Education, socioeconomic status, and intelligence are commonly used as predictors of health outcomes, social environment, and mortality. Education and socioeconomic status are typically viewed as environmental variables although both correlate with intelligence, which has a substantial genetic basis. Using data from 6815 unrelated subjects from the Generation Scotland study, we examined the genetic contributions to these variables and their genetic correlations. Subjects underwent genome-wide testing for common single nucleotide polymorphisms (SNPs). DNA-derived heritability estimates and genetic correlations were calculated using the ‘Genome-wide Complex Trait Analyses’ (GCTA) procedures. 21% of the variation in education, 18% of the variation in socioeconomic status, and 29% of the variation in general cognitive ability was explained by variation in common SNPs (SEs ~ 5%). The SNP-based genetic correlations of education and socioeconomic status with general intelligence were 0.95 (SE 0.13) and 0.26 (0.16), respectively. There are genetic contributions to intelligence and education with near-complete overlap between common additive SNP effects on these traits (genetic correlation ~ 1). Genetic influences on socioeconomic status are also associated with the genetic foundations of intelligence. The results are also compatible with substantial environmental contributions to socioeconomic status. •Generation Scotland is a large family-based cohort of ~ 24,000 people.•We investigate the genetic influences on education, SES, and intelligence.•Both DNA-based (subset of ~ 6500) and pedigree-based analyses are used.•Genetic effects on SES and education are linked to the genetic basis of intelligence.•There are also substantial environmental effects on all three traits. The causes of the associations between these three variables are poorly understood . All thrTo better understand how intelligence, education, and socioeconomic status are related, we can examine the genetic foundations of these variables and their associations. Twin studies indicate that intelligence has a high heritability in adulthood  around . HoweverAlthough educational attainment is often treated as an environmental factor , common More so than education, socioeconomic status is viewed as an environmental variable. However, one may ask whether variation in the different measures that are used to describe people's socioeconomic status have some genetic contributions.Apart from socioeconomic status's role as a main effect  that could be partially genetic in origin, it also has a potentially important role as a moderator of genetic influences on intelligence. Socioeconomic status has been shown to interact with intelligence test scores to yield different heritability estimates of intelligence within different social strata. Such gene–environment interactions were described by Here, we perform univariate and bivariate genetic analyses for education, socioeconomic status, and intelligence. Previous univariate and bivariate heritability studies of these variables have mostly been based on twin and family studies. These models assume random mating of parents and identical environmental exposure for the twins. Such assumptions have been questioned, and the samples used in such analyses are not necessarily population-representative. We do not wish to suggest that the conclusions based on these samples and models are incorrect, but there is now a new and independent way to study genetic contributions to, and genetic correlations between, complex traits that requires neither these assumptions nor such non-representative samples. Molecular (SNP) data derived from testing DNA samples can now be used in unrelated, population-based samples to estimate the degree to which phenotypic similarities can be explained by genetic similarities. The present study's sample, Generation Scotland: the Scottish Family Health Study , is a la2http://www.generationscotland.org/.Generation Scotland: the Scottish Family Health Study  is a fam2.1All components of GS:SFHS received ethical approval from the NHS Tayside Committee on Medical Research Ethics (REC Reference Number: 05/S1401/89). GS:SFHS has been granted Research Tissue Bank status by the Tayside Committee on Medical Research Ethics (REC Reference Number: 10/S1402/20), providing generic ethical approval for a wide range of uses within medical research.2.2Selection criteria for genome-wide analysis of 10,000 participants were: Caucasian ethnicity, born in the UK (prioritising those born in Scotland), and full phenotype data available from attendance at a Generation Scotland research clinic. The participants were also selected to have chosen to consent to be re-contacted for new research and all can be linked to National Health Service medical records using the Community Health Index (CHI) number. The selected samples include 446 DNAs that have also been exome sequenced primarily for analyses of depression (150 extreme [early onset] recurrent major depressive disorder [rMDD]), cognition  and 146 family representatives of sequenced participants with MDD, comprising parent–child trios and quads. A further 3234 DNAs from trios and quads, 807 participants with measured rMDD, and 5513 unrelated participants make up the 10,000 sample total.2.3Blood samples  from GS:SFHS participants were collected, processed and stored using standard operating procedures and managed through a laboratory information management system at the Wellcome Trust Clinical Research Facility Genetics Core, Edinburgh . The yieTo remove a shared environment influencing the associations, a genetic cut-off of 0.025 was specified, which excludes individuals who are more closely related than second cousins . Rare SN2.4f, which was based on the scores from the DST, LM, and VFT.Four cognitive tests were administered to GS:SFHS participants. This included measures of processing speed , verbalEducation was measured as years of full time, formal education, on an ordinal scale from 0 to 10.http://www.scotland.gov.uk/topics/statistics/simd/) was used to assess socioeconomic status. Briefly, small areas in Scotland are ranked according to seven domains: income, employment, health, education, geographic access, crime, and housing. The range of ranks goes from the most deprived (rank 1) to the least deprived (rank 6505). A rank-based inverse normal transformation was applied to SIMD scores, pictured in The Scottish Index of Multiple Deprivation 2009  was used to assess socioeconomic status. Briefly, small areas in Scotland are ranked according to seven domains: income, employment, health, education, geographic access, crime, and housing. The range of ranks goes from the most deprived (rank 1) to the least deprived (rank 6505). A rank-based inverse normal transformation was applied to SIMD scores, pictured in Supplementary Fig. 1, prior to the analyses.The Scottish Index of Multiple Deprivation 2009  were adjusted for age, sex, and, in the molecular analyses, population stratification  using linear regression. Residuals from these models were used as the dependent variables of interest. The number of ancestry components was determined by comparing the log-Likelihoods and residual errors  from linear regression models of the three traits on age, sex, and up to twenty principal components . Based oPrior to the main analyses, the traits  were adjusted for age, sex, and, in the molecular analyses, population stratification  using linear regression. Residuals from these models were used as the dependent variables of interest. The number of ancestry components was determined by comparing the log-Likelihoods and residual errors  from linear regression models of the three traits on age, sex, and up to twenty principal components . Based on these data, the optimal number of components to adjust for was six.range = 23,673), giving familiality estimates for the traits. Briefly, genetic and environmental variances are partitioned using a linear mixed model that compares phenotypic covariance between pairs of relations. Relatedness is defined theoretically: e.g., the genetic correlation between parent and offspring is 0.5 and 0.25 for grandparent and grandchild. To account for siblings sharing environmental variance, a maternal random effect was considered. This improved the model fit in all analyses with the exception of those (univariate and bivariate) involving SIMD. However, even in the models where the fit was improved, the effect size for the maternal component was small. We did not model the potential shared environmental effects in the extended pedigrees. The models fitted here were run in ASReml-R .In the molecular approach, univariate genome-wide complex trait analyses  (GCTA) w3Genome-wide SNP data were available on 6815 unrelated Generation Scotland participants; median age 57 years (IQR 49–63), and 4002 (59%) were female . The majThe family-based pedigree analyses  (f) or crystallised-type intelligence (MHVS), the education estimates decreased to 0.15 (SE 0.06) and 0.08 (SE 0.05). There were smaller differences in the corresponding partialled effect sizes for SIMD: estimates were 0.15 (SE 0.06) and 0.13 (SE 0.06), respectively.The variance in education and SIMD explained by common SNPs was similar: 0.21 (SE 0.05) and 0.18 (SE 0.05), respectively  , and crystallised-type intelligence (a single test — MHVS) components (Supplementary Table 5). The genetic correlations between education and both gf and MHVS were large, 0.83 (SE 0.17) and 0.92 (0.11), respectively. The genetic correlation between SIMD and gf was small: 0.18 (SE 0.19). The corresponding figure for SIMD and MHVS was larger and significantly different from zero: 0.42 (SE 0.14). Further sensitivity analyses that accounted for the over-sampling of individuals with depression had no effect on the results (Supplementary Tables 6 and 7).To account for potential domain-specific variability, sensitivity analyses were performed for g split into general fluid-type intelligence , 26% of which was shared with genes that influence intelligence. Around 24% of their phenotypic correlation can be explained by common SNPs. This is important for those studying inequalities in health, because cognitive ability and socioeconomic status are correlated predictors of health outcomes. Thirdly, we found a slight difference between the proportion of variance in g explained by SNPs for those above or below the median socioeconomic level (31% versus 15%). This is in the same direction as reported by We note that, because we examined only a defined, albeit large set of common SNPs and not other types of genetic variation, the univariate estimates of heritability provided here by the GCTA analyses are lower-bound estimates of narrow sense heritability, such as are contributed to by genetic variants in linkage disequilibrium with those common SNPs that were typed. However, assuming that the genetic correlation is the same for tagged and untagged causal variants, then the GCTA-derived estimate of this quantity will be unbiased .f, are lower than those obtained in a sample of mid-to-late-life adults (f component has been based on different tests in different studies), and the small (only three) set of cognitive tests used to form gf in the present study. By contrast the vocabulary-based estimate of crystallised intelligence had a higher proportion (42%) of variance explained by common SNPs. This figure is similar to those reported previously  and education. Moreover, the bivariate heritability estimates suggest that common SNPs explain 24–59% of the phenotypic correlations. Bivariate GCTA estimates between childhood intelligence (at ages 7 and 12) and family-based socioeconomic status (at age 7) have been presented in the study of The genetic correlations from the bivariate GCTA indicated notable pairwise associations among the traits. In particular, there was a close to a complete overlap between the additive genetic effects that influence education and g. The strength of the relationship can also be seen in the univariate GCTA analysis for education that had additional adjustments for gIn conclusion, using DNA SNP data we found modest genetic contributions to differences in socioeconomic status that have measurable association with the genetic basis of intelligence differences and education. The implication from these analyses is that socioeconomic status and education have, in part, genetic causes that are shared with the genetic contributions to measured intelligence. In a discussion article on the causes of the association between intelligence and education  we notedHistogram of Scottish Index of Multiple Deprivation (SIMD) ranks from 2009 in the full Generation Scotland cohort of 23,673 and the unrelated, genotyped subset of 6815 subjects.Comparison of log-likelihoods and residual errors for linear regression models of age-, sex-, and population stratification -adjusted cognition, education, and SIMD.Supplementary Table 1: Pearson correlations of general intelligence, g, between pairs of relatives.Supplementary Table 2: Age- and sex-adjusted univariate pedigree models for cognition, education, and social class with and without maternal effects.Supplementary Table 3: Age- and sex-adjusted bivariate pedigree models for cognition, education, and social class.Supplementary Table 4: Age-, sex-, and population stratification-adjusted univariate GCTA models for cognition, education, and social class.Supplementary Table 5: Age-, sex-, and population stratification-adjusted bivariate GCTA models for cognition, education, and social class.Supplementary Table 6: Age- and sex- and population stratification-adjusted univariate GCTA models for cognition, education, and social class excluding those with depression.Supplementary Table 7: Age-, sex- and population stratification-adjusted bivariate GCTA models for cognition, education, and social class excluding those with depression.The following are the supplementary data related to this article.http://dx.doi.org/10.1016/j.intell.2014.02.006.Supplementary data to this article can be found online at


A pearl millet inbred germplasm association panel (PMiGAP) comprising 250 inbred lines, representative of cultivated germplasm from Africa and Asia, elite improved open-pollinated cultivars, hybrid parental inbreds and inbred mapping population parents, was recently established. This study presents the first report of genetic diversity in PMiGAP and its exploitation for association mapping of drought tolerance traits. For diversity and genetic structure analysis, PMiGAP was genotyped with 37 SSR and CISP markers representing all seven linkage groups. For association analysis, it was phenotyped for yield and yield components and morpho-physiological traits under both well-watered and drought conditions, and genotyped with SNPs and InDels from seventeen genes underlying a major validated drought tolerance (DT) QTL. The average gene diversity in PMiGAP was 0.54. The STRUCTURE analysis revealed six subpopulations within PMiGAP. Significant associations were obtained for 22 SNPs and 3 InDels from 13 genes under different treatments. Seven SNPs associations from 5 genes were common under irrigated and one of the drought stress treatments. Most significantly, an important SNP in putative acetyl CoA carboxylase gene showed constitutive association with grain yield, grain harvest index and panicle yield under all treatments. An InDel in putative chlorophyll a/b binding protein gene was significantly associated with both stay-green and grain yield traits under drought stress. This can be used as a functional marker for selecting high yielding genotypes with ‘stay green’ phenotype under drought stress. The present study identified useful marker-trait associations of important agronomics traits under irrigated and drought stress conditions with genes underlying a major validated DT-QTL in pearl millet. Results suggest that PMiGAP is a useful panel for association mapping. Expression patterns of genes also shed light on some physiological mechanisms underlying pearl millet drought tolerance. Pennisetum glaucum (L.) R. Br.] is the sixth most important global cereal crop which is grown by subsistence farmers in the semi-arid regions of sub-Saharan Africa and the Indian subcontinent [Pearl millet [ontinent . It is tontinent –4. It alontinent , 6. Furtontinent .A tremendous phenotypic variability exists in cultivated pearl millet for many agronomic traits such as flowering time, panicle length, grain and stover characteristics, and for tolerance to many biotic and abiotic stresses , 9. DespWhile conventional linkage mapping has identified a number of important quantitative traits in pearl millet, it has been severely limited by the resolution provided by two (or a few) parent-derived mapping populations. Due to small (79–125) population sizes and the early generations used, resolution has been in the range of 10-30cM in these studies. Therefore, there is need to explore and venture into improved alternatives for allele mining for improving pearl millet open-pollinated varieties and hybrids. Association mapping, also known as linkage disequilibrium (LD) mapping, offers an alternative means of allele mining, which utilizes ancestral recombination events in germplasm collections or natural populations to make marker-phenotype associations . This apRecently, a pearl millet germplasm association panel (PMiGAP) comprising 250 inbred lines has been assembled from a large set of 1000 diverse breeding lines and accessions of landraces, elite cultivars and mapping population parents, collected from wide geographical range in Africa and Asia . It is aThe permission to carry out field experiments was obtained from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, India and phenotyping was carried out at fields of ICRISAT, Patancheru, Hyderabad, India.Two hundred and fifty inbred lines of PMiGAP, representing 23 countries across three continents , were us2 (GNPM), stay green (SG) and leaf rolling (LR). These traits were measured as described in [The 250 PMiGAP entries were assigned to four precocity groups  so that flowering time would not compound the results of drought tolerance. They were phenotyped as test cross hybrid trials during the summer seasons (January to May) of 2011 and 2012 in the experimental farm of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), located in Patancheru, Telengana, India . Both seasons experiments were laid out in alpha-lattice designs with two replications in 3 test environments. Individual plots were 4.0 m long; net (harvested) plot area was two rows by 3.0 by 0.6 m. Four checks; 843-22A x ICMR 01004, 843-22A x ICMR 01029, 863B x ICMR 01004 and 841A x D 23 were repeated across trials and used in calculating adjusted means of the traits. The 3 test environments consisted of two terminal drought stress treatments, an early-onset and a late-onset of terminal stress, plus a common, fully-irrigated non-stress treatment. Drought stress in the more severe early-onset treatment was initiated at 50% flowering, by withholding irrigation from about 1 week before flowering. Drought stress in the late-onset treatment was initiated during early grain filling by withholding irrigation from approximately 50% flowering. These entries were evaluated for 16 morphological, morpho-physiological and agronomical traits. The investigated traits were grain yield (GY), panicle yield (PY), panicle harvest index (PHI), time to 75% flowering (FT), plant height (PH), panicle length (PL), panicle diameter (PD), panicle number (PN), number of tillers per plant (TPP), biomass yield (BY), grain harvest index (GHI), thousand grain weight (TGW), grain number per panicle (GNPP), grain number per mribed in , 15. Brith Edition. The minimum, maximum and mean values of each trait in each environment were calculated using summary statistics option in GenStat. Normality of the data for each trait was checked by drawing normal plots. For each environment, the restricted maximum likelihood (REML) analysis was performed with replications as a fixed effect and entries as random effects. For combined environment analysis, REML model was used with environments and replications*environments as fixed effect and entries and entries*environments as random effects. The REML model produced best linear unbiased predictors (BLUPs) and variance components for all traits. The variance components were used for calculating broad sense heritability (h2) for traits in each environment and over combined environments. For each environment h2 was calculated as: h2 = Vg/ (Vg + Verr/r) where Vg is genotypic variance and Verr is the error variance and r = number of replications for a single environment. For combined environments, h2 was calculated as Vg / ((Vg + (VGxE /n) + (Verr /nr)) where Vg is genotypic variance, VGxE is genotype x environment interaction variance, Verr is the error variance, n = number of environments and r = number of replications. The phenotypic correlations among traits were obtained using Minitab 15 (http://www.minitab.com/en-IT/products/minitab/).All phenotypic analysis was conducted using GenStat ver. 14Genotyping of PMiGAP entries with genome wide SSR markers was done using M13 tailed (5’CACGACGTTGTAAAACGAC3’) forward primers as described previously . The PCRet al. [et al. [et al. [Genotyping of PMiGAP entries with InDel markers was done as described by Sehgal et al. . For SNPet al.  were amp [et al. . Sequenc [et al. . All thehttp://taylor0.biology.ucla.edu/structureHarvester/index.php) to estimate ∆K [The Bayesian model-based population structure analysis implemented in STRUCTURE v2.2  was usedimate ∆K . It is aThe assessment of genetic diversity was conducted using the software POPGENE version 1.32 . The folhttp://www.maizegenetics.net). LD significance was determined with 100,000 permutations for each locus. The squared correlation coefficients (r2) between loci were used for quantifying LD. LD was calculated for each candidate gene separately.Linkage disequilibrium was estimated with the software program TASSEL 3.0 (http://www.maizegenetics.net), employing both the general linear model (based on the Q- or PCA-matrix) and the mixed linear model (based on Q- or PCA-matrix and the kinship matrix).A pairwise kinship matrix was estimated using the program SPAGeDi . NegativP values [P-values were found to be highly stringent. Hence considering the potential risk of type II error, we used another criterion as described in Pasam et al. [P-values obtained within the bottom 0.1 percentile of the distribution are significant. A threshold P-values of 0.05 corresponded to the bottom 0.1 percentile in the present study, which was then used to declare significant MTAs.The significance of marker trait associations (MTAs) was initially based on FDR-adjusted P values  with cutm et al. . Based oth day after sowing (a week after panicle emergence) and continued for 7 days. Leaves were harvested in liquid nitrogen from the plants in both control and stress treatments, and stored at -80°C. Total RNA was isolated from the frozen leaves of the drought-stressed and control materials using RNeasy Plant Mini Kit  with manufacturer’s protocol. RNA was treated with RNase free DNase  to remove genomic DNA contamination followed by inactivation of DNase with RNeasy Min Elute cleanup kit  according to the manufacturer’s instructions. The quantification of the purified RNA was done at 260 nm in a spectrophotometer. The quality of the RNA was checked by both gel analysis, and 260/280 and 260/230 nm spectrophotometric ratios. The RNA was stored at -80°C before use. One micro gram of total RNA was used to synthesize cDNA using iScriptTM cDNA synthesis kit  as per manufacturer’s protocol. Real-time PCR was performed using the SYBR Green PCR master mix kit  according to the manufacturers’ recommendations. The following PCR program was used; 10 min at 95°C, 40 cycles at 95°C for 15 s, 54°C for 1 min and 72°C for 40 s, with a final cycle at 72°C for 7 min. Relative mRNA accumulation of each candidate sequence was compared to the control by using the standard comparative Ct (2-∆∆Ct) method. Average Ct value was calculated for 3 replicates for the housekeeping actin gene and for each candidate sequence in controlled and stressed cDNA samples. ∆Ct was then determined by taking a difference of the two values.Seeds of three contrasting genotypes, H 77/833-2 (drought susceptible), PRLT 2/89-33 (drought tolerant) and ICMR 01029 (near isogenic line introgressed with drought tolerance QTL on LG2 from drought tolerant parent PRLT 2/89-33) were sown in 8 inch pots filled with compost in a controlled environment tuned with the following conditions: 10 h light/14 h dark, temperature 20°C (night)/28°C (day) and relative humidity of 60% (day)/80% (night), suitable for pearl millet growth. Three replicates per genotype were sown in the pots and plants were grown till maturity. Water stress was initiated by withholding water supply on the 45Xibmsp09/AP, Xibmsp07/AP, Xibmcp11/AP and Xibmsp43/AP) to 16 (Xipes0236), were detected across 250 pearl millet accessions which represent an average allele number (A) of 7 per locus (e) for 37 markers was 2.71. The difference between A and Ae indicates that a large proportion of the alleles (54.8%) had frequencies lower than 5%, with 40.1% of them  present in a single genotype. The Shannon’s information index ranged from 0.20 (Xicmp 3056) to 2.21 (Xpsmp2203), with a mean of 1.06 for all loci. The Nei’s gene diversity ranged from 0.07 (Xibmsp31/AP) to 0.87 (Xpsmp2203), with a mean of 0.54 for all loci.A total of 37 SSR and InDel markers, covering all the seven linkage groups, were used to evaluate the genetic diversity in PMiGAP. A total of 259 alleles, ranging from 2  included sixteen accessions from west and east Africa, one accession from southern Africa, one accession from central Africa and two accessions from India. The eight accessions whose source was ICRISAT gene bank were also included in this population. These eight accessions  shared either a pedigree or geographic origin with other west and east African accessions in population A. For instance, both ICMP 451-P6 and ICMP 451-P8 are derived from LCSN 72-1-2-1-1, a selection made in Burkina Faso (West Africa). The accession ICMP 85410-P7 is a derivative of a cross based on a germplasm from east and west Africa. Similarly, PRLT 2/89-33 is an inbred derived from the ICRISAT Bold Seeded Early Composite, based predominantly on Iniadi landrace germplasm from West Africa. Population B (green) was a large group comprising of 48 accessions from seemingly diverse geographic origins, including 19 accessions from west Africa, 5 accessions from east Africa, 4 accessions from southern Africa, 2 accessions from central Africa, 9 accessions from India and one accession each from USA, Yemen and South Africa. The six accessions  from ICRISAT gene bank were also part of this large group. As expected, 843B and its derivative ICMB 89111 (bred at ICRISAT using 843B as one of the parents) were grouped together in the population B. The lines with similar traits, but with diverse origins, were generally characteristic of this population. For example, accessions from Yemen (IP 20349) and USA (IP 21155) have characteristic features of productive tillers > 12. The accessions from India and Niger included in this population are either salinity tolerant  or thermo-tolerant . Similarly, three accessions from east and southern Africa  possess characteristic features of thick panicles (>50mm). Two early maturing accessions from west Africa and India  were also part of this population. Population C (blue) comprised of 9 accessions from west Africa, 2 accessions each from east and central Africa and 5 accessions from Asia (India and Pakistan). Population C also contained a large (11) number of breeding lines released by ICRISAT for different breeding programs. For example, 81B-P6 and ICMB 90111-P6 are downy mildew (DM) resistant selections which have been widely used as mapping population parental lines (MPPLs). Similarly, LGD 1-B-10, P310-17-B, P1449-2-P1 and WSIL-P8 developed by ICRISAT have been widely used as MPPLs. Another two lines, SOSAT-C88 and Raj 171, released by ICRISAT in west coast of Africa and India, respectively, were also included in this population. Population D (yellow) comprised of 44 accessions predominantly from West Africa (18) and 4 accessions each from east, central and southern Africa and one accession each from South Africa and India. This population also included a large number of breeding lines/cultivars developed and released by ICRISAT. The lines Okashana 1 (released in Namibia), GICKV 93191 and WC-C75 (released in India), 863B , IP 18293-P152 (MPPL), ICML 1 and ICML 2 (resistant to ergot) and five others were part of this subpopulation. Population E (purple) contained 50 accessions, with 60% of them originating from west (21) and east (8) Africa. The remaining accessions included 3 accessions from central Africa, 9 accessions from India, 1 accession each from Pakistan and USA and 7 breeding lines/cultivars released by ICRISAT. Similar to population B, lines with similar traits yet diverse origins were grouped in this population. For example, salinity tolerant lines from India and Niger  and early maturing (<37 days) lines from Togo and Pakistan  were part of this population. Population F (turquoise) was the largest group with 51 accessions of diverse origins including 15 accessions from west Africa, 9 accessions each from east and central Africa, 11 accessions from India, 1 accession each from south Africa and Pakistan, 2 Tifton accessions (Tift 186 and Tift 383) from USA and a few breeding lines released from ICRISAT. Again, lines with similar traits, regardless of geographic origins, were grouped in this population. For example, drought tolerant lines from Uganda and Ghana , lines with sweet stalk from India, Cameroon, Burkina Faso and Nigeria , lines with purplish-black seed colour from Sudan , lines with yellow endosperm from Burkina Faso  and lines with thick panicle from Zimbabwe and Burkina Faso ; all were included in population F.The model-based STRUCTURE analysis was performed for K populations varying from 1 to 15. ∆K showed the highest likelihood at K = 6  and topoThe distance-based principal component analysis (PCA) showed grouping fairly consistent with model-based grouping, except for population D. The population D was merged with populations F and A (results not shown). Genetic diversity parameters calculated for six subpopulations are given in The effect of drought environment was evident on all measured traits  Tables. The heritability was generally high (>0.75) or moderately high (0.50–0.75) for all the traits under control conditions . The herTraits GNPM, PN and GNPP showed significant differences among the six subpopulations under all treatments. Populations C and D had the highest GNPP and GNPM under both control and two drought stress environments while population B had the highest PN under all treatments. Other phenotypic traits were not significantly different among the subpopulations (results not shown).Previously, we mapped 17 genes as SNP and CISP markers on LG2 in the drought tolerance QTL region . In thisr2; P<0.01). When LD was calculated within each subpopulation, the frequency of pairs of loci with significant LD (P<0.05) was reduced by more than half.The extent of LD was assessed among all 1,575 pairs of loci  for all accessions as well as for the six subpopulations separately. Across all accessions, 28% of the total marker pairs were in LD (based on squared correlation coefficient r2) values were calculated  in previous study [The two model approaches, general linear model (GLM) and mixed linear model (MLM), were compared for all traits using both Q-matrix and three PCAs in both models. The results were same irrespective of the matrix (Q- or PCA-matrix) chosen in the models. The QQ plots of traits  suggesteus study  showed sSemi-quantitative PCR was conducted on four candidate gene sequences  using drought tolerant (PRLT 2/89-33) and susceptible (H77/833-2) parents of a mapping population which was used to identify DT-QTL on LG2 , 15, andA pearl millet germplasm association panel (PMiGAP), comprised of 250 landraces, elite cultivars and mapping population parents, collected from a wide geographical range in Africa and Asia, was recently established from the global pearl millet germplasm collection of ICRISAT . The preet al., [et al., [et al., [et al., [Across the 250 PMiGAP entries examined, we obtained a total gene diversity value of 0.54. This value is close to gene diversity estimates obtained previously for a pearl millet world collection from Africa and Asia , 39 and et al.,  and Mari[et al.,  for a wo[et al., . The low[et al.,  in addit[et al.,  used a h[et al., –41 but l[et al.,  for the Comparison of genetic diversity parameters, estimated based on twenty-six geographic origins, revealed the highest observed and effective number of alleles in genotypes from India followed by Burkina Faso, Nigeria, Niger and Mali . The expThe STRUCTURE analysis revealed six sub-populations in PMiGAP . This waThe candidate gene-based association mapping is a promising approach to bridge the gap between quantitative and molecular genetic approaches for complex trait dissection . Using tr2 varied extremely from gene to gene in the present study  to upto 0.32 . These results are not unexpected considering that linkage disequilibrium estimates vary according to the target region in the gene  and number of polymorphic sites [r2 in the candidate genes (CGs) ranged from 0.024 to 0.21 [r2 was 0.23 for CGs in our study, which is comparable to r2 obtained for CGs in sorghum [Lolium perenne [The linkage disequilibrium measure nt study . For exaic sites , 49. In  to 0.21  and 0.0  to 0.21 , respect sorghum  and Loli perenne . Since fXpsmp2059, Xipes0152, Xicmp3056, Xipes0117 and Xipes0218 with many drought tolerance traits (results not shown) which confirms previous results of association of these markers with DT-QTL based on bi-parental fine mapping population [We tested both GLM, MLM models for all traits, and obtained QQ plots to choose the best model for association analysis. It has been reported that directly fitting both Q/PCA (structure) and K (kinship), without testing the model, may overcorrect population structure and familial relatedness for some traits and results in type II error . No signpulation .+ channels in guard and mesophyll cells [Out of 39 SNPs and 7 InDels from 17 genes genotyped in PMiGAP, significant associations were obtained for 22 SNPs and 3 InDels from 13 genes considering all treatments together (one irrigated and two drought stress treatments) . Of thesll cells –56. Simill cells . Many rell cells –63. HoweXibmsp15/AP1.1 (A/G) and Xibmsp15/AP1.2 (A/G) in Zn finger CCCH type are associated with panicle diameter (PD) and thousand grain weight (TGW), respectively, under both irrigated and late drought stress conditions. Since both PD and TGW are highly correlated with grain yield, these two SNPs can be used for marker-assisted selection (MAS) in pearl millet for improving yield under both irrigated and water-limited environments. The SNP in serine/threonine protein kinase gene, however, was not associated with the same trait under both irrigated and drought stress conditions (2 (GNPM) under irrigated conditions and with panicle harvest index (PHI) under late stress conditions. Hence, these SNPs can be used for MAS either in specific environment or in combination with other SNPs from genes showing significant associations with GNPM and PHI. Similarly, the SNP in protein phosphatase 1 gene can be used for MAS for grain yield, panicle harvest index and panicle yield under drought stress conditions. For ubiquitin conjugating enzyme, we obtained significantly associated SNPs for grain harvest index, panicle yield and thousand grain weight under irrigated conditions and for stay green, grain number per panicle and grain harvest index under late drought stress conditions. The significant association of SNPs in ubiquitin conjugating enzyme (UBC) gene with stay green trait under terminal drought stress confirms the importance of this gene during leaf senescence [Except for protein phosphatase 1 regulatory subunit SDS22, we obtained significant associations of SNPs in the remaining three genes under both irrigated and drought stress conditions. For example, SNPs nditions  i.e. it nescence , 64.We obtained positive correlation of grain yield with stay green under terminal drought stress treatments  which suUBC gene was observed in drought tolerant lines as compared to the sensitive genotype. These results indicate that key signaling enzymes and ubiquitin proteasome system are repressed in pearl millet under terminal drought stress in order to keep the photosynthetic machinery intact.Of the genes that underlie DT-QTL, Zn finger CCCH type has been functionally characterized in rice , 70. TraLHCP gene have also been reported to be associated with plant height, spike length, number of grains per spike, thousand grain weight, flag leaf area and leaf color under well-watered conditions in barley [LHCP gene with grain yield and yield components, and stay green traits under terminal drought stress is an important finding of the present study. This InDel can be used for MAS in pearl millet to select high yielding genotypes having ‘stay green’ phenotype under terminal drought stress.We obtained significant association of an InDel in putative chlorophyll a/b binding protein gene with grain yield, thousand grain weight and panicle yield under both early and late drought stress conditions and with grain harvest index, panicle diameter, panicle harvest index, stay green and leaf rolling under late stress conditions . Fig 5  is a key enzyme in fatty acid biosynthesis. Fatty acid biosynthesis involves the cyclic condensation of two-carbon units derived from acetyl-coA [2 by the enzyme ACoC. The tight regulation of ACoC controls the overall rate of fatty acid synthesis. It has been shown in barley that ceasing activity of ACoC inhibits growth and development of seedlings as there is not enough fatty acids to contribute to membrane structures [ACoC gene in PMiGAP entries and found significant association of this SNP with many traits , dipeptidyl peptidase IV (DPPIV), oligopeptidase B (OPB), and acylaminoacyl peptidase (ACPH). Of these, POP has been demonstrated to confer drought stress tolerance . The rolSimilar to the results demonstrated in this study, many genes have been reported to be associated with drought tolerance using association mapping –84 thus Correlation among traits is commonly used by breeders for either simultaneous improvement of correlated traits or for reducing undesirable side effects when improving only one of the correlated traits. The negative correlation is undesirable by breeders specifically when the goal is to simultaneously increase the trait values of two negatively correlated characters. Many examples can be cited for such negatively correlated traits. For example, a negative correlation exists between grain yield and protein content in maize , durum wUBC, LHCP and PhyC are the key regulatory and downstream genes in the DT-QTL interval found to be associated with the stay green trait in the present study Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


The current genetic makeup of Latin America has been shaped by a history of extensive admixture between Africans, Europeans and Native Americans, a process taking place within the context of extensive geographic and social stratification. We estimated individual ancestry proportions in a sample of 7,342 subjects ascertained in five countries . These individuals were also characterized for a range of physical appearance traits and for self-perception of ancestry. The geographic distribution of admixture proportions in this sample reveals extensive population structure, illustrating the continuing impact of demographic history on the genetic diversity of Latin America. Significant ancestry effects were detected for most phenotypes studied. However, ancestry generally explains only a modest proportion of total phenotypic variation. Genetically estimated and self-perceived ancestry correlate significantly, but certain physical attributes have a strong impact on self-perception and bias self-perception of ancestry relative to genetically estimated ancestry. Latin America has a history of extensive mixing between Native Americans and people arriving from Europe and Africa. As a result, individuals in the region have a highly heterogeneous genetic background and show great variation in physical appearance. Latin America offers an excellent opportunity to examine the genetic basis of the differentiation in physical appearance between Africans, Europeans and Native Americans. The region is also an advantageous setting in which to examine the interplay of genetic, physical and social factors in relation to ethnic/racial self-perception. Here we present the most extensive analysis of genetic ancestry, physical diversity and self-perception of ancestry yet conducted in Latin America. We find significant geographic variation in ancestry across the region, this variation being consistent with demographic history and census information. We show that genetic ancestry impacts many aspects of physical appearance. We observe that self-perception is highly influenced by physical appearance, and that variation in physical appearance biases self-perception of ancestry relative to genetically estimated ancestry. Understanding the basis of a variation in human physical appearance has been a topic of long-standing research interest. However, little is known about the genetic basis of most of this variation. An exception is pigmentation, which has been the focus of considerable research, particularly in Europeans Latin America provides an advantageous setting in which to examine the impact of genetic variation on physical appearance. The region has a history of extensive admixture between three continental populations: Africans, Europeans and Native Americans In this paper we present the first phase of a research program focused on the genetics of physical appearance in Latin Americans. We base this program on a sample of over 7,000 individuals ascertained in five countries: Brazil, Chile, Colombia, México and Perú. Information was obtained for a range of socio-demographic variables, physical attributes and self-perception of ancestry. Here we report analyses based on individual mean genome admixture proportions. Coordinate-based spatial analyses illustrate the significant variation in ancestry existing across Latin America, in agreement with demographic history and census information. Significant effects of ancestry were detected for most of the phenotypes examined, and the direction of these effects agrees with the phenotypic differentiation of Africans, Europeans and Native Americans. Finally, we observe that certain phenotypes have a strong impact on self-perception and that these phenotypes bias self-perceived relative to genetically estimated ancestry.Summary descriptive statistics for the study sample collected are presented in We estimated individual African/European/Native American admixture proportions with data for 30 highly informative SNPs using the ADMIXTURE program −5), Chile , Colombia , Mexico , Perú . Few volunteer birthplaces were thus located in sparsely populated regions (e.g. Amazonia) and geographic interpolation of ancestry in those regions should be regarded with special caution.Consistent with previous studies, we observe extensive variation in ancestry between countries  as well The Brazilian sample  shows wi−16 for the three ancestries): African and European ancestry decreases with altitude , while Native American ancestry increases (r = 0.48).To evaluate the statistical significance of the observed spatial variation in ancestry we calculated Moran's Index (I) of association between each individual ancestry component and spatial location. These were significant for the three ancestries in all countries . Since the three ancestry components are not independent, we also calculated canonical correlation coefficients between ancestry and geographic location. These were also significant for all countries . The variation in ancestry seen in the admixture maps of The Kriging interpolation scheme used in building the maps of −3 using a conservative Bonferroni multiple testing correction, −2). Higher African ancestry is associated with: increased height, higher skin pigmentation and greater hair curliness. The proportion of phenotypic variance explained by ancestry is highest for skin pigmentation (19%) followed by hair shape (8%) and color of eyes and hair  but at most 1% for the other phenotypes.Regression of phenotypic variation on genetic ancestry (taking Native American as reference) demonstrates a significant effect for most of the traits examined  and age  on height, with height increasing for individuals born more recently at a rate ∼1 cm every 10 years . Higher European ancestry is associated with reduced eye fold and an overall smaller face (centroid size).−6 assuming a conservative Bonferroni multiple testing correction; −10) involve mainly distances between landmarks placed on the lips and nose. Face shape variation, independent of size, was assessed via Principal Components (PCs) of procrustes 3D coordinates. Significant effects of European ancestry were detected for PCs 1 and 3–5, while African ancestry impacts on PCs 1, 2 and 4  are commonly used across Latin America in national censuses and other population surveys. We contrasted genetic ancestry and skin pigmentation (as measured by the melanin index) across these four self-estimated categories for the countries sampled . Within −6, Europe: r = 0.48, P<2.2×10−6, Africa: r = 0.32, P<2.2×10−6. However, there is a trend for higher self-perceived Native American and African ancestry to exceed the genetic estimates  and the negative sign of the regression coefficients reflects the orientation of bias seen in Contrasting self-perceived (ranked into five bands at 20% increments) and genetically estimated continental ancestry we observe a moderate, but highly significant, correlation: America: r = 0.48, P<2.2×10stimates . Similarstimates . To explstimates , using gstimates . As expeMost of the phenotypic traits that show ancestry effects  also havth century, the population of what is now called “Latin America” has undergone major demographic changes within the context of a highly diversified physical and social environment Since the late 15www.ine.cl; geoftp.ibge.gov.br; www.igac.gov.co; www.censo2010.org.mx, www.indepa.gob.pe). Altogether, these data underline the extensive genetic structure existing within and between Latin American countries. It is possible to relate this genetic heterogeneity to well documented historical factors th century of a caste system regulating marriages th century The interpolated ancestry maps obtained  are consEDAR, is in high linkage disequilibrium with the p.Val370Ala variant in the HapMap dataset and is strongly associated with hair type in our sample, after accounting for ancestry  with hair greying , hair and eye color and hair type are traits with little environmental sensitivity and show large differences between continental populations ancestry , suggestRecent genome-wide association analyses in Europeans have implicated loci for variation in height and related anthropometric traits The ancestry effects that we detect for facial features , but not for head circumference, agree with the notion of a greater developmental and evolutionary constraint on neuro-cranium than on facial variation. This is also in line with proposals that human facial features include a range of environmental adaptations Our joint analysis of genetic, phenotypic and self-perception variation emphasizes the strong impact of physical appearance on self-perception. Comparison of skin pigmentation across self-perceived ethno/racial categories shows remarkable consistency between countries, underlining the weight given to this trait in self-perception In conclusion, our study sample illustrates the extensive geographic variation in genetic ancestry seen across Latin America, reflecting the heterogeneous demographic history of the region. The highly significant impact of genetic ancestry on physical appearance is consistent with some of the phenotypic variation seen in Latin Americans stemming from genetic loci with differentiated allele frequencies between Africans, Europeans and Native Americans Recruitment took place mainly in five locations: México City (México), Medellín (Colombia), Lima (Perú), Arica (Chile) and Porto Alegre (Brazil). With the exception of Chile, most subjects recruited in these cities were students and staff from the universities participating in this research. In Chile about 2/3 of the subjects recruited were professional soldiers. In Brazil ∼10% of samples were collected in smaller towns of the states of Rio Grande do Sul, Bahia and Rondonia. Adult subjects of both sexes were invited to participate mainly through public lectures and media presentations. Maps showing the number of volunteers in each unique birthplace are presented in A physical examination of each volunteer was carried out by the local research team using the same protocol and instruments at all recruitment sites. We obtained: height, weight, head, hip and waist circumference, cheilion-cheilion width and sellion-gnation height. All measurements were obtained in duplicate and the mean of the two measurements retained for further analyses. We recorded eye colour into five categories , and natural hair colour into four categories . Balding in males was recorded using a modified Hamilton scale as: 0) no hair loss, 1) frontal baldness only, 2) frontal hair loss with mild vertex baldness, 3) frontal hair loss with moderate vertex baldness, and 4) frontal hair loss with severe vertex baldness. Similarly, graying was recorded along a five point scale: 0) for no greying, 1) for predominant non-graying, 2) for ∼50% graying, 3) for predominant greying and 4) for totally white hair. Due to the small number of individuals in categories 1–4 for male pattern balding and greying, we pooled these categories so as to contrast only two categories (presence or absence of the trait). Macroscopic hair type was categorized by visual inspection as 1-straight, 2-wavy, 3-curly or 4-frizzy. A quantitative measure of constitutive skin pigmentation (the Melanin Index) was obtained using the DermaSpectrometer DSMEII reflectometer . Measurements were obtained from both inner arms and the mean of the two readings used in the analyses.http://www.photomodeler.com/ Eos Systems Inc, Vancouver, Canada) , left angle (−45°), frontal (0°), right angle (45°), right side (90°) were taken from ∼1.5 meters at eye level using a Nikon D90 camera fitted with a Nikkor 50 mm fixed focal length lens. The frontal facial photographs were used to score  the presence of an eye fold along the upper eye lids using a three point scale: 0) absence 1) partial  and 2) full .All photographs were annotated manually with 36 anatomical landmarks and 3D landmark coordinates extracted using the software Photomodeler ( Canada) . LandmarA structured questionnaire was applied to each volunteer. We obtained information on two indicators of socioeconomic position . The firThe questionnaire included items exploring self-perception of ethnicity in the categories: “Black”, “Native”, “White” and “Mixed”, and self-perception of African, European, and Native American ancestry proportions. This was explained as a personal estimation of the proportion of ancestors that had a particular continental origin. We proposed a five point scale, expressed in 20% per cent brackets (and in words): 1) 0–20% (none or very low), 2) 20–40% (low), 3) 40–60% (moderate), 4) 60–80% (high) and 5) 80–100% . The questionnaire also recorded information on the place of birth of the volunteer.http://www.cs.rpi.edu/~drinep/HGDPAIMS/WORLD_5000_INFAIMs.txt. Of these, allele genotype data is available in Native Americans for 3,848 markers www.lgcgenomics.com/). In a sample of Colombians recently included in a genome-wide association study that used Illumina's 610 chip In order to select 30 markers highly informative for estimating African/European/Native American ancestry, we started from the list of 5,000 markers, highly informative for world-wide continental ancestry estimation, identified by Paschou et al (2010) Individual African, European and Native American ancestry proportions were estimated using the ADMIXTURE program https://developers.google.com/maps/documentation/geocoding/). The GeodesiX software  was used for the geocoding query. We used the Global Rural-Urban Mapping Project version 1 data set  to attribute census size to these localities  for each placename were obtained via the Google Maps Geocoding API (http://www.esri.com/software/arcgis). The cartographic database was geo-referenced to the SIRGAS geodesic system  using a Universal Transverse Mercator projection. Corel-DRAW X3  was used to edit the map images. When a geographic location had multiple data entries (i.e. volunteers), the Kriging interpolation scheme uses the mean ancestry at that location. The correlation between the standard deviation of individual ancestry variation (at locations with more than 10 samples) and census size was tested using Spearman's rank correlation . Statistical significance was obtained via permutation of individual birthplaces.Geographic maps displaying spatial variation in individual admixture were obtained with Kriging interpolation using the software ArcGis 9.3  separately. This index tests for spatial uniformity of a variable using standard autocorrelation models and we evaluated significance by permuting birthplace locations for every individual maintaining constant the number of individuals sampled per location. To assign a single value to each location we used the average ancestry, recalculating this average after every permutation. Secondly, we used canonical correlation analysis. A disadvantage of Moran's method is that the three ancestry variables are not independent, complicating the interpretation of p-values. Canonical correlation allows one to combine the three ancestries into a single variable: it is the maximal correlation between two sets of linear combinations of multiple variables. In our case, the three ancestries constitute one set and the geographical coordinates (latitude & longitude) constitute the second set. Adding quadratic and cubic powers of the geographic coordinates improved the fit, consistent with the curved shape of the ancestry gradients and the existence of regions with markedly different ancestry. Adding a fourth power did not improve the fit any further. P-values were obtained by permutation as above.To evaluate the effect of ancestry on phenotype we used multivariate regression models including basic covariates . Depending on the trait we used multiple linear  or logistic (for binary traits) regression. The categorical traits in 2 from this full model. A baseline regression model with only the covariates was also performed, leaving out ancestry, and the difference in R2 in the two models was taken to be the proportion of variance in the phenotype explained by ancestry. Standard errors of the individual ancestry estimates (provided by the ADMIXTURE software) were incorporated in the multivariate regressions via the errors-in-variables model Regression results corresponding to the ancestry variables are presented in To evaluate the relationship between self-perceived and genetically estimated ancestry we performed a bias analysis. This bias was defined as self-perception minus the estimated genetic ancestry. Overestimation therefore means that self-perception exceeds the genetic estimate, while underestimation indicates that self-perception is lower than the genetic estimate. Each genetic ancestry estimate was obtained as a percentage (proportion), while self-perception was recorded into five bands at intervals of 20%. The bias in self-perception was therefore considered zero if the percentage of genetic ancestry fell within the chosen self-perception interval. Otherwise, bias was measured to be the distance of the closest boundary of the self-perception interval to the genetic ancestry percentage. We then performed multivariate linear regression of the bias on the genetic ancestry estimates and other variables . The advwww.r-project.org) All statistical analyses were performed using R Click here for additional data file.Figure S2(A) Supervised and (B) Unsupervised ADMIXTURE runs with K = 3.(DOCX)Click here for additional data file.Figure S3PCs 1–5 obtained from 3D coordinates of facial landmarks, represented by (A) Scatterplots and (B) Facial morphs.(DOCX)Click here for additional data file.Figure S4Birthplace maps of study volunteers in (A) Brazil, (B) Chile, (C) Colombia, (D) México and (E) Perú.(DOCX)Click here for additional data file.Figure S5Populated locations (points) and Population sizes in (A) Brazil, (B) Chile, (C) Colombia, (D) México and (E) Perú.(DOCX)Click here for additional data file.Figure S6Scatterplots of (A) Skin pigmentation (Melanin Index) and (B) Height (in cm), and European genetic ancestry.(DOCX)Click here for additional data file.Table S1Allele frequencies at 30 SNP markers selected for African, European and Native American ancestry estimation and the absolute difference in reference allele frequency (Δ) between continental populations.(DOCX)Click here for additional data file.Table S2Population samples used for genetic ancestry estimation.(DOCX)Click here for additional data file.Table S3Coefficients and P-values for regression of pair-wise inter-landmark distances and ancestry.(DOCX)Click here for additional data file.Table S4Frequency (and %) of four ethnicity categories in each country, mean individual genetic ancestry and Melanin Index in each category.(DOCX)Click here for additional data file.Text S1Variation in ancestry across countries and individuals.(DOCX)Click here for additional data file.Text S2Correlation of socioeconomic position with ancestry.(DOCX)Click here for additional data file.Text S3Regression of height (in cm) on ancestry and covariates.(DOCX)Click here for additional data file.Text S4Regression of hair shape on marker rs260690 and covariates.(DOCX)Click here for additional data file.Text S5Description of face shape changes associated with PCs 1–5 of 3D landmark coordinates.(DOCX)Click here for additional data file.Text S6Obtaining population size at individual birthplaces.(DOCX)Click here for additional data file.


Miscanthus.Increasing demands for food and energy require a step change in the effectiveness, speed and flexibility of crop breeding. Therefore, the aim of this study was to assess the potential of genome-wide association studies (GWASs) and genomic selection (i.e. phenotype prediction from a genome-wide set of markers) to guide fundamental plant science and to accelerate breeding in the energy grass Micanthus sinensis genotypes, and related SNVs to phenotypic data for 17 traits measured in a field trial.We generated over 100 000 single-nucleotide variants (SNVs) by sequencing restriction site-associated DNA (RAD) tags in 138 Confounding by population structure and relatedness was severe in naïve GWAS analyses, but mixed-linear models robustly controlled for these effects and allowed us to detect multiple associations that reached genome-wide significance. Genome-wide prediction accuracies tended to be moderate to high (average of 0.57), but varied dramatically across traits. As expected, predictive abilities increased linearly with the size of the mapping population, but reached a plateau when the number of markers used for prediction exceeded 10 000–20 000, and tended to decline, but remain significant, when cross-validations were performed across subpopulations.Miscanthus breeding programs may be feasible.Our results suggest that the immediate implementation of genomic selection in  Cost-efficient genotyping protocols based on next-generation sequencing  and genoWith increasing demands for food and energy, decreasing land base and changing environmental conditions . Genomic4 grass Miscanthus is a promising energy crop because of its broad adaptability, potentially very high yields and low requirements for agricultural inputs , but captured high levels of genetic variation across a range of phenotypic traits related to phenology, biomass productivity and cell wall composition.The tropical CM. sinensis genotypes and generated over 100 000 ‘RAD-Seq' single-nucleotide variants (SNVs); (2) confirmed patterns of putatively neutral population structure detected in our previous study, but also used the power of the RAD-Seq markers to add substantial new details, including the hypothetical geographical origins for a large number of accessions with unknown sampling locations; and (3) assessed the potential of GWASs and genome-wide prediction to guide biological discovery and accelerated breeding in Miscanthus.Building on this study, we: (1) sequenced restriction site-associated DNA (RAD) tags in 142 M. sinensis Anders. genotypes based on previous analyses of single-nucleotide polymorphism (SNP) data  data .r) and genetic correlations among all pairs of traits , were affixed to digested templates, polished and amplified via a polymerase chain reaction. The resulting RAD libraries were run on two Illumina  HiSeq platforms at Ambry Genetics  and the Oregon State University Center for Genome Research and Biocomputing  using Illumina 1 × 100-bp chemistry.RAD library preparation was performed by Floragenex, Inc. . Briefly, genomic DNA from each genotype was digested with the restriction endonuclease Sorghum bicolor genome (Miscanthus pseudo-reference of RAD sequence data (Supporting Information Methods S1). This assembly contained 48 426 clustered RAD sequences, representing c. 4.1 Mb of Miscanthus genomic sequence. To further reduce the potential of identifying spurious SNVs from low-quality sequences, reads were trimmed from the 3′ end to a total length of 85 bp. SNV detection then proceeded as described previously. These two approaches resulted in the initial identification of 142 539 and 223 046 SNVs, respectively.Sequencing adaptors and barcodes were removed using Floragenex software tools, resulting in 94-bp RAD fragments. These trimmed fragments were then aligned to a reference genome sequence using Bowtie (plink … –indep-pairwise 50 5 0.2), as well as to estimate linkage disequilibrium (LD) (plink … –ld-window-r2).We used custom scripts and VCFtools . For inir2 ≥ 0.8 (i.e. based on genotypic correlation). Four of the 142 sequenced genotypes were identified as outliers along four different and highly significant axes of variation (P < 10−5 from tests based on the Tracy–Widom distribution) based on the default settings of the smartpca program within the EIGENSOFT package  approach of K) from one to six. Each run consisted of 10 000 burn-in iterations and 10 000 data collection iterations. We used the DISTRUCT program  divided by the standard deviation of the respective trait BLUPs (we used AvgeSen.9). We chose values of k so that the expected PVE would be between 0.01 and 0.20 in 0.01 increments, and performed 1000 simulations for each of these values. Naïve estimates of PVE were calculated as the R2 from simple linear regression of the trait BLUP, including simulated additive effects, on SNV genotypes. This was only performed for SNVs that were significantly associated with the simulated trait BLUP at α = 10−5.We used the efficient mixed-model association expedited approach EMMAX; . To cont 0 and a  to genotTo assess the relative severity of confounding across traits, we also ran models that only included the IBS kinship matrix, or did not include any terms to account for population structure and relatedness . In addition, we also used the multi-locus mixed-model (MLMM) approach of We used the R package rrBLUP  for genor) as Pearson's correlation of BLUPs calculated directly from field data and those obtained from the marker data using ridge regression (Accu) by dividing predictive abilities by the square root of the broad-sense heritability (H) of the respective trait (b0) and slopes (b1) of the simple linear regressions of BLUPs calculated from field data on those estimated using ridge regression. The last two measures, although not practically meaningful, can be indicative of model deficiencies and/or non-random partitioning of genotypes into training vs test populations  within the training population, or based on their rrBLUP-estimated effects within the training population.To assess the potential of improving the performance of genome-wide prediction by selecting the most informative markers, we compared predictive abilities between cross-validations in which 10, 100, 1000 or 10 000 markers were chosen randomly, based on their GWAS significance  appeared to be located either inside or within 2 kb of genes.Our genotyping approach resulted in the detection of over 100 000 putative SNVs . A large proportion of these SNVs did not satisfy even our ‘liberal’ filtering criteria, but even after ‘stringent’ filtering, over 20 000 informative loci were available for downstream analyses Table . At a fiSorghum and Miscanthus, we used the sliding window LD-based pruning option of PLINK to calculate that 35 700 of the 53 174 SNVs detected based on alignments to Sorghum and ‘liberal’ filtering had no other SNV within a 25-SNV up- or downstream window that was linked at r2 > 0.2. Similarly, the 121 771 SNVs detected based on alignments to a Miscanthus pseudo-reference and ‘liberal’ filtering were located on 36 223 unique RAD tags. Although our data were not appropriate for detailed estimation of LD and historical rates of recombination , analyses based on SNVs located on the same RAD tag suggested that fine-scale LD is substantial  extends to at least several hundred bp , which explained between 2.9% and 1.2% of the total SNV variation. As expected, PCA  and PC2  being strongly correlated with the source longitudes of genotypes with known sampling locations. The differentiation between these two subpopulations was reflected in fine-scale patterns of LD, with r2 decaying slightly more slowly in each subpopulation than in the overall population . When such associations were detected, their estimated effects tended to be upwardly biased by as much as an order of magnitude .P values (i.e. quantile–quantile (QQ) plots in Figs S4, S5). This effect was particularly strong for traits characterized by strong genetic differentiation (i.e. high QST between ‘Continent’ and ‘Japan’ subpopulations) and/or significant correlation with geographical variables or primary eigenvectors of population structure , without apparently compromising statistical power relative to models that only included the IBS matrix .Naïve GWAS analyses that ignored the effects of population structure and relatedness  consistently resulted in severely inflated P < 10−5) for SNVs resulting from alignments to the S. bicolor genome . Four of these associations  reached genome-wide significance after Bonferroni correction for multiple testing (P < 0.05/53 174 ≈ 9.4 × 10−7), whereas another 13 had an estimated false discovery rate < 0.05 , with 53 SNVs reaching suggestive (P < 10−5) and two SNVs  reaching Bonferroni-corrected genome-wide significance (P < 0.05/121 771 ≈ 4.1 × 10−7).Using this conservative approach , we detected 35 putative associations . Furthermore, when PC1 and PC2 were included as covariates in MLMMs, the selection procedure for AvgeSen.9 favored a model including six significantly associated SNVs .Results from the MLMM-GWAS approach were generally consistent with those from the single-locus EMMAX analyses . However, the MLMM approach allowed us to detect Bonferroni-corrected associations for two additional traits  between the sets of SNVs obtained based on different alignments , with further increases resulting in little or no improvement . However, the extent of this difference varied considerably among traits  and relatively stringent alignment criteria resulted in a greatly unbalanced genome coverage  and more sophisticated models , seven associations consistently reached genome-wide significance across a range of GWAS analyses and adjustments for multiple testing , all appeared to align within 8–16 Mb of the putative dwarfing locus dw2, whose location was recently supported by a GWAS for plant height in S. bicolor ? Because no single unambiguous explanation can be provided, we hypothesize that several factors may underlie this observation. First, LD in sinensis  does nots genome . Thus, as genome , which wMiscanthus breeding programs. Our genome-wide prediction results illustrate several important points. First, as expected from both theoretical and empirical studies (DryMatter.9 was moderately heritable (H2 = 0.54), yet genome-wide prediction of this trait consistently failed  nearly reached its theoretical maximum , as well as Cellulose.8, and promising for most other traits . Second, our results clearly suggest that substantial further improvements in predictive ability are more likely to come from using larger training populations (Miscanthus genome (Although GWAS results are expected to improve in the near future (i.e. with the use of larger populations and denser genome coverage), the ability to predict phenotypes from a genome-wide set of markers is likely to have immediate impact on  H2 = 0.5, yet gen H2 = 0.5, yet gen H2 = 0.5, yet gen H2 = 0.5, yet gen H2 = 0.5, yet gen H2 = 0.5, yet genulations  than froulations . Howevers genome . The obss genome . Thus, ts genome  clearly M. sinensis to SNV markers obtained through RAD-Seq genotyping. Despite the relatively small size of our experimental population (N = 138), results from GWASs were promising and suggest that this approach will be instrumental for the dissection of complex phenotypic traits. On a more immediate time-scale, results from our genome-wide prediction analyses suggest that the application of genomic selection in Miscanthus may be feasible, and we are therefore validating this finding in our accelerated breeding program.In summary, we related high-quality phenotypic data for 17 traits in a population of


Patinopecten yessoensis) through simulation and real data analyses using six statistical models. Our simulation analysis revealed that the prediction accuracies obtained using the 2b-RAD markers were slightly lower than those obtained using all polymorphic loci in the genome. Furthermore, a small subset of markers obtained from a reduced tag representation (RTR) library presented comparable performance to that obtained using all markers, making RTR be an attractive approach for GS purpose. Six GS models exhibited variable performance in prediction accuracy depending on the scenarios , but Bayes-alphabet and BLUP-based models generally outperformed other models. Finally, we performed the evaluation using an empirical dataset composed of 349 Yesso scallops that were derived from five families. The prediction accuracy for this empirical dataset could reach 0.4 based on optimal GS models. In summary, the genotyping flexibility and cost-effectiveness make 2b-RAD be an ideal genotyping platform for genomic selection in aquaculture breeding programs.The recently developed 2b-restriction site-associated DNA (2b-RAD) sequencing method provides a cost-effective and flexible genotyping platform for aquaculture species lacking sufficient genomic resources. Here, we evaluated the performance of this method in the genomic selection (GS) of Yesso scallop ( High-density markers ensure that the linkages between markers and quantitative trait loci (QTLs) are tight so that recombination does not cause them to decay rapidly1012131415Patinopecten yessoensis), which is cultured mainly in Liaoning and Shandong provinces of China, has been among the most important commercial shellfish since its introduction from Japan in the early 1980 s22232426272829Yesso scallop , and low-density marker panels (LD-SNPs) consisting of 5 k SNPs located in reduced tag representation (RTR)-BsaXI tags , which contain approximately 250, 50, and 5 markers per Mb, respectively. Among all SNPs, 5,000 loci were randomly chosen as candidate QTLs with the allelic effects sampled from a normal distribution with a mean of 0 and a variance of 1. The initial breeding population was simulated for 1,000 generations according to the Fisher-Wright population model with the genetic parameter values specified in 1 for three panels with different statistical models are summarized in 1 − > G2) are provided in We first investigated whether the marker densities generated by the 2b-RAD method are sufficient to capture the QTL effects using GS models. We generated high-density marker panels (HD-SNPs) consisting of 250 k SNPs spaced evenly along the Yesso scallop genome, medium-density marker panels (MD-SNPs) consisting of 61 k SNPs located in BsaXI tags , a substantial increase in accuracy was observed with the inclusion of more individuals in the training set. For example, the prediction accuracy for BayesB increased by 12% with a sample size of 500 (10 families), and by 14% with a sample size of 1,000 (20 families), indicating that sample size and phenotype heritability should be considered simultaneously in the implementation of GS. Overall, even for a dataset consisting of 250 individuals (5 families), acceptable prediction accuracies (over 0.8) could be obtained by selection of optimal statistical models .Given the reliance of many aquaculture breeding schemes on sib testingp values among the five families for SL, SW, and SH were statistically significant, with values of 2e-6, 2e-6 and 3.6e-3, respectively. For all individuals, 2b-RAD reads and mapping rates were summarized in The real dataset was composed of 349 Yesso scallop individuals that were derived from two full-sib families and three bi-parental families. Box and whisker plots exhibited the first and third quartiles of shell length (SL), shell width (SW), and shell height (SH) among the five families . AccordiThe prediction accuracies assessed using five-fold cross-validation for the entire population are shown in th of total BsaXI sites in the Yesso scallop genome are targeted by using adaptors with 5′-NNA-3′ overhangs. Thus, the sequencing cost can dramatically decrease compared with that cost associated with the use of a standard BsaXI library, and the prediction abilities in this case remain acceptable  The genetic architecture. It is challenging to generate a simulated dataset that mostly resembles to a real case because genetic backgrounds of real breeding families/populations are usually unavailable in practice. Most of existing GS models do not consider non-additive effects and may partly misclassify the non-additive effects into the random error term, resulting in a decrease of the additive heritability. To explore this possibility, we performed an additional simulation analysis  in compa32In this study, we evaluated a wide range of GS models for their potential use in aquacultural GS projects. It is currently challenging to find a statistical model that is optimal for all breeding projects, as each model has its advantages and disadvantages depending on the scenarios 3134Our simulation and empirical analyses support 2b-RAD to be a powerful and cost-effective genotyping platform for GS implementation in aquaculture breeding programs. Comparison of six GS models revealed variable performance in prediction accuracy depending on the scenarios , but Bayes-alphabet and BLUP-based models generally outperformed other models though additional, larger studies are required to verify these suggestive findings.in silico analysis was created from the draft genome sequence of Yesso scallop . We first introduced SNPs at a rate of 2% by adding alleles to the diploidized genome. Hence, approximately 5,000 loci were randomly chosen as candidate QTLs and the allelic effects were sampled from a normal distribution with a mean of 0 and a variance of 1. The 2b-RAD method was then used for in silico marker genotyping. Two marker panels with different marker densities were generated by extracting all BsaXI tags  and RTR-BsaXI tags  generated using selective adaptors with the 5′-NNA-3′ overhangs. Only SNPs located in the BsaXI tags were considered for subsequent simulation analysis.The simulation dataset for 1000 were selected as sires and dams and randomly mated in pairs with 20 offspring/pair to generate G1. This process was repeated to generate G2. For all of the samples in G1 and G2, the markers were genotyped and the traits were recorded. Phenotypic records were generated by adding the genetic values to a normally distributed error term and the variance was determined by heritability. The second simulation was composed of 20 full-sib families with each one containing 50 offspring by mating 10 males and 10 females from G1000 randomly. Different from the second experiment, the third simulation considered not only the additive genetic effects, but also dominant effects.A breeding population was simulated for 1000 generations according to the Fisher-Wright population model using the quantiNemo softwareet al.h2) of each trait were obtained as the ratio of additive variance (σa2) to the total phenotypic variance (σa2 + σe2) using the REML algorithmA total of 349 Yesso scallop individuals that were derived from two full-sib families and three biparental families were included in 2b-RAD sequencing and genotyping. These families were established with assistance of the Dalian Zhangzidao Fishery Group Corporation. Growth-related traits including shell length , shell width , and shell height  were measured for all samples at the age of 15 months. The 2b-RAD libraries were prepared following the protocol developed by Wang 1 were considered as the training set, and the samples in G2 were used as the validation set. The prediction accuracy r was calculated using the correlation between the true breeding values (TBVs) and the estimated breeding values (EBVs) by sampling the training and validation sets for 100 times. For the empirical data analysis, the prediction accuracy r was adjusted according to the Equation To validate the accuracy of family/population-based prediction, we divided all of the samples into five subsets. Four of the subsets (80%) were used to estimate the marker effects, whereas the remaining subset (20%) was used as the validation set. For the over-generation prediction, all of the samples in Gy is the observed phenotype and h is the square-root of heritability.where Six GS models including G-BLUP, BayesA, BayesB, Random Regression Best Linear Unbiased Prediction (RR-BLUP), Least Absolute Shrinkage And Selection Operator (LASSO), and Bayesian LASSO (BL) were used to estimate the marker effects. The basic model is as follows:y is the vector of the phenotype for a given trait, μ is an intercept, Z is a design matrix assigning individuals to families, w is the vector of the family effect, and X is a design matrix allocating records to the SNP effects, in which element Xij = 0, 1, or 2 if the genotype of individual i at the jth SNP is AA, AB, or BB, respectively.where For the traditional BLUP method, the genetic effect is defined as following:It follows thatG is the kinship coefficient matrix determined by the pedigree information.Where Different from the traditional BLUP method, G-BLUP estimates the kinship coefficient matrix based on the genome-wide genotyping information.G = XX’k with a common choice of k as follows:where pj is an estimate of the frequency of the allele codes at the jth marker. Therefore, the representation of G-BLUP is given by the following model:where The posterior mode of this approach can be rewritten as:In other words, the method can be understood easily by replacing the standard pedigree-based numerator relationship matrix used in the traditional BLUP approach with a marker-based estimate of additive relationships.For the BayesA approach, the following prior assumption regarding the distribution of SNP effects made:πg, and normally distributed with a mean equal to 0 and a locus-specific variance with probability (1–πg). BayesA is a special case of BayesB in which πg = 0.For BayesB, a priori SNP effect is assumed to be zero with probability g is assumed to be normally distributed, and the direct solution of equation For RR-BLUP, a vector of SNP effects e2/(σg2/k), k = 2pi(1-pi) and pi is the allelic frequency of the ith marker.where λ = σg is assigned a prior double exponential (DE) distribution:For the BL approach, e2 is assigned a scaled inverse chi-square prior distribution.And the residual variance σg is a solution to an optimization problem of the following formFor the LASSO approach, the genetic effect The RR-BLUP method was implemented using the rrBLUP packageHow to cite this article: Dou, J. et al. Evaluation of the 2b-RAD method for genomic selection in scallop breeding. Sci. Rep.6, 19244; doi: 10.1038/srep19244 (2016).


Pleiotropy, which occurs when a single genetic factor influences multiple phenotypes, is present in many genetic studies of complex human traits. Longitudinal family data, such as the Genetic Analysis Workshop 18 data, combine the features of longitudinal studies in individuals and cross-sectional studies in families, thus providing richer information about the genetic and environmental factors associated with the trait of interest. We recently proposed a Bayesian latent variable methodology for the study of pleiotropy, in the presence of longitudinal and family correlation. The purpose of this work is to evaluate the Bayesian latent variable method in a real data setting using the Genetic Analysis Workshop 18 blood pressure phenotypes and sequenced genotype data. To detect single-nucleotide polymorphisms with pleiotropic effect on both diastolic and systolic blood pressure, we focused on a set of 6 single-nucleotide polymorphisms from chromosome 3 that was reported in the literature to be significantly associated with either diastolic blood pressure or the binary hypertension trait. Our analysis suggests that both diastolic blood pressure and systolic blood pressure are associated with the latent hypertension severity variable, but the analysis did not find any of the 6 single-nucleotide polymorphisms to have statistically significant pleiotropic effect on both diastolic blood pressure and systolic blood pressure. Abundant pleiotropy has been reported for complex human traits . HoweverThere have been some recent developments in methods proposed for joint analysis of multiple phenotypes. For example, Weller et al  applied Longitudinal family data, such as Genetic Analysis Workshop 18 (GAW18), combine the features of longitudinal studies in independent individuals and studies using families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest than cross-sectional studies  Project 2) that were sequenced. Among the 464 individuals, 396 individuals have at least 1 blood pressure measure . The length of time between 2 consecutive measurements ranges from 3 to 9 years, and the number of family members varies from 11 to 36. For the phenotypes of interest, we added a value of 15 to SBP and a value of 10 to DBP for those individuals who took antihypertensive medication . Among tULK4, close to each other, and likely to be in strong linkage disequilibrium (LD). Therefore their statistical association results are expected to be similar. The other 2 SNPs (rs7640747 and rs743395) were found to be significantly associated with hypertension, but only suggestively with SBP or DBP. These 2 SNPs are also close to each other in high LD and near ITGA9.To detect SNPs with pleiotropy effect, we focused on a set of 6 SNPs from chromosome 3  that had been reported to be significantly associated with either DBP or the binary hypertension trait, based on a large-scale genome-wide association study of blood pressure and hypertension under an additive genetic model assumption . Table 1Ys are SBP and DBP, the covariates include the genotype of the SNP, age, and sex, and the continuous LV U represents the conceptual hypertension severity level. We began our analysis by considering all possible models that include the 3 covariates, where each covariate has either direct effects on the phenotypes Ys or indirect effect through U. Table α. For comparison, we also report the highest posterior density interval (HpdI) for testing the factor loadings and the fixed effects.We then applied the Bayesian LVM method to analyze 1 SNP at a time, assuming an additive genetic model. For each SNP of interest, the phenotypes α1, the coefficient for the SNP, covering zero. Results for other SNPs are characteristically similar to what's reported here for rs9816772. Therefore, our analysis did not detect a pleiotropy effect on both DBP and SBP for any of the 6 SNPs that were previously reported to be associated with DBP or the binary hypertension trait.Based on the results shown in Table In this paper, we investigated the Bayesian LV methodology recently proposed by our group to joint model multiple phenotypes, in the presence of serial and familial correlations . The proU is introduced to characterize the within-subject correlation between the 2 phenotypes and can be interpreted as the underlying hypertension severity variable. Pleiotropy is detected if a genetic marker is found to have a significant effect on U. To detect SNPs with pleiotropic effect on SBP and DBP, we investigated a set of 6 SNPs from chromosome 3 that were previously shown to be significantly associated with either DBP or the hypertension trait. However, we did not see statistically significance evidence for pleiotropy effect for any of the 6 SNPs.We applied this method to the GAW18 data, jointly analysing SBP and DBP. A LV c ain equation (2.4) allows us to model the familial correlation of the underlying disease trait. However, this formulation does not take into account the different degrees of genetic relationship among the individuals within a family. Thus, alternative models incorporating the kinship matrix will be considered in the future. Our future work also includes developing alternative models that adjust for the unequally spaced time measurements, and more efficient algorithms so that a genome-wide search for pleiotropic SNPs can be performed. (The current computation time is about 1.7 minutes per SNP.) Evaluating the performance of the Bayesian LVM method using the simulated data, and extension of the method to joint analysis of multiple rare variants are also of interest.The incorporation of family-specific random effect The authors declare that they have no competing interests.Study design: LX, RVC, ADP, and LS. Analysis: LX and AD. Manuscript drafting: LX and LS. All authors read and approved the final manuscript.