Penaeus vannamei) breeding lines with data collection of 52, 248 individuals from 481 fullsib families. During grow-out test period, 10 days intervals of survival data were considered as separate traits. Two survival definitions, binary survivability (S) and continuous survival in days (SL), were used for the genetic analysis of survival records to investigate; 1) whether adding more survival time information could improve estimation of genetic parameters; 2) the trajectory of survival heritability across time, and 3) patterns of genetic correlations of survival traits across time. Levels of heritability estimates for both S and SL were low (0.005–0.076), while heritability for survival day number was found to be similar with that of binary records at each observation time and were highly genetically correlated (rg > 0.8). Heritability estimates of body weight (BW) for BL2019 and BL2020 were 0.486 and 0.373, respectively. Trajectories of survival heritability showed a gradual increase across the grow-out test period but slowed or reached a plateau during the later grow-out test period. Genetic correlations among survival traits in the grow-out tests were moderate to high, and the closer the times were between estimates, the higher were their genetic correlations. In contrast, genetic correlations between both survival traits and body weight were low but positive. Here we provide the first report on the trajectory of heritability estimates for survival traits across grow-out stage in aquaculture. Results will be useful for developing robust improved pacific white shrimp culture strains in selective breeding programs based on field survival data.Survival can be considered a relatively ‘old’ trait in animal breeding, yet commonly neglected in aquaculture breeding because of the simple binary records and generally low heritability estimates. Developing routine genetic evaluation systems for survival traits however, will be important for breeding robust strains based on valuable field survival data. In the current study, linear multivariate animal model (LMA) was used for the genetic analysis of survival records from 2-year classes (BL2019 and BL2020) of pacific white shrimp ( Annual global production reached ∼4.4 million tons with a commercial value of 26.7 billion USD in 2020, which ranked as the most important traded food commodity across the aquaculture sector  genetic parameters from two different survival definitions for the binary traits of survivability and the continuous trait of survival time; 2) the trajectory of heritability for survival traits across the grow-out test period; and 3) genetic correlation patterns for survival traits across grow-out stage and their correlation with growth. Results from the current program will provide insight into selection breeding of robust culture strains based on field survival records in aquaculture species.Pacific white shrimp , with the selection target being for local farm environments in China based on a family selection approach. Foundation populations were produced in 2015 sourced from 12 hatchery lines in China representing four genetic populations as evidenced from a population structure analysis . In 2015 studies .In the current study, genetic analysis data were sourced from two different year classes 2019 (BL 2019) and 2020 (BL 2020) in the breeding nucleus line. The BL2019 line, consisted of 243 full-sibs families generated from 157 sires and 243 dams over a 1-week period, while the BL2020 line consisted of 238 full-sibs families produced by 143 sires and 238 dams . The meaDead individuals from the breeding lines were collected three times per day across the grow-out period and pedigree information of mortalities was recorded from visible implant elastomer (VIE) tags. At the end of the grow-out test stage, all remaining harvested individuals in the test system were scored for body weight, pedigree of family ID, and gender.Data collected on survival from each 10 days interval were considered as separate traits over the grow-out period. Survival phenotype was recorded using two definitions in the genetic analysis. First, survival phenotype at the end of each 10 day period was coded as a binary trait of survivability, with 0 for dead individuals and 1 for live individuals. Secondly, survival data for each 10 day period was considered to be a continuous trait of survival time indicating how many days individuals survived over the test period. For example, live individuals in each breeding line at surviving to 100 days were coded as 100d and dead individuals at 86 days or 56 days were coded as 86d or 56d, respectively. Therefore, survival data records from BL2019 breeding line across the 117 days grow-out period were coded as 12 survivability binary traits  and 12 continuous traits of survival  for each observed time window of 10 days. Similarly, the 98 days records of survival data from BL2020 considered 10 survival binary traits  and 10 continuous traits of survival days , respectively. At the conclusion of the grow-out tests, phenotype of body weight for each shrimp was collected as a continuous trait (BW), while data from the final population census of live shrimps were recorded as a binary survival trait (SUR).R package ; β is the vector of fixed effects including sex, tanks and family batches; α is the vector of random additive genetic effects; e is the vector of random residual errors; and, X and Z are known incidence matrices relating observations to the fixed and random effects mentioned above. Both vector of α and e are assumed to be multivariate normal distribution with mean zero and variances as:σα2 and σe2 are the random additive variances and error variances, respectively. A is the numerator relationship matrix based on pedigree information, and I represents an identity matrix. Total variance (σp2) was calculated as the sum of random additive genetic variance (σα2) and random residual components (σe2). Heritability (h2) was calculated as the ratio of the random genetic variance to the total phenotype variance, pr) between two traits was calculated as: rg) between two traits was calculated as:rp and rg were displayed as heat maps in R package  in the genetic analyses. Equations of univariate animal models can be written as follows:f is the vector of random full-sib family effects and W is the corresponding design matrices; other symbols represent the same vectors as described in Model 1. Compared with Model 3, there was no random full-sib family effect in Model 4. Likely statistical significance for the random full-sib family effects were analysed using a likelihood ratio approach. After running the two above univariate Models, significance for the random full-sib family effects were compared to the final log-likelihood (Maximum log L) using a Chi-square (χ2) test.Two linear univariate animal models were developed for the significance test of the full-sib family effect (p < 0.01) in mortality among families for the two breeding lines investigated here. Means of daily mortality records for BL2019 and BL2020 were 0.1% and 0.17%, respectively , survival days (SL), final population census of survival (SUR), and body weight (BW) are presented in ± 0.036 and 0.373 ± 0.031, respectively.Heritability estimates for final population census survival (SUR) were very similar for S and SL at the final testing stage, while for body weight (BW) in BL2019 and BL 2020, they were 0.486 rp) and genetic (rg) correlations with SE for S, SL, SUR, and BW are provided in rg for S, estimates between different times were moderate to high and ranged from 0.475 (S1 vs S12) to 0.999 (S11 vs S12) in the BL2019 breeding line, and 0.277 (S1 vs S10) to 0.999 (S9 vs S10) for BL 2020, respectively. Estimates of rg for SL between different times were much closer compared with that of S in both 2-year breeding lines. As expected, estimates of rg between SUR and S, or SUR and SL were much lower, and in most cases were only moderate correlations likely due to the issues referred to unobserved mortality events when estimating SUR vs. S/SL. In contrast, estimates of rg for BW and all survival related traits (S/SL/SUR) were low but all positive. We consider that this result implies that selection directed on survival related traits would not experience potentially negative tradeoff effects on growth traits. Patterns of phenotype correlations (rp) among S, SL, SUR, and BW were similar to those for rg but were always slightly lower than for rg  in the genetic analysis. Among 26 comparison for BL2019, 24 tests were not significant for the likelihood ratio tests (c2 (χ21df). Data interpretation for genetic parameters for S1 and SUR however, showed limited potential for improving an animal model fit for full-sib family effects (Model 3) because the new results for S1 and SUR genetic parameters showed large associated SE estimates . Similarly, there was limited potential for improving animal model fit for full-sib family effects in the genetic analysis of survival data from BL2020 based on the result of likelihood ratio tests.The likelihood ratio test suggested that there were very limited full-sib family effects  and the continuous trait of survival time (SL) were relatively high. These findings will be useful for genetic analyses of survival data in field environments and potentially contribute to the development of relatively robust farmed aquatic strains used in aquaculture. An issue we identified in the analysis however, was missing records of some individual mortality events across our test periods. This reflects in general the difficulty of collecting survival data for aquaculture species in the field when working in commercial test environments.Records of daily mortality in breeding lines are not only important for genetic evaluation of survival traits, but also for assessment of general husbandry management of population health status in breeding lines. Changes in daily mortality here were similar to those reported in our previous study with the mortality rate varying between 0.1% and 0.15% . DesigniThe highest daily mortality peaks in both test years occurred in late grow-out stages , periodsIn the current study, we observed a gap in bias between survival estimators across the test period and the final population census data. Collecting survival data in natural ‘field’ conditions can be difficult in aquaculture breeding programs compared with that in terrestrial farm animals because most mortality events are under water and consequently, may not be detected. While survival phenotype derived from controlled experimental challenge tests can be more accurate , the useP. vannamei selection lines were reported to range from 71% to 82.2% during the grow-out test stages  that effectively removes time of age effects and common environmental effects in the genetic analysis. This can enhance the accuracy of the animal model results generated. We therefore chose a linear multivariate animal model (LMA) approach for our genetic analyses rather than other more sophisticated linear animal models such as a random regression model or repeatability model to fit age effects of time for the survival data set here.Heritability estimates for S1 to S12 and SL1 to SL 12 were consistent with results reported from other pacific white shrimp breeding programs. via adding more survival time information to mortality events, similar findings have been reported for genetic analysis of survival data in experimental challenge tests on aquaculture species  were not significant in the current study indicating that linear multivariate animal model (LMA) without full-sib effects was effective for routine genetic evaluation here. This finding agrees with a systematic review paper on c2 estimation in aquaculture breeding suggests that full-sib effects contribute only a small proportion of total phenotypic variance at earlier growth stages for growth related traits, but for growth traits in an individual’s later stages or with other phenotypic traits, full-sib effects (c2) are not significant in most cases and are essentially zero (c2) (Common full-sib effects (lly zero . As an e (c2) .rg/rp among body weight, body length, and other morphological growth traits  at each time window . In an ah traits . Patternrg among S1 to S12 and SL1 to SL12) in the current study were moderate to high. There was a trend here for a gradual decrease in rg between survival traits with the time. These patterns for rg were in accordance with trajectories of genetic correlations for growth traits across ages in other aquaculture studies  scored and generally low heritability estimates. Developing routine genetic evaluation systems for survival traits however, will be important for developing robust strains using valuable field survival data. Firstly, survival data collection from commercial lines can be used directly for improving overall survival rate in target breeding lines . MoreoveP. vannamei and potentially for other farmed aquatic species in the future.In conclusion, we developed routine genetic evaluations for survival data in selective breeding pacific white shrimp line in China. Results of heritability and genetic correlation estimates indicate that both survival definitions for binary traits and continuous traits of survival time can be used effectively for genetic analysis of survival data. Binary survival records following with linear multivariate animal models (LMA) provide a feasible routine genetic evaluation approach in practical commercial breeding programs because of the simple data recording, reduced computing time required, and the ability to combine performance assessment of multiple continuous phenotypic traits in the genetic analysis. While heritability estimates for survival traits here were generally low, they showed a gradual increasing trend across the grow-out period. Genetic correlations for survival traits during grow-out tests were moderate to high, and the closer the times were between estimates of two survival traits, the higher were the genetic correlations. Genetic correlations between survival traits and body weight were low but all estimates were positive. In summary, we first reported the trajectory of heritability estimates for survival traits across grow-out stage in aquaculture genetics, which would be useful for how to use survival data in field situations to develop more robust culture strains of


The growing global demand for wheat for food is rising due to the influence of population growth and climate change. The dissection of complex traits by employing a genome-wide association study (GWAS) allows the identification of DNA markers associated with complex traits to improve the productivity of crops. We used GWAS with 10,045 single nucleotide polymorphism (SNP) markers to search for genomic regions associated with grain yield and related traits based on diverse panels of Ethiopian durum wheat. In Ethiopia, multi-environment trials of the genotypes were carried out at five locations. The genotyping was conducted using the 25k Illumina Wheat SNP array to explore population structure, linkage disequilibrium (LD), and marker-trait associations (MTAs). For GWAS, the multi-locus Fixed and Random Model Circulating Probability Unification (FarmCPU) model was applied. Broad-sense heritability estimates were high, ranging from 0.63 (for grain yield) to 0.97 (for thousand-kernel weight). The population structure based on principal component analysis, and model-based cluster analysis revealed two genetically distinct clusters with limited admixtures. The LD among SNPs declined within the range of 2.02–10.04 Mbp with an average of 4.28 Mbp. The GWAS scan based on the mean performance of the genotypes across the environments identified 44 significant MTAs across the chromosomes. Twenty-six of these MTAs are novel, whereas the remaining 18 were previously reported and confirmed in this study. We also identified candidate genes for the novel loci potentially regulating the traits. Hence, this study highlights the significance of the Ethiopian durum wheat gene pool for improving durum wheat globally. Furthermore, a breeding strategy focusing on accumulating favorable alleles at these loci could improve durum wheat production in the East African highlands and elsewhere. The testing locations represent the country’s major and most suitable durum wheat growing environments. The soil texture of each site is characterized as heavy clay for Akaki and Chefe Donsa and clay for Holeta, Kulumsa, and Sinana. The test sites are classified into ME (Mega environment)2:SW(Spring Wheat) high rainfall areas that receive more than 500 mm of rainfall during the crop growing cycle as defined by CIMMYT’s Wheat Breeding Program . Among t2.32 with 2.5 m x 0.4 m (two rows with 20 cm spacing). The space between the plots was 20 cm. A seed rate of 150 kg ha−1 and fertilizer rate of 50 kg N ha-1 and 100 kg of P2O5 ha-1 was applied to each plot. In order to maintain genotype uniformity (since the genotypes were mostly landraces with possible seed admixture), the genotypes were grown on different plots for two consecutive crop growing seasons (2017-2018) at Sinana agricultural research center, and individual plants that appeared to differ in any of the clearly visible phenotypic traits were removed.The experiment was laid out using an alpha lattice design with two replications containing 21 incomplete blocks with a block size of 20, according to 2.4In this study, phenotyping was conducted by applying the previously described methodology for evaluating wheat genetic resources . The tra2.5th genotype in the jth incomplete block with the lth replication of each environment for a particular trait was described as:Before further analysis, data were evaluated by the Shapiro–Wilk test to assess if they fit into the normal distribution. Furthermore, based on the results from the normality test, the homogeneity test was performed for the scored data in the experiment as described in ijl is the phenotypic response of the ith genotype in lth incomplete block within jth replication, µ is the overall mean, τi is the fixed effect of genotype i, βj is the random effect of the jth replicate, γl is the random effect of the jth incomplete block nested in the lth replication, and ξijl is the residual error.where YThe combined ANOVA across environments inference was computed for all the response variables as follows:ijlk is the observed phenotypic trait for ith genotype in lth incomplete block within jth replication at the kth environment, µ is the overall mean, τi is the fixed effect of genotype i, βl is the random effect of jth replication, γj is the random effect of the lth incomplete block within jth replication, Ek is the random effect of environment k, βγlj is random effect of incomplete block l nested in replication j, γEjk is random effect of replication j in test environment k, τEik is random effect of interaction between genotype i and environment k, and ξijlk is a random residual effect. For the sake of simplicity, we assumed that all the underlying random effects residuals are normally distributed with zero mean and are independent homoscedastic.where YThe best linear unbiased estimates (BLUEs) of measured traits for each genotype from each environment were obtained using META R software . The estl is the number of environments, and r is the number of replications.where https://www.traitgenetics.com/index.php/service-products. Based on a specific durum wheat cluster file developed by TraitGenetics that differentiates durum wheat from bread wheat, markers accurately scored for the A and B genomes were recorded.A single spike representing each genotype was collected during field phenotyping for genotyping. Five healthy seeds from each spike were taken to represent each genotype and were planted in 3 L pots in a greenhouse at the Swedish University of Agricultural Science (SLU), Alnarp, Sweden. A total of ten 6 mm leaf discs sampled from five two-week-old seedlings of each genotype were collected in each well of a 96-deep well plate and freeze-dried using the CoolSafe ScanVAC Freeze Dryer according to the instructions provided by Trait Genetics. The freeze-dried samples in 96-well deep well plates were sent to TraitGenetics  for DNA extraction and subsequent genotyping. A standard cetyltrimethylammonium bromide (CTAB) protocol was used to extract DNA from the leaf samples in TraitGenetics’ lab. The 420 genotypes were genotyped using an Illumina Infinium 25k wheat single nucleotide polymorphism (SNP) array following the manufacturer´s protocol. The details of the SNP array can be found at Several criteria were used to filter the genotypic data obtained before further analysis. TASSEL 5.2.80 software  was used2.7The number of subgroups among the 420 genotypes was inferred by principal component analysis (PCA) and model-based clustering methods, which were computed by Genome Association and Integrated Prediction Tool (GAPIT) 3.0  and STRU2) for pairs of SNP markers was computed according to 2 value for each chromosome  was considered as a cut-off point to represent a limit of QTL between pairs of markers as indicated in previous studies for Ethiopian durum wheat panels (Information on the pattern of linkage disequilibrium (LD) within a genetic material of interest is necessary to determine the marker density required for a genome-wide scan . Accordiromosome . The meacay rate . The r2 t panels .2.8GWAS was conducted using best linear unbiased estimates (BLUEs) for nine phenotypic traits and 10,045 SNP markers to identify marker-trait association (MTA). The BLUEs for grain yield, spike length, and grain-filling period were calculated by considering days to heading (DTH) as a covariate in order to control the effect of heading time, as suggested in previous studies . The anap< 0.01  to identify genes related to significant MTAs. The nucleotide position extending from 1-5 cM up and downstream from the SNP position was used for searching the potential candidate genes, as previously reported for wheat (Triticum turgidum (Svevo.v1) reference genome)  .33.1-1, while the mean thousand kernel weight was 40.9 g. On average, 17.5 spikelets per spike were recorded across the environments (-1) was observed at Chefe Donsa, followed by Sinana (8.2 t ha-1), whereas the lowest mean grain yield was recorded at Holeta (4.4 t ha-1) (p<0.01) impact of genotype, environment, and genotype by environment interactions on all traits evaluated  and GFP (H2 = 0.98), indicating that these traits are highly heritable.The estimates of genotypic variance σg2 and p< 0.01) for DTH with DTM, SPP, SPL, PHT and NET, for SPP with SPL, NET and GYD, and for SPL with PHT and NET  and 5,238 (52%) were distributed on the A and B genomes, respectively Table 2.The marker density was 1.01, 0.96, and 0.98 Mbp per marker for the A, B, and whole genomes, respectively. The SNP markers used in this study covered a total size of 9.86 Gbps, with chromosomes 1A and 2B having the smallest (584.2 Mbp) and largest (789.4 Mbp) regions Figure 33.42 ≥ 0.3  to 26.2% (r2 = 0.211 for chromosome 3B) of all marker pairs on the corresponding chromosomes  was observed on chromosome 7A. The r2 values of marker pairs progressively declined as the physical distance between them increased on each chromosome  of the entire variation in the data set and grouped the genotypes into two subpopulations  had a Q value of 1, which indicates 100% fitting the grouping with modern cultivars. Furthermore, one modern cultivar (G405), which was derived from related to landraces (according to their pedigree information), was grouped with landraces. The allelic divergence between the two subpopulations inferred by STRUCTURE was 0.27. On average, the expected heterozygosity of subpopulation-1 (Cl - I) and subpopulation-2 (Cl - II) was 0.22 and 0.32, respectively. Subpopulation-1 had a mean F3.6Considering all test locations and combined data over locations, GWAS was able to identify 179 significant MTAs for the nine traits , 12 significant MTAs were identified from the GWAS of combined data from the five locations Table 4.The GWAS scan detected four significant MTAs across test environments for DTM. Of these MTAs, Kukri_rep_c73477_888 on chromosome 6A was previously reported  and was GWAS revealed two significant MTAs for GFP on chromosomes 3B and 7B Figure 53.6.2The GWAS analysis revealed 14 MTAs significantly associated with plant architecture traits Figure 6The MTA analysis for spike length (SPL) revealed five significant MTAs. Among the significant MTAs, Tdurum_contig45715_1246 on chromosome 1B was previously identified . The rem3.6.3GWAS for grain yield and yield-related traits evidenced 18 significant MTAs Figure 7For GYD, GWAS revealed four significant MTAs on chromosomes 1B, 5A, 5B, and 7A. The proportion of phenotypic variance explained by the significant MTAs ranged from 1.74% (RAC875_c57656_170 on chromosome 7A) to 44.95% (IAAV3365 on chromosome 5A). Alleles of the high signal MTAs  had a highly significant effect on grain yield , whereasThe genome-wide association analysis identified seven significant MTAs for TKW on chromosomes 1B, 3B, 5A, 6A, and 7A. The phenotypic variance explained by the associated SNPs ranged from 1.05% to 10.6%. Among the MTAs significantly associated with TKW, two  were previously reported. However, the other five MTAs  were novel. The effect of alleles on locus AX-158564275  revealed a highly significant difference in TKW (3.6.4q.gwas.01 to q.gwas.37) are provided in the first column of q.gwas.15 and q.gwas.20) and one QTL (q.gwas.08) for TKW were previously described based on Ethiopian durum wheat germplasm  overlap with more than one trait evaluated in this study (q.gwas.30)  is considered to be a QTL. Significant SNPs within the ranges of 10 Mbp apart are considered to refer to the same QTL. Based on this approach, 37 QTLs were identified for the 44 significant MTAs Table 5.ermplasm . GenomicTRITD7Av1G01175, TRITD7Av1G017240, and TRITD7Av1G017550 , which encode Growth-regulating factor, Zinc-finger CCCH domain protein TE, and NAC domain-containing protein, respectively, were associated with DTH and DTM. The genes TRITD1Bv1G168480 and TRITD7Bv1G057630 encode WRKY transcription factor and Flowering Locus T/Terminal Flower 1-like protein, respectively, were reported to regulate DTH. The TRITD1Bv1G000110 gene, on chromosome 1B, which encodes Tryptophan aminotransferase-related protein 2, was shown to be associated with SPP and GYD , which is consistent with the results of previous research on durum wheat  positive correlation with SPP, and TKW, indicating that the simultaneous selection of desirable characteristics of these traits could lead to the improvement of grain yield in this crop. However, GYD negatively correlated with DTH and PHT, indicating that late-heading genotypes generally have lower grain yield than early-heading types. However, the early-heading types appear to have a more extended grain-filling period, as a very low but significant positive correlation was obtained for GYD versus DTM. TKW exhibited a moderate positive correlation with GYD, and GFP, implying that direct improvement of these traits may improve the former, which contributes to enhancing GYD. Conversely, TKW had a negative relation with DTH and DTM. Thus late-maturing cultivars will have a relatively low TKW.The present study found that GYD had a moderate but significant (2 = 0.2) in their study on diverse Ethiopian bread wheat germplasm. This disparity could arise due to the type and density of markers, genomic regions the markers cover, and differences in the sample used in these studies. However, 2 = 0.11. Likewise, The LD among the SNP marker pairs showed a sharp decline within the physical distance ranging from 2.02 to 10.4 Mbp, with an average of 4.26 Mbp. This decline in LD is far below the results of previous research using Ethiopian durum wheat landraces , which rThe population clustering inferred by STRUCTURE and PCA divided the genotypes into two sub-populations, similar to the results of earlier research . Based oq.gwas.02 (0.3 – 5.8 Mbp) on chromosome 1B, q.gwas.21 (544.7 – 554.9 Mbp) on chromosome 5A, q.gwas.32 (609.2 – 619.2 Mbp) on chromosome 7A, and q.gwas.33 (703.3 – 714.2 Mbp) on chromosome 7A. Among them, q.gwas.02, q.gwas.21, and q.gwas.32 are novel QTLs, as these genomic regions have not previously been reported for their association with grain yield. The putative QTL q.gwas.33 on chromosome 5B is co-localized within the same genomic region of a QTL reported by q.gwas.33 is also identified for spikes per plant , TRITD5Bv1G246830 (KH domain containing protein), TRITD5Bv1G247760 (NBS-LRR disease resistance protein –like protein) and TRITD5Bv1G246270 (Glycosyltransferase).Using GWAS, different genomic regions associated with grain yield were identified in the present study. The putative QTLs identified for this trait are er plant , kernel er plant , kernelser plant , fusariuer plant , yellow er plant , and steer plant . This QTq.gwas.02 (RFL_Contig3481_1669) for GYD, identified in this study, overlaps with QTLs for several traits such as total root number and length (q.gwas.21 on chromosome 5A (MTA for IAAV3365 SNP and GYD) is a novel major QTL for grain yield, explaining the largest proportion of phenotypic variance (r2 = 44.95%) as compared to all other putative QTLs reported here. This is a highly significant result of this study, which needs to be validated through further research, including fine mapping to pinpoint the gene(s) responsible for this QTL. Interestingly, the genomic region of this QTL overlaps with previously identified QTL for number of kernels per spike (TRITD5Av1G205000 (an ABC transporter) gene is one of the potential candidate genes behind this QTL (q.gwas.21). This is because previous research indicated that ABC transporter genes affect grain formation in wheat during heading and also modulate the ripening of the heads  on chromosome 1B, q.gwas.15 (541.4–551.4 Mbp) on chromosome 2B and q.gwas.16 (721.4–725.8 Mbp) on chromosome 3A. For TKW, five novel putative QTLs, i.e., q.gwas.18 (58.8–68.0 Mbp) on chromosome 3B, q.gwas.19 (803.2 – 812.9 Mbp) on chromosome 3B, q.gwas.22 (431.8–442.1 Mbp) on chromosome 5A, q.gwas.26 (524–534 Mbp) on chromosome 6A, and q.gwas.34 (681.9–692 Mbp) on chromosome 7A were identified. Fine mapping of these genomic regions is required to identify the genes responsible for these QTLs for SPP and TKW. However, for four of the five novel QTLs for SPP, we were able to identify potential candidate genes, i.e., TRITD1Bv1G000110 (Tryptophan aminotransferase-related protein 2), TRITD1Bv1G167110 (UDP-Glucose Pyrophosphorylase 1), TRITD2Bv1G184650 (ethylene-responsive transcription factor), and TRITD3Av1G275580 (E3 ubiquitin-protein ligase). TRITD2Bv1G184650 has been reported to regulate the initiation and development of spikelets in wheat, particularly when the temperature is low , q.gwas.14 (94–104.6 Mbp), q.gwas.20 (721.4–725.8 Mbp), and q.gwas.30 (2–35 Mbp) were found co-localized with previously reported QTLs for these traits on chromosomes 1B, 2B, 4A, and 7A, respectively  with a QTL identified based on Ethiopian durum wheat germplasm   Table 5.via GWAS discovered novel and previously identified genomic regions (putative QTLs) associated with DTH. Of these, q.gwas.01 (522.7–528.6 Mbp of chromosome 1B), q.gwas.09 (753.4–757.6 Mbp of chromosome 2A), and q.gwas.30 (2–35 Mbp Mbp of chromosome 3B) were previously reported for this trait . This gene involved in regulating leaf senescence. It is also known to have major roles at various stages of wheat development affecting productivity and product quality and was predicted to interact with DTH. Similarly, the TRITD7Av1G017240 (zinc finger CCCH-type transcription factor) gene, which promotes wheat flowering, was also identified in this study. Hence, it would be worthwhile to conduct further research on this genomic region to identify the gene involved in q.gwas.01 and to understand the relationship between leaf senescence and DTH in durum wheat.The marker-trait association analysis conducted is trait . These Qis trait . Hence, bidopsis . Hence, ly shown , the q.gq.gwas.25 (601.5–615.3 Mbp of chromosome 6A) was reported in a previous study on wheat (q.gwas.10) and 7A . The TRITD6Av1G220960 (Ethylene receptor) gene, which is located within the genomic region of q.gwas.25, is a potential candidate gene for q.gwas.25. Previous research has suggested that ethylene receptors are most likely related to the duration of seed development and maturation; i.e., the duration embryo development (TRITD7Av1G011750 (growth-regulating factor), TRITD6Av1G017240 (Zinc finger CCCH domain protein TE), and TRITD7Av1G017550 (NAC domain-containing protein) genes have been found in the genomic regions of QTLs for DTH identified in this study, and are potential candidate genes for the corresponding QTLs. Studies have shown that these genes are mainly involved in regulating growth, development, biotic and abiotic stress adaptation in wheat, rice, and other crop plants and may also determine the variation in phenological traits in wheat and rice.A previously known genomic region and three novel genomic regions (putative QTLs) associated with DTM were found on chromosomes 2A, 6A, and 7A. These QTLs explained 2–33% of the variation in DTM. The QTL designated as on wheat . The novelopment . Howeverelopment . Similarelopment , TRITD7Aq.gwas.03 (580.6–585.4 Mbp of chromosomes 1B), q.gwas.04 (669.5–674.7 Mbp of chromosomes 1B) and q.gwas24 (628.2–630.9 Mbp of chromosomes 5B), similar to the previous reports (TRITD1Bv1G191400 (Zink finger protein) could be a candidate gene underlying the q.gwas.03 QTL for PHT. Previous research revealed that this gene is significantly associated with improved salt tolerance and regulates stress resistance in wheat, Arabidopsis, and other plants , TRITD1Bv1G189570 (encoding Receptor-like protein kinase), and TRITD5Bv1G0001780 (Cytochrome P450-like protein).MTA analysis for PHT identified four putative QTLs,  reports . This str plants . Other pq.gwas.06; 314.3–318.8 Mbp), which was previously reported by q.gwas.11 on chromosome 2A (489.5–492.5 Mbp), QTL q.gwas.12 on chromosome 2A (522.4–534.5 Mbp), QTL q.gwas.13 on chromosome 2B (185.5–195.6 Mbp), and QTL q.gwas.23 on chromosome 5A (526.3–534.1Mbp) were found for this trait. These are likely to be novel QTLs since the corresponding genomic regions are not associated with SPL in previous studies. The potential candidate genes for the SPL QTLs include TRITD2Av1G189490 (encoding Acyl-CoA N-acyltransferase), TRITD2Av1G190600 (encoding Ring finger protein), and TRITD2Bv1G109560 (encoding E3 ubiquitin-protein ligase). A report from a previous study revealed that E3 ubiquitin proteins have a potential role in modulating crop productivity by influencing growth, development, and important agronomic traits  that explained 44.95% of the variation in grain yield, q.gwas.10 (for SNP Kukri_rep_c73477_888) that explained 32.9% of the variation in days to maturity and q.gwas.28 (for SNP Kukri_c60966_261) that explained 30.7% of the variation in the grain-filling period are the key findings of this study. Additional research is needed to validate these key findings, including fine mapping to determine the underlying genes and their subsequent functional analysis. The addition of SNP markers associated with the target traits of this study is highly beneficial for genomic-led breeding of durum wheat.In the present study, we evaluated the diverse germplasm of Ethiopian durum wheat using multi-environment trials (MET) data that are genotyped with the Illumina Infinium 25k wheat SNP array to unravel genomic regions associated with its phenological and plant architecture traits as well as grain yield and yield related traits using GWAS. The GWAS identified 44 significant MTAs, including 26 novel genomic regions. The combined analysis of variance revealed significant effects of genotype, environment, and genotype-by-environment interaction on the target traits. The study also confirmed several previously reported QTLs. The identification of a large number of novel QTLs in this study indicates the presence of novel alleles of the genes underlying these QTLs, which probably confirms the distinctness of the Ethiopian durum wheat gene pool from other durum wheat gene pools. The major significant QTLs, such as via precision breeding. In addition, the identified MTAs could be used in marker-assisted breeding, fine mapping, and cloning of the underlying genomic regions and putative QTLs in durum wheat germplasm.The results could empower the sustainability of durum breeding by unlocking genomic regions governing complex plant characteristics. Most importantly, the results obtained in the present study could contribute a major role in understanding the durum wheat genome and improving genetic resources for breeding this crop, which in turn, supports global food security. The newly identified genes will also advance the understanding of genomic regions associated with essential characteristics used in durum wheat breeding. The identified novel variants suggest a potential use of Ethiopian durum wheat in durum wheat marker-assisted breeding. The study also provided new insight into the genetic architecture of grain yield and related traits. It indicated the potential of the diverse Ethiopian durum wheat gene pool for future improvement programs. Hence, the identified MTAs and candidate genes could be used to understand the genetic basis of genomic regions of important traits and to accelerate the development of new cultivars with high grain yield and agronomically essential traits https://www.traitgenetics.com/index.php/service-products. The genotypic data of the 420 genotypes studied can be obtained upon request.The genotypic data presented in this study were generated using a commercially available Illumina Infinium 25k wheat SNP array whose details can be found at Conceptualization: BM, KT, RO, MG, EJ, TH, Methodology: BM, MG, KT, RO, EJ, TH, CH, Data curation: BM, Formal analysis: BM, Visualization: BM, MG, Investigation: BM, MG, RO, EJ, Resources: KT, RO, EJ, MG, TH, Funding acquisition: KT, RO, EJ, MG, TH, Project administration: KT, RO, MG, EJ, TH, Supervision: KT, RO, EJ, MG, TH, CH, FH, Writing original draft: BM, Writing-review, and editing: BM, RO, EJ, MG, KT, TH, CH, and FH. All authors have read and approved the final version of the manuscript.


The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion. The mechanisms underlying many genetic variants associated with human traits are often unknown. Here, the authors identify the developmental stage-, organ-, tissue- and cell type-specific associations between genetic variation and gene expression in cardiac tissues, and describe how these associations affect complex cardiac traits and disease. Many approaches have been developed to identify causal variants and genes, including annotating GWAS loci with their closest gene1, prioritizing variants that overlap regulatory elements active in cardiac tissues3, and integrating GWAS variants with adult cardiac eQTL (expression quantitative trait loci) signals4. However, these approaches have been limited in part due to the fact that regulatory elements can regulate the expression of multiple genes over hundreds of kilobases6; and regulatory variants frequently work in a spatiotemporal context not captured in previous eQTL analyses11. For some GWAS traits and diseases, efforts have been made to fine map each GWAS locus and identify potential associations between genetic variation, gene expression, and disease14; however, the cardiac field still lacks a resource for conducting in depth annotations of GWAS loci. Here, we integrate GWAS signals for multiple cardiac traits with gene expression data for different cardiac developmental stages, tissues, and cell types across hundreds of individuals and demonstrate the power of this comprehensive resource for fine mapping causal regulatory variants and understanding the molecular mechanisms underlying cardiovascular GWAS traits and disease.Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiac traits and diseases22, indicating that cardiac regulatory variants function, and hence affect, the expression of a gene and its associated cardiac traits and disease, across a range of developmental stages and in different cellular contexts26. With the development of cell type deconvolution techniques28, bulk RNA-seq enables the characterization of cell type-specific gene expression as well as the expression of both genes and associated isoforms30. Furthermore, the GTEx consortium has generated bulk RNA-seq from hundreds of adult cardiac samples from multiple tissue types and whole genome-sequenced the donors25. Although several fetal-associated factors, such as low birth weight, maternal preeclampsia, under- and malnutrition and oxidative stress in utero, have been associated with increased risk of developing cardiovascular disease as adults32, large numbers of human fetal cardiac specimens are not readily available, and functional genetic variation in fetal heart cell types remains largely unstudied. To overcome ethical and availability issues associated with the use of fetal samples, iPSCORE has developed strategies to employ induced pluripotent stem cell (iPSC) derived cardiovascular precursor cells (iPSC-CVPCs) from hundreds of whole-genome sequenced individuals to study early developmental cardiac traits and disease35. We and others have shown that iPSC-CVPCs display epigenomic and transcriptomic properties similar to that of fetal cardiac cells41, and their differentiation results in both cardiomyocytes and epicardium-derived cells33. We have also previously deconvoluted the proportions of cell types in ~1000 bulk RNA-seq samples from fetal-like iPSC-CVPCs33, adult healthy heart and arteria42, and heart failure43, and demonstrated the power of this approach for examining the reactivation of fetal-specific genes and isoforms during heart failure29.Gene expression has long been known to be regulated in a spatial  and temporal  specific manner29 cardiac expression datasets enables the genome-wide mapping of regulatory variant functions in a spatial-temporal-specific manner. We performed an expression quantitative trait locus (eQTL) analysis on 966 deconvoluted bulk RNA-seq samples, including adult  and fetal-like (iPSC-CVPCs) tissues, and identified cardiac eQTL signals associated with 11,692 eGenes and 7165 eIsoforms. Less than half of the eIsoforms shared the same eQTL signal with their associated gene, indicating that molecular mechanisms underlying the associations of genes and their isoforms with cardiac traits and disease are different in many cases. By leveraging information about the source and cellular composition of each sample, we were able to detect more than 2578 eQTLs that function in a spatiotemporal manner. We exploited the cardiac eQTLs to investigate the molecular underpinnings of five cardiac traits and diseases from the UK BioBank and found that many of the cardiac GWAS signals colocalized with eQTL signals that function in a specific cardiac stage, organ, tissue, and/or cell type context. We also observed that three of the cardiac traits were enriched for eQTLs that function in specific spatiotemporal contexts, including pulse pressure with fetal-like, arteria, and smooth muscle cell eQTLs; pulse rate with adult, heart, and atrial eQTLs; and atrial fibrillation with left ventricle and cardiac muscle cell eQTLs. We used the colocalized eQTL and GWAS signals for fine mapping, which allowed us to identify a potential causal variant for 210 cardiac GWAS loci. Overall, our study serves as a comprehensive resource mapping cardiac regulatory variants that function in spatiotemporal context-specific manners to alter gene expression and affect cardiac traits, thereby providing potential molecular mechanisms underlying the associations of hundreds of GWAS loci with cardiac traits and disease.In this study, we show that the combined deconvoluted fetal-like iPSCORE and adult GTEx33 and integrated with RNA-seq data for 786 adult cardiac tissues, including atrial appendage, left ventricle, aorta, and coronary artery from 352 individuals included in the GTEx Consortium42 (Supplementary Data 44). An eGene may have multiple different eQTL signals, each associated with a different underlying causal variant, which can be detected by regressing out the genotype of the lead variant of the primary and subsequent conditional eQTLs. We obtained conditional eQTLs for 4394 eGenes , including 1315 with two conditional eQTLs, 395 with three, 160 with four, and 74 with five  and identified 7165 with at least one eQTL , including 988 with one or more conditional eQTL  from 139 individuals included in the iPSCORE collection45  for five hypotheses: H0) neither trait is associated; H1) only trait 1 is associated; H2) only trait 2 is associated; H3) both traits are associated, but with different underlying causal variants; and H4) both traits are associated with the same underlying causal variants). For 3221 eIsoforms (56.2% of the 5744 associated with eGenes) the most likely hypothesis was 4, indicating that the eIsoform and eGene eQTL signals share the same causal variant  shared a common eQTL signal, with their lead variant (rs28473516) having posterior probability of association (PPA) > 99% of being causal for both gene expression and isoform use  was located ~6 kb downstream of the 3′ end of the gene, whereas the lead variant for its isoform , which is not in LD with rs61876335 , was a synonymous coding variant , while 938 were not eGenes (corresponding to 1421 eIsoforms). For the 2909 eGenes with eIsoforms, to examine if the same genetic variants were associated with both gene expression and isoform use, we performed a colocalization analysisuse Fig. . On the ant Fig. . For thep ≈ 0), whereas isoform eQTLs are more likely to overlap gene bodies, including introns (p = 1.9 × 10−87), UTRs (p = 7.8 × 10−88), splice donor sites (p = 9.0 × 10−19), splice acceptor sites (p = 1.1 × 10−31) and exons  is a G > A substitution that likely disrupts the splice site for exon 19  and stage  and stage , suggesting that this spatiotemporal eQTL signal is iPSC-CVPC-specific, whereas the spatiotemporal eQTL signal for ADAM15 is significant in both iPSC-CVPC  and adult heart , when tested independently. Since the signal in iPSC-CVPC is stronger than adult, we labeled this spatiotemporal eQTL as “iPSC-CVPC-associated”. We found 814 stage- eQTL signals (combined -specific and -associated) for eGenes and 297 for eIsoforms (Bonferroni-corrected p < 0.05). Of these, the majority (620 eGenes and 191 eIsoforms) had adult-specific eQTL signals; 105 eGenes and 76 eIsoforms had associated eQTLs ; and 89 eGenes and 30 eIsoforms had iPSC-CVPC-specific eQTL signals. We also observed organ- and tissue-specific or -associated eQTL signals for 1,246 eGenes and 350 for eIsoforms  or samples annotated as a specific organ (arteria and heart) or tissue  or if they were associated with specific cell types ESPNL, where the interaction was not significant , fibroblasts (p = 6.4 × 10−10) and adipocytes (p = 6.4 × 10−3). Furthermore, we observed that cardiac muscle-, heart- and left ventricle-specific eQTLs were strongly correlated with the set of myocyte eQTLs that were described by GTEx24 , tissue  and/or cell type . The difference in the enrichment between iPSC-CVPC and adult heart could suggest that fetal eQTLs are less likely to be multigenic than adult eQTLs. Furthermore, we observed that eGenes that share the same eQTL signals are significantly more likely than expected to be associated with the same stage, organ, tissue, or cell type , and IRF1-AS1  with rs7734334, a diastolic blood pressure-associated SNP54, suggesting a likely role of the gene pair IRF1/IRF1-AS1 in driving cardiac remodeling in response to high blood pressure. These results show that cardiac genes and their paired antisense RNAs are enriched for sharing eQTL signals.Since antisense RNAs are a particular class of non-coding RNAs with regulatory functionAS1 Fig. . PAX8 plAS1 Fig. . IRF1 is45 between eQTL signals for eGenes and eIsoforms and the GWAS signals for pulse rate, QRS duration, pulse pressure, atrial fibrillation, and myocardial infarction, all obtained from the UK BioBank. We focused on 1444 eGenes and 919 eIsoforms that overlapped or were in close proximity (<500 kb) with genome-wide significant GWAS signals; and to account for the potential presence of multiple independent causal variants at the same locus, we performed colocalization using both primary and conditional eQTL signals. We found that the eQTLs for 206 eGenes and 125 eIsoforms  colocalized with high PPA with at least one GWAS signal , we observed that 51 (24.8%) and 21 (16.8%), respectively, had stage, organ, tissue, or cell type eQTLs . Overall, these data show that about one-quarter of the eQTL signals that colocalize with cardiac GWAS traits function in a spatiotemporal manner.Since associations between genetic variation and complex traits can function in a spatiotemporal mannerE2 Figs. , 5b–d, a30. Significant associations were observed for three of the five traits: pulse pressure, pulse rate, and atrial fibrillation  and three eIsoforms , of which two were iPSC-CVPC-specific (AKR1B1 and TBX2-AS1), indicating that the variants at these loci associated with pulse pressure in the adult could exert their function during cardiac development. Conversely, seven adult- eGenes  and three adult- eIsoforms , of which six were adult-specific , colocalized with pulse rate, resulting in the enrichment of pulse rate for adult-eQTLs 64, and observed very similar enrichments  we computed 99% credible sets (defined as the SNPs whose sum of PPAs is >99%). In the 65 cases where multiple eGenes or eIsoforms colocalized with the same GWAS signal, we retained only the credible set with the smallest number of SNPs and, in cases of multiple credible sets with the same number of SNPs, we retained the one having the lead SNP with highest PPA. Across the five cardiac GWAS traits, we found that most credible sets  included 10 or fewer SNPs, including 28 (13.3%) having one single causal variant and 51 (24.3%) between two and five . These results show that genetic fine mapping GWAS loci by colocalization with eQTL signals reduces the number of candidate causal variants to only a handful in the majority of loci.To identify putative causal variants underlying the shared GWAS and eQTL signals we used the colocalization data to conduct genetic fine mappingble sets 3, 53.8% ble sets 3, 53.8% q = 2.29 × 10−28) and transcriptional start sites in adult heart  have been previously reported as index SNP for the same trait  and for 25 (11.9%) the lead variant was in low to moderate LD (0.2 ≤ R2 ≤ 0.8), indicating that the causal variant is likely not the reported index SNP but rather in LD with it. For the remaining 129 (61.4%) “novel” signals, the putative causal SNP that we identified was in a locus not associated with the same trait in the GWAS catalog. These results show that colocalizing eQTLs with GWAS signals from the UK BioBank identifies putative causal variants associated with cardiac traits at hundreds of loci, of which the majority are novel.We examined if the causal variants identified by our colocalization analysis correspond to lead index GWAS SNPs that have previously been described. Specifically, we investigated whether they were reported as associated with the same trait in the GWAS Catalogait Fig. . For theCEP68  with the previously identified index variant rs2723064 67. We also found that the SNP (rs2738413) with the highest PPA for atrial fibrillation and the cardiac muscle- eQTL in the intron of SYNE2  with the highest PPA for pulse pressure and the fetal-like-eQTL for PRKG1  with the highest PPA for atrial fibrillation and the cardiac muscle-eQTL for E2 Figs. , 5b–d hahe SNP rs000060 wiFLCN isoform (ENST00000389168.6_2)  with the highest PPA for pulse rate and the eQTL for ACTN2  associated with heart failure in a GWAS75 was in low R2 but high D’ with rs12724121 , suggesting that a potential synthetic association77 exists between these two variants. Our results indicate that rs12724121 may regulate pulse rate  through the altered expression of ACTN2 and, since elevated resting heart rate may cause heart failure78, our findings may explain the previously observed GWAS association between this locus and heart failure. Of note, these observations show the importance of investigating LD in terms of D’ since synthetic associations77 between variants can exist at GWAS loci.On the other hand, many of the fine-mapped GWAS signals were novel . For instance, the SNP (rs1708618) with the highest PPA for pulse rate and the eQTL for an TN2 Fig.  is anothIn summary, our fine mapping analysis identified the likely causal SNPs in 81 previously characterized GWAS loci and 129 novel GWAS loci, demonstrating the power of this approach to uncover insights into the biology of cardiac traits and disease.79 and transcribed regions for variants affecting alternative splicing80. Taking spatiotemporal information about the cardiac samples into account has provided us with the unique opportunity to identify more than 2,500 eQTL signals dependent on developmental stage, organ, tissue, and/or cell type. Using these data, we show that regulatory variants underlying eQTL signals shared between multiple eGenes are more likely to function in a spatiotemporal manner; and that eGenes which share the same eQTL signals tend to display the same spatiotemporal regulation. Our findings also indicate that fetal-like- eQTLs are less likely to be associated with multiple eGenes than adult eQTLs, which suggests that gene expression could be more individually regulated in early cardiac development. We demonstrate that, when paired genes and antisense transcripts are both eGenes, they are enriched for sharing the same eQTL signal, suggesting that a tight regulation of protein synthesis is required. Overall, we have generated an invaluable resource comprised of cardiac eQTL signals for 11,692 eGenes and 7165 eIsoforms, which can be used to understand the molecular mechanisms underlying the association of genetic variants with cardiac traits and disease.We have conducted one of the largest eQTL analyses of human cardiac samples considering both eGenes and eIsoforms and taking spatiotemporal context into consideration. We combined 180 fetal-like cardiac samples (iPSC-CVPC) with 786 adult cardiac samples from multiple tissues, including atrium, ventricle, aorta, and coronary artery. We showed that 44.9% of eIsoforms were either not associated with an eGene or had a different eQTL signal than their associated eGene; and that eGenes were enriched for being associated with regulatory variants at promoters and enhancers while eIsoforms were enriched for being associated with variants that affect post-transcriptional modifications. These results were consistent with previous studies, which described an enrichment in regulatory elements for variants affecting gene expression30. Furthermore, GWAS of multiple cardiac traits, such as atrial fibrillation and PR interval, identified loci associated with embryonic development-associated genes and genes whose mutations are known to cause serious heart defects, such as TTN, GATA4, MYH6, NKX2-5, PITX2, and TBX54. However, the extent to which regulatory variants that function in a spatiotemporal manner underlie cardiac trait GWAS signals was unknown. We showed that many eQTL signals that colocalize with cardiac GWAS traits function in a spatiotemporal manner. Furthermore, we found that three of the five traits examined in this study were enriched for eQTLs that function in specific spatiotemporal contexts. Surprisingly, pulse pressure was enriched for fetal-like-eQTLs, indicating that a subset of genetic variants associated with this adult trait exert their function during early cardiac development.Recent studies showed that certain GWAS loci are associated with tissue- and cell-type-specific regulatory elements and eQTLsAGPAT1, CBWD1, RPL13, and PRKG1). For 81 (38.6%) of the 210 GWAS signals, the SNP with the highest PPA in the credible was either the index SNP  or in LD  with the index SNP for the same trait in the GWAS catalog. For the remaining 129 (61.4%) GWAS signals, the putative causal SNP that we identified was in a locus not associated with the same trait in the GWAS catalog. These findings show that fine mapping provides the blueprint to both understand the molecular mechanisms underlying known and novel GWAS loci and to uncover insights into the biology of important cardiac traits and disease. Of note, there remains hundreds of GWAS signals that did not colocalize with eQTLs, indicating that a substantial fraction of the inherited component of human traits is not explained by regulatory variation, which is consistent with the recent findings of others81. Therefore, future studies on the functional characterization of genetic variants will likely require integration with other functional information, such as epigenomic data or trans-eQTLs to identify candidate causal variants37. Overall, we show that fine mapping using our cardiac eQTL resource identified the causal variant underlying hundreds of GWAS signals in five cardiac traits, led to an understanding of the underlying spatiotemporal context, and provided novel insights into the biology of the corresponding cardiac GWAS trait.Using colocalization between eQTL and GWAS signals, we fine mapped 210 unique GWAS signals for five cardiac traits and disease. We were able to identify one single likely causal variant  for 28 of these GWAS signals, while for an additional 51 we were able to restrict the number of likely causal variants to fewer than five. Fifteen of these fine-mapped loci were associated with spatiotemporal eQTLs, including four that were iPSC-CVPC-eQTLs  from 352 GTEx subjects25  bammarkduplicates87. To quantify TPM gene expression and relative isoform abundance, we used RSEM V.1.2.2088 with options rsem-calculate-expression --bam --num-threads 16 --no-bam-output --seed 3272015 --estimate-rspd --paired-end --forward-prob 0. Using this pipeline, we determined the expression of 62,492 genes and their corresponding 229,835 isoforms. Only 19,586 autosomal genes with TPM ≥ 1 in at least 10% of the 966 samples were considered as expressed and used for eQTL analysis. Likewise, 37,032 isoforms (TPM ≥ 1 and usage >10% in at least 10% of the 966 samples) from 10,337 expressed genes were used for isoform eQTL analysis.RNA-seq data for all 966 cardiac samples (180 iPSC-CVPC and 786 adults) was obtained from dbGaP (phs000924 and phs000424) and processed as followsnormalize.quantiles (preprocessCore 1.50.0) and qnorm functions in R, in order to obtain mean expression = 0 and standard deviation = 189.To normalize gene and isoform expression and usage across samples, we performed quantile-normalization using 29. Briefly, we determined marker genes for eight cardiac cell types  using single-cell RNA-seq91 and used their average expression levels in each cell type to deconvolute cell type proportions using CIBERSORT27.We deconvoluted cell type proportions for all 966 cardiac RNA-seq samples as followsp > 10−6), and that were within 500 kb of any expressed gene. Specifically, we expanded the coordinates of each of the 19,586 expressed genes (500 kb upstream and downstream) and extracted all variants in these regions using bcftools V.1.9 view with parameters --f PASS -q 0.01:minor. Next, we merged the resulting VCF files (bcftools merge -m none), normalized indels and split multiallelic variants (bcftools norm -m) and removed variants that were genotyped in fewer than 99% of samples (bcftools filter -i ‘F_PASS(GT!=“mis”) > 0.99). Finally, we converted the resulting VCF files to text using bcftools query and converted the genotypes from character strings  to numeric . This resulted in 4,962,200 total variants that were common between the two studies (iPSCORE and GTEx) and used for eQTL mapping.VCF files from WGS data were obtained from dbGaP (phs001325 and phs000424). We retained variants with MAF > 1% in both studies, that were in Hardy-Weinberg equilibrium . To account for genetic relatedness between samples, we performed eQTL mapping using a linear mixed model (LMM) with limix v.3.0.493 (scan function), which incorporates a kinship matrix as a random effect:Yi is the normalized expression value for sample i, βj is the effect size (fixed effect) of SNP j, Xij is the genotype of sample i at SNP j, PCim is the value of the mth genotype principal component for the individual associated with sample i, γm is the effect size of the mth genotype principal component, M is the number of principal components used (M = 20), PEERin is the value of the nth PEER factor for sample i, γn is the effect size of the nth PEER factor, N is the number of PEER factors used, Cip is the value of the pth covariate for sample i, γp is the effect size of the pth covariate, P is the number of covariates used, ui is a vector of random effects for the individual associated with sample i defined from the kinship matrix, and ϵij is the error term for individual i at SNP j.To identify genetic variants that are associated with cardiac gene expression, we performed a joint eQTL analysis using all 966 samples. For each expressed gene, we used q-values <0.05 as significant.To identify additional independent eQTL associations for a gene or isoform , we performed a stepwise regression analysis in which we re-performed eQTL analysis and included the genotype of the lead eQTL as a covariate. We repeated the analysis to discover up to five conditional associations. For each iteration, we performed the two-step procedure described above and considered conditional eQTLs with 94 that first corrects at the gene level and then at the genome-wide level. (1) For each gene, p-values were FDR-corrected using eigenMT, which takes into account the LD structure of the tested variants; and (2) across the lead variants for all the 19,586 tested genes and 37,032 isoforms, p-values were corrected for false discovery rate (FDR) using Benjamini-Hochberg’s correction and considered only eQTLs with q-values < 0.05 as significant.To perform FDR correction, we used a two-step procedure similar to the one described in Huang et al.44.Summary statistics for all eQTLs are reported in “Figshare [10.6084/m9.figshare.c.5594121]”We performed eQTL mapping using the following covariates  in the expanded Supplementary Data We performed principal component analysis (PCA) on WGS variants to determine the global ancestry of each individual in this study96 for obtaining the maximum number of eQTLs. We initially calculated PEER factors on the 10,000 expressed genes with the largest variance across all samples using the R package peer 1.0. To limit biases due to the expression levels of each gene, we divided the 19,586 expressed genes into ten deciles based on their average TPM, and selected 20 random genes for each decile, for a total of 200 genes. We next performed eQTL analysis on each of the 200 genes using the following numbers of PEER factors: 5, 10, 20, and increments of 20 between 20 and 300. These eQTL analyses of the 200 genes to determine the optimal number of PEER factors was performed using the same covariates as for the combined eQTL analysis: (1) sex; (2) normalized number of RNA-seq reads; (3) % of reads mapping to autosomes or sex chromosome; (4) % of mitochondrial reads; (5) 20 genotype principal components to account for global ancestry; (6) the selected number of PEER factors to account for transcriptome variability; and (7) kinship matrix. We observed the maximum number of eQTLs using 280 PEER factors. To further refine the optimal number of PEER factors, we performed a second eQTL analysis using 270, 275, 285, and 290 PEER factors, which determined that 285 PEER factors were optimal .To define a credible set of candidate causal variants for each eQTL association, we performed genetic fine mapping using the bedtools intersect and compared the proportions of variants overlapping each annotation between eGenes and eIsoforms using Fisher’s Exact Test. Promoters were defined as the 2000 bp upstream of the transcription start site. Exons and UTRs were obtained from Gencode V.34lift37. For the intronic variants, we calculated their distance from the closest exon to determine their overlap with splice sites.We obtained fine-mapped variants for all eQTL signals for both eGenes and eIsoforms with PPA > 0.01. We determined the overlap of each fine mapped variant and each genomic annotation using coloc.abf function from the coloc package in R45. This Bayesian method uses p-values of the variants tested for two traits (eIsoform and eGene) to calculate the posterior probabilities of association (PPA) for each of the five hypotheses at a specific locus: (1) H0: neither trait has a significant association at the tested locus; (2) H1: only the first trait is associated; (3) H2: only the second trait is associated; (4) H3: both traits are associated but the underlying variants are different; and (5) H4: both traits are associated and share the same underlying variants. Since multiple eQTL signals may be present for both eIsoforms and eGenes , for each eIsoform/eGene pair, we considered only the colocalization with highest PP-H4.For each of the 5744 eIsoforms whose associated genes had eQTLs, we performed colocalization using the To determine whether two eGenes or eIsoforms from different genes shared the same eQTL signal, we used the same colocalization approach as described above.To determine whether paired genes and antisense RNAs were enriched for sharing an eQTL signal, we obtained the gene symbols of all eGenes from Gencode. We identified 163 gene/antisense pairs where both the gene and its antisense RNA were eGenes and whose gene symbols were “Gene A” and “Gene-A-AS1”, respectively. We found that 47 of these pairs shared the same eQTL signal (PP-H4 > 0.8) and tested whether the proportion of colocalized eQTL was greater than expected between any pair of eGenes that were within 500 kb of each other  using a Fisher’s Exact Test.P-values were adjusted using Benjamini-Hochberg’s method. Enrichments with FDR < 0.05 were considered significant. This analysis was repeated for the comparison between pairs that shared the same eQTL signal (PP-H4 > 0.8) and pairs that had distinct signals (PP-H3 > 0.8). For the gene/antisense pairs with distinct signals (PP-H3 > 0.8), we used all fine mapped variants (from finemap.abf) for both genes and antisense with PPA > 0.01.To determine if eQTLs associated with gene/antisense pairs that act in the same versus opposite directions have differential enrichments, we obtained all fine-mapped variants with PPA > 0.01  and annotated each variant by its location relative to both the gene and antisense . We then used a Fisher’s Exact Test to calculate the enrichment by comparing the proportion of SNPs within the genomic region between correlated and anti-correlated gene/antisense pairs. iscan function in limix47, we implemented the following linear mixed model, which we applied to the lead variant of each eQTL signal :Yi is the normalized expression value for sample i, βj1 is the effect size (fixed effect) of SNP j, Xij is the genotype of sample i at SNP j, βk2 is the effect size of cell type k, Zik is the fraction of cell type k for sample i, βjk3 is the effect size of the interaction between genotype Xij and cell type Zik, Ciq is the value of the qth covariate for sample i, γq is the effect size of the qth covariate, Q is the number of covariates used , ui is a vector of random effects for the individual associated with sample i defined from the kinship matrix, and ϵij is the error term for individual i at SNP j.To detect eQTL associations with cell types, tissue or developmental stage, we obtained the lead variant for each significant eQTL from the combined analysis for each eGene and eIsoform and used the same linear mixed model described above and included an interaction between genotype and context. Since each eQTL signal underlies one single causal variant, the spatiotemporal context of the lead variant is sufficient to annotate the whole eQTL signal. We considered a total of 16 different contexts: two organs (arteria and heart), four tissues , eight cell types , and two developmental stages . To avoid counting these associations as “context-specific” we included the following filtering step for all spatiotemporal eQTLs .While the interaction LMM described above identifies context-specific eQTLs, an interaction would also be considered significant if the eQTL is active in all other samples but the context being tested : the eQTL signal is considered as context-specific (left ventricle-specific);eQTL is significant only for “all the other samples”: the eQTL is not considered as context-specific;eQTL is significant for both sets but its effect size is stronger  in the tested context: the eQTL is context-associated (left ventricle-associated);eQTL is significant for both sets but its effect size is stronger  in “all the other samples”: the eQTL is not considered as context-specific or context-associated.For binary contexts , we divided all samples into two sets corresponding to the two binary values (for example: (1) left ventricle; (2) all the other samples). We performed an additional standard eQTL analysis on each of the two sets. eQTL is significant only for the tested cell type : the eQTL signal is considered cell type-specific;eQTL is significant only for the bottom quartile: the eQTL is not considered as cell type-specific;eQTL is significant for both the top and bottom quartiles but its effect size is stronger  in the top quartile: the eQTL is cell type-associated;eQTL is significant for both the top and bottom quartiles but its effect size is stronger  in the bottom quartile: the eQTL is not considered as cell type-associated.For cell types, we divided all samples into quartiles and analyzed the top and bottom quartile sets. When analyzing cell types, the following four scenarios may occur:In Supplementary Figures, we show examples of eQTLs with significant interactions with stage  and cell type-specific information for 286,725 snATAC-seq peaks obtained from human adult heartsbedtools V.2.27.1 intersect. We found that 18,928 variants  overlapped snATAC-seq peaks. If multiple variants were associated with the same gene and overlapped snATAC-seq peaks, we considered only the peak that overlapped with the variant with the highest PPA.Across all eQTL associations, we obtained 201,082 fine mapped variants with PPA > 0.01  and intersected their coordinates with the snATAC-seq peaks using Using the eQTL-intersected peaks, we then tested the enrichment of cardiac muscle-associated eQTLs (corresponding to 367 eGenes) in peaks that are specific to each of the nine cell types detected in snATAC-seq, including atrial cardiomyocyte, ventricular cardiomyocyte, adipocyte, fibroblast, endothelial, smooth muscle, lymphocyte, macrophage and nervous. Specifically, we used a paired t-test comparing the RAS of peaks that are specific to a cell type compared to peaks that are specific to all other cell types. Results are shown in Supplementary Fig. 24. We downloaded myocyte interaction eQTLs (ieQTLs) for heart left ventricle from the GTEx Data Portal and calculated the Pearson correlation coefficient between effect sizes for matching eGenes using the cor.test function in R. We observed positive associations between GTEx ieQTLs for myocytes in heart left ventricle and cardiac muscle-specific/associated, heart-specific/associated eQTLs, and heart ventricle-specific/associated eQTLs  using linear regression. To confirm that eQTL signals associated with multiple eGenes were more likely than expected to be associated with the same spatiotemporal context and to ensure that enrichments were not due to a small number of signals shared by many eGenes , we also performed a permutation analysis. We counted the number of occurrences where the same spatiotemporal context was shared between two colocalizing eGenes and compared it with the distribution of 100 permutations of each eGene-spatiotemporal context association. We calculated Z-scores and converted them to https://pan.ukbb.broadinstitute.org/]”. All data was obtained in hg19 coordinates. We sorted and indexed all of the files using tabix92 and, for each trait, extracted all genome-wide significant SNPs . We found 1444 eGenes and 919 eIsoforms overlapped or were in close proximity (<500 kb) with genome-wide significant GWAS variants. We performed colocalization between each significant eQTL signal and all genome-wide significant GWAS signals within the same locus  using the coloc.abf function in R45. Colocalization tests if two genetic association signals (such as eQTL and GWAS) have the same underlying causal variant by comparing the effect sizes of all the variants genotyped at the locus of interest. Colocalization was performed only on loci that included at least one genome-wide significant eVariant and one genome-wide significant GWAS variant. For downstream analyses, we considered a trait to colocalize with a gene if the GWAS signal colocalized (PP-H4 > 0.8) with the eGene or any of its eIsoforms, while we discarded all the colocalizations with high PP-H3. All colocalization results have been deposited to “Figshare [10.6084/m9.figshare.c.5594121]”44.We obtained GWAS summary statistics for five cardiac traits from the “Pan UK BioBank repository [cluster.louvain function in igraph 1.3.5 that shared the same lead SNP or had lead SNPs in high LD (D’ > 0.8). This analysis resulted in 210 independent GWAS signals.To identify all GWAS loci that colocalized with at least one eQTL, we calculated the linkage disequilibrium (LD) between all pairwise combinations of lead SNPs across all 331 colocalizations. We next identified clusters of eQTL signals using the 14, which converts the p-value of each variant at each of the 210 GWAS loci to PPA, without considering the colocalization with eQTLs. We used the finemap.abf function from the coloc package in R45 on the GWAS summary statistics at each of the 210 loci. 99% credible sets describe the smallest set of variants that cumulatively contribute to ≥99% of the PPA in each locus.Standard genetic fine mappingcoloc.abf function in R45, which provides the PPA that each tested variant is causal for the GWAS and eQTL associations. For each colocalization , we constructed 99% credible sets as described above. For each of the 210 independent signals , we selected the credible set that had the fewest SNPs. If two credible sets had the same number of SNPs, the set containing the lead variant with the highest PPA was chosen.We performed fine-mapping on each of the 210 GWAS signals using the To test the utility of cardiac eQTLs to identify potential causal variants for cardiac traits, we performed fine mapping using two different methods:P-values were corrected using Benjamini-Hochberg across all tests for each trait. Traits were considered to be context-associated if they had an FDR-corrected p-value < 0.1 at the 0.8 threshold.Enrichment of context-associated eQTLs in cardiac traits was calculated using a Fisher’s Exact Test at multiple PP-H4 thresholds , where the contingency table consisted of two classifications: (1) if the eQTL signal was significantly context-associated (FDR < 0.05); and (2) if the eQTL signal colocalized with the GWAS trait greater than the PP-H4 threshold. 98—see Supplementary Note 2 for details on this method. We initially performed independent tissue-specific eQTL analyses on five tissues  using a linear mixed model (limix) and the same covariates as our combined eQTL approach: sex, normalized number of RNA-seq reads, % of reads mapping to autosomes or sex chromosome, % of mitochondrial reads, 20 genotype PCs, and kinship matrix, and recalculated the number of optimal PEER factors. Gene expression was quantile-normalized across all samples within each of the five datasets, and the number of optimal PEER factors was determined using the same method described for the combined eQTL analysis: we obtained 200 random genes and performed eQTL analyses using a range of PEER factors and selected the number of PEER factors for each dataset associated with the maximum number of detected eQTLs for these 200 genes. We used the following number of PEER factors: 40 for iPSC-CVPC, 60 for atrium, 70 for ventricle, 70 for aorta, and 30 for coronary artery. The kinship matrix was included as a random effect. Conditional eQTL analysis and FDR correction was performed using the same method as our combined eQTL approach  method98. We annotated each eQTL signal as associated with a tissue if the LFSR < 0.05 (Supplementary Data −30). At the least stringent LFSR threshold (LFSR = 0.05), we observed <11,500 eQTLs in each of the four adult tissues and 8856 in iPSC-CVPCs ; on the other hand, mash uses information from other tissues to improve power to detect eQTLs in a specific tissue. Therefore, mash is able to identify eQTLs that are “active” in a tissue because they occur in multiple other tissues, rather than describing tissue-specific eQTLs.lm function in R where binary state annotation was modeled as a function of SNP PPA. Enrichment p-values were FDR-corrected (Benjamini-Hochberg).Across all 210 fine-mapped GWAS loci, we obtained 2157 SNPs with PPA ≥ 0.01 and tested their enrichment in Roadmap Chromatin States for fetal heart, adult right atrium, adult left ventricle, and adult right ventricle using a linear regression model with the 66, which is a deep-learning-based framework that predicts the chromatin effects of each SNP. One of the outputs of DeepSea (version “Beluga”) is the disease impact score for each SNP, which was computed using a logistic regression model that prioritizes candidate disease-associated SNPs based on their predicted effects on transcriptional or post-transcriptional regulation. To determine whether GWAS candidate causal eQTLs are likely to impact disease, we used a linear regression model to test the association between SNP PPA and disease impact score.We provided a VCF of all 2157 SNPs with PPA ≥ 0.01 in the 99% credible sets of the 210 fine-mapped GWAS loci as input into DeepSeaFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


Homo sapiens. Our work confirms and extends links between human-gained enhancers active in fetal brain tissue and cortical surface area measured in adults, including left-hemisphere regions related to speech. The study also reveals that regions of the genome carrying introgressed Neanderthal variants make a significantly diminished contribution to connectivity of the left-hemisphere uncinate fasciculus, a white-matter tract involved in mapping sound to meaning.Which aspects of our genetic makeup contribute to distinctly human features of brain anatomy? Here, we identify associations between common DNA variants and interindividual differences in cortical circuitry, based on neuroimaging in tens of thousands of people, and assess how they relate to genomic regions of evolutionary interest, capturing different timescales along the lineage leading to  ZIC4, a gene implicated in neurogenesis. This work demonstrates how genomic investigations of present-day neuroanatomical variation can help shed light on the complexities of our evolutionary past.Alterations in brain size and organization represent some of the most distinctive changes in the emergence of our species. Yet, there is limited understanding of how genetic factors contributed to altered neuroanatomy during human evolution. Here, we analyze neuroimaging and genetic data from up to 30,000 people in the UK Biobank and integrate with genomic annotations for different aspects of human evolution, including those based on ancient DNA and comparative genomics. We show that previously reported signals of recent polygenic selection for cortical anatomy are not replicable in a more ancestrally homogeneous sample. We then investigate relationships between evolutionary annotations and common genetic variants shaping cortical surface area and white-matter connectivity for each hemisphere. Our analyses identify single-nucleotide polymorphism heritability enrichment in human-gained regulatory elements that are active in early brain development, affecting surface areas of several parts of the cortex, including left-hemispheric speech-associated regions. We also detect heritability depletion in genomic regions with Neanderthal ancestry for connectivity of the uncinate fasciculus; this is a white-matter tract involved in memory, language, and socioemotional processing with relevance to neuropsychiatric disorders. Finally, we show that common genetic loci associated with left-hemispheric pars triangularis surface area overlap with a human-gained enhancer and affect regulation of  Homo sapiens, as well as more recent shifts in brain shape  for tens of thousands of individuals, such as those collected by the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium  and in pPrior work by Tilot et al.  investign = 30,332) from the UK Biobank, including a subset of individuals from the prior work on hemisphere-averaged measures  to identify genetic variants associated with white-matter connectivity within and across hemispheres then assessed links to evolution across the same annotations as those used for cortical surface area. We demonstrated the value of integrating neuroimaging genomics with data on human-specific regulatory elements, gene expression, and evolutionary history for identifying genetic pathways of interest, using the ZIC4 gene as an illustration. Finally, we investigated human accelerated regions (HARs)  –27 and ds (HARs) , assessiures n = ,924 to iSI Appendix, Table S1). In line with previous work, common genetic variants accounted for 40.22% (SEM = 4%) of the phenotypic variation in total surface area . We observed very high genetic correlations between our independent UK Biobank subset of White British ancestry and the earlier GWAS data  , suggesting consistent genetic underpinnings of cortical anatomy in Grasby et al. . Next, we correlated the SDS results of all brain regions with results from Tilot et al. .To further minimize potential confounding effects of residual population stratification on GWAS effect size estimates, we applied the ancestry regression procedure implemented by Tilot et al. . As in tt et al.  but founFDRP = 0.004]  in the UK Biobank . GWAS results for cortical regions were controlled for the matching total hemispheric surface area and may thus be considered independent of the total measures. Common variation accounted for 39.02% (SEM = 3.14%) of total left-hemispheric and 39.37% (SEM = 3.21%) of total right-hemispheric surface area variation . Because our targeted follow-up of Tilot et al. . Significant enrichments were identified for brain regions with average to high SNP-heritability estimates; it is possible that some positive findings may have been missed for brain regions with low SNP-heritability estimates due to lack of power . Left-hemisphere cortical regions that reached significance, pars triangularis  and pars opercularis , together form Broca’s area, well-known for its roles in speech-associated functions, among others  = 0.57(0.11), FDRP = 0.005; left pars opercularis: enrichment(SEM) = 0.03(0.3), FDRP = 0.008] .After false discovery rate (FDR) correction for 43 independent GWAS traits  , after c= 0.022]  using a = 0.022] , consistP < 5 × 10−8) left-hemispheric pars triangularis–associated loci (LD blocks harboring genome-wide significant SNPs that are in LD r2 > 0.6), there are two that overlap with a fetal brain HGE. This includes the chromosome-3 locus near ZIC4, which has the lowest P value for association with this brain region  and contains a genome-wide significant SNP  that falls in an HGE element located adjacent to ZIC4  . The minor allele (T) of rs1875748 is associated both with increased surface area of the left-hemispheric pars triangularis and with increased ZIC4 expression in adult brain cortical tissue  A. We annand ZIC1 A. SNPs il tissue B. Comparl tissue C. The Hul tissue .ZIC4 expression in adult brain tissue, we further investigated the genetic architecture of the ZIC4 locus . We looked for human-specific regional deletions and insertions in the last ∼6 My of human evolution by aligning human and chimpanzee sequences for a genomic locus flanking ZIC4 45 kb upstream and downstream  but found no major genomic rearrangements. Finally, we generated a multiple sequence alignment for ZIC4 coding sequence across 36 mammalian species to run phylogenetic analysis by maximum likelihood (PAML) (ZIC4 coding sequence with the likelihood ratio test shows that 13.5% of the codons evolve under positive selection (χ2P < 0.001) .Given the association between the human-derived allele at rs1875748 and d (PAML)  to test P < 5 × 10−8 and SNPs that are in LD r2 > 0.6, comprising 2,596 loci in total) from all full and regional left- and right-hemisphere surface area GWASs. There were 10 loci coinciding with HARs and 23 loci located within AMH-derived DMRs . Intriguingly, 5 of the 10 loci that overlap with HARs are significantly associated with left or right hemisphere lateral orbitofrontal surface area, while loci coinciding with AMH-derived DMRs are associated with a range of cortical regions including superior parietal, lateral occipital, and pars orbitalis.Finally, we applied our overlap analysis to HARs and AMH-derived DMRs (two interesting evolutionary annotations that could not be reliably investigated with partitioned heritability) using an extended list of genome-wide significant loci .This study made use of imaging-derived phenotypes generated by an imaging-processing pipeline developed and run on behalf of the UK Biobank , 59. We https://www.well.ox.ac.uk/~gav/qctool_v2/). In each of the three study samples, variants with a minor allele frequency <0.1%, Hardy–Weinberg equilibrium P value <1 × 10−6, imputation quality INFO score <0.7 (included in the imputed UK Biobank data) and multiallelic SNPs were excluded.For all individuals, imputed variant genotype data  were extracted and variant-level quality control and SNP statistics were computed for each dataset using qctool v2.0.2  implemented in the R package PhenoSpD  to calcuhttps://github.com/bulik/ldsc/wiki/LD-Score-Estimation-Tutorial). SDS  between trait-increasing SDS values and GWAS Z-scores was applied. This analysis was applied for 33 cortical regions segmented with FreeSurfer  using Benjamini–Hochberg FDR . SDS  analysisal). SDS  was theneeSurfer  and fulleeSurfer , P valuehttps://github.com/bulik/ldsc/wiki/Partitioned-Heritability). For the replication study, proportion of heritability, heritability enrichment, and SNP proportions within fetal cortical HGEs active at the seventh postconception week (https://github.com/bulik/ldsc) to enhance reliability of heritability estimates. For HGEs, we merged HGEs active in human fetal cortical tissue (r2 = 1). The archaic deserts . HGEs active in fetal cortical tissue, archaic deserts and Neanderthal introgressed alleles were controlled for the baselineLD v2.2 model provided by the Alkes Price group  , BRAINEAC (www.braineac.org), CommonMind consortium  was used to visualize the association between rs1875748 and ZIC4 expression in adult cortical brain tissue. Neanderthal and Denisovan allele states for SNPs of interest were derived from the ancient DNA genotype data provided by the Max Planck Institute for Evolutionary Anthropology   with clumped GWAS SNPs. We then identified the loci that overlap with HAR or AMH-derived DMRs by using the findOverlaps function from the GenomicRanges R package  . Using tnsortium , and psynsortium . Loci wensortium  and Schmnsortium . GTEx V8dertal/) . Sequenc, DOTTER  for alig, DOTTER . We used, DOTTER  to detecth PLINK , and sel package .Genetic and evolutionary analyses were conducted on the computing cluster of the Max Planck Institute for Psycholinguistics. Results were parsed and organized with bash scripts. Statistical analyses were conducted with R, Python, and Linux Shell. Plots were generated using R packages plotly and ggplot2.Supplementary File