Tyrp1 and Gpnmb to the iris transillumination defect (TID) in five age cohorts of BXD mice. Using systems genetics, we also evaluated the role of other known pigmentation genes (PGs). Mapping studies indicate that Tyrp1 contributes to the phenotype at all ages, yet the TID maps to Gpnmb only in the oldest cohort. Composite interval mapping reveals secondary loci viz. Oca2, Myo5a, Prkcz, and Zbtb20 that modulate the phenotype in the age groups up to 10–13 months. The contributions of Tyrp1 and Gpnmb were highly significant in all age cohorts. Moreover, in young mice, all six gene candidates had substantial interactions in our model. Our model accounted for 71–88% of the explained variance of the TID phenotype across the age bins. These results demonstrate that along with Tyrp1 and Gpnmb, Oca2, Myo5a, Prkcz, and Zbtb20 modulate the TID in an age-dependent manner.We investigated the contributions of  Iris pigmentation is a complex genetic trait controlled by a network of genes whose coordination and regulation are poorly understood. Pigmentation abnormalities are clinically significant; they can lead to albinism, iris transillumination defects (TIDs), and potentially pigment dispersion glaucoma. We demonstrate for the first time the simultaneous contribution of multiple genes to the TID phenotype. The outcome of our studies using age-specific cohorts to elucidate the impact and interactions of the known pigmentation genes and their control over the progression of TID is unparalleled. Identifying which genetic modifier(s) regulates the disease during its progression will be significant in formulating targeted therapeutic options.Genetic variations in mice with regard to the color coat have had a significant influence on the discovery of genes influencing pigmentation  and Dct   and Myo5a (myosin VA) have also been found to be critical for melanogenesis. The pigmentation pattern of the iris is a complex genetic trait controlled by an intricate network of genes whose coordination and regulation are poorly understood.Pigment synthesis takes place in the melanosome, a lipid-bound organelle within melanocytes. As mouse strains with coat color variations harbor mutations that influence melanosomes on a whole, these mutations may also affect the iris and give rise to abnormal ocular phenotypes. However, there could also be differences in coat and the iris phenotypes, which could mainly be due to difference in the development of iris and the coat. Specifically, melanocytes influencing coat color are solely derived from neural crest, whereas pigment cells in iris are of diverse origin . Genetic abnormalities in neural crest would thus affect both iris and coat, while that affecting neuroepithelial cells and bone marrow would only affect the iris, thereby leading to a difference in the phenotypes. . They have also shown that ISA is characterized by deterioration of the iris stroma, and an accumulation of stromal pigment and cell debris in the drainage structures and is caused by a recessive mutation in the tyrosinase-related protein 1 gene (Tyrp1b)  has served as the preeminent mouse model for pigment dispersion syndrome (PDS) . Collectively, these 80 BXD strains are among the largest RI sets, and they have been used extensively in genetic and genomic studies of the eye and central visual system  strains of mice are a useful resource for identification of the genetic sources of variation in phenotype, in our case, the severity of TID , we are confident that this study is the first of its kind to simultaneously evaluate the contribution of multiple pigmentation genes to the TID phenotype  using our large family of BXD recombinant inbred mice and a large repository of sequence variation data and bioinformatics analysis tools available on http://GeneNetwork.org (http://www.genenetwork.org/webqtl/main.py). We also sought to evaluate the plausible role of other known pigmentation genes along with potential genetic interactions using systems genetics along with mathematical modeling.The parental strains from which the BXD recombinant inbred mice were generated—B6 and D2—vary in their degree of TID. B6 mice have no ocular aberrations, while D2 harbor mutations in We categorized the level of TID into five grades encompassing the full range of the phenotype. Representative photographs of each grade are shown in Grade 0: The iris appears intact when illuminated with a broad beam of light, and no light is transmitted through the iris when a narrow beam of light is shined through the pupil;Grade 1: The iris is not visibly aberrant when illuminated with a broad beam of light, and a very small amount of light is transmitted through the iris, typically in the mid-peripheral region of some areas, when a narrow beam of light is shined through the pupil;Grade 2: The iris appears to be slightly thinned, and the pigmentation is slightly patchy when illuminated with a broad beam of light, and a small amount light is transmitted through the iris, typically in the mid-peripheral region of some areas, when a narrow beam of light is shined through the pupil;Grade 3: The pigmentation of the iris is noticeably patchy, and the pupil has started losing its contour when illuminated with a broad beam of light, and diffuse light is transmitted across the entire iris when a narrow beam of light is shined through the pupil; andGrade 4: The iris has a very patchy pigmentation pattern, and the pupil has lost its contour when illuminated with a broad beam of light, and an equal amount of light is emitted through the iris 360° when a narrow beam of light is shined through the pupil.r > 0.95, data not shown). Because our data lacked both gender and sidedness biases, we pooled all data for each BXD strain into a single data set that was stratified only by age.After optimizing the TID grading scale, we applied it to mice from all BXD strains in each age group, as well as parents and F1 crosses. To determine whether data could be pooled across genders and within a mouse, separate analyses were performed on mice from each age cohort. Within each group, no significant differences were observed between females and males  . CompletTyrp1 and Gpnmb, several findings became clear . Finally, BXD strains carrying mutant versions of both Tyrp1 and Gpnmb had significant TIDs at all ages that were less than the D2 parent, which lends support for the theory that the B6 genetic background harbors protective genes , a gene known to be causative for the TID in D2 mice , the second gene known to be causative for the TID in D2 mice , Gpnmb (G), Myo5a (M), Oca2 (O), Prkcz (P), and Zbtb20 (Z), were highly significant in all age cohorts except the oldest one, that is, 13+ months of mice, where only G and T were found highly significant. With regard to the genetic interactions, we found significant cross-talk between the predictors at all age groups. The most common of those interactions involved Tyrp1, Gpnmb, or Oca2. The extent of genetic interactions was lowest in age cohort 13+ months. At this age cohort, none of the predictors other than Tyrp1 and Gpnmb were highly significant. The detailed analyses are presented in sas statistical software, which showed identical results (data not shown).The six candidate genes that met our inclusion criteria were subjected to general linear modeling to evaluate the contribution of each gene individually as well as any possible interactions between the pigmentation genes. These results demonstrate that at different time points during the progression of the TID, all six genes contribute to the phenotype . In someTo gain an understanding of polygenetic nature of a quantitative phenotype, the contributions of multiple genes must be simultaneously evaluated. This can be an arduous task under the best circumstances and nearly impossible when using a genetically diverse reference panel. In this study, we demonstrate for the first time the simultaneous contributions of multiple genes to the TID phenotype. Using our large repository of phenotypic and genetic data along with the bioinformatics tools available on GeneNetwork, we have generated a mathematical model that accounts for 71–83% of the genetic factors that modulate the TID.Tyrp1 and Gpnmb—cause severe iris atrophy and pigmentary dispersion, respectively, in D2 mice  affect eumelanin/pheomelanin synthesis, but do not affect intracellular trafficking of the mutant protein, leading to a mild iris phenotype . It is our goal to explore the genetic modulation of the TID phenotype in greater depth using an innovative Quantitative Trait SNPs approach, which may shed light on the remaining genes associated with the TID phenotype. Similar methods can be used to determine the genetic complexities of other phenotypes including polygenetic diseases such as glaucoma, age-related macular degeneration, Alzheimer's disease, and diabetes.Our mathematical model accounts for 71–88% of the explained variance of the TID phenotype across the age bins that we investigated, leaving approximately 10% of the genetic contribution toward the TID unaccounted for by these six genes. We used up to a six-way analysis to check for the best possible combination of genes in our mathematical model. Lowest error values and highest per cent observed total variances were seen using a three-way analysis; hence, we chose a 3-way model for our interaction studies. The results were in agreement with the mapping studies as seen in the Mice were handled in a manner consistent with the Guide for the Care and Use of Laboratory Animals , and all studies were approved the Animal Care and Use review board of the University of Tennessee Health Science Center, an AAALAC-accredited facility. Mice were maintained at a temperature of 20–24°C on a 12:12 light/dark cycle with 35–40% humidity in a specific pathogen-free (SPF) environment at the University of Tennessee Health Science Center Animal Facility. Animals were weaned at 25 days of age, housed in same-sex cages (2–5 mice per cage), and were fed 5% fat Agway Prolab 3000 rat and mouse chow.A total of 3564 mice were used in this study: 3280 mice from 73 BXD RI strains; 127 D2 mice; 83 B6 mice; 51 B6D2F1 mice; and 23 D2B6F1 mice. The gender distribution was relatively equal with 1965 females and 1599 males being included. Because the transillumination defect of D2 mice can be progressive , we have helped genotype a total of 480 strains using a panel of 13,377 SNPs. These SNPs have been combined with our previous microsatellite genotypes to produce consensus maps for the expanded set of BXD using the latest mouse genome assembly as a reference frame for marker order (Mouse Build 36 - UCSC mm8). The order of markers given in the BXD genotype file is essentially the same as that given in Build 36. (Files were updated from mm6 to mm8 in January 2007.). Post-July 2007, we have been using NCBI37/mm9 assembly obtained from NCBI and the Mouse Genome Sequencing Consortium.http://www.genenetwork.org/reference.html; http://www.genenetwork.org/links.html; http://www.genenetwork.org/tutorial/WebQTLTour/; and http://www.genenetwork.org/glossary.html. These links would provide a more comprehensive list of references (some with pdf links), tutorials, glossary of terms, and an exhaustive list of resources used for the functioning of GeneNetwork.A total of 88 strains were genotyped using the full set of SNPs, and 7482 of these were informative. Informative in this sense simply means that the C57BL/6J and DBA/2J parental strains have different alleles. To reduce false-positive errors when mapping using this ultradense map, we have eliminated most single genotypes that generate double-recombinant haplotypes that are most commonly produced by typing errors (‘smoothed’ genotypes). For this reason, the genotypes used in the GeneNetwork differ from those downloaded directly from Richard Mott's web site at the Wellcome Trust, Oxford. Due to the very high density of markers, the mapping algorithm used to map BXD data sets has been modified and is a mixture of simple marker regression, linear interpolation, and standard Haley–Knott interval mapping. When two adjacent markers have identical SDPs, they will have identical linkage statistics, as will the entire interval between these two markers . Readers are also directed to the following links for further support with GeneNetwork: Previous studies have shown that the anterior segment disorder of DBA/2J mouse includes pigment dispersion and iris atrophy with associated synechiae  and photodocumented with a digital camera . For assessment of TIDs, a small beam of light was shone directly through the slit of the dilated pupil of the mouse, and the iris was examined for the ability of reflected light to pass through depigmented areas of the iris. All photographs were taken with identical camera settings and were prepared with identical image software processing. After optimizing the TID grading scale, we applied it to mice from all BXD strains in each age group, as well as parents and F1 crosses. To determine whether data could be pooled across genders and within a mouse, separate analyses were performed on mice from each age cohort. We also stratified TID by both age and the genotypes at datadesk 6.3 .The grade of TID of left and right eyes of all mice was measured and pooled separately within each age group. A paired t-test was used to statistically compare the values of TID grades between eyes. Likewise, a paired t-test was used to compare the TID grades between females and males of each age group. All statistical evaluations were performed using h2 = 0.5Vg/(0.5Vg + Ve)  a set of 3795 markers. Linkage is reported with genome-wide significance levels based on 2000 permutation tests. Two types of QTL mapping analyses–simple mapping using the Haley–Knott regression equation, and composite interval mapping–were utilized in this study. Simple interval mapping was performed to illustrate the significance of any QTLs that regulate the TID. As a secondary analysis, composite interval mapping which controlled for the influence of Tyrp1 was also performed with the goal of identifying any secondary QTLs that may have been masked by the major QTL on Chr 4. Each of these analyses produced a LRS score, which is a chi-square statistic that provides a measure of the linkage between variation in the phenotype and genetic differences at a specific genetic locus. Logarithm of odds values can be obtained simply by dividing the LRS by 4.61. Further, we conducted an exhaustive search on the MGI phenotype database using ‘iris’ and ‘pigmentation’ as the search terms to shortlist known pigmentation gene candidates playing a role in iris-related diseases. We examined all the genes obtained within the secondary QTL peaks in composite mapping  between the parental strains as determined via the variant browser link on GeneNetwork;The gene had expression differences between the parental strains as determined via the RNA-Seq link on GeneNetwork; andThe mean expression level of the gene across the BXD strains was >7, in the age group in which its LRS score was at or above the suggestive levels in GeneNetwork.DataDesk 6.3 statistical software . Different permutations and combinations of the pigmentation genes  were included into the model, and the contribution of individual genes and potential interactions were studied. A general linear model using OLS with up to six-way interactions was used to evaluate the contributions of individual genes as well as potential interactions between the various genes. General linear models are a flexible generalization of ordinary linear regression that allows for response variables that have other than a normal distribution. The GLM generalizes linear regression by allowing the linear allows the magnitude of the variance of each measurement to be a function of its predicted value. The model was devised with the following formula, wherein the variance in the TID was computed as follows: V = (P1+P2+P3+P4+P5+P6) + (P1*P2) + (P1*P2*P3) + (P1*P2*P3*P4) + (P1*P2*P3*P4*P5) + (P1*P2*P3*P4* P5*P6) + E; where, V = Variance; P = Predictors ; and E = Error term. Significance levels of P ≤ 0.05 were considered highly significant, while significance levels of 0.05 ≤ P ≤ 0.1 were considered marginally significant. The predictors yielding the highest variance, and the lowest error terms were selected for modeling. We also validated our results from DataDesk using sas statistical software with the option, anova for unequal cell size for each outcome, and Type III sum of squares.A general linear model using OLS with up to six-way interactions was used to evaluate the contributions of individual genes as well as potential interactions between the various genes. The identical analysis was run on all possible combinations of the six pigmentation genes. The analysis that produced the highest explained variance was used in our model. The explained variance was calculated as /total variance*100. For all age groups, inclusion of all six pigmentation genes provided highest explained variance . Only th


We compare heritability estimates based on pedigree-based relationships to those based on genome-wide SNPs. We find that, in a family-based study, estimates of heritability using SNP data are nearly identical to estimates based on pedigree information, and range from 0.50 for FEV1 to 0.66 for FEV1/FVC. Therefore, we conclude that genetic factors account for a sizable proportion of inter-individual differences in pulmonary function, and that estimates of heritability based on SNP data are nearly identical to estimates based on pedigree data. Finally, our findings suggest a higher heritability for FEV1/FVC compared to either FEV1 or FVC.Asthma and chronic obstructive pulmonary disease (COPD) are major worldwide health problems. Pulmonary function testing is a useful diagnostic tool for these diseases, and is known to be influenced by genetic and environmental factors. Previous studies have demonstrated that a substantial proportion of the variation in pulmonary function phenotypes can be explained by familial relationships. The availability of whole-genome single nucleotide polymorphism (SNP) data enables us to further evaluate the extent to which genetic factors account for variation in pulmonary function and to compare pedigree- to SNP-based estimates of heritability. Here, we employ methods developed in the animal breeding field to estimate the heritability of forced expiratory volume in one second (FEV Airway diseases are a major health burden, and one of the leading causes of death in the United States and worldwide  have identified several loci that are associated with pulmonary function and are biologically plausible candidates, such as TNS1, GSTCD, HTR4, AGER, and THSD4 , FVC , and FEV1/FVC as the primary phenotypes of interest.Residents of Framingham, MA, USA, have been recruited since 1948 to participate in a long-term study to understand the risk factors for heart disease  less than 0.5% were excluded. The remaining missing genotypes were imputed by sampling from a binomial distribution using the empirical MAF estimate under the assumption of Hardy-Weinberg Equilibrium. Given the low genotype missingness (approximately 1%), we do not expect that a more robust method of imputation will significantly affect the results. Genotypes from 444,938 SNPs were considered in the analysis. The first two principal components (PCs) of approximately 1000 markers that are informative for the within-Europe geographical/ancestral origin of European and European-American individuals   consisted of the residual of linear regressions with FEV1, FVC, and FEV1/FVC as outcomes, and sex, age, PC1 and PC2, and cohort (to account for changes in spirometry measurement techniques) as predictors. Phenotype residuals were modeled according to an additive model of the form yi = β0 + ui + εi where β0 is an intercept, ui is an additive genetic effect, representing the collective additive actions of genes potentially affecting the trait of interest, and εi is a component of the phenotype that cannot be explained by additive genetic effects. Stacking all the above equations from i = 1 to i = n  into vectors, we havey = ′, u = ′ and ε = ′ are vectors of phenotype, additive genetic effects, and model residuals, respectively.The outcome , where σ2a is an additive genetic variance and A is an n-dimensional matrix whose entries are pedigree-derived additive relationships (twice kinship coefficients).Following the standards of the additive infinitesimal model Fisher, , we assuG, whose entries were: xij is the count of allele coded as 1 for the ith individual at the jth SNP, xkj is the count of allele coded as 1 for the kth individual at the jth SNP, θj is the estimated frequency of the allele coded as 1 at the jth SNP, and p is the number of SNPs considered . Therefore, in this model we have: u = g ~ N , where σ2g is the genomic variance.In this model, we replace the matrix of pedigree-derived additive relationships A, with a marker-derived estimate A give the expected patterns of genetic similarity between pairs of individuals. However, for any given pair of individuals, the expected and realized proportion of allele sharing will differ because of Mendelian sampling . The correlation between FEV1 and FVC is 0.95. However, the correlation between each of these and FEV1/FVC is much lower .The estimated coefficients for sex, age, and cohort and their statistical significance are shown in Table A plot of the G-based  relationship coefficients for different levels of A-based  relationship coefficients is shown in Figure 1 is accounted for by the variation captured by the SNP-based relationship matrix. We obtain a slightly higher estimate when using the pedigree-based matrix. For FVC, we find that 54% of the phenotypic variation is accounted for by the SNP-based relationship matrix, while 56% is captured by the pedigree-based matrix. For FEV1/FVC, we find substantially higher estimates of heritability overall: 64% using the pedigree-based relationship matrix, and 66% using the SNP-based relationship matrix.Heritability estimates are shown in Table 1. The predicted values are based on both fixed effects , and random effects . The correlation between these estimates is approximately 0.86, 0.86, and 0.87, respectively, for FEV1, FVC, and FEV1/FVC.Finally, we assessed the correspondence, on an individual level, between predicted genetic values derived from pedigree and from markers. Figure 1/FVC are considerably higher than for either FEV1 or FVC. It may be that by taking the ratio of FEV1 and FVC, much of the variation due to environmental factors is removed, compared to just FEV1. Indeed, it does appear that the correlation of both FEV1 and FVC with FEV1/FVC is rather low. Additionally, it may suggest a higher heritability for obstructive lung diseases such as COPD in which FEV1/FVC is typically reduced, as opposed to restrictive lung diseases such as fibrosis, in which FEV1/FVC is not typically decreased since both FEV1 and FVC are decreased together  and a deviation (d) from the expected value given by the sampling of alleles at meiosis . Therefore, the average value of Gij is simply Aij. On the other hand, Hill and Weir  increases as Aij does (simply because large chunks segregate together), and this is why we observe larger variability of Gij around its mean when Aij is larger.The relationship observed between the values of the A- and G-based relationship coefficients is not unexpected. We can think of realized genomic relationships as random variables whose realized values depend on the expected value  than SNP-based estimates since pedigree information could capture more shared environmental factors than SNP information. However in this study, we find that both estimates of heritability are essentially identical, except for a slightly higher SNP-based estimate in the case of FEVWe have shown that the heritability of pulmonary phenotypes is substantial, and that the use of genome-wide SNPs in a family-based study results in essentially identical estimates of heritability as those obtained using pedigree information. Both heritability estimates could be confounded with common environmental effects that may result in inflated heritability estimates, although this is likely more of a concern in the pedigree-based estimates.In addition to estimating overall genetic variance, the use of genome-wide SNP information also has the potential to further our understanding of the genetic basis of pulmonary function and diseases such as asthma and COPD. Given that these traits are likely highly polygenic, it will be important to continue using high-dimensional methods (both at the level of sample size and of predictors) to identify causal loci, and to better understand the genetic architecture of these traits. These causal variants are likely to be numerous and located across the genome and may be at lower frequencies than SNPs in GWAS. Improved knowledge of the genetic basis of pulmonary function could then lead to improved individualized prediction of airway disease and to targeted therapeutic options.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Prunus persica L.) are highly perishable; they ripen and deteriorate quickly at ambient temperature. Storage at low temperature (0–5 °C) is a common strategy used to slow the ripening processes and to extend shelf life. However, if susceptible varieties are held too long at a low temperature, they will not ripen properly and will develop chilling injury (CI) symptoms like mealiness (M), flesh browning (FB), and flesh bleeding (FBL). Understanding the genetic control of these traits to produce CI resistant cultivars will greatly benefit producers, shippers and consumers. In this study, we evaluated a population of 51 individuals from Pop-DG across 4 years with CI traits observed in one or two time points to detect molecular marker association with selected 960 single-nucleotide polymorphisms (SNPs) from 1,536 SNPs chip. Genotypic and phenotypic data were analyzed by general linear model and mixed linear model to see comparative results from both analyses. Among 960 SNPs used, 22 SNPs were found associated with CI susceptibility traits like M, FB, and FBL. Many SNP markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis.Peach and nectarine ( Prunus persica) is a species of Prunus, a genus that also includes nectarine, plum, apricot, cherry, and almond belonging to the subfamily Prunoideae of the family Rosaceae. It is considered one of the genetically most well characterized species in the Rosaceae, and it has distinct advantages that make it suitable as a model genome species for Prunus as well as for other species in the Rosaceae  are highly perishable; they ripen and deteriorate quickly at ambient temperature. Storage at low temperature (0–5 °C) is a common strategy used to slow the ripening processes and to extend shelf life. Susceptibility of stone fruit to chilling injury (CI) is highly influenced by the genetic background of the cultivar , flesh browning (FB), and flesh bleeding (FBL)  is a powerful approach to identify the causal genetic polymorphisms underlying complex traits  Batsch] and related Prunus species. To date only a limited number of genetic markers, including SSRs have been available, this issue was additionally addressed by an international consortium  in a coordinated effort to perform genome-scale SNP discovery in peach using next generation sequencing platforms to develop and characterize a high-throughput Illumina Infinium® SNP genotyping array platform . The samples were sent to the DNA Technologies Core at the UC Davis Genome Center (http://dnatech.genomecenter.ucdavis.edu/dna_quant.html), and a second DNA quantification was done on a Molecular Devices Analyst plate reader with Pico Green (Invitrogen Molecular Probes). Phenotypic evaluation was carried out similar to our previous work  and ‘Georgia Belle’ (pollen parent), was used in this study. ‘Dr.Davis’ is a modern canning peach cultivar and ‘Georgia Belle’ is a century-old fresh market peach cultivar which contrasts for many fruit quality and other CI related traits. ‘Pop-DG’ was created and managed at Kearney Agricultural Center . This orchard was established in 1998 containing 51 verified hybrids. Each progeny genotype was represented by two trees in the orchard; the leaves were collected from parents and 51 Pop-DG populations, any one of the orchard tree and frozen in liquid nitrogen and stored at −80 °C until used. Peach DNA was extracted from new leaves by the CTAB method . Once the final SNPs were selected, their reproducibilities and heritabilities were examined for Pop-DG population. Linkage group (LG) was constructed from final set of most informative markers and individuals from Pop-DF and Pop-DG population , except that the Gen-Call score threshold level for inclusion of data into clusters was changed from 0.15 to 0.25 . The deviations from expected allelic Mendelian inheritance ratios at all loci for each progeny were measured according to our previous paper  ranging from 1 to 10, based on burn-in period and the number of Markov chain Monte Carlo (MCMC) replications after burn-in, which were all assigned at 100,000. The correct estimation of k was provided by joining the log probability of data [Ln P(D)] from the STRUCTURE output and an ad hoc statistic Δk  was generated for further analysis. To account for the population structure, four statistical models were evaluated: the general linear model (GLM) model without considering Q and K, the Q model with considering Q (not shown), the K model with considering K (not shown) and mixed linear model (MLM) model considering Q and K .  for CI traits. The two models were compared based on different levels of significance across years at one or two time points. GLM analysis uses a Σ-restricted model that is a good test of marker effects, even when the data are unbalanced  at least 3 years were considered without considering the time points.GWAS have been more successful in crop plants than in humans . Significance levels observed in both time points in year 2007 were less when compared with other 3 years. All four SNPs that showed association for M were on chromosome 4 (Table P < 0.0001) followed by MLM model (P < 0.01). Three SNPs namely UCD_SNP_1084, UCD_SNP_1441 and UCD_SNP_1172 should have contributed for higher significance levels in both models  were on chromosome 4 . Even though large number of markers showed association for FB, level of significance was less when compared with M for all SNPs showing association. For trait FB significance levels observed in both time points in year 2007 was less when compared with other 3 years (Table P < 0.1) followed by GLM model (P < 0.01). Trait FB did not show higher significance levels (P < 0.0001) of association for all 12 SNPs  of association using both models compared with FBL  (Table P < 0.1) followed by GLM model (P < 0.01). Even though same SNPs  co-associated for trait M, level of significance observed was not similar  Table . Similarlar Fig. . The co Q and K and MLM model considering Q and K analysis did not show major difference in P values for all the traits under study. There was no additional markers pop out in either analysis when compared with one another but P values obtained from MLM analysis were lower than GLM  also resulted with same number of markers for association of various traits under study. Studies on soybean using various statistical models evaluated: the GLM model without considering Q and K; the Q model with considering Q; the K model with considering K; the MLM model with considering Q and K. MLM model performed a little better than the K model. So, the GWAS for soybean yield and yield components with the MLM model (Q + K) was used to correct for population structure and genetic relatedness, using 1,142 SNPs and 209 haplotypes  database has been developed specifically to study CI  is one of the effective approach for connecting phenotype and genotype in plants. It enhances previous QTL information for MAS in rice  for chilling injury requirement (CR) based on a two-way pseudo test cross mapping strategy, in which two high-density apricot maps were constructed using a total of 43 SSRs and 994 amplified fragment length polymorphism (AFLP) markers that span an average of 502.6 cM with an average marker interval of 0.81 cM. Twelve putative CR QTLs were detected using composite interval mapping, a simultaneous multiple regression fit and an additive by-additive epistatic interaction model without dominance. An average of 62.3 ± 6.3 % of the total phenotypic variance was explained. QTLs corresponding to map positions of differentially expressed transcripts and candidate genes controlling a majority of the QTLs were shown to be stable between both Prunus species, as well as similar trends in their QTL effects, with the allele for increasing the trait value mostly originating from the high chill parents. The comparative QTL mapping strategy presented reveals the transferability of genetic information between two Prunus species, the characterization of stable QTLs, the utility of the maps to consolidate each other and to further validate previously identified CR QTL as a major controlling factor driving floral bud break.Another study on apricot (Identifying the genetic variants that underlie complex traits was very essential to plant genetics. Two main approaches are available for mapping the relevant genes and identifying the variants associated with this complex trait: linkage mapping in families and population-based genetic association studies (Agrama et al. Q and K and MLM model considering Q and K analysis. A total of 22 SNPs marker-trait associations were identified across 4 year in one or two time points by analyzing both models. However, further studies are necessary to confirm the association results in specific biparental crossing populations and consequently avoid identifying spurious associations. All three SNP markers namely UCD_SNP_1084, UCD_SNP_1441 and UCD_SNP_1172 will be investigated based on their position in genome, and it would be interesting to see if these SNPs are located in regulatory region or coding region in some candidate gene of interest. The presence of considerable levels of synteny among many rosaceous species suggests that even for those plant species with fewer genomic resources, candidate-gene association mapping coupled with QTL mapping studies and comparative mapping would be feasible and highly valuable. Knowledge acquired in one species can then be extended to others. Establishing a model species and refining its genome sequence and identifying regions associated with important traits could even be used for other related species of Rosaceae by conducting comparative genomics. Our future studies include localization of genes based on SNP marker-trait association for CI traits.In our present study, association genetics was performed for CI susceptibility traits using GWAS based on GLM model without considering


Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we establish a theoretical benchmark by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective  Boehnke  based on Boehnke . Typically test statistics and p-values are reported for each marker on the genomic array, and genome-wide significance for a SNP is declared if the p-value after Bonferroni correction is below a threshold for a desired family-wise error rate. Commercially available genomic arrays interrogate the genotypes of individuals at hundreds of thousands or even millions of loci, and p-values less than 5×10−8 are usually required to achieve genome-wide significance. Obviously, these levels of significance are difficult to reach unless the signal is very strong or the sample size is very large. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) can be incorporated into statistical tests, and thereby increase the power to detect association under the same family-wise error rate.Genome-wide association studies (GWAs) are a prominent approach to search for single-nucleotide polymorphisms (SNPs) associated with disease or other phenotypes. To date, results from more than 1000 GWAs have been reported, identifying over ten thousand DNA loci to be statistically associated with one or more of hundreds of phenotypes investigated , but for simplicity still refer to G as the genotype. In this setting G is the random variable with E(G) = π, the relative frequency of G = 1 in the study population. As usual, the parameters μG and θG describe the relationship between x and π via the link F, with θG being the parameter of interest. We denote the disease status indicator for individual i ∈ {1, …, n} by ix, and the genotype for individual i by ig. Thus, ∑ ix is equal to the number of cases and n − ∑ ix is equal to the number of controls.In a case-control setting we assume the retrospective risk relation to beF is inverse-logit. To test the hypothesis of no genotype/phenotype association at a specific marker H0 : θG = 0  we use the score test statisticg . As in Equation (2) above, the relevant score statistic ish. Setting E(H) = ξ, the conditional distribution of H given G is ph|g = Pr (H = h|G = g), providing a measure of the LD between G and H. Consequently ξ = p1|0 + π (p1|1 − p1|0) andIn addition to ZG and ZH and the correlation between G and H under the null hypothesis (θG = 0) and local alternatives, and defer the derivations for global alternatives to the supplementary material.In the following, we derive the relation between the correlation of the test statistics GZ, HZ) = E (GTHT). Using Equations (2) and (4) we have thatUnder the null hypothesis of no association, cov(The last line follows fromp1|0 = p1|1 = p (no linkage between G and H), and equal to nx (1 − x) π (1 − π) if p1|0 = 0 and p1|1 = 1 (perfect linkage). If π and ξ were known then the denominators DG and DH are constants, and thusAssuming that the participants are unrelated and that G and H (linkage equilibrium) and H is not independently causal, then H is not associated with disease status. If H is in LD with G, an association with the disease status is induced by G.Thus, under the null hypothesis and local alternatives, subject to the approximation that For a local alternative . Note that ΔH is induced, so that in case of linkage equilibrium (p1|1 = p1|0) we have ΔH = 0. Also note that θH depends on both the odds ratio at the causal marker (θG) and the covariation of the genotypes.If Z =  be the score test Z statistics from an LD block with K markers.Let Zmax = maxk ≤ K1 ≤  {|Zk|}. The null distribution of Zmax depends on the correlation matrix R of the test statistics Z, and for large samples we have Z ~ NK. The two-sided p-value for Zmax can be derived from this multivariate distribution by calculatingR is the cumulative distribution function of the multivariate normal distribution with mean vector 0 and correlation matrix R, K1 is a vector of ones of length K, and the symbol ° denotes the dot product.We define the maximum z-statistic as p-value in a set of K markers as K times the p-value stemming from the most significant marker as given by Zmax = maxk≤K1≤ {|Zk|}.We compute the Bonferroni L′ Z) is optimal. Generalizing from the two-locus case to a block of K SNPs with πk = pr(Gk = 1) for k ∈ {1, …, K}, we defineWe consider a block of correlated markers as the region of interest and assume that one of these SNPs is biologically associated with the trait of interest. The statistical associations at neighboring markers are thus controlled by the strength of the correlation between the causal marker and other loci in the analysis. With locus-specific Z-scores being approximately normally distributed, a linear combination }2 = L′ΔΔ′L subject to L′RL = 1, with the correlation matrix R computed from the genotype correlation structure. This is equivalent to finding the HL = R−1/2H = R−1/2ΔΔ′R−1/2. A standard matrix theory result  yields that H, and we have Lopt = R−1/2Zopt = L′optZ.We need to identify the Lopt depends only on the relative sizes of the Δs. If Δk ≡ Δ, then Lopt = R−11 / (R−1++)1/2 where R−1++ is the sum of all entries of R−1. Further, in the case that all minor allele frequencies in the block are the same, then all kB are identical and Δ is the product of a constant and the row (or column) of R corresponding to the locus which is biologically associated with the trait of interest. (This follows from an extension of Equations (10) and (11) to more than two markers.) In this case, H has a degenerate form with all but one entry  equal to 0. This yields that Lopt is simply a vector equal to 1 at the causal locus, and zero otherwise, i.e., Zopt = Zcausal for sets of markers with equal minor allele frequency. Thus, even though the associations in the remaining markers of the block are only induced by the causal SNP, there is in general more information in the optimal linear combination of score statistics than in the statistic from the causal locus alone, since the minor allele frequencies across a set of markers are virtually always non-identical, unless the markers are in perfect LD .Note that E(Zopt) = L′optΔ = (Δ′ R−1Δ)1/2, and thus the non-centrality isWe also note that the expectation of the optimal linear combination is K is very large, care is needed in computing R−1. However, in most situations either R will be relatively small (limited to the size of an LD block) or will have considerable structure with many zeros and non-communicating subsets and so only matrices of small to medium size will need to be inverted.If Zopt above we consider a linear combination of z-scores, albeit without any prior knowledge of the location of any causal variant. This lack of knowledge comes into play in choosing a value of Δ, and we assume a uniform prior over the set of possible causal variants. In this case, the expected non-centrality isk) with default settings that produce a linearly weighted kernel, with weights inversely proportional to minor allele frequency.For comparison with the above methods, we also include the sequence kernel association test (SKAT), a widely used method for SNP-set analysis based on a logistic kernel-machine approach that allows for flexible, covariate adjusted relations between a genotype and the outcome of interest . We simulated 5-locus haplotype blocks with exchangeable  and auto-regressive lag-1 (AR1) correlation structures, with correlations between 0 and 0.8. For all markers in the haplotype blocks we chose constant minor allele frequencies in this simulation, set at either 5 or 25%. One causal marker was selected and haplotypes were sampled to generate cases and controls as given by the genetic risk model, using a variety of odds ratios . We generated 50,000 samples of 1000 cases and 1000 controls, and carried out marker-specific score tests to generate sets of test statistics.We simulated a “naive” population under a dominant disease model using the R package opt), and the “agnostic” linear combination of the test statistics assuming equal prior probabilities for each marker in the block to be causal (combeq). In addition, to mimic some limited biological information available, we show results for a linear combination of test statistics assuming equal prior probabilities for the causal and one additional marker, narrowing the set of potentially causal markers to two out of five (combpair). For the compound symmetry simulations each pair of markers that contains the causal one is equivalent. For the auto-regressive lag-1 simulations, we show a pair of markers with correlation ρ and a pair of markers with correlation ρ2. Estimates of type I error and power are the fraction of simulations with p-values lower than the set significance level assuming two-sided tests. We also include the results derived for the Bonferroni correction with the significance level divided by the number of markers assessed. In addition to the typical significance level α = 0.05, we also assessed the different methods using a much stricter significance level for type I error control, as is usually done in GWAs. These extreme tail probabilities were estimated using importance sampling .We investigated the type I error and power for the approach using the maximum z statistic, the optimal linear combination of the test statistics with known causal locus  and both minor allele frequencies considered Figure . For muceq and Bonferroni, and thus, is a practical and more powerful method than conventionally employed approaches.As expected, the optimal linear combination with correctly specified causal locus outperforms all other methods, yielding the largest power for odds-ratios of 1.1, 1.4, and 1.7. For this method, the estimated power was virtually constant for all magnitudes of correlations across markers within a block, for both simulated compound symmetry at low Figure  and highpair occasionally yields modestly higher power than the maximum z-statistic method, but this is typically only the case when the two markers are in strong LD. However, for large effect sizes and weak correlations in the LD block, combpair performs at times even worse than multiple comparison correction via Bonferroni, and particularly noticeable at low minor allele frequencies  of 4251 European American participants in the Lung Health Study, a NHLBI-supported multi-center randomized clinical trial in the United States and Canada to determine whether or not a program of smoking intervention and use of an inhaled bronchodilator could slow the rate of decline in pulmonary function in smokers with mild airflow limitation . This block is part of a previously identified region of strong LD observed in a Caucasian population , with the exception of the conservative Bonferroni correction. The optimal linear combination with correctly specified causal locus again outperformed all other methods, yielding the largest power for odds-ratios of 1.1, 1.25, 1.4, and 1.7 (bottom of Table ρ = −0.14), while in the second setting all markers have about the same minor allele frequency, and are much more strongly correlated.For the seven marker block with the larger minor allele frequencies and stronger LD we observed similar results (bottom of Table R2of 0.97), and yields unsatisfactory power when the markers are not in strong LD (R2of 0.05 between markers 12 and 3). The performance of SKAT again is affected by the distribution of minor allele frequencies in the block and the observed LD. While the causal marker 12 has an appreciable minor allele frequency of 0.28, some other markers show much less variation, and thus receive more weight in the default settings. Here, the lowest minor allele frequency (MAF = 0.07) is observed for marker 3, which is in very low LD with the causal marker (R2of 0.05). Overall, and similar to previous results, the largest gain of power for the optimal linear combination relative to the other methods is seen at lower odds ratios.The simulation on the 24 marker block yields similar results in general, but some qualitative differences are noteworthy. The optimal linear combination again performs best overall, although for odds ratios of 1.4 the maximum z-statistic shows slightly higher power Table , likely We evaluated three approaches to controlling multiplicity in GWAs: standard Bonferroni, the correlation-calibrated maximum statistic, and a theoretical benchmark: the optimal linear combination of locus specific test statistics which requires knowledge of the causal locus. Computation of the latter two depends on the correlation among the test statistics; the performance of each depends on this correlation. We reiterate that the correlation among the test statistics is essentially identical to the biological correlation amongst the genotypes (the LD structure) and this can be estimated. For an additional comparison to the above methods, we included the sequence kernel association test (SKAT), a widely used method for SNP-set analysis based on a logistic kernel-machine approach that allows for flexible, covariate adjusted relations between a genotype and the outcome of interest.Simulations show that the two correlation-dependent approaches are well-calibrated under the null hypothesis. As expected, unless the correlations are very small, the Bonferroni approach is conservative. In the context of the test Z-scores being well approximated by a multivariate normal distribution, the optimal linear combination dominates all other approaches, but this optimality is quite fragile, depending on having identified the causal locus or one in high LD with it. If the causal locus is poorly selected, our linear combination using “best guess” weights as one example, the properly-calibrated Max statistic often performs better, sometimes by a substantial amount with these relations depending on the magnitude of the correlations, their pattern (compound symmetry or auto-regressive), and the magnitude of the genotype-phenotype association. We do see gains in power using a linear combination where we have narrowed down the set of candidate loci in our block, particularly in the case of very small effect sizes. The haplotype-based simulations produce similar comparisons, but with generally smaller differences amongst the approaches.Overall, the calibrated maximum method is very effective at maintaining power compared to use of a linear combination. However, when the causal locus is correctly specified, the optimal linear combination can confer a considerable increase in power. Therefore, there is some room for error and we have also provided an approach by which it is possible to specify prior probabilities on the loci and then use the induced, optimal linear combination. As we have shown, if there are two markers in a block that have a higher prior likelihood of being associated with disease , putting higher weights, or all weight, on these will provide robustness to mis-specification of the causal locus, while providing more power than the Max test in some cases, especially for very low effect sizes. However, our equally weighted case is equivalent to giving each locus equal prior probability and its generally poor performance indicates that some focus is needed.R2values are mathematically not possible between SNPs of very different allele frequencies. On the other hand, the power in the simulations with lower minor allele frequencies and weaker LD was lower for SKAT than the power of both the maximum and the optimal linear combination tests. We conjecture that this is due to the default weighting scheme in SKAT—up-weighting less common variants—while in our simulation the marker with the lowest minor allele frequency and thus the highest weight had only a very weak correlation to the assumed causal marker. Thus, we believe that the default SKAT is most useful for blocks with high LD, and for association tests under the common assumption of higher penetrance for lower allele frequency variants. We also note that SKAT allows for weighted burden tests, which we did not consider in this manuscript.We also found that the sequence kernel association test (SKAT) run with its default values is a very competitive method in settings when LD within a block is strong—which also implies similar minor allele frequencies between markers, as high One challenge for all methods of this type is the dependence on having pre-defined blocks of interest. There are several existing methods for estimating LD-structures (e.g., Stephens et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A total of 55 marker-trait associations were detected between 26 SSRs and seven lint yield traits, based on a mixed linear model (MLM) and Bonferroni correction . Of which 41 could be detected in more than one environment and 17 markers were simultaneously associated with two or more traits. Many associations were consistent with QTLs identified by linkage mapping in previous reports. Phenotypic values of alleles of each loci in 41 stably detected associations were compared, and 23 favorable alleles were identified. Population frequency of each favorable allele in historically released cultivar groups was also evaluated. The QTLs detected in this study will be helpful in further understanding the genetic basis of lint yield and its components, and the favorable alleles may facilitate future high-yield breeding by genomic selection in Upland cotton.Linkage disequilibrium based association mapping is a powerful tool for dissecting the genetic basis underlying complex traits. In this study, an association mapping panel consisting of 356 representative Upland cotton cultivars was constructed, evaluated in three environments and genotyped using 381 SSRs to detect molecular markers associated with lint yield and its components. The results showed that abundant phenotypic and moderate genetic diversities existed within this germplasm panel. The population could be divided into two subpopulations, and weak relatedness was detected between pair-wise accessions. LD decayed to the background ( The tetraploid species Gossypium hirsutum L. , commonly referred to as Upland cotton, accounts for 95% of the world's cotton production Cotton is the most important natural textile fiber source globally. The worldwide economic impact of the cotton industry is estimated at approximately $500 billion per year with an annual utilization of about 27 million metric tons of cotton fiber. In recent years, demand for cotton fiber in the world market has dramatically increased, stock and use ratio dropped to 37% in 2010, compared to 55% in 2009. While cotton acreage has declined worldwide in the past few years, mainly due to strong competition from other crops as well as production costs  declined at <10 cM in the landrace stocks and >30 cM in variety germplasm, but at r2≥0.2 which reduced to ∼1–2 cM and ∼6–8 cM, respectively G. hirsutum varieties 2≥0.1 and reduced to ∼5–6 cM at r2≥0.2 and an average of ∼20 SSR markers was associated with each main fiber quality trait in two environments. Zeng et al. carried out an association mapping study between 86 SSR markers and fiber traits using an exotic germplasm population of 260 lines derived from multiple crosses among Gossypium tetraploid species and found 59 markers were significantly associated with six fiber traits Lint yield is a complex trait in cotton, which is controlled by a large number of quantitative loci (QTLs). It is becoming progressively more difficult to improve lint yield using conventional breeding methods. Fortunately, the development in applied genomics research has provided alternative tools to improve efficiency in plant breeding programs. Molecular markers linked to the causal genes and QTLs can be used for marker-assisted selection (MAS) and/or genomic selection (GS) Although AM has been successfully used to detect the QTLs underlying quantitative traits in some crops, from a breeding standpoint, detecting associated loci is just the first step; analyzing the genetic effects of alleles and identifying favorable alleles will be more beneficial for target trait improvement. Breseghello & Sorrells identified several potentially beneficial alleles for kernel size and milling quality by comparing the average phenotypic value with specific alleles and null alleles in a soft winter wheat population A total of 356 representative Upland cotton cultivars and breeding lines were selected from the cotton germplasm collections in our laboratory and the Cotton Research Institute, Chinese Academy of Agricultural Sciences (CRI-CAAS), and assembled to construct an AM panel. The population consisted of 348 cultivars developed in China, seven introduced from the U.S., including the genetic standard line TM-1, and one introduced from Uganda. According to their release year, the 348 Chinese cultivars could be divided into the following six groups: I ; II ; III , IV ; V ; and VI . The cultivars introduced from abroad, DPL 15, DPL 16, STV 2B, King, Foster 6 and Uganda 3 were used as a check group for genetic diversity and allele transmission evaluation, because they had been used as the main founder parents in China's Upland cotton breeding programs and are the progenitors of many cultivars All of the accessions were planted in the following three environments to evaluate phenotypic performance: (1) Jiangpu Breeding Station, Nanjing Agricultural University, Nanjing, in 2009 (designated as E1), (2) Dafeng Agronomy Farm, Yancheng, Jiangsu Province (E2), and (3) Zhengzhou Agricultural Research Institute, Zhengzhou, Henan in 2010 (E3). The first two locations were in the Yangtze River cotton-growing region, and the third was in the Yellow River region. A randomized complete block design with single row plot and two replications was used in all field trails. The sowing dates were from late March to early April in different years and locations, and seedlings having up to 3–4 leaves were transplanted from seedbeds to fields, with 20 plants per row, a 30 cm plant-to-plant spacing, and 80 cm between rows. For most of the accessions are non-BT cottons, chemical control were used for preventing from bollworm damage and field managements were adjusted to local practices.Field planting has been approved by Nanjing Agric Univ.. No specific permissions were required for these locations/activities since they are pure-line cultivars and the field studies did not involve endangered or protected species.Ten consecutive plants in the middle of each row were tagged for trait measurement. Yield traits evaluated included: lint yield , seed cotton yield , bolls per plant (BN), boll weight , lint percentage , lint index  and seed index .G. hirsutum is an allopolyploid species, SSR markers often yield complex band patterns and some of them had been located to more than one locus. To measure the complex band patterns of large scale genotypes, the band pattern in TM-1  was treated as a check and the following criteria were used to assign the alleles to the corresponding loci: 1) when only one fragment was amplified in each accession, the fragments were regarded as alleles belonging to the single locus; 2) when multiple fragments were amplified in each line and the bands showed an obvious co-segregating relationship among different samples, they were regarded as alleles belonging to the same locus; and 3) when multiple bands produced in each line did not co-segregate among different accessions, the corresponding fragments in TM-1 that had been mapped to the reference map and co-segregated among different accessions were measured, other bands were discarded. According to the above criteria, the band pattern in TM-1 was designated as 1, the same patterns were also designated as 1, and the different ones were designated as 2, 3, 4, 5 and so on, thus the alleles from all accessions on each locus were measured. Markers with more than 10% missing data were not used in further analysis.Young leaves from each of the 356 accessions were collected and stored at −20°C. Total genomic DNA was extracted from the leaf samples as described by Guo et al. k) ranging from 1 to 10. The correct estimation of k was provided by joining the log probability of data [LnP(D)] from the STRUCTURE output and an ad hoc statistic Δkk, each accession was assigned to a subpopulation for which the membership value  was >0.5 r2 of each pair of SSR loci was calculated using the software package TASSEL 2.1 with rare alleles  treated as missing data r2 was estimated for total, linked and unlinked markers both in the entire panel and each subpopulation, respectively. The 99th percentile of r2 distribution for unlinked markers, which determined whether LD is due to physical linkage 2r values of each pair of SSR loci were plotted against map distance (cM), and LD decay was estimated.Summary statistics including the total number of alleles, the number of alleles per locus, and gene diversity values were calculated using the software PowerMarker 3.25 hB2) of each trait was estimated with the variance components. Correlation coefficients between traits were calculated with PROC CORR.Statistical analysis of all phenotypic data across three environments was performed with SAS 8.0 software (SAS Institute 1999). Analysis of variance (ANOVA) of all phenotypic data was calculated with PROC GLM, based on the trait means for each line across the three environments. Decomposition of variance components  was evaluated using PROC VARCOMP, and the broad-sense heritability  in the present panel and a total of 415 alleles were detected . The allk kept increasing with k value and did not show any peak. The Δk value showed a much higher likelihood at k = 2 than at k = 3–10  value corresponding to each hypothetical k = 3–10 Figure 1k = 3–10 . Then, tFor the kinship coefficient values, 86.85% was less than 0.05, 8.56% had a range of 0.05–0.10, and the remaining 4.59% showed various degrees of genetic relatedness (data not shown). Based on the results of the relatedness analysis, a K matrix was constructed for association mapping.r2 was calculated for total, linked and unlinked markers . Moreover, 21.03% of the linked locus pairs and 18.18% of the unlinked pairs showed significant LD (P≤0.01) with the average r2 of 0.0160 and 0.0101, respectively. In the subpopulation P1 and P2, the average r2 of locus pairs was 0.0151 and 0.0104, respectively, and the proportion of significant LD (P≤0.01) was 5.10% and 10.78%, respectively. In the entire panel and subpopulations, both average r2 and proportion of significant LD for linked loci were all higher than those for unlinked markers  for all the seven traits. The mean coefficient of variance for FY, SY, BN, BW, LP, LI and SI was 29.09%, 23.19%, 19.36%, 9.11%, 9.33%, 12.48% and 9.02%, respectively, demonstrating that there was a high degree of diversity in lint yield traits of Chinese Upland cotton cultivars. The broad sense heritabilty (B2h) for the seven traits had a range of 27.34–75.77% in the reference population , indicating that LP was less impacted by environmental factors than the other six traits.Seven traits for lint yield and its components were measured in 356 Upland cotton accessions across three different environments. Each trait varied widely Table 2,Phenotypic correlation analysis showed that there were significant positive correlations between lint yield and its most components, while the negative correlation between lint yield and SI was also significant Table 3.α = 0.01 (−log10P = 2) level, a total of 195 significant associations were detected between 82 SSR markers and seven lint yield traits  and population structure (Q), implemented in TASSEL software. At the d traits . Among td traits .P≤0.05/145, −log10P≥3.46) was used to obtain an appropriate significance threshold In this study, 145 markers were used for detecting association, so the same statistical test was performed 145 times at a significance level of 0.01, and the experimental type I error rate would be much higher than 0.01. To overcome this problem, the Bonferroni correction  A suitable association mapping panel should embrace as much phenotypic and genotypic diversity as can be reliably measured in common environments Many crops have a long and complex history of domestication and breeding, such as Upland cotton, and complex population structures may confound AM Gossypium hirsutum accessions, the genome-wide LD (r2≥0.1) declined at <10 cM in the landrace stocks and >30 cM in variety germplasm, but at r2≥0.2 which reduced to about 1–2 cM and 6–8 cM, respectively. In another panel composed of 335 G. hirsutum variety germplasm, the genome-wide LD extended up to 25 cM at r2≥0.1 and reduced to about 5–6 cM at r2≥0.2 r2 of locus pairs was 0.0103, and 18.29% were significant (P≤0.01) (r2 = 0.1182) within 12–13 cM  level  for the Bonferroni correction was adopted to reduce the experimental type I error rate induced by multiple tests P≤0.05/145, −log10P≥3.46), 55 associations remained significant and 74.55% (41 of 55) could be detected in more than one environment  level . It is v01 −log10P = 2 levAlthough the markers used in different studies are different, and QTL mapping results are not easy to be compared, some of the marker associations detected in this study were consistent with QTLs for lint yield and its components that had been mapped previously by conventional linkage mapping. The locus JESPR204 associated with LY on chromosome 13 (detected in 2 environments) was located in the same region as a QTL identified by Wu et al. hB2 = 75.77%). The phenotype of complex traits often results from the combined actions of multiple genes and environmental factors, all these can easily lead to lost heritability Moreover, in our study, seventeen markers were co-associated with two or more different traits, and most of the lint yield-associated markers were associated with at least one of its components, which coincided with phenotypic correlations among these traits. This could result from pleiotropy of a single causal gene or tight linkage of multiple causal genes. We found that 10 of 14 markers associated with LP were detected in all three environments, which was consistent with the phenotypic statistical analysis that LP possessed the highest broad-sense heritability Click here for additional data file.Table S2Detailed information of 145 polymorphic SSR markers.(DOC)Click here for additional data file.Table S3Genetic diversity in different historically released cultivar groups.(DOC)Click here for additional data file.Table S4P≤0.01, −log10P≥2.0) associated with lint yield traits and their explained phenotypic variation across three different environments.SSR markers significantly ((DOC)Click here for additional data file.


There were also significant genetic contributions to individual change scores from before to after the move (h2 = 0.48). These results indicate that genetic regulation of novelty seeking was modified by the level of environmental stress, and they support a role for gene–environment interactions in a behavioural trait with relevance for mental health.Considerable attention has been paid to identifying genetic influences and gene–environment interactions that increase vulnerability to environmental stressors, with promising but inconsistent results. A nonhuman primate model is presented here that allows assessment of genetic influences in response to a stressful life event for a behavioural trait with relevance for psychopathology. Genetic and environmental influences on free-choice novelty seeking behaviour were assessed in a pedigreed colony of vervet monkeys before and after relocation from a low stress to a higher stress environment. Heritability of novelty seeking scores, and genetic correlations within and between environments were conducted using variance components analysis. The results showed that novelty seeking was markedly inhibited in the higher stress environment, with effects persisting across a 2-year period for adults but not for juveniles. There were significant genetic contributions to novelty seeking scores in each year ( Chronic and acute stressful life events are known to increase the risk for psychopathology in human populations , but notIn this report, we take advantage of a major environmental change for a pedigreed nonhuman primate colony to assess genetic influences on a measure of exploratory behaviour within and across environments differing in the level of stress. In early 2008, the Vervet Research Colony (VRC) was moved across country to a new facility to expand access to the colony for life span and metabolic research. The change involved the acute stress of the move, as well as an ongoing increase in the level of handling and group disturbances in the new location. A significant increase in hair cortisol from before to 6 months after the move provided independent evidence that the two locations differed in level of environmental stress  (age 1–24 years) living in 16 multigenerational, matrilineal social groups in the VRC between 2006 and 2009. All subjects were born at the original VRC facility and raised in relatively stable social groups that were managed to reflect the natural social composition of vervet groups in the wild. Current VRC colony members are descendants of 57 wild caught founders  captured in St. Kitts and Nevis, West Indies, and introduced into the colony between 1975 and 1983. The colony management practice of maintaining females in stable matrilines and transferring males between groups has produced one large, complex, extended pedigree spanning eight generations.Subjects include 503 vervet monkeys (2 of ground area (mean = 61 m2), each with its own attached indoor shelter. The outdoor corrals had one or two large platforms and multiple perches, climbing structures and enrichment devices. Care was provided by a small and consistent staff of animal technicians. There were no changes in adult male group membership in 2006 and 2007. The animals were captured and anesthetized three times during the 2-year period, for an annual veterinary examination and a magnetic resonance imaging (MRI) assessment. The only other group disturbances in the 2 years prior to the move were for behavioural tests and infrequent clinical interventions.The original VRC facility, established in 1975, was bounded by a high, opaque fence in a quiet park-like setting on the grounds of the Veterans Administration Greater Los Angeles Healthcare System, Sepulveda campus. The monkeys were housed in 16 outdoor enclosures varying in size from 30 to 117 m2) for each group with a platform, shelves for sitting, and enrichment devices that were similar in design and material to those in the pre-move environment; and (2) adult female and immature monkeys remained in the same social groups after the move and there were no significant changes in the matrilineal dominance hierarchies. Eleven of the 16 groups had new breeding adult males introduced in the new environment in January–February 2008, a procedure that has been used throughout the history of the colony to prevent inbreeding and mimic the natural processes of male emigration and immigration. M.J.J. served as scientific manager of the colony at both locations.In January–February 2008, the entire colony was moved across country by ground transport to a newly constructed facility at the Wake Forest University Primate Center. The 16 social groups were housed in 2 new buildings, with 8 enclosures per building. Similarities between the old and new facilities included: (1) a large outdoor area  more than a threefold increase in handling and group disturbances for veterinary examinations, sample collection and cognitive/behavioural testing; (2) an increase in the number and unpredictability of individuals involved in animal handling and care; (3) exposure to other groups, increased noise levels and potential sleep disruption in the indoor areas; (4) exposure to colder winter temperatures and confinement to the indoor area when the temperature fell below 35°F; and (5) multiple changes in diet composition and palatability.Both facilities were fully accredited by the American Association for the Accreditation of Laboratory Animal Care (AALAC), and the colony was managed in accordance with the The Home Group Novelty test is a standardized procedure to measure free-choice novelty seeking in the home enclosure. During the tests presented here, the door to the indoor area was closed, so all group members were present in the outdoor area. A novel and potentially threatening object was placed in an open container and positioned outside the chain link fence of the home enclosure, within reach of the animals and away from any of the preferred sitting or resting places. The identity of each animal observed within 1 m of the novel object was coded for each minute in a 30-min test session. A team of two to three observers familiar with identifying individual monkeys made a consensus determination of who was within 1 m. The area within 1 m of the object only occupied a small portion of the home enclosure, and it required voluntary choice on the part of the monkeys to approach the object.The Home Group Novelty test was repeated once per year between 2006 and 2009, with predator-like objects as the novel stimuli . Groups were tested in the summer or fall (June–October) in each of the four test years. The first post-move tests were conducted in June 2008, 4–5 months after relocation to the new environment. The second post-move tests were conducted in October 2009. Features of the outdoor areas and placement of the novel objects did not differ between the two environments. L.A.F. and M.J.J. managed the tests and recorded the data at both locations, and the primary observers in the post-move environment were trained at the pre-move facility.The latency to approach was determined by the first minute an animal was recorded within 1 m of the novel stimulus, and time near was measured by the number of 1-min intervals the animal was observed within 1 m. Animals which approached within 1 m in the first minute were given a latency score of 0 and those who never approached were assigned a latency score of 30. A composite score combining latency and time near was computed for each year [novelty seeking score = (30 − latency) + time near], with high scores indicating a quicker approach and greater interest in the novel object. Animals that never approached within 1 m had a score of 0.n = 408; 2007, n = 404; 2008, n = 406; 2009, n = 381, with 290 subjects measured in all 4 years. Age was categorized into five groupings based on maturational stage and behavioural change: juvenile (1–3 years), adolescent (4 years), young adult (5 years), middle adult (6–12 years) and older adult (13–24 years). Yearlings born in the post-move environment were not included as subjects. Pre-screening of the data indicated that there were no significant main effects or interactions between novelty seeking score and subject sex, dominance rank, or the addition of new adult males to the group in the prior 6 months. The final model included novelty seeking score as the dependent variable, animal identity as the subject variable and age, site, test year nested within site, and age × site interaction as fixed factors.To accommodate changes in the subject pool and subject age across the 4-year period, the effect of year and environment on novelty seeking scores were evaluated using a mixed model approach. The number of subjects evaluated per year was as follows: 2006, Standardized residuals, controlling age group, were computed for each test for the bivariate genetics analyses. The pre-move mean and post-move mean novelty seeking scores were computed from the average of the two standardized residual scores within each site. The pre–post change score was the difference of the pre-move and post-move mean scores.The VRC pedigree was constructed using a panel of 14 highly polymorphic microsatellite markers to determine paternity and verify maternity (c2) by incorporating a matrix identifying individuals that were raised by the same mother. Final models were the most parsimonious and included covariates and shared maternal effects only if their contribution to the variance met a threshold value of P≤ 0.05 using log likelihood ratio tests. Values of P≤ 0.05 were considered significant.Heritability and genetic correlations were estimated by variance component models using SOLAR , a computer package designed for variance component and linkage analysis in pedigrees of arbitrary size and complexity . The varBivariate analysis was performed to estimate the genetic correlation between novelty seeking scores within and between environments, using SOLAR. For this analysis, the phenotypic variance–covariance matrix is partitioned into the additive-genetic and environmental variance–covariance matrices using the full pedigree information. The additive genetic correlation (rhoG) estimates the additive effects of shared genes on the covariance of novelty seeking score for the 2 years within each environment, and for the mean scores between environments. A genetic correlation significantly different from zero indicates that the two measures share susceptibility genes. A genetic correlation of one indicates that the same genes are controlling individual differences in novelty seeking behaviour across tests  and site  and a significant interaction of age and site , with the effects of site considerably greater for adults than for juveniles aged 1–3 years. There was a significant effect of test year within site for the pre-move tests  but not for the post-move tests . In both post-move years, the novelty seeking scores for adolescent and adult animals remained considerably below the pre-move levels.P < 0.001; pre-move 2007 to post-move 2008, ICC = 0.55, df = 352, P < 0.001; post-move 2008–2009, ICC = 0.66, df = 362, P < 0.001; pre-move mean to post-move mean: ICC = 0.52, df = 289, P < 0.001).While there were large mean differences in novelty seeking scores between sites, individual differences remained relatively consistent across years. The intra-class correlation coefficients were as follows: pre-move 2006–2007, ICC = 0.78, df = 363, h2 ranging from 0.35 to 0.43 .There were significant genetic contributions to variation in novelty seeking scores in each test year, with  to 0.43 . Age wasP (from 0) <0.001), and a relatively low genetic correlation between sites  = 0.06). Both of the within-site values were significantly greater than the between-site values . This suggests that there is a high degree of overlap in the genes influencing novelty seeking within each environment, with different genetic influences operating between the two environments. There was also a significant genetic component to the individual pre–post change scores (h2 = 0.48, Bivariate analyses within and between sites were conducted on the standardized residuals for each year, and for the pre-move and post-move means. The results, shown in The move from a low stress to a higher stress environment was associated with a dramatic decline in exploratory behaviour of vervet monkeys in a free-choice novelty test. The stressors in this study were not experimentally controlled, but they did involve changes in handling procedures, sleep environment, climate and diet that were largely unpredictable and uncontrollable from the animals' perspective. CMS protocols in rodents use a variety of similar stressors, including intermittent and unpredictable changes in the dark/light cycle, food availability, temperature or group membership. CMS studies produce behavioural changes that are consistent with depressive-like symptoms, including anhedonia, learned helplessness and reductions in exploratory behaviour in rats and mice . The resAn unanticipated finding from the present study was the relative resilience of juvenile monkeys to the environmental change. Juvenile novelty seeking was only slightly attenuated in the first year after the move, and did not differ from pre-move levels in the second year in the new environment. Adults, in contrast, showed a 70% mean reduction in novelty seeking scores that was maintained in the second year. While there were significant effects of environment on novelty seeking in this study, the results also verified the trait-like nature of novelty seeking, and showed the role of genetic influences within a consistent environment. Inhibition of behaviour and latency to explore novel objects have been shown to be heritable traits for infant and juvenile rhesus monkeys in novelty challenge tests (Nonhuman primate models of the effects of environmental stress have used a variety of experimental paradigms, including group reorganization among adults, permanent or temporary separation of infants from their mothers, and variable work load for the mothers to assess the effects of stress on behaviour and development . An impoComplex behavioural disorders and conditions typically involve many genes with small effects, and replication of published effects of specific candidate genes on stress vulnerability and resilience in human studies has proven difficult , and theIn other studies, genetic correlations have been used to identify genetic links between diagnosis and endophenotypes for childhood and adult psychiatric disorders, including attention deficit disorder, alcohol dependence and schizophrenia (Admittedly, there were limitations to the current study. The animals were subject to a diverse set of stressors in the new environment, and there was no control for changes over time unrelated to the move. Therefore, it is not possible in the current paradigm to ascribe the observed change in behaviour to any specific stressor. Research with mouse models of behavioural traits have showed that small changes in housing and test conditions can have significant and strain-dependent effects on outcome . In the In summary, the results of this study were consistent with the hypothesis that an increase in environmental stress inhibits voluntary exploration and novelty seeking in vervet monkeys, with more pronounced effects on older than younger individuals. The results also showed a strong genetic contribution to behavioural vulnerability to stress, and provided evidence that genetic regulation of novelty seeking behaviour is modified by the level of environmental stress. This supports a role for gene–environment interactions in a behavioural trait with relevance for psychopathology, and underscores the difficulty in identifying genes that are consistently associated with complex behavioural traits across environments.


CYP2C19 gene alone has identified approximately 2,000 reference single nucleotide polymorphisms (rsSNPs) containing 28 registered alleles in the P450 Allele Nomenclature Committee and the number continues to increase. However, knowledge of CYP2C19 SNPs remains limited with respect to biological functions. Functional information on the variant is essential for justifying its clinical use. Only common variants  that represent CYP2C19*2, *3, *17, and others have been mostly studied. Discovery of new genetic variants is outstripping the generation of knowledge on the biological meanings of existing variants. Alternative strategies may be needed to fill this gap. The present study summarizes up-to-date knowledge on functional CYP2C19 variants discovered in phenotyped humans studied at the molecular level in vitro. Understanding the functional meanings of CYP2C19 variants is an essential step toward shifting the current medical paradigm to highly personalized therapeutic regimens.More than 30 years of genetic research on the  CYP2C19 is highly polymorphic, this may account for much of the variability in the pharmacokinetics of drugs metabolized by CYP2C19. CYP2C19 metabolizes a number of drugs, including the antiulcer drug omeprazole , resulting an aberrant splice site  and mephenytoin metabolism  and the number of functionally known SNPs. A summary of CYP2C19 variants that have been discovered and characterized in phenotyped human subjects is presented in Figure CYP2C19 pharmacogenetics into the clinical decision making process.The majority of other alleles have been discovered in various human projects by using DNAs obtained from normal populations. CYP2C19*2 and CYP2C19*3 are the most common and defective variants ; this is due to rapid clearance of the drugs. Although most variation in the drug response can be explained by these three variants , high interindividual variation in subjects homozygous for CYP2C19*1 has also been observed  of the allele and the low or undetermined allele frequency in the study population. Therefore, information on the functional significance of the variant allele would help researchers to include more candidate SNPs for genotyping in their clinical studies. This would ultimately improve translational quality in clinical settings and would be a beginning step for launching personalized medicine because physicians could easily communicate with patients using biological evidence for the drug response prediction instead of explaining the statistical risk without biological significance.Because CYP2C19 polymorphisms. Altered CYP2C19 activity can be, at least in part, predicted by the CYP2C19 genotype. The most extensively used polymorphic variants for genotype and phenotype association studies have been CYP2C19*2, *3, and *17 . Briefly, diazepam is demethylated by CYP2C19  and amoxicillin  was 100% in CYP2C19 PMs, 60% for those who were heterozygous for one mutant allele, and 29% in individuals homozygous for the CYP2C19*1 allele (Furuta et al., CYP2C19 PM genotype’s impaired metabolism of PPIs, which leads to higher PPI plasma concentrations in PM individuals (Furuta et al., CYP2C19 to the metabolism of various PPIs differs. For example, in terms of plasma drug concentration-time curves, the ratio of the area under the curve of PMs versus EMs decreases in the following order: omeprazole, pantoprazole, lansoprazole, and rabeprazole (Funck-Brentano et al., CYP2C19 genotype compared to other PPIs. Patients with CYP2C19*17 exhibit an enhanced response to clopidogrel with an increased risk of bleeding due toMany clinical drugs are metabolized by CYP2C19, and hence, the pharmacokinetics of CYP2C19 substrate drugs is influenced by CYP2C19*17 allele (Tiroch et al., CYP2C19 genotype testing. This may be due to the fact that other downstream genes, such as GPIIb/IIIa receptor genes, and other modulating factors can render a patient more sensitive or more resistant to clopidogrel despite having the same genotype. Larger-scale investigations using genotype-guided clopidogrel therapy compared to other therapy options would reveal the effectiveness of CYP2C19 genotype testing for clopidogrel therapy.the higher rate of biotransformation into the active metabolite (Sibbing et al., in vitro, which has made physicians reluctant to test these individuals in clinical trials. Therefore, more data on functional genomics are needed particularly for rare variants. Although CYP2C19 is a major enzyme for the metabolism of certain drugs and influences their pharmacokinetics, there are many other genes that can modulate or mask the genetic effect of CYP2C19 polymorphisms, including genes involved in pharmacodynamics. Fortunately, recent whole genome sequencing and exome sequencing data have identified numerous variants, and these findings have led to genome-wide association studies using dense genomic markers on chips, resulting in the discovery of new determinants of drug responses. In conclusion, the translation of CYP2C19 pharmacogenetics into clinical practice is currently limited to a small number of functional variants, although more than 2,000 variants have already been discovered. More comprehensive and diverse research covering a large number of CYP2C19 variants will lay the foundation for improved personalized medicine in the future.To date, the pharmacogenetic data on CYP2C19 clearly support that genetic variants alter the drug responses of its substrate drug. However, clinical application of CYP2C19 pharmacogenetics is limited to certain genotypes, mostly null alleles, and functionally known variants with a minor allele frequency of >5%. Clinical research using common variants only in case and control groups can enhance the statistical power of the evidence for that variant. However, the number of study populations that would benefit from pharmacogenomics research would be greatly reduced if such studies focused on common variants for strong statistical evidence. One goal of pharmacogenomics is to provide personalized medicine to each individual patient and to provide him or her an appropriate dose of the most appropriate drug. Therefore, more diversified investigations or strategies including low-frequency variants are needed to develop a standard practice for human applications. This may be ineffective or unrealistic for personalized medicine if clinical trials wait for the enrollment of a certain number of participants carrying the target genotype to satisfy the statistical power when assessing the role of that particular variant. The biological function of most rare variants remains unknown or has not been investigated The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.