These include well-studied lipid genes such as SORT1, CETP, and PSRC1, as well as genes that have been implicated in cardiovascular phenotypes, such as CCL22 and ICAM1. The majority (40/59) of significant associations colocalized with expression quantitative trait loci (eQTLs), indicating a possible mechanism of gene regulation in lipid level variation. To fully characterize the genetic architecture of lipid traits in diverse populations, larger studies in non-European ancestry populations are needed.Plasma lipid levels are risk factors for cardiovascular disease, a leading cause of death worldwide. While many studies have been conducted in genetic variation underlying lipid levels, they mainly comprise individuals of European ancestry and thus their transferability to non-European populations is unclear. We performed genome-wide (GWAS) and imputed transcriptome-wide association studies of four lipid traits in the Hispanic Community Health Study/Study of Latinos cohort , replicated top hits in the Multi-Ethnic Study of Atherosclerosis , and compared the results to the larger, predominantly European ancestry meta-analysis by the Global Lipids Genetics Consortium . In our GWAS, we found significant SNP associations in regions within or near known lipid genes, but in our admixture mapping analysis, we did not find significant associations between local ancestry and lipid phenotypes. In the imputed transcriptome-wide association study in multiple tissues and in different ethnicities, we found 59 significant gene-tissue-phenotype associations (P < 3.61×10 Lipid levels are a major risk factor for cardiovascular disease, the leading cause of death in the United States . While lTo increase our understanding of the genetic architecture of lipid traits in non-European populations, we chose to study the Hispanic Community Health Study/Study of Latinos (HCHS/SoL) , 8. Phenhttps://github.com/WheelerLab/px_his_chol.Here, we expand upon GWAS by integrating eQTL data to predict transcriptomes in multiple tissues and in multiple ethnicities for HCHS/SoL and replication cohorts, the Multiethnic Study of Atherosclerosis (MESA) and GLGC, to further investigate the biological effects of these variants. We performed a linear mixed model GWAS for each of the four lipid phenotypes  in HCHS/We sought to understand the genetically regulated architecture underlying lipid traits in Hispanic populations. The genetic diversity within HCHS/SoL and other Hispanic populations has been extensively described previously and thus we concentrated on calculating cohort-specific principal components to be used as covariates in our analyses –18. We cWe performed GWAS using a linear mixed model implemented in the software GEMMA  to invesIn the fine-mapping joint analysis, we found multiple significant, independent SNPs associated with the four phenotypes. These SNPs include 12 with CHOL, 7 with HDL, 0 with TRIG, and 10 with LDL, totalling 29 associations . There wr2 = 0.632) with rs199768142, which is implicated in total cholesterol levels . Th. Th67]. Within the MESA cohort, individuals were recruited from six urban centers in the US: Baltimore, MD; Chicago, IL; Forsyth County, NC; Los Angeles County, CA; northern Manhattan, NY; and St. Paul, MN. Genotype data were collected from blood samples using an Affymetrix 6.0 SNP array and phenotype data from Exam 1 were used. MESA individuals include those that self-identified as “Black, African-American” , “White, Caucasian” , and “Hispanic” . Cohorts underwent quality control and imputation as previously performed by our group .http://csg.sph.umich.edu/willer/public/lipids2013/. This cohort is >95% European and contained 196,475 individuals. The original study had quantile-normalized phenotypes [Summary statistics from the Global Lipids Genetics Consortium were downloaded from −6), and restricted analyses to SNPs on autosomes only. Unlike the typical quality control process for non-admixed populations, we did not remove principal component outliers nor did we remove individuals with >0.125 identity by descent in HCHS/SoL as this would have included >66% of the sample [For quality control, we merged the two HCHS/SoL permission groups from dbGaP and ran all processes in PLINK. All of the genotypes are in genome build GRCh37/hg19. We removed individuals with genotyping rate < 99%. Additionally, we removed SNPs with failed heterozygosity (outside ± 3 standard deviations from the mean), with failed Hardy-Weinberg equilibrium ..Fig 1). −8 [We used the software GEMMA for the GWAS portion of the study. We ran separate univariate linear mixed models for each of the four phenotypes and included the relationship matrix as random effects. For the covariates used as fixed effects within the model, we included self-identified region, use of lipid or other cardiovascular medication, and the first 5 of 20 principal components as previously conducted in this cohort . P value−8 . We repoSince local ancestry inference for a population with three or more origins requires reference populations, we used Iberian from Spain (IBS) and Yoruba from Ibadan, Nigeria (YRI), both from 1000 Genomes Phase 3, as representatives for European and West African ancestry, respectively . The EurIn ADMIXTURE, we ran the 1000G American (AMR) populations with the Native American sequences, 1000G IBS, and 1000G YRI as reference populations, and kept 1000G AMR individuals with > 90% native American ancestry to use as a Native American reference panel for uniformity with 1000G (NAT), including 4 Mexicans from Los Angeles, USA and 23 Peruvians from Lima, Peru . In totaTo prepare our genotypes from local ancestry estimation, we used HAPI-UR to infer haplotypes with a maximum HMM window size of 64 . We calc−5 [We converted the RFMix output to the three continental ancestry allelic estimations . We tested each ancestry allele count for association with each phenotype to observe how local ancestry may be associated with each lipid trait using a univariate linear mixed model including relatedness as random effects and the first 5 principal components, lipid medications, and region as fixed effects . Genome-−5 .http://predictdb.org/. The other set of PrediXcan models we used were made from monocyte gene expression in MESA [http://predictdb.org/, and all models in the analyses were filtered by protein coding genes, R2 > 0.01 and predictive performance P < 0.05. Individuals used to train the AFHI, ALL, and HIS models are also included in the MESA lipid trait replication cohort [We used the software PrediXcan to produce predicted expression from genotype for both HCHS/SoL and MESA. The models we used include 44 tissue models from the GTEx V6 tissues with at least 70 individuals  each, with an average of 5,179 genes predicted in each tissue , 13, 30. in MESA . Each ofn cohort .-notsnp. This was performed in GEMMA rather than base PrediXcan as base PrediXcan does not have an option to include a relationship matrix, which is important in a heavily related and structured cohort like HCHS/SoL [After the 49 predicted expression files were made using PrediXcan, we ran them as a pseudo-genotype in a univariate linear mixed model in GEMMA using the command HCHS/SoL . We agai−8, 0.05/, and in the replication populations, MESA and GLGC, as P < 0.05 within the model. This significance threshold is conservative, since many tissues share eQTLs [Since we did not have the GLGC genotypes, we used the software S-PrediXcan, which is an extension of PrediXcan that takes GWAS summary statistics as input . We ran re eQTLs .R2 value, while a high P3 probability indicates independent signals between an eQTL and a GWAS hit and a high P0, P1, or P2 indicates an unknown association [https://github.com/hakyimlab/summary-gwas-imputation/wiki/Running-Coloc [We performed a colocalization analysis by applying the software COLOC to the lipid GWAS results and eQTL data from GTEx and MESA to determine whether eQTLs within gene prediction models and GWAS hits were shared . We subsociation . Analyseng-Coloc .S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S1 FigPC1 vs. PC2 is plotted for each individual separated by their self-identified region. From previous observations and studies, Hispanic populations have multiple continental ancestries due to a previous history of colonization and slavery: African , Native American , and European . Caribbean populations, such as the Cuban, Dominican, and Puerto Rican groups, tend to be mainly admixed between African and European, while mainland populations such as Mexican, Central American, and South American, tend to be mainly admixed between Native American and European.(TIF)Click here for additional data file.S2 FigPrincipal components 1, 2, and 3, explain 16.934%, 11.060%, and 5.494% of the variance, respectively. All other principal components explain < 5% of the variance each. All analyses used 5 PCs as fixed effects, as previously used in analyses of HCHS/SoL.(TIF)Click here for additional data file.S3 FigGenomic control lambda values (λ) indicate little genome-wide deviation from the significance expectation line in any of the GWAS results.(TIF)Click here for additional data file.S4 FigPrediXcan results for all 44 GTEx tissue models and 5 MESA monocyte populations are combined. Each point is a gene-tissue or gene-population association. Genomic control lambda values (λ) indicate little genome-wide deviation from the significance expectation line in any of the PrediXcan results.(TIF)Click here for additional data file.


Variants within these regions appear, by influencing brain architecture, to predispose both to left-handedness and to certain neuropsychiatric diseases.The brain signature and genetic basis of handedness are unclear. Wiberg  MAP2 and MAPT, as well as WNT3 and MICB, all implicated in the pathogenesis of diseases such as Parkinson’s, Alzheimer’s and schizophrenia. In particular, with rs199512, we identified a common genetic influence on handedness, psychiatric phenotypes, Parkinson’s disease, and the integrity of white matter tracts connecting the same language-related regions identified in the handedness–imaging analysis. This study has identified in the general population genome-wide significant loci for human handedness in, and expression quantitative trait loci of, genes associated with brain development, microtubules and patterning. We suggest that these genetic variants contribute to neurodevelopmental lateralization of brain organization, which in turn influences both the handedness phenotype and the predisposition to develop certain neurological and psychiatric diseases.Ninety per cent of the human population has been right-handed since the Paleolithic, yet the brain signature and genetic basis of handedness remain poorly characterized. Here, we correlated brain imaging phenotypes from ∼9000 UK Biobank participants with handedness, and with loci found significantly associated with handedness after we performed genome-wide association studies (GWAS) in ∼400 000 of these participants. Our imaging–handedness analysis revealed an increase in functional connectivity between left and right language networks in left-handers. GWAS of handedness uncovered four significant loci , three of which are in—or expression quantitative trait loci of—genes encoding proteins involved in brain development and patterning. These included microtubule-related  One of the most remarkable features of human motor control is that ∼90% of the population has had a preference for using their right hand over the left since at least the Paleolithic period , and thiNeuroanatomical studies of human handedness have been equivocal, most likely owing to small- to medium-sized study populations . ConversAnother unresolved issue is whether such a population bias in handedness is under genetic influence. While left-handedness runs in families , which are distinct individual measures that can be used for correlation with other phenotypes, or for genetic analysis  handedness phenotype and IDPs; (ii) genotype and handedness; and (iii) handedness-related genotypes and IDPs. All UK Biobank imaging data were processed following pipelines designed to create a set of IDPs that summarizes the information across all brain structural and functional modalities . The desWe tested the effects of self-reported handedness directly in UK Biobank (Data Field 1707), and results were Bonferroni-corrected for multiple comparisons across all 3144 IDPs. This analysis was performed on the subset of imaged UK Biobank participants that had been preprocessed using the pipelines mentioned above (second release: ∼9000), by directly contrasting 721 left-handers with 6685 right-handers .n = 38 332) versus right-handers (n = 356 567); (ii) non-right handers [left-handers + ambidextrous (n = 44 631)] versus right-handers (n = 356 567); and (iii) left-handers (n = 38 332) versus non-left-handers [right-handers + ambidextrous (n = 362 866)].After performing quality control of UK Biobank genotype , we undertook three genome-wide association analyses across 547 011 genotyped single nucleotide polymorphisms (SNPs) and ∼11 million imputed SNPs, with genetic sex and genotyping platform used as covariates: (i) left-handers .To identify the biological pathways and gene ontologies enriched in this genome-wide association study (GWAS), we performed a SNP-based enrichment analysis and a gene-based analysis. We then analysed the average expression of the mapped genes across 53 tissue types, to gain insight into the relative tissue expressions of these mapped genes in a broad range of tissues. We also performed linkage disequilibrium (LD) score regression on summary-level statistics for the left- versus right-handers GWAS to estimate the SNP heritability, and to estimate the genetic correlation between handedness and various neurological and psychiatric diseases from publicly available summary-level GWAS data. Finally, we looked for correlations with clinical phenotypes collected directly from the entire UK Biobank population  and loci (n = 4).For the genotype–imaging study, we used BGENIE v1.2 to carry out GWA analyses of the significant loci for handedness against each of the IDPs [see URLs in the As all the participants’ diffusion images are non-linearly registered to a common space (These significant voxelwise results in the white matter were then subsequently used as starting points (seed masks) for the virtual reconstruction and identification of the tracts to which they belong. For this, we ran the probabilistic tractography tool from FSL (probtrackx) with default settings  yielded numerous significant results, all but one using resting-state functional MRI measures .The top 10 associations were all measures of functional connectivity between pairs of resting-state networks (‘edges’), the most prevalent being the homologue of the language network in the right hemisphere, encompassing Broca’s area (BA44 and 45), regions around the superior temporal sulcus, as well as premotor and primary motor regions centred around the tongue and mouth. Overall, these functional connectivity results showed, in left-handers, (i) a stronger connectivity between right and left (homologous) language networks A and B; P = 4.1 × 10−9; rs45608532, 22q11.22, P = 1.4 × 10−8; rs13017199, 2q34, P = 3.3 × 10−8. Comparing non-right-handers  versus right-handers uncovered one further locus: rs3094128 at 6p21.33, P = 2.9 × 10−8, and replicated the association at rs199512. Comparing left-handers versus non-left-handers, did not yield any new associated loci, but replicated the three loci from left- versus right-handers (2) of handedness explained by all the SNPs in the left- versus right-handers GWAS to be 0.0121 (standard error 0.0014).In the genotyped UK participants, we discovered three genome-wide significant loci by comparing 38 322 left-handers versus 356 567 right-handers: rs199512, 17q21.31, -handers . We calcMAP2 (microtubule-associated protein 2), rs3094128 is ∼1.2 kb downstream of TUBB (tubulin beta class 1), ∼1 kb upstream of FLOT1, and an eQTL of MICB. rs199512, which lies in an intron of WNT3, is an eQTL of MAPT and MAPT-AS1, and is located within a large LD block within a common inversion polymorphism referred to as the MAPT (microtubule associated protein tau) locus  of u) locus .P ≤ 5 × 10−5) in the same GWAS showed that the top two enrichments were for Parkinson’s disease (P = 2.6 × 10−19) and neurodegenerative disease (P = 2.8 × 10−12); of the top 10 enrichments, eight were for neurological disorder phenotypes , Parkinson’s disease , and at a trend level with anorexia nervosa  and bipolar disorder  (Puncorrected = 0.004)  . Then, b= 0.004) .WNT3, MAPT, MAPT-AS1), this SNP yielded many highly significant associations with measures of white matter structural connectivity (diffusion imaging IDPs)  . In partng IDPs) C.Through our top SNP associated with handedness, rs199512, we have identified a common genetic influence on handedness, Parkinson’s disease and many mental health phenotypes , we found no effect of handedness on motor networks. While there might be confounds to such functional connectivity measures  language networks that differ between left- and right-handers . Our finmeasures A and B.MAP2, TUBB/MICB, WNT3/MAPT). In particular, microtubules —as integral components of the neuronal cytoskeleton—play a key role in neuronal morphogenesis and migration. WNT3 has also been shown to act as an axon guidance molecule and, strikingly, as a gradient for retinotopic mapping along the medial-lateral axis  as well as FSLnets-like functional connectivity measures, such as the Human Connectome Project (awz257_Supplementary_DataClick here for additional data file.


Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score.Comparison of all modeling approaches indicated a need for bias correction in marginal models and similar power for each method, with quadratic inference functions providing a minor decrement in power compared to generalized estimating equations and linear mixed-effects models. Longitudinal measurement provides numerous benefits for evaluating the association between genetic variation and human health. For example, the inclusion of multiple time points allows for the prospective study of time-varying covariates and an improved understanding of disease and trait progression over time. However, the promise of applying longitudinal data to genetic-based problems is tempered by the analytic challenges inherent to the approach. Particularly when incorporating related subjects, statistical methods should account for both the within-family correlation structure and the correlation among repeated measurements. The added complexity of large genetic data sets and influence of family structure makes computational limitations an additional barrier to consider. When global DNA methylation data are added to traditional candidate single-nucleotide polymorphism (SNP) genotypes these analytic and computational concerns are exacerbated.Linear mixed effects (LME) models and generalized estimating equations (GEEs) are two broad and commonly used strategies for analyzing clustered/longitudinal data. In LME models, beyond parameterizing the correlation structure of repeated measurements, the correlations induced by familial relationships can also be explicitly modeled. This is often done using a kinship matrix to parameterize the degree of within-family relatedness. GEEs can also be used for analyzing individual-level outcomes within any given family (or cluster) assumed to be correlated . When thAlthough longitudinal and other multivariate methods have been considered and compared when evaluating SNP genotypes in previous Genetic Analysis Workshops (GAWs) –9, the rg2 = 0.025 to 0.125). The causal SNP effect was conditionally related to methylation at the nearby CpG site, with complete expression when that CpG site was fully unmethylated and complete suppression when fully methylated. The other 5 SNPs tested were noncausal and their corresponding CpG sites shared a simulated random variability with the 5 simulated causative CpG markers. This analysis included 680 individuals from 164 families with complete data at each site tested.We used pharmacoepigenetic data from 200 simulated posttreatment replicates from the GAW20 data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. Simulation solutions were known prior to analysis. Details regarding the GOLDN study from which these data were simulated are provided elsewhere . We examSeparate models tested the interaction of CpG-by-SNP site (interaction test = CpG × SNP). The methylation site analyzed was posttreatment CpG located closest to the candidate SNPs. Both SNP and CpG sites were standardized prior to analysis. All analyses also included the covariates age, sex, and study center. Therefore, the full model included the CpG × SNP interaction, its constituent single-order terms, and the other covariates . We address two different outcome settings: (a) posttreatment TGs, using pretreatment TGs as a model covariate (henceforth referred to as “Pre/Post”) and (b) a post/pretreatment TG score (henceforth referred to as “Change”). These settings mean that the temporal nature of the study was not explicitly included in the design and was evaluated as a more general multivariate rather than longitudinal method. A log transformation was applied to TG values prior to all calculations.make kinship function of the “kinship2” package in R [lmekin function of the “coxme” package in R incorporated this kinship matrix for use in the LME model [First, we used LME modeling to evaluate TG treatment response. The LME model assumed CpG, SNP, and other covariates as fixed effects and a random effect of family parameterized by the kinship correlation matrix obtained using the age in R . The lmeME model . In contith family with ni members is denoted by E(TGi| Xi) = μi linked to covariates though the identify function, xij = [1, xij1, …, xp − 1)ij [Each of the 5 SNP–CpG interactions from the simulation that were simulated causal for TG response were tested using a nominal 5% significance threshold. A Bonferroni correction was also calculated based on the genome-wide methylation data to increase generalizability to the typical experimental setting. This resulted in a significance threshold of 1.08 × 10To evaluate false-positive rates, we examined the 5 simulated noncausal SNP–CpG interactions. A nominal 5% significance threshold was first used. The same Bonferroni correction was then applied for genome-wide correction. The Type I error rate was computed by recording the number of times the noncausal loci were significant across all tested sites and simulations.g2, with > 90% power observed at the chromosome 1 site (hg2 = 0.125) but only 29% power observed at the chromosome 10 site (hg2 = 0.025). Both LME settings adequately controlled for Type I error rates, with the Change setting proving a more conservative test (α = 0.038) than the Pre/Post test (α = 0.048).Table g2 were also observed. Both GEE and QIF at the two outcome settings adequately controlled for Type I error rates after applying the bias-correction methods. Removal of the bias-correction methods improved power, particularly for the QIF models, but also resulted in inflated Type I error rates.A similar pattern of results was observed when testing the GEE and QIF models. Again, in all instances, the Pre/Post setting provided improved power compared to the Change setting. Consistent improvements in power with increases in simulated hFew causal sites were identified as significant after applying the genome-wide correction across all models and settings .Run times were estimated as 0.5, 1.25, and 0.4 s/site for the LME, GEE, and QIF models, respectively. Consequently, epigenome-wide testing  would take approximately 64, 160, and 52 h for the LME, GEE, and QIF models, respectively.g2 was high) and a nominal significance threshold was applied. Covarying baseline performance provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score in all instances. This outcome suggests that settings similar to those typically used for more complex longitudinal designs involving multiple time points will provide comparable or greater power as change score computation. Note, however, that this is also a function of the simulation structure and needs to be evaluated with other models of data generation. Comparisons between baseline adjustments and change scores for commonly employed longitudinal methods have been examined extensively in other settings [The primary purpose of the present analysis was to evaluate 3 methods  for analyzing clustered, family-based data, including genomic and epigenomic measures. Acceptable power was generally observed across the 3 methods and 2 outcome settings when the effect sizes were large . In addition, estimates of heritability are not generated that would have allowed for evaluation of bias. Finally, we did not take full advantage of the correlation structure within subjects and focused instead on a more general multivariate method with repeated measurements. Future research should evaluate longitudinal data with more measurements to determine if these results extend to those settings.These limitations outstanding, the present analysis represents one of the first evaluations of QIF models for analyzing genetic data. Our findings suggest that such methods may provide comparable power and adequate control of Type I error rates. Although computational estimates did not include the time necessary for manipulating and cleaning the large data sets incumbent to genetic analysis, these estimates suggest that each model could be scaled up to the epigenomic level. Future studies can further evaluate these models to identify ways to improve computational efficiency for application to large-scale genetic and epigenetic data.


Due to the extensive marbling of its beef, Hanwoo (Korean native cattle) has continuously gained popularity and has become a mainstay in South Korea’s animal industry. In any beef cattle production system, reproductive performance is one of the main economic aspects taken into consideration. Therefore, genetic parameter estimates are necessary to obtain indices in order to maximize the response to selection, which in turn could lead to higher profitability. To date, knowledge on the genetic parameters for reproductive traits in Hanwoo cattle is still limited. Therefore, this study estimated the variance components, heritability, phenotypic, and genetic correlations of age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL) of Hanwoo cattle. This was done using single-trait and multi-trait animal models. Results revealed the low heritability estimates for AFC, CI, DO, and GL in both single-trait and multi-trait models, which indicated the probable slow response of these traits due to direct selection. Moreover, phenotypic and genetic correlations varied from low to high among the reproductive traits of interest. Nevertheless, heritability estimates and genetic correlations shown in this study will prove to be vital as initial estimates are considered in the genetic improvement program of Hanwoo cattle.Genetic parameters for the reproductive traits of Hanwoo cattle were estimated using data obtained from 15,355 cows in 92 herds across South Korea, which were inseminated from May 1997 to July 2016. An “average information” restricted maximum likelihood (REML) procedure that fit in single-trait and multi-trait animal models was used to estimate the variance components of age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL). Results showed the low estimates of heritability for all reproductive traits from both single-trait and multi-trait models. Estimates of heritability for AFC were 0.08 and 0.10 with single-trait and multi-trait models, respectively, while the estimates of heritability using the same animal models ranged from 0.01 to 0.07, 0.01 to 0.09, and 0.10 to 0.16 for CI, DO, and GL, accordingly. While AFC showed positive genetic correlations of 0.52 and 0.46 with CI and DO, respectively, the estimates of genetic and phenotypic correlations of GL with AFC were close to zero. Moreover, phenotypic correlations of GL with CI and DO were also close to zero; however, the corresponding genetic correlations were 0.13 and –0.38 for CI and DO, respectively. These estimated variance components and genetic correlations for reproductive traits can be utilized for genetic improvement programs of Hanwoo cattle. Hanwoo (Korean native cattle) is a mainstay in the South Korean beef industry. Its beef is very popular for its marbled fat, tenderness, juiciness, and characteristic flavor . CurrentThe prevailing Hanwoo cattle breeding program mainly focuses on the genetic improvement of the carcass and meat quality traits. This is essentially due to its economic importance, in addition to its data availability and ease of analysis procedures. However, reproductive traits are also pivotal, usually with significant impacts on the economic success of production ,4. The lAs genetic parameter estimates are essential in obtaining indices for an optimized response to genetic selection, the present study aimed to estimate the variance components, heritability, and phenotypic and genetic correlations of age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL) of Hanwoo cattle utilizing both single-trait and multi-trait animal models.Data were obtained from reproductive traits of 15,355 Hanwoo cows, calved from May 1997 to July 2016, and obtained from 92 herds in nine South Korean provinces. The approval of the Animal Care and Use Committee was not necessary in this study since all data were acquired from an existing database. Four reproductive traits were analyzed including age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL). Cows with an AFC between 540 and 1000 days, a CI between 300 and 600 days, a DO between 25 and 200 days, and a GL between 260 and 310 days were retained in the final analysis. The accompanying pedigree consisted of 74,445 individuals and was 10 generations deep. The means, standard deviations, and minimum and maximum values for AFC, CI, DO, and GL are shown in The data were first analyzed via the ordinary least squares using the MIXED procedure from the statistical software SAS  in order to determine the significance of fixed effects and their interactions. These fixed effects and covariates tested for significance in each trait where the herd in which the animal was raised, the parity of the cow, the year-month and year-season of birth and calving, the sex of the calf, and the age of the cow (months) were covariates. Based on preliminary analyses, herd, parity, and year-month were included as contemporary groups in the models. 1, and Z2 are incidence matrices related to fixed, additive genetic, and permanent environmental effects, respectively. Vector a was assumed to follow a normal distribution, with N variance components and their corresponding standard errors. Heritability (h2) was defined as the ratio of genetic variation that is due to additive genetic variance to total phenotypic variance . Differences in the estimated variances of additive genetic and residual effects for CI, DO, and GL were observed among parities. Although there were no large differences in the estimate of heritabilities for CI, DO, and GL among the note parities, the additive genetic and residual variances of CI and DO from those of parities 2 and 3 were higher than those of parity 1. Heritability estimates using the single-trait model ranged from 0.01 to 0.07, 0.01 to 0.09, and 0.10 to 0.16 for CI, DO, and GL, respectively. Permanent environmental effects only accounted for a small portion of the total variability in this analysis.Results of the heritability estimates with the multi-trait model for all reproductive traits were not significantly different from those found with the single-trait model, as shown in In succeeding sections, only the results from the multi-trait animal model were compared to the estimates from the related literature, owing to the fact that: (1) the present study did not involve the differentiation of the genetic parameter estimates from different models and (2) the estimated heritability for all the reproductive traits were similar in single-trait and multi-trait models.The heritability estimates for reproductive traits attained in this study were low because of a large, unexplainable portion of residual variance. Even though studies related to the estimates of variance components and heritability for reproductive traits of Hanwoo cattle are scarce, the observed low heritability estimates were comparable with previously reported estimates in other beef cattle breeds. Cavani et al.  and BuzaA number of associated studies have shown heritabilities of a similar magnitude in beef breeds for CI, DO, and GL. Using the Bayesian procedure, Ulhôa et al.  reportedTherefore, the results of this present work indicated that these traits of interest would be expected to respond gradually to direct selection. This was in concordance with Aby et al. , who empBos Taurus cattle, respectively. In contrast, Cavani et al. [The genetic and phenotypic correlations between DO and CI were unsurprisingly high and positive. Hereafter, all correlations involving DO were similar to those involving CI. The moderate and positive genetic correlation of AFC with CI found in this study implies that animals that deliver their first calf earlier tend to have a reduced calving interval. However, these genetic correlations between AFC and CI presented high standard errors, which means that no reliable conclusion can be drawn from the relationships of these traits. Several reports described variations in the genetic correlations between these traits. Both Oyama et al.  and Berri et al.  and Merci et al.  reportedThe genetic correlation of 0.04 between GL and AFC was in agreement with other preceding studies . GestatiThe selection of Hanwoo cattle for reproductive traits such as AFC, CI, GL, and DO may render low magnitudes and long-term responses. Nevertheless, the economic importance of these traits should not be overlooked. Therefore, these reproductive qualities should be further developed and improved so that they can reach their optimal levels through enhanced management techniques. Further, data that records direct measures of these traits for implementation of genetic prediction should be improved. The variance component estimates and genetic correlations for reproductive traits obtained in this study can be utilized for future genetic improvement programs concerning of Hanwoo cattle.


Helianthus annuus L.) under well-watered and water-limited  conditions, with a particular focus on root morphology. Water limitation reduced seedling size and produced a shift toward deeper rooting. These effects varied across genotypes, and we identified 13 genomic regions that were associated with traits of interest across the two environments. These regions varied in size from a single marker to 186.2 Mbp and harbored numerous genes, some of which are known to be involved in the plant growth/development as well as the response to osmotic stress. In many cases, these associations corresponded to growth traits where the common allele outperformed the rare variant, suggesting that selection for increased vigor during the evolution of cultivated sunflower might be responsible for the relatively high frequency of these alleles. We also found evidence of pleiotropy across multiple traits, as well as numerous environmentally independent genetic effects. Overall, our results indicate the existence of genetic variation in root morphology and allocation and further suggest that the majority of alleles associated with these traits have consistent effects across environments.With climate change and an ever-increasing human population threatening food security, developing a better understanding of the genetic basis of crop performance under stressful conditions has become increasingly important. Here, we used genome-wide association studies to genetically dissect variation in seedling growth traits in cultivated sunflower ( Plants are faced with a variety of abiotic challenges throughout their lives. Water limitation is perhaps the most important of these stressors, with drought having an enormous impact on crop yields and agricultural sustainability –4. UnforResponses to water limitation are genetically complex due to the involvement of numerous biochemical, molecular, and physiological mechanisms affecting plant growth and development ,4,23. ReOf particular interest to many researchers has been the relationship between root-related traits and performance under water limitation. It has been observed that plants often increase their root biomass allocation and alter their root morphology under water limitation, presumably to improve water uptake from the soil  under well-watered vs. water-limited conditions.In most cases, root traits have been found to be under complex genetic control, with multiple QTL of small to moderate effect ,46. MoreCultivated sunflower, which is one of the world’s most important oilseed crops, is a deep-rooted plant that has the potential to tap soil moisture reserves that are inaccessible to many other crops ,57. It iH. annuus germplasm collections maintained by the USDA’s North Central Regional Plant Introduction Station (NCRPIS) and the French National Institute for Agricultural Research (INRA), is composed of 288 lines that capture ca. 90% of the allelic diversity present within the cultivated sunflower gene pool (We used the sunflower association mapping (SAM) population developed by  and descene pool . It inclIn the fall of 2015, we germinated individual sunflower seedlings from the SAM population and planted them in three replicates across two treatments  in a growth room in the Miller Plant Sciences Building at the University of Georgia . Due to space constraints, the three biological replicates were grown sequentially, with all entries being planted in a completely randomized fashion within treatments. For each replicate, eight seeds per line were scarified to promote germination, placed on damp filter paper in a Petri dish, and kept in the dark. After 24 hours, seed coats were removed and the seeds were returned to the dark. After 24 additional hours, the Petri dishes were brought out into the light for 72 hours and, five days post-scarification, two viable seedlings (one per treatment) were selected for transplant into individual 50 mL Falcon tubes containing beach sand as a growth substrate. Prior to transplanting, the Falcon tubes were pre-drilled with two ⅛-inch holes 2 cm from the base to allow drainage.To accommodate the 576 seedlings per replicate, the experiment was split into four trays per treatment, with each tray containing 72 randomly assigned lines. These trays were arranged in an alternating fashion by treatment in the growth room to minimize variation across treatments. After planting, all tubes were top-watered daily with a solution of deionized (DI) water and one g/L of Jack’s All Purpose 20-20-20 aqueous mix  to facilitate establishment. Following establishment, seedlings were separated into the two treatments: the well-watered control (conditions maintained as above) and the water-limited (stressed) scenario. Water limitation was implemented as an osmotic stress using polyethylene glycol , which is a high molecular weight polymer that induces an osmotic challenge ,62. The Throughout the experiment, the temperature for both treatments was kept at 20°C with a 16h:8h light:dark cycle. Seedlings were harvested and subjected to detailed phenotypic characterization seven days after initiation of the differential watering regime. This procedure was then repeated two more times for the second and third replicates.A total of 12 morphological traits were measured for each individual. These included stem, leaf, and root traits, as well as metrics of overall growth. At harvest, seedlings were gently uprooted and their roots were rinsed to remove soil substrate. Stem height and diameter (just above the soil line) were measured using Fowler 6"/150mm Ultra-Cal IV Electronic Calipers . The seedlings were then divided into their component parts by removing all leaves and cotyledons as well as intact root tissue. Stem, leaf, and cotyledon tissue was dried at 60°C for 72 hours in a forced air drying oven and then weighed to determine biomass and biomass fractions.Intact root systems were stained with toluidine blue for four minutes to facilitate image analysis. Stained roots were then measured for taproot length using a ruler, and severed 2 cm below the emergence of the first basal root, to establish sections for upper and lower root allocation, (see P < 0.001). While this treatment effect could be a real biological phenomenon, we observed an accumulation of PEG residue on the aboveground tissues, likely due to droplets of the PEG solution having dried onto these tissues following watering. Notably, at a PEG concentration of 82.5 g/L, the evaporation of one mL of solution would leave behind 82.5 mg of residue, so even a few drops of dried PEG solution could produce a measurable increase in mass. The belowground tissues were not subject to such accumulation, as the soil was kept constantly wet throughout the experiment and the root tissues were carefully washed prior to imaging. Thus, to protect against a potential methodological artifact in terms of biomass estimation, we did not analyze aboveground or total biomass values any further. Instead, we used stem height and diameter as proxies for overall plant performance; stem diameter in particular is known to be a good predictor of biomass accumulation in sunflower [Overall seedling growth was investigated using biomass production. Total biomass was calculated as the sum of the dried leaf, stem, and root tissue. Organ biomass fractions were also determined from these data. It should be noted, however, that total and aboveground biomass traits as well as leaf mass fraction (LMF) and stem mass fraction (SMF) were increased under stressed conditions, while root mass fraction (RMF) was similar under both treatments; all of these traits also had significant line-by-treatment interactions . Briefly, following sequence alignment against the HanXRQ sunflower genome assembly , single https://github.com/masalia). We used the Efficient Mixed-Model Association eXpedited (EMMAX) algorithm [Q) and familial relatedness , to test for significant associations. Model effects and likelihood values for individual SNPs were extracted from the EMMAX output, and Manhattan plots were generated using the R package qqman [Q) for all 213 lines was estimated based on the entire filtered SNP set using the bioconductor package SNPRelate [k) matrix was estimated via EMMAX for all 213 lines using all filtered SNPs. Both Q and k were then incorporated into our GWA studies to protect against spurious associations.Association mapping was conducted for all phenotypic traits in both treatments using a collection of custom R scripts . It should, however, be kept in mind that the lack of a detectable effect for a given trait/treatment could be a false negative due to the highly conservative correction for multiple comparisons employed herein and/or the relatively limited sample size. To further explore this possibility, we identified the most significant SNP  within each associated region and queried its P-value for other traits within the same treatment as well as the same trait in the alternate treatment. These P-values were then converted into a percentile rank amongst all SNPs for the alternate trait/treatment of interest to provide an indication of the likelihood that the region of interest had additional phenotypic  effects. While this approach does not protect against the outright failure to detect a real genetic effect, it helps protect against incorrectly concluding that a significantly associated region has no effect on other traits, or in the alternate treatment.Given the non-independence of linked markers within the dataset, a traditional Bonferroni correction to protect against false-positives due to multiple comparisons would result in overly conservative significance thresholds. We therefore used the PLINK v1.9 indep-pairwise linkage disequilibrium (LD) variant pruning function  to estimFinally, the relative effect size of an associated region on a particular trait in a given treatment was estimated as the follows:r2 ≥ 0.80. This threshold has previously been found to be sufficient for defining independent blocks in association studies  along a chromosome to determine reasonable boundaries for each associated region. This allowed us to extend significant regions based on observed patterns of LD, rather than restricting our focus to intervals demarcated by SNPs that exceeded the highly conservative significance thresholds employed herein. Significantly associated regions were thus defined as spanning contiguous blocks of significant SNPs plus any suggestive SNPs that collapsed into these blocks based on the LD threshold . To visualize these blocks, we estimated r2 values between all possible pairs of significant and suggestive SNPs for each associated region using PLINK v1.9 with default settings and displayed the results using ggplot2 [Rather than treating all significant SNPs as independent associations, we delineated the boundaries of significantly associated genomic regions based on observed patterns of LD using LDSelect v1.0 . This ins e.g., –88). We . We r2 ≥ ggplot2  in R (Enhttps://www.heliagene.org/HanXRQ-SUNRISE/). To protect against the exclusion of potentially relevant genes near the boundaries of an associated region, we included an additional gene flanking both the beginning and end of the region. This feature was implemented as a “buffer” flag (b), with a default setting of one. This results in a list of all genes within the associated region plus (2 × b) additional genes. For single marker associations, the list thus included (2 × b) genes if the marker fell between genes, or 1 + (2 × b) genes if it fell within a gene.We used a custom script (Enchilada Suite: GUAC) to compile a list of genes underlying each associated region using the annotation of the HanXRQ sunflower genome assembly v1.2 ; https:/P < 0.0001; P < 0.001) and positively correlated with each other, as well as with taproot length, root biomass, and TRL. In contrast, taproot length, root biomass, and TRL were all negatively correlated with root biomass allocation and TRL allocation. Within the stressed treatment, many of these relationships held, though there were more significant correlations, including SRL being negatively correlated with stem diameter and root biomass. Across treatments, all traits were significantly and positively correlated with themselves under the alternate conditions, and similar trait-trait correlations to those seen within treatments also held across treatments . The first four, which accounted for 7.2%, 6.2%, 4.1%, and 3.1% of the total genotypic variation, respectively, are shown in SNPRelate identified 27 principal components corresponding to population structure  was estimated to be 89,009 or ca. 13.9% of the total. Genome-wide association studies were performed for all traits using the filtered SNP set separately for the control and stressed treatments. See In terms of evidence for pleiotropy, only a single genomic region  spanning ca. 10.3 Mbp had a significant effect on two different traits: root biomass and TRL; both under stressed conditions. Within this region, the same marker (HanXRQChr08:80922968) was the most significantly associated SNP for both traits, suggesting that variation in these two traits might have a common genetic basis. Interestingly, these were also the only associations for size-related traits for which the major allele had a negative effect on the trait value, indicating that plants carrying the more common allele at this locus have a lower root biomass and shorter TRL under stress as compared to plants carrying the rare allele. There were no instances in which the same chromosomal region had significant effects on one or more traits when comparing across treatments.P-values of the most significant SNP from each significantly associated region for evidence of possible effect on other traits (whether or not these traits had significant associations of their own), or in the other treatment. The results of this analysis are presented in P-value of the focal SNP fell in the top 1% of all P-values for one another trait. Similarly, there were eight additional instances in which the P-value of the focal SNP fell in the top 5% of all P-values for another trait, and six additional instances in which the P-value of the focal SNP fell in the top 10% of all P-values for another trait. Overall, for 10 of the 13 associated regions, the focal SNP fell in the top 1% of all P-values for at least one (and as many as three) other traits, suggesting that these regions might have otherwise undetected effects on multiple traits. For all size-related traits, the effects were always in the same direction . For example, on Chr10 and Chr13, the regions associated with stem diameter under stressed conditions  had strongly suggestive effects  on taproot length, root biomass, and TRL in the same treatment. In both cases, the major allele produced an increase in stem diameter as well as in all three of the other traits. The only instances in which the sign of the effect changes were for allocation-related traits, which are expressed as a ratio and thus not directly indicative of size.As noted in the Materials and Methods, the Bonferroni correction employed herein results in a highly conservative significance threshold that could result in false negatives, even after adjusting for non-independence amongst markers. We thus interrogated the P-value of the focal SNP for a given association in one treatment fell in the top 1% of all P-values for that same trait in the other treatment. These associations corresponded to stem diameter, taproot length, and TRL. Similarly, there were three and one additional cases in which the P-value of the focal SNP for a given association fell in the top 5% or 10% of the P-values for that same trait in the other treatment, respectively. In all cases, the original association was identified under stressed conditions, with a suggestive effect under control conditions. Moreover, in all cases, the focal SNP had the same direction of effect across treatments. For all associated regions, exact pleiotropic percentile values and direction of effects can be found in Looking across treatments, there were five instances in which the The 13 associated regions contained 5,302 unique genes based on the HanXRQ sunflower genome annotation v1.2 . Of thesOf the 5,302 genes underlying significant associations, 1,511 corresponded to proteins of unknown function or uncharacterized proteins  that were identified in this study exhibited substantial variation in physical size, ranging from a single marker to 186.2 Mbp in length . This paInterestingly, for all but one of the associations underlying size-related traits, the major allele resulted in increased plant growth, which suggests that selection for increased vigor during the evolution of cultivated sunflower may be responsible for the relatively high frequency of these alleles . The solTaken at face value, our results suggest a paucity of pleiotropic effects, with all but one genomic region influencing just a single trait . MoreoveP-values in the alternate treatment), indicating that the associations we detected included a mix of environmentally dependent and independent genetic effects.In terms of cross-treatment comparisons, our results were similar to the cross-trait comparisons. While we only detected particular associations in one treatment or the other, suggesting the possibility of pervasive genotype-by-treatment interactions, nine of thirteen associated regions had evidence supporting a possible effect in the alternative treatment . While tGiven the large size of some of our associated regions, along with the large number of genes contained within such regions , the ideAux/IAA repressors, thereby promoting auxin-responsive genes [The above caveats notwithstanding, it is worth noting that under control conditions, two cytochrome P450 genes were found to be associated with stem diameter on Chr12 , thUnder stressed conditions, a pyruvate dehydrogenase E1, which was identified out of 133 genes in the region corresponding to Stem.Diameter.13.2, was directly adjacent to the most significant SNP underlying that association. This enzyme is the rate-limiting step for the whole pyruvate dehydrogenase complex, which links carbon output from glycolysis to the Krebs cycle, thereby influencing energy metabolism . We alsoOur results have revealed that sunflower harbors substantial variation for seedling root morphology and growth traits under both well-watered and water-limited conditions. Osmotic stress resulted in decreased aboveground growth accompanied by a shift towards deeper rooting, with significant line-by-treatment interactions across our diversity panel. While this work provides superficial support for pervasive trait- and treatment-specific genetic effects, closer inspection of the data has revealed evidence of pleiotropic effects across multiple traits, as well as numerous examples of environmentally independent genetic effects. Overall, our results suggest the existence of little in the way of tradeoffs between aboveground and belowground growth characteristics, and further suggest that the majority of alleles have consistent effects across treatments. As such, it appears that selection for alleles conferring deeper rooting in a given treatment will likely not come at the cost of aboveground growth, and that selection for high performance in one environment may result in improved performance under other conditions. We also identified candidate genes underlying these associated regions, some of which correspond to genes involved in various aspects of plant growth/development and response to osmotic stress.S1 FigPopulation structure is illustrated using the first four principal components based on the genome-wide collection of SNPs.(PDF)Click here for additional data file.S2 FigRelatedness values were calculated using all SNPs in EMMAX. Warmer  colors indicate higher relatedness. Along the left border are genotype classification information for heterotic group and market type.(TIF)Click here for additional data file.S3 FigColors alternate by chromosome, dots correspond to SNPs, and the horizontal red line indicates the adjusted significance threshold.(PDF)Click here for additional data file.S4 Figr2. Plots separated per chromosome, per trait, per treatment. As described in the methods we used LDSelect to determine the boundaries of the region showing strong LD with significant SNPs. Significant SNPs are marked in yellow, all others are suggestive.Colors along the diagonal indicate similar groups of markers based on (PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


We explore how advances in our understanding of the genetics of complex traits such as educational attainment could constructively be leveraged to advance research on education and learning. We discuss concepts and misconceptions about genetic findings with regard to causes, consequences, and policy. Our main thesis is that educational attainment as a measure that varies between individuals in a population can be subject to exactly the same experimental biological designs as other outcomes, for example, those studied in epidemiology and medical sciences, and the same caveats about interpretation and implication apply. Broadly, the questions that are addressed are as follows:Many other disciplines wish to address questions that stem from genetic findings. Those questions can be summarized as “What can we do with this information?” A biologist may wish to understand the function of a particular genetic mutation, a medical researcher may wish to create therapeutics to cure a disease, and an epidemiologist may wish to identify a modifiable risk factor and influence health policy. In this review/perspective, we address recent findings about biology and education, and how these findings could be useful for researchers and practitioners working in education, science, and learning.2 but it only recently became feasible to start identifying some of the DNA variants responsible for this genetic variation.3 In this review article, we discuss how, if at all, such advances could constructively be leveraged to advance research on education and learning.Behavior–genetic studies of twins and adoptees consistently find genetic factors accounting for at least a modest share (up to 40%) of differences in educational outcomes across individuals in developed, Western countries,4 We review the methodology of these studies and their findings, and we discuss what can and cannot be concluded from heritability estimates of educational phenotypes, such as years of schooling, measures of scholastic achievement, psychological characteristics, learning disabilities, or neurodevelopmental disorders.To set the stage, we begin by reviewing evidence from behavior–genetic studies on the heritabilities of various traits related to learning, memory, and education. A heritability is a statistical parameter describing the proportion of variation in a population that is accounted for by genetic factors.3 An important lesson emerging from studies to date is that single polymorphisms almost always have small effects—and for this reason, their detection requires very large samples.5 As larger and larger discovery samples become available, the number of credibly established associations with a wide range of outcomes will continue to grow. The larger samples will also allow researchers to construct increasingly powerful predictors, called polygenic scores (PGSs), from genetic information.Our ability to reliably detect specific genetic polymorphisms associated with various behavioral traits has increased dramatically in recent years.Finally, we also discuss how these and future findings from this literature may be put to productive use in research on education and learning. We organize our discussion around three broad classes of potential contributions.R2 ≈ 7%, implying a correlation between predictor and outcome of ~0.26), but this number is likely to increase substantially in the years ahead.First, controlling of genetic factors in empirical research will become increasingly possible, thus strengthening the credibility of many research designs . Currently, a PGS for educational outcomes has a modest predictive power  in the genome. At each locus, individuals are endowed with two alleles, one inherited from the mother and one from the father. We can arbitrarily designate one of these to be the reference allele, and define person i’s genotype at locus j, xij,  by the number of reference alleles person i is endowed with .To explain heritability, a simple and highly stylized causal model is useful; our treatment follows Benjamin i’s outcome:j indexes the loci, βj is the causal impact of an additional copy of the reference allele at locus j (this impact need not be the same across time and space), and Ui is an environmental variable. If Yi is normalized so its standard deviation is 1, and Gi and Ui are uncorrelated, the total variance in Yi is the sum of two components: a genetic factor (h2≡var(Gi)) and a non-genetic factor (u2≡var(Ui)). The parameter h2—known as (narrow sense) heritability—is simply the R2 from the population regression of the outcome on the J genotypes.We assume the following causal model for person h2 of a trait without knowing the specific genes responsible for the heritable variation. Prior to the widespread availability of molecular data, most such efforts have relied on comparisons of the resemblance in the observable characteristics (“phenotypes”) of various pairings of relatives who vary in their degree of environmental and genetic resemblance. In these studies, a commonly made assumption is that the distributions of Gi and Ui are the same across all types of siblings. Randomly ordering the two members of a sibling pair and denoting the second member of a pair by a prime, most heritability estimates are based on identifying conditions that have the general form:One can make inferences about the h2 and u2. In general, assumptions about 7 whereas inferences about On the left-hand side of Eq. k = MZT) with the resemblance of dizygotic twins reared together (k = DZT). In studies attempting to estimate heritability from twin data, a commonly made assumption is that Eq. For example, most twin studies compare the resemblance of monozygotic twins reared apart .To illustrate some canonical findings from the literature, Fig. 11 Our measure of socioemotional skills is based on a professional military psychologist’s assessments of the conscript’s ability to function in the military, with higher scores assigned to recruits, which the psychologist perceives as independent, emotionally stable, able to function in a group and willing to take on responsibility.12 Lindqvist and Vestman12 document that the variable  is a much stronger predictor of labor market outcomes than the personality dimensions measured by standard personality scales.Our measure of cognitive skills is derived from the conscript’s score on four cognitive tests  and is highly correlated with what is sometimes referred to as general intelligence.In these analyses, two brothers are classified as “reared apart” if they lived in separate households during every census undertaken before the age of 18, and “reared together” otherwise. We use information about biological parents to classify siblings reared in the same household as full brothers , half-brothers , and adoptees . Two broad patterns are evident from Fig. Until recently, data such as those in Fig. 13Though the data in Fig. What should we infer from data such as those plotted in Fig. u2 is low, the environmental interventions can be effective . For example, the provision of eyeglasses could yield large returns in a population where very few children have eyeglasses and the heritability of eye sight is near 100%.15It does not follow logically from the observation that when 16 Factors that may be important inputs to a person’s learning process—such as motivation, ability to concentrate, or time spent reading books—are all under some genetic influence. But the extent to which genetic risk factors for, say, concentration difficulties actually translate into worse scholastic performance is likely to depend in subtle ways on the design of the education system. To take an even starker example, the education system’s treatment of boys and girls has changed dramatically over the course of the 20th century, along with changes in norms and attitudes.Jumping to policy conclusions from high heritabilities is problematic for a second, distinct reason. Genes may sometimes—perhaps often—influence complex outcomes through channels that are modifiable . The fact that genes influence an outcome does not imply they must do so through some narrowly physiological process.What then, should one conclude from data such as those in Fig. 17 Most schooling systems eventually separate children into different tracks , often using past scholastic performance as a major screening device, and we suspect the proposition that some sort of screening at some point is desirable, which has broad agreement. But for the reasons articulated by Goldberger and Jencks, we do not believe any obvious relationship exists between the data pictured in Fig. Data such as those in Fig. Calls for “genetically sensitive” school are sometimes met by concerns that advances in genomics will be used to justify denying children educational opportunities as opposed to help children better realize their potential. All schooling systems already have at least some features that can be characterized as “genetically sensitive” in the sense that heritable child characteristics (such as school grades) are used to assign children to the environments. Children with poor eyesight are (hopefully!) supplied with glasses; children with low grades are more likely to be required to retake a grade level; children with learning disabilities are sometimes educated by specially trained teachers who rely on different pedagogies and teaching materials; eyesight, test scores, and virtually all learning disabilities have some degree of heritability.Ultimately, what matters is whether these interventions generate benefits that can justify their costs, not whether the bad eyesight or learning disabilities are ultimately caused by genes. In our opinion, it is only to the extent that genetic information makes it possible to tailor more effective interventions that genetic data may be a useful supplement to systems already in place.9 Today, most of these studies are hypothesis-free scans for associations between some outcome and millions of genetic variants (each of which is separately tested for association with the outcome). The large number of hypotheses tested means a variant is considered associated if the p-value for association is below the genome-wide significance threshold, 5 × 10−8.In the last decade, data on molecular-level genetic markers that differ across individuals, called single-nucleotide polymorphisms (SNPs), have become much more widely available as costs of measuring SNPs have plummeted. As a result, research has begun to identify specific SNPs that account for some of the heritable variation in anthropometric traits, common diseases, and, in a handful of cases, behavioral outcomes such as educational attainment or smoking.In the early years of genome-wide association studies (GWASs), the fact that most of the studies published at that time had identified only a small number of SNPs was commonly (mis)interpreted as evidence that GWAS is a flawed approach. Within medical genetics, today it is increasingly understood that an ever-increasing number of SNPs associated with complex outcomes are being identified as the sample sizes have grown. This empirical regularity is illustrated in Table 20 whereas more recent research,21 based on a sample of 253,000 individuals, identifies 697. The first genome-wide association study of educational attainment was conducted in a discovery sample of 101,069 individuals, and identified one significant association with years of schooling.9 The authors conducted the follow-up study in a discovery sample size of 293,723 individuals, and identified 74 loci associated with years of schooling completed. In a combined analysis of the discovery and replication samples , the number of independent loci further increases to 162. Thus, educational attainment appears to follow a pattern that is qualitatively similar to medical and anthropometric traits.Consider the example of height. Early studies of height identified 10–20 SNPs at genome-wide significance,βjs (which are unobserved) with estimates obtained from an independent sample.28 For most complex traits, the combined explanatory power of the genome-wide significant associations uncovered so far is very modest, even if combined into a polygenic score. For example, the 162 loci, found to be associated with years of schooling jointly, explains less than 1% of variance across individuals; the analogous figures for BMI and height are 12.5 and 3%, respectively.Medical geneticists have developed methods to construct PGSs that can be used to exploit the joint effects of many genetic variants. Most commonly, such PGSs are constructed using some version of Eq. 32Following the publication of some of the earliest GWASs, the gap between the explanatory power of the variants identified and estimates from twin studies prompted a spirited debate on the causes of the “missing heritability”.Though the missing heritability seems unlikely to have a single explanation, researchers now broadly agree a substantial fraction of the heritability was not “missing,” but was rather hiding in the form of SNPs whose effects were so small they evaded detection even in discovery samples of hundreds of thousands of individuals. This consensus is based on several convergent lines of evidence, of which the qualitative patterns described in Table p-value threshold than genome-wide significance. The fact that prediction accuracy improves when variants that failed to reach genome-wide significance are added suggests many of these marginal associations represent true associations of genetic variants that will reach genome-wide significance in sufficiently large samples.Most studies to date have found the predictive power of PGSs is maximized if the markers included to generate the scores are selected using a more liberal βj falls as larger and larger samples reduce estimation error. Figure left panel shows the results for body mass index BMI and years of schooling. An important interpretational caveat is that our analyses and projections are for samples of European ancestry. Because the original GWASs were conducted in samples of European ancestry, a PGS derived from the GWAS results would have substantially lower predictive power in non-European populations.33Also consistent with the hypothesis of “hidden” heritability, the predictive power of PGSs has increased as larger and larger discovery samples have become available. Intuitively, larger samples enable constructing PGSs with greater predictive power, because the expected deviation between entclass1pt{minimaN ≈ 100,000) explain around 3% of variation in independent samples. As sample sizes have increased to N ≈ 300,000 (BMI) and N ≈ 400,000 (EduYears), the predictive power of PGSs has increased to about R2 ≈ 7%. For height, the qualitative patterns are similar, but the level of predictive power is higher at all sample sizes .For BMI and EduYears, PGSs with weights derived from the first large-scale GWASs , this formula can be calculated quantitatively. Figure Figure R2 and N from the largest meta-analysis of the trait available to date . These meta-analyses were performed by combining summary statistics from dozens of cohorts that vary across important dimensions, including the birth cohort of the genotyped respondents, the phenotype measures used, country, and exact genetic ancestry.Much of the uncertainty about the ultimately attainable predictive power stems from uncertainty about the SNP heritabilities. The projections in Fig. et al.29 This discrepancy is likely at least in part due to: (i) phenotypic heterogeneity across cohorts included in the meta-analyses, (ii) genetic heterogeneity across cohorts, (iii) gene-by-environment interactions across cohorts, and (iv) undetected errors in the meta-analysis. Most of these sources of downward bias can be mitigated  if the discovery sample is restricted to a single, genetically and environmentally homogenous cohort. Therefore, with the advent of resources such as the UK Biobank, the numbers in Fig. The resulting heritabilities  are approximately one-third lower than the estimates based on the method of Yang PGSs for educational attainment might first prove valuable as control variables in randomized-control trials on the impact of an intervention on some scholastic outcome. Controlling for a score could improve the statistical precision of the study by reducing the amount of residual variance.37 Substantial uncertainty remains about the causal effects of many of these interventions, partly because many of the studies were conducted in small samples. As a result, credibly ruling out that any observed difference between the outcomes of treated and untreated subjects is due to chance fluctuations (as opposed to an effect of the intervention) is often difficult. The concern about small sample size applies especially to some of the most influential studies on the effectiveness of early childhood interventions, such as the HighScope Perry Preschool Study and the Abecedarian Project.Education researchers frequently use randomized controlled trials to study the effectiveness of various interventions.et al.9 consider the decision problem of a hypothetical researcher whose objective is to maximize the statistical power to detect a treatment effect and who must choose between the following two options:NX experimental participants, a proportion p of whom are randomly assigned the treatment. For all study participants, a set of baseline characteristics explaining 10% of outcome variance is available.Conduct a randomized controlled study in a sample of qNX experimental participants, a proportion p of whom are randomly assigned the treatment. For all study participants, a set of baseline characteristics explaining 10% of outcome variance is available. Additionally, a PGS whose incremental predictive power is X% is available.Conduct a randomized controlled study in a sample of Rietveld X, the authors ask for what value of q, the investigator is indifferent between the two options. Clearly, the answer to this question is most interesting for a realistic choice of X. According to Fig. et al.9 show (SOM Table S27) that under the assumption that the PGS and baseline characteristics are uncorrelated, the investigator should be indifferent between the two options at q = 0.87. Thus, the PGS in this example allows a cost-saving equivalent to reducing the sample by 13%. The cost savings are smaller if the baseline characteristics are correlated with the PGS, because in that case, the incremental explanatory power of the PGS will be smaller than 12%.For a range of values of et al.9 note, for very expensive interventions, even a modest reduction in sample size may yield substantial cost savings. By contrast, in studies where the outcome of interest is a variable that can be measured accurately and inexpensively through a single survey question, it is unlikely that PGSs will prove valuable as control variables. In some cases, it may also be desirable to gather data on participants’ DNA after the intervention has already occurred. Such data could be used to obtain a more precise estimate of the treatment effect by controlling for the PGS. Normally, controlling for variables gathered after an intervention risks biasing the estimated treatment effect, but this concern does not apply to a variable derived from time-invariant characteristics (a participant’s DNA sequence).Yet, as Rietveld Moving beyond randomized controls, confounding is always a lingering concern in observational studies. Even researchers who are fundamentally uninterested in how genes influence educational outcomes may benefit from genetic data. For example, imagine a researcher who observes that students who live in districts with better-funded schools have higher test scores than students in regions with poorly funded schools and wonders to what extent the differences are due to the causal impact of schools. Advances in genetic knowledge might eventually lead to the investigation of a set of genetic markers that can plausibly be argued to capture some of the unobserved differences in ability. Clearly, our researcher would have a much stronger case for assigning the difference in test scores to a causal impact of schools if she could show that genetic markers believed to proxy for abilities were balanced across the two school regions.38To many, the primary justification for investing resources in gene-mapping efforts is that gene discoveries may implicate biological systems, thus accelerating efforts to develop effective drugs. Many medical conditions that impair learning—including dyslexia, autism spectrum disorders, epilepsy, or neurodevelopmental disorders—are substantially heritable.41 But compared to height or education, these studies have been conducted in relatively small samples. The explosion in data availability in the coming years is exceedingly likely to result in a steady accumulation of associations that replicate reliably, with a qualitative pattern resembling that shown in Table 42The GWASs of neurodevelopmental disorders conducted to date have identified only a modest number  common variants at genome-wide significance.The discovery of genetic associations with neurodevelopmental disorders may prove valuable for a number of reasons.43 Imperfect understanding of such differences could conceivably sometimes cause doctors and scientists to use a single label for a diverse set of conditions that are observationally difficult to distinguish but genetically heterogeneous. In such cases, distinction between the subtypes of the disease may yield therapeutic benefits.42For example, they could help researchers come up with disease classifications that better reflect the underlying genetic aetiologies as opposed to shared symptoms.44 For example, the variants identified in the largest GWAS of educational attainment show evidence of enriched association with a range of neurocognitive disorders and brain function. Strikingly, genes found in previous work to harbor rare de novo (and hence rare) mutations for neurodevelopmental disorders are also statistically more likely to harbor common variants that influence normal-range variation in educational attainment.45Conversely, in some cases, there may also be cases in which substantial genetic overlap is identified between traits previously believed to have distinct genetic aetiologies.Progress in genetic research will undoubtedly make possible, in principle, improvements in our ability to predict a range of behavioral outcomes from genetic data. For complex traits, being realistic about the amount of predictive power, we can expect with discovery samples comprising millions of individuals is important. For example, even for a highly heritable phenotype such as height, the results in Fig. 46 The study reported that PGSs were positively associated with reading and speaking skills in early childhood, subsequent scholastic achievement, and an index of economic security in adulthood. A mediaton analysis revealed that about half the effect of the PGS on the adult outcomes was mediated by measured cognitive and noncognitive skills. Children with higher PGSs were more likely to be raised in households with higher socioeconomic status and perhaps most intriguingly: the PGS predicted the likelihood that a child raised in a household with lower socioeconomic status would be upwardly mobile.PGSs may similarly prove valuable in research on the lifecycle development of skills, as illustrated by a recent study with extraordinarily rich phenotypic data measured in individuals tracked up to four decades.et al.6, if genetic screening can eventually sufficiently predict dyslexia, parents with children with substantially elevated risk for dyslexia could be given the option of enrolling their children in supplementary reading programs, years before a formal diagnosis of dyslexia.In some cases, knowledge of genetic risk may help parents more effectively to choose environments for their children . To borrow an example from Benjamin In conclusion, this perspective summarizes the current state of knowledge on the genetics of education and reviews how empirical findings should be interpreted.Strong evidence shows genetic factors account for a substantial proportion of variation in educational attainment and its many precursors. A number of individual genes and biological pathways have been identified that underlie this variation. This biology clearly implicates the brain, often pointing to pathways shared with neurological and psychiatric disorders. Empirical results from GWASs can be used to construct genetic predictors that may have uses in experimental education studies, intervention studies, and studies of causality.Though we believe advances in genetic knowledge in the years ahead will likely prove to have increasing utility for researchers across multiple fields, it bears repeating that the heritability of educational attainment per se is not relevant for evaluating the likely consequences of some change to education systems or policy. After all, the heritability of human height is robustly and consistently estimated to be around 80%, and yet huge secular changes in the population’s average height have occurred since industrialization.


An alternative to F1 hybrids are bulks of F2 seed from selfed F1 plants (F1:2). Seed production for F1:2 bulks requires no hybridization system because the number of F1 plants needed for producing enough F1:2 seed for multi-environment testing can be generated by hand-pollination. This study evaluated the suitability of F1:2 bulks for use in training sets for genomic prediction of F1 level general combining ability and hybrid performance, under different degrees of divergence between heterotic groups and modes of gene action, using quantitative genetic theory and simulation of a genomic prediction experiment. The simulation, backed by theory, showed that F1:2 training sets are expected to have a lower prediction accuracy relative to F1 training sets, particularly when heterotic groups have strongly diverged. The accuracy penalty, however, was only modest and mostly because of a lower heritability, rather than because of a difference in F1 and F1:2 genetic values. It is concluded that resorting to F1:2 bulks is, in theory at least, a promising approach to remove the significant complication of a hybridization system from the breeding process.Developing training sets for genomic prediction in hybrid crops requires producing hybrid seed for a large number of entries. In autogamous crop species ( Since the pioneering work of Genomic prediction methodology  are currTriticum aestivum), rapeseed (Brassica napus), cotton (Gossypium hirsutum), sugar beet (Beta vulgaris), rice (Oryza sativa), and sunflower (Helianthus annuus), as well as many horticultural species. Here, hybrid seed production requires elaborate hybridization systems , instead of the F1 hybrids directly. There are examples of commercial use of F2 hybrid seed in cases where F1 hybrid seed production is not economically feasible . This opm or f will be used to indicate the origin of the allele  in both populations , arbitrarily labeled “male” . Because alleles are assumed to be identical in state in both populations, it is not necessary to distinguish them by origin for the purpose of describing possible genotypic values. Let x and columns y of Consider a three by three matrix Similarly, let e.g., The correlation between e.g., The GCA of a male inbred with genotype e.g., The quantities ulations . Therefoz is an arbitrary constant. The “duplicate factor model” involving additive and dominant gene action as well as all forms of epistatic interactions  markers of 209 maize inbred lines from the Dent (123) and Flint (86) heterotic groups of the maize breeding program of the University of Hohenheim in Germany formed the starting point of the simulation. This data set is available publicly as supplemental material to a publication by Biparental families of DH lines derived from elite inbred parents are the predominant population type encountered in early stages of breeding programs  were drawn from a Normal distribution with mean zero and standard deviation of d were drawn from z was drawn from z thus have expectations of 0.25 and 1.00, respectively. Those settings for the distributions of a, d, and z were chosen to ensure that the various gene action effects have the same magnitude in all gene action models. For example, with th of the mean absolute value of the elements of a particular instance of The homozygous effects The genotypic values of all F1 hybrids from the full factorial of al table . AccordiThe DH from 20 male and 20 female families, chosen at random from the 40 male and female families created, were used for building the training set. Each of the 500 DH from the 20 families from one heterotic group was paired at random with one DH from the 20 families of the opposite heterotic group, resulting in a set of 500 hybrid combinations . This deμ. Male and female additive main and additive by additive epistatic interaction effects are u indexing the parent and v the marker), The following mixed model was fitted to the databination , p. 276.variance . This movariance  and its The performance of all F1 hybrids from the full factorial was predicted using BLUP as When predicting the GCA of DH lines, The whole simulation was repeated independently 2,500 times for each heritability and population structure scenario and results averaged. All computations were conducted within the R statistical computing environment .https://doi.org/10.25387/g3.7493996.The SNP genotypes of the Dent and Flint inbred lines used as the initial population and the physical map of the SNP are available from the online supplement of i.e., it decreased with increasing allele divergence and decreasing degree of dominance . F1:2 bulks derived from F1 hybrids heterozygous at one or both loci, however, will exhibit an average performance that is intermediate between z and 0.0. With increasing allele frequency divergence, relatively more F1 hybrids are heterozygous at one or both loci, leading to a greater frequency of cases with differing F1 and F1:2 performance and hence a lower correlation.Both the simulation as well as the theoretical results indicate that the correlation between F1 hybrids and F1:2 bulks can be expected to be high across a wide range of scenarios  and 4. Aic model . Here, mTwo factors will lessen the impact of strongly diverged pairs of loci with duplicate gene action on the overall correlation for complex traits. First, is the genetic variance generated by loci pairs with duplicate gene action considerable lower than for pairs of loci with complementary gene action but similar divergence and magnitude of gene action effects compare  and 4I. i.e., partial dominance), the correlation is always +1 and that very high degrees of dominance are required to reach this “band” for low or moderate divergence of allele frequencies. As can be seen from the example in File S2 (and from File S1) as well, loci that exhibit this behavior will contribute relatively little to the variance of GCA effects in the affected heterotic group. Thus, to have an impact on the overall GCA correlation, the vast majority of loci controlling a complex trait must be within the specific combinations of allele divergence and degree of dominance defining this “band” and the alleles increasing genotypic value must consistently have a lower frequency in the same heterotic group. This seems unlikely for a complex trait controlled by hundreds or thousands of genes. The low contribution to GCA variance in the heterotic group in which Under dominant gene action, the correlation between F1 and F1:2 GCA effects in the heterotic group with lower frequency of the allele that increase genetic value can take values of -1 within a narrow band defined by particular combinations of allele divergence and degree of dominance . An explThe theoretical results show that to the degree that The observation that the genetic variance is lower among F1:2 bulks than among F1 hybrids  is also d is in essence halved because only 50% of the segregants within a F1:2 bulk derived from a heterozygous F1 will have a genetic value of d and the genetic values of the homozygous segregants will cancel in the determination of the mean value of the bulk.Finally, the theoretical results show that the proportion of SCA to total genetic variation in the F1 hybrids decreases with increasing interpopulation divergence for the dominance , the dupThe theory developed here in principle applies also to QTL with large effects on the trait of interest. An exception, however, are major QTL that affect plant stature and morphology. A prime example of such QTL are “dwarfing genes” . Their si.e., F1 and F1:2 training sets phenotyped with same number of locations and replications, the latter is thus expected to have a lower heritability. Prediction accuracy is expected to decrease with decreasing heritability of the training set phenotypes , are strongly influenced by the phenotypic observations available for them . The other advantage is that when the crosses are made at random, they comply with the assumptions of a gene-orthogonal population  and the seeding rate (= number of seeds planted per area unit) of the crop in question. Both will determine how many F1 plants, which have to be generated by hand pollination, are required to produce the amount of F1:2 seed needed for conducting multi-environment field trials. All else equal, the proposed approach will be more promising for crops with high seed multiplication rate and low seeding rate, like rapeseed and sugar beet, than for crops with a low multiplication rate and high seeding rate, which tends to be the case for many small grain cereals. Other factors influencing the economical viability of F1:2 production for research purposes include flower morphology, determining the ease with which hand pollination can be carried out, and ultimately also the availability and cost of labor. Those biological and practical factors, however, are highly crop specific and, as far as labor costs are concerned, also depend on the geography of the breeding program. A detailed economic cost-benefit analysis of the F1:2 approach is therefore not within the scope of this study.In cases where neither the implementation of a hybridization system nor the production of F1:2 seed is feasible on a large scale, inbred per-se evaluation might be considered. The correlation between inbred per-se and testcross performance is, however, typically expected to be low for complex traits like grain yield , for example, often result in only partial sterility of the female parent, meaning that a certain proportion of the harvested seed is in fact the result of a self pollination. The degree with which this happens is genotype dependent (i.e., without the ability of compensating for the reduced heritability of F1:2 bulks and with strong divergence between heterotic groups, should the prediction accuracy observed here for GCA and hybrid performance of tested and untested individuals be high enough to facilitate genetic gain and identification of superior hybrids. The modest accuracy penalty when using F1:2 bulks might therefore be a reasonable price to pay for the prospect of removing the significant complication and resource requirement of a hybridization system from the breeding process.Even in the “worst-case” scenario however,