2D risk for carriers of the nonsense variant.For studies of quantitative traits, it has been shown that the power to detect rare variant associations can be increased by sampling from the extremes of the trait distribution ,28. To ddisease) . For insdisease)  sampled -workers  chose salleagues  selectedAlthough extreme sampling may boost the statistical power of a study to detect associations, data analysis often requires sophisticated statistical techniques to remove sampling bias ,34. FurtOwing to a variety of demographic forces , many subpopulations around the world have undergone extreme population bottlenecks, and have become isolated and remained so for many generations ,36. ThesBRCA1 and BRCA2 mutations, which occur at high frequency in the Ashkenazi Jewish population and are associated with risk for breast and ovarian cancer [Recently, a study of WGS data from 1,795 Icelanders identified a non-coding, low-frequency variant associated with prostate cancer . The risn cancer . Howeverad hoc filtering criteria, although there has been recent progress in developing unified and rigorous methods for analyzing sequence data from pedigrees [A different type of design for identifying disease-causing rare variants is to study a family with multiple affected members. Often referred to as ‘family studies’, such a design involves sequencing co-affected family members and searching for overlapping variants that co-segregate with the condition of interest. Both linkage-based and genetic association methodologies are amenable to family studies. This type of design has been very successful in identifying large effect, highly penetrant mutations that underlie Mendelian disorders ,42. Howeedigrees . If the edigrees .n number of trios provides the same statistical power as a case–control design, with n cases and n controls [In addition to co-segregation analysis, genotype data from trios (an affected offspring and his or her parents) are often used in studies with a family-based design. The transmission disequilibrium test (TDT)  has beencontrols . For coma priori which design will be most powerful. Both population-based studies (such as extreme phenotype sampling or population isolates) and family designs are powerful and useful in differing contexts and should serve as complementary, rather than competing, approaches for uncovering the genetic contribution of rare variants to complex traits.The underlying genetic architecture of the trait of interest determines which study design is best powered to detect the association . For mosP-values <5 × 10−8 are generally labeled as significant genome-wide. The HapMap Project established this significance threshold by examining common genetic variation in populations of European, African and Asian ancestry [α = 5 × 10−8 is the Bonferonni-corrected threshold for a million tests). However, because rare variants are more numerous and less correlated with each other than common variants, a simple α = 5 × 10−8 threshold is not enough to declare significance in association studies that target rare variants. Thus, RVASs suffer from an increased multiple testing burden and a decrease in statistical power owing to the rarity of individuals carrying these variant alleles. It is generally recognized that a better strategy for analyzing rare variants is to combine them within units of association, defined using gene annotations, genomic coordinates or functional characterization (see below). Because rare variants are traditionally grouped by genes, these tests are referred to as gene-based tests and use α = 2.5 × 10−6 as a significance threshold in a genome-wide search .In the GWAS literature, single nucleotide polymorphisms (SNPs) that reach association ancestry . The resCED9 alleles in Caenorhabditis elegans and their effect on programmed cell death) and Mendelian genetics  that the same gene can carry alleles that have dramatic phenotypic consequences, but in opposite directions. To allow for this scenario, tests that consider the distribution of genetic effects  for a group of variants were developed. For instance, the widely used SKAT test offers flexibility in terms of covariate adjustment, study design  and different variant prioritization/weighting strategies [We can divide most gene-based tests into two main categories: burden and variance-component tests . In its rategies . Recentlrategies  combinedrategies ). In parrategies -60. Inderategies .As for GWASs of common variants, large sample sizes are required to identify significant associations in RVASs. Because ethical concerns usually prevent the sharing of genotype and phenotype information between collaborators, genetic consortia tend to analyze summary statistic data; that is, association results for each variant across all participating studies . This apin silico genotyping) is a statistical technique for predicting genotypes at variants that are not directly measured [Owing to the costs associated with genotyping or sequencing large numbers of samples, many studies of rare variant associations do not have sufficiently large sample sizes for a well-powered analysis. Genotype imputation can be used to increase sample sizes by imputation of sequence variants (from a sequencing study) into large numbers of samples with genome-wide array data. Genotype imputation  of the variants that were considered by each study ,72.LCT locus and circulating white-blood-cell counts as well as between variants in the MPL gene and platelet counts. Imputation of ESP data was also used to find loci associated with height [APP and Alzheimer's disease and between rare variants in PDX1 and T2D were identified [In addition to GWAS meta-analyses, genotype imputation can be used to search for rare variants associated with complex traits. Auer and co-workers took sequence data from the ESP and imputed them into over 13,000 samples with genome-wide array data . This aph height . By sequentified ,75. The entified , and theentified . These sStudy-specific reference panels can also enhance the quality of imputation for rare variants. Duan and colleagues  showed tImputation of sequence variants from related individuals or extended pedigrees can also boost imputation accuracy. The Genome of the Netherlands project sequenced 769 Dutch samples at 14× coverage, derived from 231 trios and 19 quartets, resulting in 998 unrelated haplotypes. Imputation of these variants into other Dutch as well as English and Italian samples showed improvement over 1000 Genomes imputation , highligImputation accuracy decreases with MAF, making it difficult to impute very rare variants. However, imputation accuracy also increases with the size of the reference set. With larger reference sets, imputation of very rare variants should become more accurate. To this end, there has been a recent effort to establish a Haplotype Reference Consortium that contains sequence data on over 30,000 subjects . With thA gold-standard association study would include directly measured genotypes from tens or even hundreds of thousands of study samples. However, even though the cost of sequencing continues to fall, it is not currently feasible directly to genotype rare variants across the genome in tens of thousands of samples. Thus, for the foreseeable future, genotype imputation is likely to remain an efficient and cost-effective means of inferring genotypes and increasing sample sizes for association studies.In addition to the choice of statistical approach to test for rare variant associations, another important consideration is whether one should include all discovered rare variants in a group-based analysis. Many tools and resources exist to annotate DNA sequence variants Table . For insPPARG [PPARG in approximately 20,000 individuals and identified 49 new rare non-synonymous variants. In aggregate, these rare variants were not associated with T2D risk, even when considering in the analysis their frequency or in silico predictions. The authors then tested the effect of each rare PPARG non-synonymous variant in an adipocyte differentiation assay, and noted that only 12 of these variants were truly functional. Interestingly, individuals that carry these rare functional PPARG alleles have a sevenfold increase in their odds of developing T2D [Bioinformatic methods based on conservation, structural information and/or amino acid physico-chemical properties have been developed to estimate the likelihood that a given missense variant is detrimental ,101. ButPPARG . In thatping T2D . This exNon-coding variants are even more difficult to ascertain, and until recently the estimation of their biological impact relied mostly on conservation scores. Publically available data generated by the ENCODE, Roadmap Epigenomics and FANTOM5 projects now offer an alternative strategy to focus on ‘more likely’ functional non-coding variants in genetic association studies -91. ThesP < 5 × 10−8 are now considered requirements for novel associations to be published [LDLR variant (MAF = 0.5%) associated with LDL-cholesterol that is polymorphic only in Sardinians. In such cases, attempting to replicate the association by discovering other variants in the same gene that are associated with the same phenotype in other populations might be the only approach to confirm the initial genetic association.An important consequence of GWASs has been the standardization of the statistical evidence needed for an association to be accepted. An independent replication of results and a genome-wide cutoff of ublished . For pubublished  reportedRare variant associations are typically tested in two ways: by considering each variant individually ; or by aggregating rare variants into a single unit  and utilizing any number of aggregate rare variant association tests -55. Thusin silico genotyping  of the selected variant, although the latter option is suboptimal. If the association signal from the independent population is statistically significant, then the discovery has been replicated. Note that the proper threshold for statistical significance for replication is P < 0.05/n, where 0.05 is the traditional significance threshold for a single test and n is the total number of variants brought forward for replication; this corresponds to the Bonferroni correction for multiple testing.Replication of single variant associations can be done in a multitude of ways. Using an independent population , it is possible to perform selected genotyping of the variant of interest, selected genotyping of a perfect tag SNP, or There are several different options for replication of aggregate or ‘gene-based’ associations. Liu and Leal  considerAPOC3 gene and triglyceride levels using exome sequencing in 3,734 subjects. For replication of this finding, four loss-of-function variants in APOC3 were included on the Illumina ExomeChip and successfully genotyped in 41,671 additional subjects. The gene-based test replicated at P = 7 × 10−6. In contrast, Emond and co-workers [DCTN4 gene and time to Pseudomonas infection in individuals with cystic fibrosis. This discovery was made via exome sequencing and was replicated by Sanger sequencing of the entire gene.Both strategies for replication have been successful in practice. Crosby and co-workers  describe-workers  discoverPCSK9, which led to the development of new therapies to treat hypercholesterolemia [PCSK9 with LDL-cholesterol and coronary artery diseases, TREM2 and APP with Alzheimer’s disease), our early findings suggest that most rare variants will have small effect sizes on phenotypes, and may therefore have only limited value in predictive medicine. But that should not undermine the intrinsic value of RVASs. For one, such experiments may lead to the identification of new genes implicated in human diseases. For instance, using the ExomeChip approach, we identified a series of rare missense variants in the chemokine receptor gene CXCR2 that are associated in aggregate with low white-blood-cell counts in individuals of European ancestry [CXCR2 had not been implicated in white-blood-cell biology by GWASs. We also confirmed that rare familial mutations in CXCR2 cause congenital neutropenia through an effect on leukocyte migration [With the technology and analytic tools now in place, and motivated by successes such as the identification of low-frequency coding variants in erolemia -9, RVASsancestry . CXCR2 higration .TM6SF2 that is associated with total cholesterol levels and alanine transaminase, and that explains GWAS signals at the locus for coronary artery and non-alcoholic fatty liver diseases [RVASs of coding variants are also useful to explore GWAS loci further. Indeed, one of the limitations of GWAS findings is that they usually highlight non-coding SNPs in LD with many other markers across large genomic intervals that may contain several genes. Although we generally accept that most GWAS SNPs have regulatory functions , it is sdiseases ,23. We aNew technological advances now enable human geneticists to explore the contribution of low-frequency and rare genetic variants in phenotypic variation. For obvious reasons, the scientific community initially focused on the 2% of the human genome that is protein coding, identifying new genes implicated in common human diseases and complex traits, or new alleles located in previously implicated loci (either by GWASs or Mendelian genetics). While we continue to explore coding variation, we need to start considering how to tackle the remaining 98% of the human genome. Indeed, there is no reason why rare non-coding variants should not influence human phenotypic variation. However, there remain challenges ahead in terms of developing statistical methods and experimental tools to distinguish rare neutral variants from functional variants. Without doubt, some of the progress in this field will come from studying other populations (isolates or different ethnic groups) or using different designs (such as family-based designs) where rare variants might be more common. We also need to develop phenotype-relevant high-throughput assays in cells or model organisms to characterize the biological impacts of these rare variants. Despite these challenges, we believe that studying the influence of rare genetic variants in human biology is a worthwhile endeavor. The premise of RVASs was that rare genetic variants would have strong effect sizes on phenotypes; with a few exceptions, this is clearly not what we observe. However, we should not forget that we do not need to find strong-effect alleles to gain insights into human biology, disease pathophysiology, or to consider and develop new therapeutic strategies .