Although psychiatric phenotypes are hypothesized to organize into a two-factor internalizing–externalizing structure, few studies have evaluated the structure of psychopathology in older adults, nor explored whether genome-wide polygenic scores (PGSs) are associated with psychopathology in a domain-specific manner. We used data from 6003 individuals of European ancestry from the Health and Retirement Study, a large population-based sample of older adults in the United States. Confirmatory factor analyses were applied to validated measures of psychopathology and PGSs were derived from well-powered genome-wide association studies (GWAS). Genomic SEM was implemented to construct latent PGSs for internalizing, externalizing, and general psychopathology. Phenotypically, the data were best characterized by a single general factor of psychopathology, a factor structure that was replicated across genders and age groups. Although externalizing PGSs  were not associated with any phenotypes, PGSs for major depressive disorder, neuroticism, and anxiety disorders were associated with both internalizing and externalizing phenotypes. Moreover, the variance explained in the general factor of psychopathology increased by twofold (from 1% to 2%) using the latent internalizing or latent one-factor PGSs, derived using weights from Genomic Structural Equation Modeling (SEM), compared with any of the individual PGSs. Collectively, results suggest that genetic risk factors for and phenotypic markers of psychiatric disorders are transdiagnostic in older adults of European ancestry. Alternative explanations are discussed, including methodological limitations of GWAS and phenotypic measurement of psychiatric outcome in large-scale population-based studies. In the United States, common psychiatric disorders such as major depressive disorder (MDD) are among the top 10 leading causes of disability and injury4. Among older adults, psychiatric disorders have pronounced effects on physical health and mortality5. Moreover, the 12-month prevalence of having any psychiatric disorder in older adulthood is substantial, with recent estimates of 11.5%6. As the number of Americans older than 65 years is projected to double in the coming decades7, more research is needed to understand the presentation and etiology of psychiatric illness in older adults.Psychiatric disorders impact health, wealth, and wellbeing across the life course9. A robust literature suggests that this comorbidity may be explained by an overarching phenotypic meta-structure that includes separate but correlated internalizing  and externalizing  factors10. These comorbidity patterns align with phenotypic differences between internalizing disorders, characterized by elevations in negative affect11, and externalizing disorders, characterized by behavioral disinhibition12. Alternatively, a single factor (or a bifactor) model that explains shared variance across all psychiatric disorders has also been supported13, and may emerge in developmental stages where symptoms are less prevalent 14. Yet examinations of the meta-structure of psychiatric comorbidity have focused primarily on child and younger adult samples. The lack of attention to older adults is a striking omission given the still substantial and impairing rates of psychiatric disorders in this population6. Moreover, given clear gender  and age  differences in the prevalence of psychiatric disorders16, more research is needed to determine how the meta-structure of psychopathology varies across these demographic groups in older adults.Psychiatric disorders show marked comorbidity across developmental stages17, and data from genome-wide association studies (GWAS) has been leveraged to identify single-nucleotide polymorphisms (SNPs) that are unique to internalizing or externalizing disorders18. Yet, there is also evidence of shared genetic risk across internalizing and externalizing domains21, including data from a psychiatric cross-disorder GWAS meta-analysis showing that genetic risk variants are enriched for biological processes core to many psychiatric conditions21.Genetic risk for psychiatric disorders may also align in a two-factor meta-structure. Twin and family designs suggest that additive genetic risk accounts for the two-factor internalizing–externalizing meta-structure23, polygenic score (PGS) estimation is one tool that can be used to capture psychiatric polygenicity. A PGS is constructed as sum score of risk alleles that an individual has, weighted by the risk allele effect size from a GWAS in an independent sample24. Although PGSs are constructed for a specific phenotype , PGS analyses have revealed widespread cross-phenotype correlations25. For example, a phenome-wide analysis in young adults indicated that a PGS of depressive symptoms was associated with several phobias and generalized anxiety disorder but not externalizing phenotypes, whereas a PGS for smoking initiation was associated with antisocial behavior but not internalizing phenotypes26. For researchers studying the etiology of psychiatric illnesses, such widespread associations present a methodological challenge: which PGS best captures genetic risk for a single psychiatric disorder? Given this issue of overlapping genetic risk, quantitative approaches to combining PGSs are needed. Recent methodological innovations have enabled users to construct better-performing PGSs by taking advantage of cross-trait correlations29. However, such cross-trait “latent” PGSs have only been applied to cohorts aggregated across age groups21. In large, population-based samples of older adults, the relative performance of individual PGSs and latent PGSs for specific psychiatric outcomes and general psychopathology is yet unknown.As psychiatric disorders are highly polygenic We assessed the meta-structure of psychopathology in a large population-based sample of 6003 older adults from the Health and Retirement Study (HRS). We examined whether two-factor phenotypic models fit the data better than one-factor models and further probed the invariance of these models across gender and age. Second, we examined whether there was polygenic specificity in the associations between PGSs for psychiatric  outcomes and behaviors indexing psychopathology . Next, we implemented Genomic SEM to derive latent PGSs based on the genetic architecture of GWAS summary statistics and present the first analyses of how these latent cross-trait PGSs perform in a large population-based sample of older adults. Based on research in younger samples, we hypothesized that a two-factor model would fit the phenotypic data better than a one-factor model and that PGS-phenotype associations would be hierarchically organized, such that PGSs for internalizing disorders would be more strongly associated with internalizing outcomes, and PGSs for externalizing disorders would be more strongly associated with externalizing outcomes.30. Launched in 1992, the HRS introduces a new cohort of participants every 6 years and interviews ~20,000 participants every 2 years. Eligible participants for the current study  were of genetically European ancestry  and participated in the Leave-Behind Psychosocial Questionnaire31 in 2010 or 2012. Participants younger than 51 years were excluded because they were not part of the original sampling frame, as were participants who completed the Leave-Behind Psychosocial Questionnaire in institutional settings, and participants who were born before 1930 . Within the analytic sample, 52% earned a high school diploma and 27.4% earned a four-year college degree or higher. Beyond the exclusion criteria listed above, compared to the total HRS, the analytic sample had a higher proportion of women (χ²(1) = 11.085, p < 0.001), but did not differ on years of schooling . Informed consent was obtained for all participants, and study procedures were approved by the Institutional Review Board at the University of Michigan. All HRS phenotypic data and PGSs are publicly available at https://hrs.isr.umich.edu/data-products. Quality-controlled genetic data are available on dbGaP (https://www.ncbi.nlm.nih.gov/gap/).Data were drawn from the HRS, a nationally representative longitudinal panel study of over 43,000 adults over age 50 and their spouses31. A complete wave of data was constructed using the 2010 and 2012 data collections. Depressive symptomatology and drinking frequency were taken from RAND HRS 2010 and 2012 Fat Files33. All phenotypic data are publicly available through the HRS website (http://hrsonline.isr.umich.edu/).Although the HRS was not explicitly designed to study psychiatric outcomes, several available measures capture dimensional symptoms of psychopathology and related traits. Measures were drawn from the Leave-Behind Psychosocial Questionnaire, a self-reported questionnaire administered to a random 50% of the core HRS participants at each biennial wave during face-to-face interviews34, anxiety symptoms35, and depressive symptoms36; externalizing psychopathology was captured by impulsivity37, trait and state anger38, and the number of drinks per day  from the genetic data collection years of 2006, 2008, and 2010. Genetic ancestry was determined in a two-stage PCA process wherein the final European American sample included all self-reported non-Hispanic whites that had PC loadings within ±1 SD of the mean for eigenvectors 1 and 2 in the PC analysis of all unrelated study subjects. PCA was then used again within the European American sample to estimate the top 10 “ancestry-specific” PCs .A random subset of the ~26,000 total participants was selected to participate in enhanced face-to-face interviews and saliva specimen collection (for DNA) in 2006, 2008, 2010, and 2012. Genotyping was conducted by the Center for Inherited Disease Research (CIDR) in 2011, 2012, and 2015. Genotype data on over 15,000 HRS participants was obtained using the llumina HumanOmni2.5 BeadChips , which measures ~2.4 million SNPs. Individuals with missing call rates >2%, SNPs with call rates <98%, HWE 41, any anxiety disorder42, MDD43) and externalizing  psychopathology were constructed using well-powered, European ancestry GWAS summary statistics . Although GWAS summary statistics are available for other psychiatric disorders , we did not construct these PGSs because these phenotypes were not measured in the HRS. A PGS for height was included as a negative control49. To construct PGSs, SNPs in the HRS genetic data were matched to SNPs with reported results in each GWAS  at each SNP, multiplied by the effect size for that SNP estimated from the GWAS meta-analysis. All SNPs were coded to be associated with increasing disease risk. To simplify interpretation, the PGSs were normalized within the European ancestry sample. All analyses in which PGSs were combined with phenotypes included the top 10 ancestry-specific genetic principal components as covariates.PGSs of internalizing . SNPs can be integrated into the modeling framework to estimate new SNP effects on cross-trait genetic liability, thus allowing for the generation of new PGSs for latent traits. Using the same GWAS summary statistics used to construct PGSs in the HRS, we estimated and compared one-factor and two-factor models of genetic risk for psychopathology. Following Genomic SEM, we constructed models of latent PGSs within the HRS, using the same methods described above. It is important to note that traditional confirmatory factor analyses could not be used to evaluate the structure of PGSs because many of the original GWAS included the same participants. Although LD-score regression20 can be used to determine cryptic relatedness by evaluating the cross-trait LD-score regression intercepts, our analyses revealed substantial sample overlap  to construct latent PGSshttps://osf.io/c9uj8/. All analyses and visualizations were conducted in R Statistical Software50. To increase generalizability and avoid overfitting the data, the analytic sample (N = 6003) was divided into two random samples of n = 3002 and n = 3001. One data set  was used to estimate phenotypic one-factor and two-factor models using confirmatory factor analyses; the second data set  was used to replicate the best-fitting factor structure. Confirmatory factor analysis is a theory-driven form of structural equation modeling that can be used to capture the shared variance among observed correlated variables to estimate unobserved latent factors51. The model fitting procedure compares the model implied covariance matrix to the observed covariance matrix, allowing users to compare model fit using several indices. We considered model fit acceptable if the root mean square error of approximation (RMSEA) < 0.06, and the Comparative Fit Index (CFI) and Tucker Lewis Index (TLI) >0.9052. One-factor and two-factor models were compared using ∆CFI and ∆RMSEA as alternatives to chi-square difference testing, which is sensitive to large sample sizes53; a ∆CFI > −0.01 and ∆RMSEA > 0.015 indicates significant depreciation of model fit55. All models were estimated using maximum likelihood estimation with robust standard errors in the lavaan package56. Maximum likelihood estimation can be used to account for missing data  and outperforms other approaches to missing data such as listwise deletion and multiple imputation57.Analytic code for the current paper is available at 58 was used to estimate measurement invariance across gender  and age. To examine invariance across age, we split the sample into three groups: middle age (51–64 years), young–old (65–74), and old–old (75–83). Previous research has documented developmental differences by these age groupings, including environmental effects on depressive symptoms59, self-rated health60, and familial social support61. Increasingly stringent models of invariance across groups are tested: (a) configural invariance—same underlying structure with all parameters freely estimated across groups, (b) metric invariance—invariant loadings across groups, (c) scalar invariance—invariant factor loadings and intercepts across groups, and (d) residual invariance—invariant factor loadings, intercepts, and unique factor variances across groups62.The semTools packagen = 3001). In large sample sizes, most estimates will be significant at the 95% confidence level. Therefore, we used G*Power63 to estimate expected effect sizes; assuming 80% statistical power, an alpha error probability of 0.05, and a sample size of N = 3003, we are statistically powered to interpret models with an adjusted R2 ≥ 0.008.Linear regression was used to examine the effects of the individual PGSs and the latent PGSs (estimated using GWAS summary statistics within Genomic SEM) on latent phenotypic factors, controlling for the top 10 ancestry principal components. These analyses were conducted within the hold-out sample only (r < 0.64) than externalizing phenotypes . For example, depressive symptoms were positively associated with all the externalizing phenotypes except drinking frequency , drinking frequency loaded negatively on the latent factor(s) and was dropped from subsequent analyses (results available upon request). Figure n = 3002), which both fit the data well. Although the relative fit indices suggested that the two-factor model fit the data better than the one-factor model , the ΔCFI and ΔRMSEA were smaller than suggested values55, indicative of equivalent model fit. The association between the internalizing and externalizing latent factors in the two-factor model was very large (r = 0.82), whereas previous work in younger samples reports that the cross-domain correlation hovers ~0.5010. These results suggest that the internalizing and externalizing factors do not represent unique constructs in this sample of older adults in the HRS. Thus, we accepted the one-factor phenotypic model in the test sample. Figure n = 3001). The largest loadings for the general factor of psychopathology were negative affect  and trait anger . The general factor explained far more variance in the internalizing indicators (0.40 < R2 < 0.78) than the externalizing indicators (0.08 < R2 < 0.15).Next, we evaluated one-factor and two-factor phenotypic models. In the test sample . The PGS for height was not associated with any phenotypic measures.To address a critical issue in the field, we evaluated polygenic specificity by examining the associations between each PGS and each phenotypic measure, controlling for the first 10 ancestry-specific principal components. Across all phenotypic outcomes, the predictive power of the externalizing PGSs was low in the HRS sample Fig. . The onl41, any anxiety disorder42, MDD43, alcohol dependence44, ADHD45, cannabis use46, and antisocial behavior47. Estimated SNP effects were then used to generate PGSs for latent traits in the HRS sample of older adults. Although both the one-factor and two-factor models fit the data well . Moreover, the cross-trait correlation was r = 0.64, indicating that the internalizing and externalizing latent genetic factors, though correlated, capture different underlying constructs. Owing to small negative residual variance in the two-factor model, the loading for MDD was fixed to 1. The largest loadings on the latent externalizing factor were alcohol dependence (β = 0.81) and antisocial behavior (β = 0.79). The largest loading on the latent internalizing factor, aside from MDD, was anxiety (β = 0.88). As the model fit for the one-factor model was excellent , we constructed both the latent one-factor PGS and latent internalizing and externalizing PGSs.Genomic SEM was used to fit one-factor and two-factor models of genetic risk for psychopathology, using GWAS summary statistics from well-powered studies of neuroticismR2 = 2% versus R2 = 1%; Fig. R2 was <1% and there were no associations between the latent externalizing PGS and any of the externalizing outcomes.Associations between the latent PGSs and phenotypic outcomes indicated that the latent internalizing PGS and latent one-factor PGS explained 1% more variance in the general factor of psychopathology than any of the individual PGSs that were used to construct these latent measures of polygenic risk  is also likely to improve to GWAS and resultant PGSs75.One major contribution of our results is the lack of specificity in PGS prediction of psychiatric phenotypes. It is surprising that a PGS designed to capture genome-wide genetic risk for a single disorder  was no better at predicting within a domain  than cross-domain  phenotypes. One explanation for these results is that psychiatric GWAS rarely account for comorbidity . By ignoring psychiatric comorbidity, GWAS may be identifying genetic risk factors for multiple phenotypes or clinical severity instead of a single phenotype. Examples of psychiatric genetic studies that account for comorbidity include a study of bipolar disorder and schizophrenia29 and suggest that any researcher interested in capturing genome-wide genetic risk for psychopathology should implement methods to aggregate GWAS summary statistics of similar phenotypes rather than rely on PGSs of individual disorders.Using Genomic SEM, polygenic risk organized into a two-factor internalizing–externalizing structure, although the one-factor model also fit the data well. Importantly, these latent PGSs that aggregated genetic effects across multiple GWAS explained 1% more variation in the general factor of psychopathology. As MDD was the largest loading in both the one-factor and two-factor Genomic SEM models Fig. , it may 43, all processes common to complex diseases. Moreover, biometric analyses in behavioral genetic/family designs demonstrate that a general genetic factor influences multiple psychiatric disorders (and their overlap) and explains more of the variation in psychiatric outcomes than the unique internalizing and externalizing genetic effects76. More research is needed to understand whether psychiatric polygenic risk is pleiotropic and if so, what kind of pleiotropic processes are at play. For example, biological pleiotropy would suggest that a genetic risk variant for neuroticism (or another intermediate transdiagnostic phenotype) predicts multiple disorders77. By contrast, mediated pleiotropy would suggest that a genetic risk variant predicts one phenotype , which subsequently predicts the onset of other phenotypes . Longitudinal phenotypic data and causal inferences techniques78 are needed to evaluate these hypotheses.In addition to practical implications, our results demonstrate that genetic risk for psychiatric phenotypes is transdiagnostic. Psychiatric GWAS repeatedly show that associated SNPs tend to cluster in genes underlying neurodevelopmental processes, signal transduction, and synaptic plasticity79. An “omnigenic model of complex traits” suggests that SNPs that contribute to the bulk of heritability in complex disorders are spread across the genome as common variants of small effect that contribute to cellular processes  relevant to many complex disorders. Disease-specific genetic risk variants, by contrast, are likely to be rare variants of large effect that are often not captured in GWAS of common genetic variation23. Moreover, GWAS do not capture copy number variants, which are also linked to psychiatric disorders and may function in a disease-specific manner80. Thus, it may also be that PGSs derived from GWAS of common genetic variants are not appropriate for examinations of disorder-specific etiology.A second explanation for low polygenic specificity in the current study is that PGSs are derived from GWAS of common genetic variation—most often SNPs with minor allele frequencies >1%Collectively, these results challenge the notion of specificity in the phenotypic and genetic presentation of psychopathology in older adults. The still impairing rates of internalizing and externalizing disorders during the second half of the lifespan necessitate discussion regarding the clinical utility of current diagnostic categories, particularly as we investigate psychiatric etiology using biological approaches such as genetics and neuroscience.81, we only constructed PGSs based on large GWAS meta-analyses with independent replication samples. As a result, we did not include PGSs derived from smaller GWAS of relevant phenotypes, including several studies of externalizing disorders83. Relatedly, the phenotypic measures available in the HRS are abbreviated scales, as is common in large surveys. Thus, one alternative phenotypic model that we were unable to fit is a bifactor model of psychiatric outcomes , which posits that there are internalizing and externalizing factors as well as a higher-order bifactor that captures shared variance between the lower-order factors39; more recent empirical work further suggests that there may be several higher-order bifactors that capture severity in symptoms84. Indeed, we observed a high correlation between the internalizing and externalizing factors in the HRS sample, which is thought to indicate the presence of a higher-order bifactor85. Moreover, the gold standard for measuring psychiatric symptoms and disorders is through structured clinical interviews—e.g., the Structured Clinical Interview for DSM Disorders86—or via questionnaires administered to multiple informants87. For example, several previous investigations of the structure of psychopathology in younger samples16 have relied on structured clinical interviews to measure symptoms of MDD, generalized anxiety disorder, multiple types of phobias, and panic disorder for internalizing psychopathology, and symptoms of alcohol use disorder, drug use disorder, conduct disorder, and antisocial personality disorders for externalizing psychopathology. Yet conducting structured clinical interviews is not feasible in large population-based data sets with multiple project aims. Thus, we relied on the available self-reported measures and included some constructs that capture dimensional psychopathology rather than psychiatric symptoms per se . These limitations are especially pronounced in the HRS measures of externalizing psychopathology, likely because behaviors like aggression and rule-breaking are less among older adults. Antisocial behavior in childhood further places individuals at risk of early mortality or long-term incarceration88, suggesting that individuals with the highest levels of externalizing behavior may not be represented in the HRS. Nevertheless, externalizing disorders such as ADHD and substance use disorder are still common: between 3% and 4% of adults aged 55–85 meet the criteria for ADHD89 and 3.8% of adults over aged 55 meet the criteria for substance use disorder6. The non-significant associations between polygenic risk and externalizing behaviors in the HRS may be owing to limited measures  that do not adequately capture the complexity of externalizing behaviors in this age group. Although the HRS is a large population-based study, future studies are needed to determine whether the factor structure and genetic associations reported in the current study are generalizable to the broader population of older adults or reflect artifacts of the limited phenotypic measures available in the HRS.Although the current study is the first to evaluate the meta-structure of phenotypic and genetic risk for psychopathology in older adults using a large, population-based sample, several limitations are worth noting. First, the estimation of latent factors in confirmatory factor analysis is dependent upon the quality of the indicators. Based on previous recommendations47. GWAS of psychiatric outcomes in pediatric cohorts90 are beginning to show that genetic risk alleles may vary by developmental stage. As the availability of genomic data increases, future research should consider age-stratified GWAS.Second, the GWAS summary statistics that we used to construct PGSs did not exclusively focus on older adults. Although maximizing statistical power through increasing sample size is a key consideration in GWAS, PGSs constructed from GWAS in younger samples may not generalize to older adults. This is particularly relevant considering the negative association we observed between impulsivity in the current sample and the PGS of antisocial behavior, constructed from a GWAS of adolescents and young-to-middle age adults91—a trend that reduces generalizability of all genetic work and is likely to exacerbate health disparities92. We did not include participants of African ancestry in the current study because the available GWAS were conducted in European samples and, thus, would not be comparable for methodological rather than substantive reasons.Third, our analyses only focused on a subset of the population: older US adults of European ancestry. Though the focus on older adults is a critical addition to research on the meta-structure of psychiatric disorders in adulthood, psychiatric genetics, and human genetics studies overall, are overwhelmingly EurocentricUsing multiple genome-wide PGSs for psychiatric outcomes, validated phenotypic measures, and novel analytic techniques in a relatively large, population-based sample of older adults, we showed that a single general factor of psychopathology best explained the phenotypic meta-structure of psychopathology in older adults in the HRS. Moreover, although PGSs were non-specific in their associations with internalizing and externalizing outcomes, latent PGSs that aggregated genetic effects across several disorders explained more transdiagnostic variation than any individual PGS alone. These results inform a changing conceptualization of psychiatric diagnoses and their genetic etiology—from disorder-specific to transdiagnostic and dimensional.Supplemental Material


The genetic uniformity of cultivated cotton as a consequence of domestication and modern breeding makes it extremely vulnerable to abiotic challenges brought about by major climate shifts. To sustain productivity amidst worsening agro-environments, future breeding objectives need to seriously consider introducing new genetic variation from diverse resources into the current germplasm base of cotton. Landraces are genetically heterogeneous, population complexes that have been primarily selected for their adaptability to specific localized or regional environments. This makes them an invaluable genetic resource of novel allelic diversity that can be exploited to enhance the resilience of crops to marginal environments. The utilization of cotton landraces in breeding programs are constrained by the phenology of the plant and the lack of phenotypic information that can facilitate efficient selection of potential donor parents for breeding. In this review, the genetic value of cotton landraces and the major challenges in their utilization in breeding are discussed. Two strategies namely Focused Identification of Germplasm Strategy and Environmental Association Analysis that have been developed to effectively screen large germplasm collections for accessions with adaptive traits using geo-reference-based, mathematical modelling are highlighted. The potential applications of both approaches in mining available cotton landrace collections are also presented. The sessile nature of plants predisposes them to a constant array of environmental challenges such as drought, flooding, temperature extremes, and soil salinity and toxicity. More often than not, plants are exposed not just to one but a combination of these stresses at any given time ,2. In thIn 2007, the Food and Agriculture Organization of the United Nations reported that as much as 96.5% of the rural land area worldwide are already under intense pressure from temperature extremes and poor soils . Over tiLike most field crops, the growth, development and productivity of cotton are largely determined by the prevailing environmental conditions within a cropping season. Its overall response to abiotic challenges, however, is more complicated owing to its indeterminate growth habit and complex fruiting patterns . In receThe increasing vulnerability of cotton to emerging challenges in agriculture due to climate change is alarming, although not entirely unexpected. Like other major crops, cotton has a narrow germplasm base as a consequence of intensive selection for only a few key traits such as fiber yield and quality during domestication. This is further exacerbated by generations of industry-scale cultivation of only a few varieties with closely shared pedigrees . To ensuLandraces or traditional local varieties are a rich source of genotypic and phenotypic diversity that can be exploited to enhance the adaptability of crops to limiting environments. They are usually composed of population complexes that were not formally bred for optimum yields but were unconsciously selected for their productivity across varying seasonal environments within specific localities under traditional farming systems ,15. BecaSub1 A) and soil salinity  have been identified from the landraces FR13 and Pokkali, respectively. Breeding lines derived from both landraces have been successfully used as donors in marker-assisted introgression to improve the adaptability of elite rice cultivars to flash floods and salt stress [pi21 allele from the landrace Sensho have been transferred by marker-assisted backcrossing to various rice cultivars. Backcross inbred lines with pi21 exhibited resistance against a broader spectrum of blast races [Orobanche crenata) under field and controlled conditions. Under severe broomrape infestation, the breeding lines out-yielded Messire, although not when the weed pressure was low [The successful use of landraces in breeding for target trait improvement has been extensively documented in a multitude of species including cereal and leguminous crops ,20,21,22t stress ,18. Addist races . In peasIn cotton, the utilization of landraces in breeding efforts have yet to be exploited. This is mainly due to challenges that relate to the breeding phenology of the plant, as well as the lack of available information for the efficient identification of suitable donor parents for breeding. In this review, we discuss the potential value of landraces for target trait improvement of cultivated cotton, as well as the major challenges and current status of their utilization in breeding programs. Gene bank mining tools, i.e., Focused Identification of Germplasm Strategy and Environmental Association Analysis that have been developed to effectively screen and evaluate the genetic merit of large germplasm collections in the absence of phenotypic data are described. Both strategies capitalize on geo-reference data that are available for conserved accessions to mathematically model associations between the environment and traits related to local adaptation. Finally, we highlight the potential applications of both tools for screening cotton landraces in search of donor parents with potential values for biotic and abiotic stress tolerance breeding.Cultivated cotton is an important commodity in the global economy. It is primarily cultivated as a major source of natural fiber for textile production, as well as of oil and protein byproducts that are used as food and feeds . To dateGossypium are entrusted to the US National Cotton Germplasm Collection (NCGC) [In the last century, concerted efforts among cotton-growing countries to collect and preserve cotton germplasm as a way of safeguarding the resiliency and productivity of the crop against possible, adverse consequences of genetic uniformity have considerably grown . Repositn (NCGC) ,28. Initn (NCGC) . G. hirsutum and G. barbadense, as well as a diverse panel of 395 Gossypium accessions from the NCGC indicated a greater genetic diversity in the landraces compared to the cultivated accessions of cotton [G. hirsutum but also demonstrated their potential to germinate even under critically low temperature [Independent assessments of genetic diversity contained in the subsets of cotton collections across the world have highlighted the immense intra- and interspecies variation available in the landrace accessions. In particular, molecular marker profiling of a reference set composed of 1933 cultivated and wild-type accessions of f cotton ,30. Simif cotton . Aside ff cotton ,33,34,35perature . A separDespite the dynamic properties and immense genetic potential of cotton landraces, their utilization in actual breeding programs remains limited. One of the most important factors in cotton adaptation is its photoperiod response. In the NCGC, most cotton landraces have innate photoperiod sensitivity and require short days to flower. When cultivated in the summer under temperate environments, the longer days prevent them from flowering. Without transitioning into a reproductive phase, the landraces cannot be crossed with any cultivated cotton genotype . To overAside from the limited capacity to directly use landraces in crossing experiments with cultivated cotton, the lack of sufficient information on the potential agronomic value of the conserved accessions also contributes to its low utilization in breeding. Selection of candidate germplasm that can be used as donor parents in breeding programs is conventionally based on phenotypic evaluation of individual accessions for target traits. Given that large-scale screening of whole collections for important agronomic characteristics is both expensive and time-consuming, a significant part of germplasm collections remains un-phenotyped. Consequently, their selection and utilization in actual breeding programs becomes severely limited. With cotton landraces, phenotypic evaluation particularly for traits that are expressed at the reproductive stage is doubly challenging due to the photoperiodicity of the plants. To aid in the mining of gene banks for candidate accessions that can be used as donor parents in cotton breeding, strategies have been developed that capitalize on the ecological information associated with curated accessions instead of phenotypic data alone. The succeeding sections will focus on two of these strategies viz. the Focused Identification of Germplasm Strategy and Environmental Association Analysis.The Focused Identification of Germplasm Strategy or FIGS is a gene bank mining tool that was jointly developed by the International Center for Agricultural Research in the Dry Areas (ICARDA), the Vavilov Institute, the Nordic Genetic Resources Center and the Australian Winter Cereals Collection. The primary objective in developing this strategy is to provide breeders with a tool to sort through large gene bank collections for accessions having specific traits of interests in the absence of actual phenotypic or agronomic evaluation data . The appAlternatively, FIGS can also benefit from limited phenotypic data that is available even for a small subset of germplasm collection . In a FIEurygaster integriceps) on the ICARDA wheat collection of 8376 geo-referenced accessions identified a total of 534 lines with likely resistance to the pest. Candidate accessions were selected from sites with a recorded history of Sunn pest occurrence and with the agro-climatic profile that supports high population density of the insect. Initial field screening of the 534 lines reduced the selection to 57 accessions of which eight bread wheat and one durum wheat were determined to possess resistance to the pest at the vegetative stage. These lines are currently being used as donors for resistance to Sunn pest, a major pest of wheat causing 100% yield losses in East Europe and West and Central Asia [Blumeria graminis (DC) Speer f.sp. tritici) [Puccinia graminis Pers.) [Diuraphis noxia Kurdj.) [Pyrenophora teres Drechs.) infecting barley [Following the development, testing and refinement of the approach, FIGS has been successfully used to identify novel donors for resistance to various pests and diseases of crops including wheat and barley. A FIGS search for natural resistance to Sunn pest  ,49, stems Pers.)  and Russ Kurdj.)  afflictig barley  were ideAside from resistance to phytophagous pests and pathogens, a set of fava bean germplasm with traits that are related to drought adaptation has been successfully identified using the FIGS approach. Using combined geographical imaging system and agro-climatic data, a collection of 9545 accessions of fava beans were separated into two different subsets. The first subset originated from environments with low moisture content and therefore are likely to possess drought tolerance while the second subset was sourced from locations with higher moisture content. Actual evaluation of both subsets for morphological and physiological characteristics that are related to water use efficiency showed differentiation in both sets that are in agreement with the groupings obtained using FIGS. The result of the study demonstrates the effectivity of FIGS in enhancing the discovery and deployment of new genes regulating traits of interest . It alsoAlthough the use of FIGS has facilitated the selection of plant accessions possessing target traits, it should be noted that the tool is not without its weaknesses ,54. The Environmental association analysis (EAA), also known as environmental association mapping or genome-environment association, is an approach that aims to identify functional polymorphisms and genetic variants associated with local adaptation to diverse climatic and edaphic factors. This framework is under the domain of landscape genomics, which has a more general scope of identifying environmental factors that shape adaptive genetic variation ,56,57,58Like FIGS, EAA capitalizes on the availability of geo-referenced genetic materials, mostly a set of landraces that were sourced from stressful and diverse agro-ecological zones. Using the geographical coordinates of the collection site for each landrace, data on multiple environmental, soil and topographic parameters are collected from publicly available databases. Information on bioclimatic variables for example, are freely available and can be downloaded from the WorldClim database ,65, wherThe EAA framework follows the same underlying principle as the typical genome-wide association study (GWAS) except that phenotypic data is replaced with environmental variables . In the To date, the body of work on EAA is steadily growing. Applications of EAA on the world’s staple crops such as maize , sorghumMaturity 1 and Tannin 1 which have been associated with agro-climatic traits in sorghum. Findings showed that Maturity 1 was most strongly associated with the minimum temperature of the coldest month. Tannin 1 was strongly associated with a number of bio-climatic gradients, with strongest associations with mean temperature of the warmest quarter [Vegetative to generative transition 1 (Vgt1) [Vgt1 has been found to be evolutionary conserved across the maize–sorghum–rice lineages [ZCN8 which encodes the maize florigen [ZmCCT [EAA is also a powerful tool to identify genetic variants and validate known genes involved in local adaptation. Using environmental variables collected from over 1900 geo-referenced landraces of sorghum, Lasky et al.  genetica quarter . Similar1 (Vgt1) ,71, a cilineages . Navarrolineages  also ideflorigen ,73 and Zn [ZmCCT , a well-DREB2A [AtCLB [CAMTA [OREI and ORS1, which regulate drought responses in rice [RHD3 that plays an important role for root growth and development [XET) and STAR1 loci, which regulates aluminum tolerance in Arabidopsis [Lastly, applications of EAA has also demonstrated its ability to identify novel candidate genes underlying local adaptation. Bandillo et al.  identifiDREB2A , AtCLB [A [AtCLB , and CAMB [CAMTA . SimilarB [CAMTA  identifi in rice , and gra in rice . SNP ass in rice  identifielopment . Lasky eelopment  identifibidopsis  and ricebidopsis . If thesFusarium oxysporum f. sp. vasinfectum Race 4 (FOV4) was identified in several fields in El Paso and Hudspeth counties in Texas. This presents a significant threat to the major cotton producing region in the US. Unlike most races of Fusarium, FOV4 survives in soils with neutral to alkaline pH and does not require the presence of root-knot nematodes to infect cotton plants. Because other plants are able to host FOV4 without getting infected, the pathogen can no longer be eliminated once it is introduced in the field. To date, cotton cultivars with resistance to FOV4 are unavailable, and no form of chemical or cultural control have been effective in managing the disease [Improving baseline production and product quality remains a focal point for cotton breeding. However, requirements for the adaptability of new cotton varieties to temperature extremes, reduced precipitation and soil salinization are becoming a necessity as climate change intensifies the extent and magnitude of current and emerging agro-environmental challenges. In the Southern High Plains for example, the suboptimal temperature that bookends the short growing season has become more unpredictable in recent years, further narrowing the window for cotton planting in the region. The shorter growing season inevitably exposes the cotton plant to cold stress either during the germination or the boll maturation stage . Increas disease .Amidst the burgeoning threats to cotton production, there is a growing interest in identifying new sources of genetic variation that can be used to enhance the adaptability of the crop to marginal environments. Panels of mutant lines and obsolete cotton varieties have been evaluated for their physiological responses to a multitude of environmental challenges including cold and drought stress but not the landraces ,34,35.Breeding for complex traits requires the effective identification of potential donor parents that can provide significant genetic gains in the long-term. Landraces possess both genetic heterogeneity and local adaptation that make them an ideal germplasm source for breeding climate-resilient cultivars. Although they have been largely replaced by modern, high-yielding varieties, a substantial portion of genetic diversity from crop landraces has been maintained in germplasm banks across the world. FIGS and EAA are gene bank mining tools that have been developed to circumvent the most significant challenge in utilizing germplasm collections—the phenotyping bottleneck resulting in the lack of agronomic data for relevant breeding traits. Without information on the potential breeding value of conserved germplasm accessions like landraces, selection of potential donor parents for breeding would be a futile exercise of finding needles in a haystack. FIGS and EAA use detailed information about the geographical origin of conserved accessions, as well as the historical climatic and edaphic data associated with each collection site instead of using phenotypic data as a basis for selection. These information provide the basis to model environments where adaptive traits such as tolerance to drought, salinity and heat stress are most likely to have evolved. The main difference between the two strategies is that EAA combines genomic data with landscape variables to identify genetic variants that are strongly associated with specific environmental conditions in a geo-referenced population. This not only provides an opportunity to understand the molecular basis of adaptive responses in landraces but also to clone and functionally validate causal genes/QTLs regulating the adaptive responses prior to their utilization in breeding programs. The NCGC of USDA alone has curated approximately 2500 accessions of cotton landraces from diverse agro-ecologies in North and South America, Africa, Asia, Australia and Europe. Agronomic evaluation for all the maintained accessions is still lacking but core passport data for each line is available and are publicly accessible from the US National Plant Germplasm System (NGPS) database . These dFor FIGS, selection of suitable cotton landraces can be initiated by filtering the collection to include only accessions with available data on longitudinal and latitudinal coordinates for collection sites. Single accessions from each set of coordinates may be selected to avoid duplicates in the panel that will be used for the search. Environmental parameters associated with the coordinates for each collection are available through WorldClim ,65 and ITo increase the resolution of germplasm searches, genomic scans of the filtered subset of landraces can be performed following the framework of EAA. High density SNP chips such as the CottonSNP63K  and CottGermplasm subsets that have been identified to possess fitness advantage under stress  can then be screened and validated in common garden experiments. Most cotton landraces are photoperiod sensitive , making At the minimum, the application of FIGS to mine landrace collections of cotton will allow the strategic and more focused identification of a subset of landraces that are most likely to have the target traits of interest. Combined with genomics scans under the framework of EAA, the search resolution increases, resulting in the potential identification of candidate loci or genes underlying local adaptation to specific climatic or edaphic conditions. To improve the adaptability of cultivated varieties, the selected germplasm accessions with validated adaptive loci can be directly used in breeding programs as donor parents or for germplasm enhancement strategy. Genetic information on the validated, functional SNP variant(s) regulating the fitness advantage of a landrace to specific environments can also be used for gene-editing. Lastly, the genotype data generated through EAA can be also be used in combination with GWAS for highly complex quantitative traits using the available phenotypic data in NGPS for the extracted cotton panel.


The role of genetics in determining measured differences in mean IQ between putative racial groups has been a focus of intense discussion and disagreement for more than 50 years. While the last several decades of research have definitively demonstrated that genetic variation can influence measures of cognitive function, the inferences drawn by some participants in the controversy regarding the implications of these findings for racial differences in cognitive ability are highly dubious. Of equal importance, there is no compelling scientific rationale for focusing on and devoting substantial effort to determining mean differences in intelligence or other cognitive functions between groups with incompletely defined and dynamic (and therefore not definitively definable) boundaries. This first-semester course was Social Sciences 15, an introduction to psychology taught by Professors Roger Brown and Richard Herrnstein, both eminent researchers in the field. Not surprisingly, given my status as a freshman, I had been completely unaware of their reputations or the research upon which those reputations rested.The Atlantic ”.Even a quote from a highly regarded economist regarding the relative importance of “nature” vs. “nurture” in determining the traits of progeny contains a number of doubtful assumptions about heritability and the factors that influence phenotypes .“I asked Justin Wolfers, father of Matilda, for his take on the power of nature versus nurture.”“Justin WOLFERS: Uh, who cares? You do the best you got with what you got. So if it’s 80 percent nature, it still leaves me with 20 percent. If it’s 20 percent nature, it leaves me with 80 percent. And either way, I want to get that part of the puzzle right”.While I would agree with Professor Wolfers that as a parent, you do the best you can to raise your children irrespective of heritability values, his comment seems to imply or at least might be interpreted to mean that heritability values can be meaningfully applied to individuals, that “nurture” corresponds entirely to “what parents do,” and that “nature” is solely about what genes are inherited from parents. None of these claims are completely accurate.A third example comes from an exchange between Ezra Klein and Sam Harris. . Klein i“We don’t know of an environmental intervention that reliably changes people’s IQ. Murray is right about that”.I believe that Harris is suggesting the non-existence of environmental interventions that reliably boost IQ scores. If so, perhaps he is, strictly speaking, correct on that point, meaning that IQ scores cannot be increased through social programs. However, I will note that James Heckman, an economist at the University of Chicago, a Nobel laureate, and an extremely eminent investigator of the outcomes attributable to social interventions, has provided strong evidence for the ability of some interventions to increase life success in several respects in ways that some might have assumed would require greater cognitive ability . I belie“While Perry failed to permanently increase a crude IQ measure of the treated, simplistic measures of cognitive achievement prove to be poor indicators of life success. Children treated with early childhood education have significantly better life outcomes than untreated children. Treatment in Perry significantly increased the participants’ employment, health, cognitive and socioemotional skills and reduced the male participants’ criminal activity, especially violent crime. Improvements in childhood home environments and parental attachment are seen as an important source of the long-term benefits of the program”.I also have to note that there is strong evidence that exposure to various environmental influences can reliably decrease effective IQ. For example, a meta-analysis of studies on environmental lead (abbreviated as Pb by chemists) exposure and IQ published this year presents overall results from seven reports that met the inclusion criteria for the new assessment . The aut“The full-scale IQ score[s] are inversely proportional to the blood Pb values…”(1)The evidence is overwhelming that genetic variation at many genomic loci substantially influences variation in intelligence and other cognitive capacities or behaviors. Some of this impact is mediated directly by biochemical mechanisms involving the gene products specified by the implicated genes. Still, some of these influences may arise through interactions between genetic and environmental factors and be, at least in part, culturally mediated.(2)In spite of this first point, the heritability of a phenotype within a population does not determine the extent to which genetic variation is responsible for mean differences between populations that can be differentiated from one another with respect to factors that exert significant influence on variation in a phenotype of interest. Furthermore, the genetic variation that influences variation in measures of cognitive performance within a population is not necessarily identical to the genetic variation that influences variation in mean measures of cognitive performance between populations.(3)Related to the preceding point, accepting a role, even an important role, for genetic variation in influencing variation in intelligence and other cognitive capacities or behaviors in no way necessitates concluding that individuals with what are defined as lesser apparent capabilities cannot be helped by changes in environmental factors.(4)Evidence of genetic influence on mean measures of cognitive performance between putative racial groups does not necessarily imply that the relevant genes are directly involved in determining brain functioning.(5)The implicit assumption of some participants in the controversy that genes involved in influencing IQ in some putative racial groups are clearly and unalterably superior to those same genes in other racial groups, creating a static hierarchy, is unjustified.(6)Although there may be no convincing evidence that intentional interventions can directly raise putatively inherent IQ, there are clearly environmental factors that can decrease putatively inherent IQ. Therefore, eliminating or reducing the impact of such factors, especially early in life, can potentially increase IQ relative to what it might have been if those negative influences were not eliminated or attenuated.(7)There is no compelling scientific reason to study the mean values for measures of cognitive or behavioral phenotypes associated with putative racial groups because individuals should be evaluated as individuals.My thesis addresses seven critical and common misunderstandings:What then is heritability and how does heritability differ from “being genetic?” One can also ask what it means to claim that a phenotype is heritable to any given extent?Heritability is a measure of the extent to which phenotypic variation, i.e., variation in a well-defined trait or characteristic (phenotype) of an organism, is attributable to genotypic variation ,13. GeneFor example, the number of fingers on human hands, which is five in the vast majority of cases, is highly dependent on the functions of proteins that are encoded by human genes, but much of the variation in that number results from accidents involving power tools or machines, i.e., environmental influences. The variation in the lengths and number of fingers on human hands that results from genetic variation, and there is such variation ,15, has (1)The value of the heritability for a given phenotype is a property of a particular population and not of any single individual.(2)The extent of the impact of allelic variation at a particular locus on variation in a trait, such as happiness, cognitive function, height, eye color, or skin color may either correlate or interact with  one or more environmental factors such that disentangling the relative importance of genetic versus environmental factors for individuals becomes highly challenging or practically impossible.(3)So, the heritability value for a trait might be expected to vary depending on the environmental factors operative within a population and the distributions of the impacts of those environmental factors on the members of a population, as well as the precise manner by which the trait is assessed or, if quantitative, measured.(4)Heritability can vary for a trait depending on when in the developmental process  the traits are assessed in members of a population.(5)The magnitude of heritability for a given trait is influenced by the particular allele frequency distributions at the relevant loci in the population studied and for which other populations may differ substantially.(6)As implied by the preceding points, changes in the relevant allele frequency distributions (which are not merely possible but over sufficiently long periods of time likely) or in the presence or distribution of environmental factors can lead to alterations in the degree of heritability.(7)Combining some of the above points, the value of the heritability is not an intrinsic property of a gene, a trait, or a gene-trait pair for a given population or whole species.(8)It is not generally possible to delineate all relevant environmental factors or assess how extensively they affect all members of a population.(9)no implications for how amenable to change that phenotype will be if exposed to altered environmental inputs, such as medical or social interventions that were not previously present in the study population.As noted above, even a very high percentage of heritability for a specific phenotype has (10)Therefore, determining a high heritability for a trait does not automatically argue against actions in social policy, medical, or other practical contexts whereby environmental factors would be modulated.(11)The influence of genetic variation on a trait can be indirect and be mediated almost completely by factors other than inherent biochemical mechanisms such as culturally grounded practices and/or beliefs such that what may be taken to be a genetic effect may be as or more appropriately viewed as originating, at least in part, from environmental causes, and(12)The vast majority of traits in the vast majority of people are the result of both genetic and environmental factors .If happiness, returning to the concern of Professor Brooks, as defined (measured) in some particular way, were 48% heritable, that would mean that 48% of the variation in happiness among the members of a population was attributable to variation in the alleles possessed by different individuals at one or more genetic loci in that population. Key points to note are:The following two sections  are inteA few basic aspects of gene function are also important to know. First, genes encode proteins and RNA molecules (gene products) that are frequently involved in two or more biochemical pathways that can differ in different types of cells and that can influence multiple traits. As a result of such multiplicity of gene product interactions, a mutation in a gene can have multiple phenotypic effects—a property referred to as pleiotropy. Second, the functional consequences of mutations at one locus can depend on the identities of alleles at other loci, a functional interaction between genes referred to as epistasis. Third, due to epistasis and gene-environment interactions and other factors, the phenotypic implications of possession of a particular allele may vary significantly in different individuals, referred to as variable penetrance, the likelihood given possession of a specific allele that an associated trait is observed, or expressivity, the extent to which the trait associated with the gene in question is expressed ,18.Another basic reality beyond dispute is that genetic variation affects variation in brain development and neural and cognitive functioning. A large body of experimental literature demonstrates causal connections between a variety of genetic variants of multiple types and intellectual disability. Mutations affecting the functioning of products produced by more than 100 genes on the X-chromosome alone have been associated with intellectual disability .The genetic loci involved in these cases have been demonstrated in other studies to encode proteins that participate in such processes as the formation of synapses (subcellular structures that mediate communication between neurons), synaptic plasticity, neuronal migration during brain development, chromatin remodeling, which influences the amount of gene transcription (mRNA production) and ultimately the amount of gene product translation (protein synthesis guided by the mRNA), and protein half-life in the cell. Many readers will probably note that some of the processes affected by mutations of genes influencing intellectual functioning are sufficiently general in nature to have effects on a variety of traits not obviously related to cognitive function. Genes encode gene products and are not best conceptualized as each corresponding to a single trait, highlighting the centrality of pleiotropy and epistasis to understanding the complexity of how genotypes relate to phenotypes.New York Times in February of 1997 [albus meaning “white”.Over twenty years ago, I came across an instructive example of how allelic variation can influence one or more traits through indirect mechanisms that can upend one’s intuition of what genetic influence means. The article, by Donald G. McNeil, Jr., appeared in the  of 1997 . It was Albino people with the most common form of the condition, oculocutaneous albinism, have reduced or absent melanin in skin, eyes and hair, all of which are tissues derived from the embryonic tissue layer known as ectoderm. Mutations in multiple different genes  can result in varying degrees of albinism .Although the loss of melanin in skin has no obvious or direct connection to cognitive function, the reduction of melanin in the eye can result in reduced visual acuity. In Zimbabwe, where, at least as of 1997, children may not uniformly have had ready access to spectacles to correct myopia, the decreased visual acuity associated with albinism could have reduced the ability of some affected individuals to decipher writing on the blackboard in school and otherwise undercut their efforts at learning.Beyond the effects on vision, cultural beliefs about albinos in Zimbabwe resulted in severe discrimination and even persecution. Some albino children were not even permitted to go to school because sending children to school entailed expense. Parents with limited financial resources believed their albino children would not live long enough to benefit from such education. Of course, many of these culturally based effects were ultimately elicited mostly because of the effects of genes that have no known direct effect on central nervous system function  is extremely difficult to fully delineate or assess in terms of quantitative significance. Consider the relative frequency with which children of less affluent and otherwise disadvantaged families are exposed to lead in drinking water or old paint flaking off walls, organic chemicals known to be toxic and many more of unknown impact on neural development, less optimal diets, less attentive medical care, and extreme stress including chronic violence.In this context, it is of relevance that Patrick Sharkey has published several studies demonstrating that violence in a neighborhood can depress measures of cognitive ability for school-aged (5–17 years old) children in that community ,23. In oAnother recent publication offers evidence of the potential for stressful experiences early in life to affect future behavior by influencing the rates of gene transcription and translation in brain cells . Much moThere is another problem with the supposition that knowing the heritability of group differences is information that can usefully guide educational or broader social policy formulation. The groups defined along standard racial lines are to a major extent arbitrary and the boundaries are necessarily imprecise and dynamic. Human evolution has not ceased.It is clear that human genetic variation exhibits patterns that are correlated with variation in the predominant geographic origins of the ancestors of people alive today. However, dividing the people of the world into clear, discrete racial groups is misleading because the variation across the globe is most appropriately regarded as roughly continuous, as would be anticipated from an evolved and evolving lineage of organisms that can move around.For example, there is more genetic variation among the people of Africa than among the people living on all other continents . So, lumPerhaps more importantly, individual students, just like individual patients or employees, need to be evaluated as individuals and not as representatives of some group. I see no value in knowing group means or comparing group means when the distributions of almost any trait of functional relevance to society will extend relatively far from the mean in both directions and be largely overlapping for these putative groups.For example, I have known and worked with physicians and researchers originating from countries all over the world. What matters to me in these collaborative projects or tasks is the individual insights, efforts, and capacities of these collaborators, not the supposed averages for their respective racial, ethnic, or religious groups. Any professionals who prioritize the latter over the former are handicapping their own collaborations.In 2017, John McWhorter directly addressed a central question confronting individuals who devote time and effort to assessing differences in mean IQ between large groups of people that they claim correspond to different races . Why do I have never seen a satisfactory answer as to why individuals who are prominent in discussions of putative racial differences in intelligence, such as Charles Murray, focus on mean group differences in measures of intelligence if the goal is policy formulation. Of equal importance, if defenders of research into the heritability of IQ or other measures of intelligence in different racial groups maintain that high heritability in a trait implies the impossibility of influencing that trait because “it is genetic” they are, in the general case, wrong.Consider the rationale behind the Human Genome Project (HGP). The claim that was aggressively made by the most vocal proponents of the HGP was that genes are critically important in conferring susceptibility to a huge variety of diseases and medical conditions, i.e., phenotypes. More importantly, they maintained, knowing the identities of a particular patient’s genes would guide the selection of customized therapies. It was implicitly assumed that a clinical trait that was highly heritable would still likely be modifiable by medical treatment. Although the ease of identifying or producing such interventions was arguably overstated by a number of HGP boosters , the basA genetic concept that can help make sense of these claims is the norm of reaction . For a gFor example, in investigating a phenotype of interest in agricultural plants or animals, a genotype, we can call A, that in comparison to genotype B generates a more productive phenotype in environment no. 1 might generate a less productive phenotype than B in environment no. 2. The same kinds of relationships can be found in evolutionary contexts where the phenotypes are assessed by measures of fitness and the seemingly fitter genotype in one environment is the less fit genotype in a different environment. Thus, the implicit assumption made by some participants in the arguments about genes, race, and intelligence that there is a clear and absolute hierarchy of genotypes in terms of the cognitive abilities they can produce is not justified in the absence of data that I have never seen.In closing, I would like to offer a paraphrase of the epigraph at the beginning of this article: whereof one cannot speak with insight, thereof one should consider either making the effort necessary to comprehend the relevant subject matter or remaining silent. A reasonable prerequisite for usefully discussing the implications of the genetics of cognitive ability for social policy is a solid grasp of what genes are, the frequently intricate and complex mechanisms by which they influence traits via functional interactions with one another and with environmental factors, the precise technical meanings of “heritability,” and the usefulness of measurements of heritability. From what I have seen of this controversy in recent years, it would be more productive if it were more fully informed by the key points enumerated and characterized above.


Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12–15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38–0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster,  Crassostrea angulata, is becoming an important aquaculture mollusk species in various parts of the world, including Asia and Europe  estimate the genomic heritability and genetic correlations for meat yield, color traits, harvest whole weight, and body shape and (ii) evaluate the accuracy of genomic prediction for these traits in Portuguese oysters.The Portuguese oysters used in this study originated from a breeding program to improve the whole weight at harvest in Vietnam . The oysThe oyster shells were cleaned in water before taking the measurements. The whole weight of oysters at harvest was recorded using electronic scales with an accuracy of 0.01 g.The cup and fan ratios were used as indicators of the body shape of the oysters. The cup ratio was calculated by dividing the shell width by the shell depth, and the fan ratio was calculated by dividing the shell length by the shell depth .Electronic scales with an accuracy of 0.01 g were used to measure the soft tissue weight. The condition index was calculated as soft tissue weight multiplied by 100/(whole weight minus shell weight) .via a CIELab L∗a∗b colorimeter which determines the color as a number under a device-independent 3D color model (∗ (whiteness), a∗ (redness), and b∗ (yellowness). The values of L∗ range from 0 for black to 100 for white; a∗ values are negative for green and positive for red; and b∗ values are negative for blue and positive for yellow  (The shell and mantle colors of oysters at harvest were measured using a FRU colorimeter WR10 8 mm model  via a CIor model . The thrl color) .TM  at a high marker density of 2.5 million sequences per sample. Genomic DNA was extracted and purified by Diversity Array Technology Pty. Ltd.  bridge PCR, followed by sequencing 77 nucleotide single-end reads on an Illumina Hiseq 2500. Reads with inaccurate barcode sequences were filtered out. Approximately 2,500,000 sequences per barcode/sample were used in marker calling. Finally, identical sequences were collapsed into “fastqcoll files,” which were “groomed” to correct for low-quality bases. The “groomed” fastqcoll files were used in a secondary pipeline incorporating DArT SNP and SilicoDArT (presence/absence of restriction fragments in representation), called analysis algorithm DArTsoft14, which clustered all tags from all libraries at a threshold of 3 for SNP calling. Technical parameters, especially the balance of read counts for the allelic pairs, were used to parse into separate SNP loci. Additional selection criteria included analysis of approximately 1,000 controlled cross-populations and testing for Mendelian distribution of alleles to assign consistency scores to classify high-quality/low-error rate markers. Calling quality was assured by a high average read depth per locus .The sample and marker statistics are given in Univariate linear mixed models were used to estimate the narrow sense heritabilities of the traits. The fixed effects fitted in the model were generation (age is a linear covariate nested within generation), sex determined at harvest (age based on spawning and harvest dates), and their interactions. The additive genetic effect and the residuals were used as random factors.Heritability was calculated as the ratio of Genetic correlations between traits were estimated using a bivariate version of the same model . All anaThe accuracy of genomic prediction was estimated for all traits by fivefold cross-validation analysis  with five replicates for each trait using gBLUP through the SVS Suite software . The gBLy is the vector of phenotypic observations , m = overall mean, and X is the incidence matrix consisting of fixed effects  in ß. The matrix M is the incidence matrix of genetic effects, and the genetic values are g=Ma, such that qj = 1−pj) and where m2pjqj , in whic (GBLUP) , genetick-folds of the cross-validation analysis that gave the largest R2-value were used to predict the phenotypes of individual animals with the following model:The allele substitution effects (ASE) and fixed effect coefficients obtained from iterations and  is the predicted phenotypes, X is the fixed effects matrix, where R2) from the regression analysis was used to assess the predictive ability of the gBLUP model for the traits studied. Furthermore, the animal effects were also predicted by traditional BLUP using the pedigree-based numerator relationship matrix for comparison with the genomic-based predictions described above. The pedigree summary of the oyster population in this study is given in The fivefold cross-validation using the gBLUP method was conducted using the SVS Suite . The actVCF2PopTree (FST) was calculated using the nucleotide diversities of the total and sub-populations (Nucleotide diversity was estimated using the method developed by 2PopTree  to obtai2PopTree . Fixatioulations .∗ significantly greater than zero) and was dark (L around 25% but with considerable variation). The average cup and fan ratios indicated a typical depth/width/length ratio of 1:1.6:2.9. The largest variation levels were also found in color traits of tissues and shells, especially for shell color a∗, with values from −2.07 to 54.96, and mantle color L∗, from 7.24 to 63.70.The summary statistics of the traits are shown in h2), the proportion of total variance explained by the SNP markers, ranged from 0.14 to 0.77 (∗ (red), 0.26 for b∗ (yellow), and 0.34 for L∗ (lightness). For the mantle, these traits had heritabilities of 0.54 for red, 0.49 for yellow, and 0.34 for lightness. The heritability estimates for meat yield traits were relatively high (0.43–0.77). Similarly, the estimate of heritability for whole weight was quite high at 0.45. However, the heritability estimates for body shape using the indicators of cup and fan ratios were low to moderate at 0.24 and 0.33, respectively.The genomic heritability ( to 0.77 . The herrg) of whole weight with the other traits are presented in the The positive genetic correlation between whole weight and soft tissue weight was high at 0.63, suggesting that selection for improved whole weight can result in desirable changes in soft tissue weight. However, low or insignificant genetic correlations were observed between whole weight and the other traits: color traits (−0.14 to 0.34), condition index (0.18), and body shape (0.08–0.14), implying that the selection for growth would result in little or no changes in these traits in this population. In addition, the genetic correlations between whole weight and all color traits were not only small, but they all had large standard errors and were therefore not significant. The genomic genetic correlations  were around twice those based on pedigree (h2 = 0.15–0.33) . However, estimated heritability in genomic selection can also depend on species, sample size, marker density, relationship of reference and validation population, and statistical methods for analysis  . These danalysis . It shouThe genetic correlations obtained with the GBLUP analysis were generally similar to those obtained from the traditional BLUP method . The higOur study reports, for the first time, the predictive accuracy of genomic selection for color and meat yield traits in a mollusk species. The predictive accuracies using the GBLUP model for meat yield and color traits were significantly higher than those obtained from the pedigree BLUP model, suggesting that the genomic selection for these traits is more efficient than the pedigree-based selection. The predictive accuracies using the GBLUP method for meat yield and color traits are in good agreement with those reported in banana shrimp . HoweverFST) of 0.13 among these clusters suggested the existence of a population structure. However, it is evident from To quantify the genetic variation in the data, we estimated the nucleotide diversity, which produced a value of 0.009, suggesting 0.9% variation in the population. This estimate was slightly less than that observed for the Pacific oyster (1.2%) . One of Genomic selection programs can be improved by using updated estimates of genetic parameters and benefiting from the higher predictive accuracy for quantitative complex traits, especially meat yield and color traits, in the Portuguese oyster. The favorably positive genetic correlations between whole weight, body shape, and meat yield traits found in this study suggest that there is little need to use a selection index to improve these related traits. To further illustrate, the heritability and genetic correlations from our study were used in SelAction  to simulCollectively, the results from this study showed that there are prospects for the application of genomic selection to improve a range of complex traits in the Portuguese oyster. Consequently, further studies should be carried out to collect further information on predictive power and potential genetic gain. Firstly, the training population size needs to be increased to obtain a more accurate prediction , i.e., wFrom the discussion above, the optimization of genomic selection will reduce the generation interval and increase the genetic gain in this population. Lowering the generation interval may result in an increase in inbreeding rate in the long-term. Therefore, to balance between inbreeding rate and genetic gain, genetic improvement programs have applied optimal genomic selection methodology and strategies . For theThe predictive accuracy using genomic selection was relatively high for all the traits studied. In addition, the estimates of heritability in the meat yield traits such as soft tissue weight and condition index were high, while those of color traits such as shell and mantle color were from low to high. The genomic selection for improvement of whole weight leads to desirable changes in other traits, such as meat yield and body shape traits, but will not affect color traits. Future breeding programs should combine all traits into a selection index to bring about higher revenues for aquaculture farmers and entrepreneurs.10.25907/00038. The phenotypic data is available by request from the corresponding authors.The DNA sequence data used in this study is available and can be accessed through the USC Research Bank under doi There are no requirements on ethics approval for mollusk species in Vietnam.SV, WK, and WO’C conceived and designed the experiments and selected the samples for sequencing. SV and NN prepared, drafted, and edited the manuscript. SV, CG, MA, and SB analyzed the data. SS involved in the population structure analysis. TL and LK prepared the oyster tissue samples. SS, CG, MA, MD, AG, RT, IV, and WO’C participated in editing the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model  up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme— These hybrids are generally obtained by crossing inbred lines originated from two complementary populations, called heterotic groups. Breeders’ objective is therefore to identify (i) the best single-cross hybrids among all possible crosses between existing inbred lines from the two groups and (ii) create new lines within heterotic group, from crosses of existing lines, which will improve the performance of candidate hybrids at a next generation. Models for genetic improvement of hybrid crops (e.g. maize) across two heterotic groups are typically based on the notions of general combining ability (GCA) and specific combining ability (SCA)  . The geni (and j) are identical, and all F1 descendants of i and j are identical. This is different from crosses of other species such as animals (pigs for instance) where full-sibs show genetic variation. As a result, GCA contains single locus  and multiple loci (additive by additive and higher additive interactions) effects. This is because the whole genotype (gamete) of the pure line is transmitted to the F1 descendants, including any possible epistatic combination, and regardless of whether loci in interaction are in the same or in different chromosomes. In this, GCA is different from the concept of Breeding Value in Animal Genetics, which captures the part of functional epistatic effects that is contained in the additive substitution effects, but it does not contain epistatic deviations as they are broken down by meiosis.It is important to notice, for readers not familiar with hybrid crops, that in many hybrid crops such as maize, parents are pure homozygous individuals (inbred lines). Thus, all gametes produced by within group 1 (group 2), whereas SCA are the sum of dominance, all epistatic interaction involving dominance, and any epistatic additive interaction across both groups.Informally, the GCAs within group 1 (group 2) are the sum of additive, additive x additive, additive x additive x additive… deviations e.g.With the advent of molecular markers (SNPs), it is possible to obtain better predictions based, ultimately, on similarity across lines (or pairs of lines) measured using markers, using for instance kinship matrices based on In this work we develop a new model, called GCA-model, which corresponds to e.g. Dent and Flint in the case of North European maize) are named 1 and 2. Extension to more than two heterotic groups is immediate.Here we derive the theory for genomic relationship matrices in the analysis of hybrid crosses of inbred lines from two populations. We draw on the tradition of separately modeling effects of gametes coming from each heterotic group . To deriOur aim is to split the total genotypic value of a single-cross hybrid in two statistical additive effects (one from each group), a single dominance deviation (particular to each cross) and epistatic interactions (either intra-group or across-groups). Ideally, this partition is orthogonal and all components should be estimable. Orthogonality is a definitional system that guarantees that variance components are “a priori” (before seeing the data) independent, whereas practical orthogonality depends on the information available in the data set. To our knowledge, this partition and its use in hybrid crops using markers have not been presented elsewhere.B/b and the allele origin denoted as i = 1 and j = 2, thus population 1 has We start from a genotypic model to derive statistical effects . Consided is the functional dominance value of both heterozygotes , as Genotypes in pure lines are in matrices Now, we can set up the covariance matrices.Additive covariance matrix for population 1  including GCA and SCA effects for a hybrid individual can be written as k and m and second-order epistasis, e matrix , so the Matrix In the above expression, The reasoning for population 2 is the same resulting in The dimensions of k and m, According to i and j. Let k, l position if the k-th hybrid comes from the l-th line in population 1 and The different For each pair of hybrids Scale the resulting matrix to an average diagonal of 1.approximation of Relationships for the other pairwise epistatic interactions  , representing an incomplete factorial design between Here, a brief description of the phenotypic and genotypic data set is given . It is known in animal breeding that this effect captures, among other things, genetic effects not explicitly modelled such as dominance or epistasis  that added, in succession, additive effects (Goodness-of-fit of models was compared based on the deviance information criterion (DIC), which balances model fit and model complexity to avoid overfitting  in the training set. Predictive performance of hybrids was computed separately for each group of hybrids by dividing the correlation of predicted and observed values by Predicted ability of phenotypes of “untested hybrids” for the different models was tested performing a T2-T1-T0 cross-validation scheme as in Estimation of variance components and cross-validation were performed in a Bayesian approach using the BGLR R-package . To speehttps://doi.org/10.1534/genetics.114.165860). Estimation of variance components and cross-validation were performed in a Bayesian approach using the BGLR R-package  orthogonal when “residual genetic effects” for GCA of lines are fit, regardless of the level of complexity for the genetic modelling . Using “residual genetic effects” allows to accommodate non-additive effects of lines not explicitly modelled.Some of these changes can be attributed to similarity of relationship matrices. The correlation between  effects .Similarly, For the G-model, Estimates of genomic broad-sense heritability ectively . Estimatectively . The estBased on the inspection of the trace plot and convergence diagnostic with BOA R-package , all estThe results for cross-validation are shown in In this study, the theory in the analysis of hybrid crosses of inbred lines from two populations using relationship matrices was revisited in a genomic context. Models for genomic prediction in hybrid crops using the notions of effects defined “according to origin” (GCAs and SCAs) were rederived and expressions for additive, dominant and epistatic relationships for hybrids were presented. These models were applied to a public data set to exemplify the theory and its consequences in real life.A surprising fact (to us) is that, in the classical pedigree-based methods, it is not possible to disentangle dominance deviations from across-population epistasis, whereas using markers it is possible. This seems not to have been recognized by previous researchers, leading to the wrong conclusion that in a genomic setting the relationship of dominance deviations is a product of corresponding additive relationships of parental lines. In this section we try to explain why such a difference.k (say k in hybrids (i and j) are identical, and because the lines are fully inbred, this is the joint probability that both “parents1” (ancestors from population 1) are IBD at locus k  see . This rek and l. In an IBD framework, across-population epistatic additive by additive relationship for hybrids i and j at two loci k and l (say k and that both “parents2” (ancestors from population 2) have the same genotype at locus l , which explains why It is worth noting that i.e. with markers), different relationships can be formed for each, additive substitution and dominant and epistatic deviations. Thus, contrary to pedigree-based formulations, a marker-based formulation allows disentangling of the different variance components.Thus, we have shown that the Thus, in pedigree-based models the dominance and across-population epistatic relationships are conceptually different, but the lack of other information forces to use the same estimator for both. This is not the case in marker-based models, where we can actually observe different relationships within locus or across loci from two populations.i.e. high order interactions) are included  QTL loci. However, even with a thousand records and thirty thousand markers (as in this work), it still makes sense to orthogonally define the genetic effects in the model. Not using orthogonal partitions might lead to ambiguous definitions of effects and to potential mistakes. For instance, if the additive variance is inflated, a possible consequence is that the genetic progress can be overestimated. If the dominance variance is inflated, the role of assortative mating of pairs of lines to produce a hybrid could be exaggerated. In our work we used orthogonal definitions of effects and the corresponding relationship matrices, as well as “residual genetic” In the GCA-model, after fitting the “residual genetic” In the G-model, when within-group epistatic effects were not fitted, additive variance was overestimated. Similar results were observed by Genomic relationship matrices for within and across groups epistasis (in the full GCA-model) allows to partition the genetic variance in terms of GCA and SCA effects, as was originally defined by e.g. ∼17) than their estimated values (179).Compared to the estimates of genetic variance component from odel see . This maModels with lower DIC values better fit the data, and a difference less than 7 units is often considered as irrelevant . This is because a high heritability generally results in high prediction accuracy, as was showed theoretically and empirically . InclusiWe used the splitting of cross-validation considering T2, T1 and T0  as in Assuming marker effects defined uniquely at the hybrid level (G-models) gave similar prediction accuracy than assuming gene effects according to origin (GCA-models). This result was also reported by e.g. F2) populations by crossing elite lines within groups followed by subsequent generation of inbreeding to develop new inbred lines. Therefore, the particular additive-by-additive (and higher order) epistatic combination existing in a particular elite line is not transmitted as a whole to its F2 (and further selfing) progeny, because meiosis and recombination shuffles alleles of the two parents in the cross, breaking down the original epistatic combinations present in the elite inbred lines and creating new epistatic combinations. Thus, the use of the additive variance, instead of the total GCA variance, is more appropriate for the prediction of genetic progress that is achievable by selecting within heterotic pools (The way of partitioning the genetic variance is to a certain extent a matter of convenience. Partitioning in terms of GCA (within group) is more convenient because inbred lines are actually created and selected within group. The magnitude of the GCA variance gives to the breeder an idea of how much overall genetic variation coming from the parents is expected in the hybrids. Further, splitting the GCA variance into additive and epistasis within group is relevant at the moment of planning the genetic progress in maize breeding programs. The genetic improvement in hybrid performance is through the selection of inbred lines. So that, breeders create new segregating , a more diverse individual will show more positive heterosis at the trait. De In maize, there is evidence of directional dominance , and using genomic relationships properly defined for each statistical component, allow for a proper partition of statistical additive effects, dominance deviations, and epistatic deviations, in hybrids derived from inbred lines from two populations. Contrary to common belief, using SNP genotypes, it is possible to split SCA into dominance deviations and across-groups epistasis, and to split GCA into within-line additive effects and within-line epistatic effects. Our GCA-model is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and its results  can be practically interpreted and used for breeding purposes.