Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35–43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations. The value of common mapping populations in diverse species has been recognized by quantitative genetics for decades. These common populations can be genotyped by a single research group, and genetically identical individuals distributed to the larger research community which could score traits of interest and test for associations between published genetic markers and trait values across the population. Early populations included sets of recombinant inbred lines (RILs) . RIL pop2r value of 0.1 in sorghum range from 10 kilobases to 350 kilobases depending on the population and genetic marker set employed  gene in sorghum, and chromosome 7, corresponding to dwarf3 (dw3) (teosinte branched1 (tb1) but was later fractionated into multiple partially linked loci for ear-related traits  has a large selection sweep on Chr6 that is linked to dwarf2 (dw2) (The Sorghum Association Panel (SAP) was first assembled in 2008  lines seInitially, the SAP was genotyped for only a set of 49 simple-sequence repeat markers . Here, whttp://people.beocat.ksu.edu/gpmorris/sorghum_GBS_data/readme.txt/ (https://doi.org/10.6084/m9.figshare.11462469.v5)  generated using conventional genotyping by sequencing and aligned to version 1 of the BTx623 sorghum reference genome were downloaded from 2469.v5) . After ihttps://doi.org/10.6084/m9.figshare.13143389). A total of 234 trait datasets scored across all or subsets of the SAP were employed in this study. One hundred ninety of these trait datasets were drawn from published sources as described in Supplementary Table S1. An additional 12 phenotypes were generated using sums or ratios of published trait datasets. The traits and formulas used to generate these 12 phenotypes are provided in Supplementary Table S2. The remaining 32 trait datasets employed in this study were previously unpublished datasets collected at either the University of Nebraska-Lincoln in Nebraska (19 datasets) or Clemson University in South Carolina (13 datasets). All these phenotype data are provided on FigShare , stem diameter (between the 3rd and 4th leaf), and node number were measured from a representative plant at reproductive maturity in 2016. A ratio of plant height to inflorescence/plant height to flag leaf was also calculated (Supplementary Table S2). Inflorescence architecture traits were measured using two representative plants at maturity in 2016, 2017 and included inflorescence length, rachis length, rachis diameter, number of primary branches, length of primary branches at the bottom third of the inflorescence, length of primary branches at the top third of the inflorescence, number of secondary branches on a primary branch, number of third-order branches on a secondary branch, first internode length on a primary branch, prominent awns (binary trait), and prominent glumes (binary trait). During 2017 one additional trait, infertility , was also collected . Two ratios were also calculated from each year in this dataset: inflorescence length/rachis length, rachis length/rachis diameter (Supplementary Table S2). Best linear unbiased predictors (BLUPs) for each phenotype were calculated by fitting a linear mixed model using R package lme4 , iron (ppm), prolamin as a percentage of dry weight, and seed density (grams per milliliter). All except 1000-grain weight and seed density were measured using near infrared and were evaluated in all 3 years. All phenotypes were measured in all 3 years except seed density which was measured only in 2017. For each trait, a linear mixed model was fit using the R package lme4 . Two plant height traits were measured: plant height from ground/plant base to panicle apex and flag leaf height. Six reproductive traits were measured: number of grains per primary panicle and grain yield per primary panicle, measured in all 3 years, glume tenacity , primary panicle branch length, panicle length, and exsertion in 2017. Five biochemical traits: magnesium (% dry basis), manganese (ppm), nitrogen (mg), phosphorus (% dry basis), and zinc (ppm) measured from ground grain samples in 2013 and 2014 using near-infrared spectroscopy  each trait dataset was normalized before analysis using the R package, bestNormalize version 1.4.3 . The funGWAS was performed independently on each of 234 trait datasets. The number and identity of lines evaluated for various traits varied both across and within studies (Supplementary Table S1). For each trait dataset, analyzed with each of the two genetic marker datasets, genetic markers were separately filtered to remove those markers with a minor allele frequency of <5% among individuals with recorded values for the target trait.The resulting marker sets were employed for GWAS analysis using two single-locus models; generalized linear model (PC), mixed linear model (PC+K) .Me) which will be lower than or equal to the actual number of SNPs genotyped. Statistical significance thresholds were set to 10−5.958  and 10−6.154  for 2013 and 2020 genetic marker datasets, respectively when SNPs were first filtered based on their minor allele frequencies in the set of 304 sorghum varieties shared between the two datasets. The statistical significance threshold for the 2020 genetic marker dataset when markers were filtered based on their frequency in the complete set of 343 lines genotyped in that dataset was 10−6.1379 . Trait values were available for different subsets of lines for different trait datasets. While markers were further filtered to remove low frequency markers in subset populations as described above, the same three P-value cutoffs for statistical significance were employed for all GWAS results to provide consistency across analyses. Manhattan plots were created using the CMplot R package (v3.6.2) ) software package with default parameter settings ((v3.6.2) . When mu(v3.6.2) .V in the 90 K random control set using the MashR function “estimate_nul_correlation_simple” and included the resulting correlation matrix V (V = Vhat) into our analysis. We used simple null correlation to find arbitrary patterns of correlation among various conditions. Instead of calculating correlation among all the null tests, we estimated simple null correlation among the random subset as it gives a quick approximation of the null correlation matrix. These datasets were analyzed using mashr/v0.2.40 using the most recent code revision available on github as of August 9, 2020 (lfsr) <0.001 was used to determine the number of traits associated to a given SNP , reproductive phenotypes, and seed phenotypes  values . Traits r2 of 0.1 between genotyped markers and causal variants , while it is possible to align results at the gene or region level it is not possible to confidently quantify the precise number of markers shared between the two datasets. If current marker datasets are sufficient to saturate the sorghum genome, using different marker datasets would be expected to identify signals from the same regions of the genome. However, if current marker sets are insufficient to saturate the sorghum genome, using different marker datasets to analyze the same trait datasets would be expected to identify only partially overlapping sets of genomic intervals for the same trait datasets.P-values inconsistent with expectations, when analyzed with one of the marker sets . The GWAS results for these two trait datasets were removed from downstream analyses for both marker sets.Each genetic marker dataset was employed to conduct GWAS for each of the 234 trait datasets. For each genetic marker dataset, imputation and filtering based on minor allele frequency specifically within the set of 304 shared lines were conducted using a common set of criteria . Similarly, a common trait data processing protocol was employed for each of the 234 trait datasets. This protocol incorporated normalization and outlier removal. GWAS for two of the 234 trait datasets produced questionable results, including distributions of observed N = 48 significant peaks). These 48 total significant peaks in individual GWAS with 2013 dataset localized to 26 unique regions of the genome as a result of repeated identification of the same genomic intervals in analyses of different trait datasets (N = 52 significant peaks). These 52 total significant peaks localized to 32 unique locations on the sorghum genome , seed weight (2013 genetic marker dataset) and stem size (2020 genetic marker dataset). The clustering of separate peaks may reflect increased statistical power resulting from elevated minor allele frequencies in this interval from over representation of the BTx406 haplotype among sorghum conversion lines in this region  when analyzed using the 2013 marker dataset . Of the 22 traits which exhibited at least one significant peak when analyzed with each of the genetic marker datasets, nine were height traits. Among the 32 traits which exhibited at least one significant peak when analyzed with one genetic marker dataset and no significant peaks when analyzed with the other, only two were height-related traits. The non-representative nature of height-related traits may be explained both by the presence of three segregating large effect mutations for height in this population and the large LD blocks which exist around these genes as a result of selection during the temperate adaptation process  for researchers interested in obtaining lists of loci controlling variation in specific traits. However, here we specifically present the results of MLM-based GWAS. With this larger population of individuals—343 e genome .ma1/dw2  were associated with 7, 6, and 13 traits, respectively. The peak on chromosome 2 was identified in GWAS for seed composition traits including oil, protein, and the sums and ratios of seed oil, protein, and starch and likely corresponds to the putative alpha-amylase 3 gene, previously identified in dw2  <0.001, and for which the ratio of the likelihood of one or more significant phenotypic effects at an SNP to the likelihood that the SNP had only null effects was estimated to be <104, which is referred to as the Bayes factor . The effects of dw2 on panicle length, seed weight, and leaf area have been previously reported  . In addition to plant height, dw1 is also known to alter biomass and biomass associated traits , as well as multiple metrics for root size/area . In contrast to dw3 and similar to dw2, dw1 may play equivalent roles in determining organ size for both below ground and above ground plant organ systems.Multi-trait analysis was able to recover a number of known pleiotropic features for large effect loci segregating in the population. In addition to plant height, the peak at reported  while thin yield , leaf anMulti-trait analysis also recovered a number of novel signals across the genome. The pairing and direction of effect sizes for these traits enables greater interpretability of the resulting MTAs. In some cases, these are straightforward trade-offs. The trait-associated marker located at 6.08 MB on chromosome 4 one allele is associated with longer panicles, but these panicles are also narrower and less dense. The other allele present at this locus produces shorter, fatter, and denser panicles, resulting in increases in seed moisture levels at harvest . A numbeArabidopsis thaliana, a widely used commercial source for the reference inbred Col-0 was found to contain substantial introgressions of non Col-0 origin  and algorithms for numerically quantifying specific traits are developed, the additional cost extracting measurements of non-target traits from the same sensor data is minimal. A better understanding of pleiotropic relationships for specific loci and across groups of plant traits may aid significantly in reducing inadvertent selection and prioritizing candidate loci for introgression into elite germplasm for sorghum and related species. A greater understanding of potential pleiotropic effects may help to prioritize which off target phenotypic effects should be tested for either when evaluating natural variants or when generating gene edits.As high throughput phenotyping approaches become more widely adopted, direct measurements of pleiotropy may become more feasible. Once sensor datasets are collected (


We estimated heritabilities of semen production traits and their genetic correlations with litter traits and pork production traits in purebred Duroc pigs. Semen production traits were semen volume, sperm concentration, proportion of morphologically normal sperms, total number of sperm, and total number of morphologically normal sperm. Litter traits at farrowing were total number born, number born alive, number stillborn, total litter weight at birth, mean litter weight at birth, and piglet survival rate at birth. Litter traits at weaning were litter size at weaning, total litter weight at weaning, mean litter weight at weaning, and piglet survival rate from birth to weaning. Pork production traits were average daily gain, backfat thickness, and loin muscle area. We analyzed 45,913 semen collection records of 896 boars, 6,950 farrowing performance records of 1,400 sows, 2,237 weaning performance records of 586 sows, and individual growth performance records of 9,550 animals measured at approximately 5 mo of age. Heritabilities were estimated using a single-trait animal model. Genetic correlations were estimated using a 2-trait animal model. Estimated heritabilities of semen production traits ranged from 0.20 for sperm concentration to 0.29 for semen volume and were equal to or higher than those of litter traits, ranging from 0.06 for number stillborn and piglet survival rate at birth to 0.25 for mean litter weight at birth, but lower than those of pork production traits, ranging from 0.50 for average daily gain to 0.63 for backfat thickness. In many cases, the absolute values of estimated genetic correlations between semen production traits and other traits were smaller than 0.3. These estimated genetic parameters provide useful information for establishing a comprehensive pig breeding scheme. Genetic parameters of 5 semen production traits, 10 litter traits, and 3 pork production traits in purebred Duroc pigs was estimated. Heritabilities of semen production traits ranged from 0.20 for sperm concentration to 0.29 for semen volume and were equal to or higher than those of litter traits, ranging from 0.06 for number stillborn and piglet survival rate at birth to 0.25 for mean litter weight at birth, but lower than those of pork production traits, ranging from 0.50 for average daily gain to 0.63 for backfat thickness. In many cases, the absolute values of genetic correlations between semen production traits and other traits were smaller than 0.3. These estimated genetic parameters provide useful information for establishing a comprehensive pig breeding scheme. Genetic parameters of 5 semen production traits, 10 litter traits, and 3 pork production traits in purebred Duroc pigs were estimated, providing useful information for establishing a comprehensive pig breeding scheme. VOL) and total number of sperm (NUM) per ejaculate, previous studies have estimated genetic parameters by using a repeatability animal model , number born alive (NBA), number stillborn (NSB), and litter size at weaning (LSW) have been also estimated  and grandparent (GP) farms by Specific Pathogen Free system in northern to southern parts of Japan. Sows were serviced typically three times by artificial insemination with semen collected from the same boar. Pedigree data covered 67,993 animals. Five semen production traits per ejaculate were analyzed: VOL, sperm concentration (CON), proportion of morphologically normal sperm (PROP), NUM, calculated as VOL × CON, and total number of morphologically normal sperm (NUMN), calculated as NUM × PROP. Six litter traits at farrowing were analyzed: TNB, NBA, NSB, total litter weight at birth (LWB), mean litter weight at birth (MWB), calculated as LWB/NBA, and piglet survival rate at birth (SVB), calculated as NBA/TNB. Four litter traits at weaning at 21 d after farrowing were analyzed: LSW, total litter weight (LWW), mean litter weight (MWW), calculated as LWW/ LSW, and piglet survival rate from birth to weaning (SVW), calculated as LSW/ NBA. Three pork production traits were analyzed: average daily gain (ADG), calculated as live body weight/ age at end of testing, and ultrasonically measured backfat thickness (BF) and loin muscle area (LMA) at end of testing.The 50,646 semen collection records from the sperm-rich fraction of 966 Duroc boars collected during 2000 and 2017, 8,128 litter performance records of 1,786 sows collected during 2000 and 2018, and individual records of performance testing at about 5 mo of age of 11,806 animals collected during 2004 and 2018 were used. These data were provided by CIMCO Corporation , which operates great-grandparent . For semen production traits, we first excluded semen collection records with an interval of longer than 21 d from the previous collection, and age at ejaculation of younger than 6 or older than 48 mo. Then, we extracted records with VOL and CON within the ranges of their means ± 3 SD, collecting 45,913 records of 896 boars. For litter traits, we extracted litter performance records at farrowing collected after 2001 using semen from Duroc boars and extracted those at weaning without cross-fostering, according to previous studies of Landrace and Large White pigs in the same company , b. ThisThe following repeatability animal model was fitted to the phenotypic records for semen production and litter traits:y is the vector of phenotypic records; b is the vector of fixed effects; a is the vector of breeding values; pe is the vector of permanent environmental effects; e is the vector of errors; and X, Z, and W are the design matrices relating b, a, and pe, respectively, to y. Fixed effects for semen production traits ; farm , birth year , month at end of testing , and a linear covariate of live body weight at end of testing, similar to the statistical model used in the national swine genetic evaluation in Japan.a, pe, and e were assumed as follows:Heritability was estimated via single-trait analysis and genetic correlation was estimated via 2-trait analysis. We confirmed that the estimated heritabitities from 2-trait analysis were similar to those from single-trait analysis . When esA is the additive genetic relationship matrix; and I is the identity matrix. When estimating the heritabilities of pork production traits, the mean and (co)variance of a and e were assumed aswhere a, pe, and e were assumed as follows:When estimating the genetic correlation between the 2 semen production traits and that between the 2litter traits, the mean and (co)variance of a and e were assumed aswhere a, pe, and e were assumed variance of  assumed , asa, pe, and e were assumed variance of  assumed  asVariance components were estimated using AIREMLF90 software . SEs weri in iteration k; and n is the number of parameters to be estimated.where Selection accuracies for VOL and NBA based on selection candidate’s own repeated phenotypic records were calculated using the estimated genetic parameters as:h2 is the heritability, rep2 is the repeatability, and m is the number of records. Here, we used genetic parameters estimated by single-trait analysis , but lower than those of pork production traits . Estimated repeatabilities for litter traits agreed with those of Landrace and Large White populations of the same company and in another Duroc population in Japan , 2020. WThe absolute values of the genetic correlation between pairs of semen production traits were more than twice their respective SEs, except for PROP and NUM and PROP and NUMN . The est-10. This problem may be associated with the facts that the estimated heritabilities of SVB and SVW were both low in single-trait analyses and that NBA was the numerator of SVB but the denominator of SVW. With the approximation formula extended by Estimated genetic correlations among litter traits in Estimated genetic correlations among pork production traits were weak to negligible . PreviouStandard errors of genetic correlations between the five semen production traits and the ten litter traits tended to be greater, ranging from 0.11 to 0.23 . The absAbsolute values of estimated genetic correlations between semen production traits and pork production traits were equal to or smaller than 0.14, less than twice their SEs. SNPs), rs709596309 C>T of the leptin receptor (LEPR) gene and rs80912566 T>C of the stearoyl-CoA desaturase (SCD) gene, on piglet weight at weaning in Duroc pigs. The SNP of LEPR is thought to affect BF in Duroc pigs (Absolute values of estimated genetic correlations between litter traits and pork production traits were greater than twice their respective SEs only for BF with LWB, MWB, LWW, and MWW. Previous studies estimated a weak genetic correlation between litter size traits and pork production traits in pigs . Solé etroc pigs , and maySelection accuracies for VOL were always greater than those for NBA , becausem records increased with m, whereas as As expected, selection accuracy increased as ecreased  althoughty model . Howeverty model  may be lty model .We estimated genetic parameters of semen production traits, litter traits, and pork production traits in purebred Duroc pigs. As far as we know, no such comprehensive range of genetic parameters has previously been estimated in pigs. Therefore, the results could provide novel information useful in determining a comprehensive breeding plan for future pork production. For example, when there is an antagonistic genetic correlation between traits to be improved and litter size traits, selection intensity and selection accuracy in sib-testing may be decreased through a lower number of littermates. This could be more serious for traits relating to pork quality measured after slaughter, including intramuscular fat, fatty acid composition, and pork moisture and texture , b, 2006We used a repeatability model to estimate genetic parameters of semen production traits. We estimated the heritabilities of semen production traits and their genetic correlations with litter traits at farrowing and weaning and pork production traits in purebred Duroc pigs. Estimated heritabilities of semen production traits were similar to or higher than those of litter traits. Absolute values of estimated genetic correlations between semen production traits and other traits were <0.3 in many cases, suggesting that genetic improvement of semen production traits is possible and that selection for them is unlikely to cause immediate correlated responses in other economically important traits studied. Our results could contribute to developing a future comprehensive pig breeding scheme.skac055_suppl_Supplementary_TableClick here for additional data file.


Cryptomeria japonica shows high variability among clones and cultivars, and brighter heartwood has higher value in the usage of non-laminated wood such as in traditional construction, which makes heartwood color an important trait in breeding of this species. However, the genetic basis of the interactions between genetics and the environment on heartwood color has been understudied while these are necessary for effective breeding programs in multiple environmental condition. The objectives of the present study were to evaluate the effects of genetics and environments on heartwood color and how they interact in contrasting environments, and to identify genomic regions controlling heartwood color in C. japonica across multiple environments. Heartwood color in terms of L*a*b* color space and spectral reflectance was measured in common gardens established in three contrasting sites. Quantitative trait loci (QTL) that affect heartwood color were identified using previously constructed highly saturated linkage maps. Results found that heartwood color was largely genetically controlled, and genotype-by-environment interaction explained one-third of the total genetic variance of heartwood color. The effect of the environment was small compared to the effect of genetics, whereas environmental effects largely varied among heartwood color traits. QTL analysis identified a large number of QTLs with small to moderate effects (phenotypic variation explained of 6.6% on average). Some of these QTLs were stably expressed in multiple environments or had pleiotropic effects on heartwood color and moisture content. These results indicated that genetic variation in phenotypic plasticity plays an important role in regulating heartwood color and that the identified QTLs would maximize the breeding efficiency of heartwood color in C. japonica in heterogeneous environments.The heartwood color of a major plantation tree  In forest trees, a number of studies on the inheritance and dissection of quantitative traits have focused on wood properties and growth (reviewed in ). In wooQuantitative traits, such as growth and wood properties, are characterized by genetic and environmental factors and interactions between genetics and environments –10. UndeJuglans nigra. Moya et al. [b* (blue/yellow) color in teak (Tectona grandis). The esthetic value and large variability of heartwood color, and the strong correlations of heartwood color with other wood property traits described above imply that heartwood color is suitable for tree breeding and it may be possible to improve breeding efficiency of heartwood color through molecular breeding approaches.Wood color is one of the traits that most differentiate wood properties and is considered to enhance the value of the wood. In this context, wood color variations have been studied in various tree species –16. Howea et al.  reportedCryptomeria japonica (D. Don) is an allogamous, wind-pollinated, and evergreen conifer species [C. japonica could become dark or reddish in some cultivars [L*) largely differs between different clones of C. japonica, and moisture and potassium content are correlated with heartwood color [C. japonica is largely unknown. species . This sp species  and has  species , 23, whi species , 24. It ultivars –27. For od color . In addiod color . HoweverC. japonica and identify genomic regions that control the heartwood color of this species using multiple common garden experiments established in three contrasting environments. These common gardens have been used to identify QTLs of wood property and growth traits and the climate sensitivity traits of C. japonica in previous studies [This study aimed to evaluate the relative effects of genetic and environmental factors and genotype-by-environment interaction on heartwood color in  studies , 24. ThiP < 0.01, analysis of variance; for details, see 2O, KCl), and exchangeable cations /kg) in 2017  and spectral reflectance , which are commonly used to assess wood color in various tree species, including C. japonica [Wood samples from 40 to 80 cm above ground were collected from each mapping progeny to measure heartwood color. Unedged boards (5 cm) with pith were sawn using a bandsaw and dried indoors for >1 year to attain the air-dried state. The quartersawn faces of the wood samples were planed with a hand-feed planer before heartwood color measurement. Heartwood color was measured as japonica , 32. HeaC. japonica is affected by soil potassium content [I test was conducted to investigate the level of spatial autocorrelation for each trait using the R package “spdep” [I tests. Moran’s I statistic ranges from −1 (perfectly dispersed) to 1 (perfectly aggregated). The significance of spatial autocorrelation was tested under the assumption of spatial randomness.Heartwood color can be spatially biased based on microscale environmental conditions within sites. For example, the heartwood color of  content . Thus, M “spdep” . A lineaI test for a given trait within a site  for each site and trait were predicted using the linear model for the block design. When there was a significant positive spatial autocorrelation with Moran’s r) among the genotypic means of the heartwood color traits were obtained for each site, and the level of significance of the correlations was tested with P-values calculated by the t-distribution using the R package “Hmisc.” Correlations among heartwood color traits were visualized with heatmaps of the correlation matrix for each site using the R package “corrplot” , and interactions between genotypes and sites were used as random variables to estimate variance components and 95% confidence intervals (CIs). Variance component analyses were performed using the “anovaVCA” function of the R package “VCA.” To further understand the environmental effects of three sites on heartwood color traits, the difference in phenotypic values among the sites for each trait was tested using the post-hoc analysis of Tukey’s all-pairs comparison. Phenotypic data analysis was performed using R version 4.1.1 , and mulorrplot”  and withorrplot” .p0 is the expected number of relevant variables, D is the number of variables, and n is the number of observations. p0 was set to 5 for all traits, and the sensitivity of the p0 value was confirmed by ranging p0 from 1 to 9  of the simple regression, and the contribution was defined as the phenotypic variation explained (PVE) for a QTL. Because the resolution of QTL positions depends on the size of the mapping progeny, QTLs that were closely located should be considered the same QTL. In this study, QTLs located within 5 cM were defined as the same QTL. QTL analysis and model selection were done using R version 4.1.1 [L*a*b* color space and spectral reflectance were highly correlated   was applon 4.1.1 . The leaon 4.1.1 , and theon 4.1.1 . Becauseated see , QTLs weL*a*b* color space and spectral reflectance) of mapping progenies in the three sites are summarized in L* are shown in a* and b* in the Ibaraki site, whereas no significant spatial autocorrelation was found for heartwood color traits in the Chiba site  than Chiba and Kumamoto sites  than Ibaraki and Kumamoto sites (P < 0.05). This tendency was also observed in significantly higher spectral reflectance in longer wavelengths (640–700 nm) in the Chiba site (P < 0.05). b* in the Kumamoto site was significantly smaller  than the other two sites (P < 0.05). This tendency was also observed in significantly higher spectral reflectance in shorter wavelengths (440–500 nm) in the Kumamoto site (P < 0.05).Heartwood color traits , whereas relatively small to moderate significant positive correlations were found among L*, a*, and b*  at the three study sites. Heartwood moisture content was significantly negatively correlated with all heartwood color traits at the Chiba and Ibaraki sites  are summarized in L* than a* (14.2%) and b* (18.5%). The variance components of sites were largest in a* (14.6%) and were small (1.7%–2.9%) in L* and intermedium to long wavelengths (520–700 nm). The variance components of interactions between genotypes and sites were larger in a* (13.0%) and b* (15.2%) than L* and spectral reflectance (7.9%–12.8%).The proportion of variance components of genotype, site, block, and interactions between genotypes and sites in heartwood color traits are shown in L*a* b* color space in the three sites  was identified for b* , which would be important for the improvement of this traits in tree-breeding programs.Heartwood color traits observed for mapping progenies were comparable to previous reports on cultivars used in this study . For example, Kawazumi et al.  reporteda et al.  investigis study  on the tL*, were negatively correlated with the heartwood moisture content, indicating that heartwood with higher moisture content is darker in color in C. japonica mapping progenies. This tendency is empirically known and has also been reported in previous studies [L* and green moisture content of heartwood of Yabukuguri cultivar were 72.2, 72.6, and 73.7 and 168%, 163%, and 145% in the initial spacing of 1500, 3000, and 5000 trees/ha, respectively. In addition, Sugimoto et al. [C. japonica wood depends on the density and the amount of reflection in the layer, especially at long wavelengths. This study also found a weak but significant positive correlation between reflectance at long wavelengths (>600 nm) and heartwood density at Chiba site but almost no significant correlation between reflectance at short to intermediate wavelengths (400–600 nm) and density. These results implied that moisture content is more related than density with heartwood color in C. japonica. Furthermore, these suggested that heartwood color is not only important in terms of esthetic value but also a valuable indicator that represent wood moisture which could affect drying processes of the wood [Heartwood color traits, especially for  studies , 29. Foro et al.  reportedthe wood .L*  alone only explained 3.9%, indicating that heartwood color traits are largely genetically controlled, whereas environmental differences could be minimal, which could also be suggested by the scanned images of the clones with the highest and lowest L* . The larst stand . These fL* and a*, respectively. These results indicated that the environmental difference could affect heartwood color in terms of reddish hue (red/green) but not the brightness in C. japonica. As noted above, a* was significantly higher for mapping progenies in the Chiba site, which was the only site without significant spatial autocorrelation in either heartwood color trait. These results suggested that environmental conditions in the Chiba site could have affected the reddish hue of the heartwood of mapping progenies. Although there was not sufficient evidence to evaluate the cause of these results, the soil analysis results implied that pH and potassium content in the soil of the Chiba site was higher and lower, respectively, than the other sites. These soil compositions may have altered the reddish hue of the heartwood of mapping progenies. Indeed, previous studies reported that heartwood color in C. japonica was influenced by potassium content [The effect of environmental differences on heartwood color varied among heartwood color traits. For example, environmental differences only explained 2.4% and 14.6% of the total variance in  content .C. japonica. Mori et al. [The interaction between genotype and environment explained 10.6% on average of the total variance and comprised 26% of the total genetic effects, indicating that genotype-by-environment interaction is important for understanding the genetic basis of heartwood color in i et al.  reportedL*, a*, and b*, and most of these QTLs had small to moderate effects (PVE = 7.0% on average) on heartwood color traits. This tendency was consistent with previous studies that identified QTLs for wood properties and growth traits in tree species . The nulection; , 45) of b* was detected in the three sites. In addition to this QTL, three QTLs identified for spectral reflectance of short wavelengths (<520 nm) were also detected in the three sites  than the other QTLs (6.7% on average). These findings suggested that there might be some important genes that control heartwood color traits in a variety of environments near the QTL cluster.One QTL identified for ee sites . These QL* and spectral reflectance of 400–700 nm  located in LG09 of the YI38 map was detected in heartwood color traits of 0–700 nm  and this0–700 nm . This miC. japonica. Overall, genetic effects, including genotype-by-environment interaction, had a large effect on heartwood color traits , whereas environmental effect alone explained less variance. Therefore, not only genetic effects alone but also genetic variation in phenotypic plasticity were considered important for breeding of heartwood color in C. japonica under changing environments. These findings showed that heartwood color in C. japonica has high potential and is suitable in terms of improving complex traits through breeding programs in wide range of environmental conditions. In addition, this study found a large number of QTLs with relatively small effects; however, some QTLs were expressed stably under multiple environments or had pleiotropic effects on heartwood color and moisture content. These findings indicated that molecular breeding approaches such as MAS and genomic selection are possible in improvement programs on heartwood color in C. japonica; MAS may be possible in the improvement programs conducted within a small range of environmental conditions, while further studies on identification of genes controlling heartwood color are essential for successful marker-based improvements. On the other hand, genomic selection would be effective in improvement programs under wide range of environmental conditions. These necessitates further studies with different genomic approaches such as association mapping studies [This study evaluated the relative contribution of genetic and environmental effects using multiple common gardens in contrasting environments and identified QTLs controlling heartwood color traits in  studies , 46, 47 S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(CSV)Click here for additional data file.S1 Appendix(TIF)Click here for additional data file.S2 Appendix(TIF)Click here for additional data file.S3 Appendix(TIF)Click here for additional data file.S4 Appendix(TIF)Click here for additional data file.


N=36,736).Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative—an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients  and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes.p-value=2.32×10−16, EAA p-value=6.73×10−11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group.We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals’ SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.The online version contains supplementary material available at 10.1186/s13073-022-01106-x. Linking electronic health records (EHRs) to patient genomic data within biobanks in a de-identified fashion has the potential to significantly advance genomic discoveries and precision medicine efforts  –4. HowevN=36,736 individuals genotyped at M=667,191 SNPs genome-wide using the Illumina global screening array (GSA) [The University of California, Los Angeles (UCLA) Health medical system is located in Los Angeles, one of the most ethnically diverse cities in the world. There is no ethnic majority: 48.5% of Los Angeles residents self-identify as Hispanic or Latino, 11.6% as Asian, and 8.9% as Black or African American; additionally, 37% of Los Angeles residents are neither U.S. nationals, nor U.S. citizens at birth . Therefoay (GSA) ). A detaay (GSA) , 18, 21.The EHR contains a de-identified extract of medical records  as well as demographic information such as self-identified race and ethnicity information. It is important to note that self-identified race and ethnicity (SIRE) represent social constructs that capture shared values, cultural norms, and behaviors of subgroups  that areIn this work, we leverage the unique genomic diversity of our single-center cohort to explore the interconnected effects of self-identified race/ethnicity and genetic ancestry on clinical phenotypes. We cluster individuals by genetically inferred ancestry (GIA) within the EHR-linked biobank, systematically construct phenotypes from EHR, and compute disease associations using multi-ancestry pipelines for both genome-wide and phenome-wide association studies (PheWAS). We find that genetically derived and self-identified information yield distinct subpopulations, emphasizing the distinction between GIA and SIRE. We leverage genetic and self-identified data to find extensive variation of subcontinental ancestry within ATLAS across European American (EA), East Asian American (EAA), Hispanic Latino American (HL), and African American (AA) GIA groups. For example, we find clusters of individuals with recent inferred ancestry from Filipino, Chinese, Japanese, and Korean ancestries among the EAA cluster. Such subcontinental clusters also stratify individuals according to disease groups thus emphasizing their utility in biomedical research. We perform both ancestry-specific GWAS and meta-analyses across GIA groups and recapitulate known genomic risk regions. We perform PheWAS on significant regions and describe genetic associations for liver-related phenotypes in multiple ancestry groups as well as associations with neurologic and neoplastic phenotypes that are associated exclusively in the HL GIA group. These results underscore how the utility of large-scale genetic analyses and deep phenotyping in diverse populations can make substantial medical contributions for population health.The UCLA Health System includes two hospitals (520 and 281 inpatient beds) and 210 primary and specialty outpatient locations predominantly located in Los Angeles County. The UCLA Data Discovery Repository (DDR) contains de-identified patient EHRs that have been collected since March 2, 2013, under the auspices of the UCLA Health Office of Health Informatics Analytics and the UCLA Institute of Precision Health. Currently, the DDR contains longitudinal records for more than 1.5 million patients (inpatient and outpatient), including basic patient information , diagnosis codes, laboratory tests, medications, prescriptions, hospital admissions, and procedures. The UCLA ATLAS Community Health Initiative includes the EHR-linked biobank within the UCLA Health System. Currently, there are more than 37,000 genotyped participants with their de-identified EHR linked through the DDR. Participation is voluntary and privacy is protected by de-identifying the samples. Additional information regarding recruitment, consent, sample processing, and quality control pipelines can be found in previous work , 18, 21.Self-identified demographic information is collected as a part of clinical care which is then translated to the EHR. Participants self-identify race and ethnicity via two distinct drop-down fields where there are pre-determined multiple-choice fields for race and ethnicity  from which an individual is descended and describes the genetic relationship implied by shared, large segments of genomic DNA between an individual and these ancestors . ThroughMuch of this work involves inferring the genetic ancestry information for a set of individuals. We introduce the term “genetically inferred ancestry (GIA)” to describe the genetic characterization of individuals within a group who likely share recent biological ancestors as inferred by a method of choice. We emphasize that GIA differs from genetic ancestry in that GIA depends on the inference method  and choice of reference data .The terms “Native American genetic ancestry” and “Native American GIA” refer to ancestry and/or recent biological ancestors from individuals originating from indigenous groups originally from North America, Central American, and South America. The term “Native American race” refers to the definition used by the US Census, “ a person having origins in any of the original peoples of North and South America  and who maintains tribal affiliation or community attachment” . We recoIn the context of this work, the term “African genetic ancestry” describes individuals whose recent biological ancestors originated from the continent of Africa. “African American (AA) GIA” refers to an admixed group of individuals within the USA who have recent biological ancestors inferred to be of African ancestry and thus have partial or total ancestry originating from Africa. The term “Admixed American ancestry” refers to those with recent biological ancestors from European, African, and Native American ancestries that achieved admixture in North America, Central America, and South America. Thus, Admixed American ancestry contains global proportions of all three ancestry groups. “Hispanic Latino American (HL) GIA” refers to the group of admixed individuals within the USA whose recent biological ancestors were inferred to be individuals of Admixed American ancestry. “European ancestry” refers to individuals with recent biological ancestors with origins in continental Europe. “European American (EA) GIA” refers to individuals within the USA with recent biological ancestors inferred to be of European ancestry, thus, partial or total ancestry originating from Europe. “East Asian ancestry” and “South Asian ancestry” refers to individuals with recent biological ancestors from East Asia and South Asia respectively. “East Asian American (EAA) GIA” and “South Asian American (SAA) GIA” refers to individuals within the USA with recent biological ancestors inferred to be of East Asian ancestry or South Asian ancestry.We disapprove that the term “White/Caucasian” is a preset multiple-choice option under the race field within the medical records and renounce its usage due to its erroneous origins and historically racist implications. We strongly discourage the connection of the term “Caucasian” with the discussion of race, a social construct separate from biology, and emphasize that the term does not have biological implications . For subN=36,779) were genotyped using a custom genotyping array constructed from the Global Screening Array with the multi-disease drop-in panel [Bio-samples collected from the UCLA ATLAS Community Health Initiative in the form of blood samples were de-identified and then processed for DNA extraction and genotyping. We utilized a “frozen snapshot” of ATLAS data composed of all samples processed up to 6/18/2021. ATLAS participants . We excluded samples with missingness >5% . We identified duplicate individuals  using KING 2.2.2 [N=42 individuals removed). All quality control steps were conducted using PLINK 1.9 [M=667,191 genotyped SNPs remained across N=36,736 individuals for downstream analyses. All subsequent genetic analyses in this paper utilize this QC’d set of genotypes. Additional steps of QC may be conducted before running specific analyses, as described in more detail below. A summary of the sample sizes and sets of SNPs used in subsequent analyses is described in Additional file We filtered out poor-quality markers by removing unmapped SNPs, SNPs with >5% missingness, and strand-ambiguous SNPs  up to degree 2 where individuals with kinship coefficient <0.0884 were included (“king --unrelated --degree 2”). This level of relatedness is expected since members of the same family will often be within the same healthcare system.We computed pairwise kinship coefficients to determine family relationships using King 2.2.2 . We perfN=36,736) and samples from 1000 Genomes. Specifically, we first filtered genotypes from ATLAS by Mendel error rate (“plink --me 1 1 –set-me-missing”), founders (“--filter-founders”), minor allele frequency (“–maf 0.15”), genotype missing call rate (“--geno 0.05”), and Hardy-Weinberg equilibrium test p-value (“–hwe 0.001”). The filtered genotypes from ATLAS are then merged with the 1000 Genomes phase 3 dataset [We estimated GIA membership using a 2-step clustering procedure. First, we performed principal component analysis (PCA)  on all i dataset . We alig dataset . This re dataset  with allk” hyper-parameter from k=5, 10, 15, 20. If a sample from ATLAS had >0.50 cluster membership, then the sample is reported as the genetic inferred ancestry represented in that cluster (European genetic ancestry (GA) → European GIA, African GA → African American GIA, Admixed American GA → Hispanic Latino American GIA, East Asian GA → East Asian American GIA, South Asian GA → South Asian American GIA). See For the second step, we perform clustering on the principal components to estimate GIA cluster membership for each individual in ATLAS. We use the K-nearest neighbors (KNN) algorithm where we use the superpopulation name of the samples in 1000 Genomes to define the cluster labels. The superpopulations form 5 clusters: European, African, Admixed American, East Asian, and South Asian genetic ancestry. For each ancestry cluster, we run KNN on the pair of PCs that capture the most variation for each genetic ancestry group: European, East Asian, and African ancestry groups utilize PCs 1 and 2, the Admixed American group use PCs 2 and 3, and the South Asian group use PCs 4 and 5. For each ancestry group inference, we run KNN separately. In each analysis, we use 10-fold cross-validation to select the “N=3,331) and samples from the East Asian ancestry population in 1000 Genomes. Using only the genotyped SNPs, we perform the same filtering steps as described above, namely filtering ATLAS genotypes by Mendel error rate, founders, MAF > 0.15, genotype missing call rate, Hardy-Weinberg equilibrium test, and LD pruning. Following sample- and variant-level quality control, M=36,504 SNPs remained. We also found that not restricting to only unrelated individuals does not bias our estimates . First, we perform PCA on all individuals in the EAA GIA group in ATLAS (K-nearest neighbors (KNN) algorithm where we use the population name of the East Asian ancestry samples in 1000 Genomes to define the cluster labels. The populations form 5 clusters: Han Chinese, Southern Han Chinese, Dai Chinese, Japanese, Kinh Vietnamese genetic ancestry. We run KNN using PCs 1–4 with 10-fold cross-validation to select the “k” hyper-parameter from k=5, 10, 15, 20. If a sample from ATLAS had >0.90 cluster membership, then the sample is reported as the genetic inferred ancestry represented in that cluster. Individuals who did not attain >0.90 membership in any cluster were reported as being “Ambiguous EAA.” A visualization of the inferred GIA clusters is visualized in Additional file For the second step, we perform clustering on the principal components to estimate subcontinental GIA cluster membership for each individual in the East Asian American GIA group in ATLAS. We use the N>20 for a total of 7 clusters: Chinese, Filipino, Japanese, Korean, Taiwanese, Thai, and Vietnamese. Although we do not utilize label information from 1000 Genomes, we still use the PCs computed on the merged ATLAS and 1000 Genomes dataset to keep PCA projections consistent across the 1000 Genomes-based and self-identified race-based clustering methods. We run KNN using the same procedure and thresholds as above. Again, individuals who did not attain >0.90 membership in any cluster were reported as being “Ambiguous EAA.” A visualization of the race-based inferred GIA clusters is visualized in Additional file Alternatively, we can define GIA clusters using self-identified information from the samples in ATLAS. We perform a similar approach as above, except we use ATLAS individuals’ self-identified race as the labels to define the clusters. We limit cluster definitions to self-identified race groups with p-value < 1×10−10. We also removed individuals whose EHR sex did not match PLINK estimated genotyped sex and those who had excess heterozygosity. Lastly, we used only sites with a MAF greater than 5%. In total, 418,195 SNPs were kept for IBD analysis. The merged dataset was then statistically phased using Shapeit4 [For identity-by-descent (IBD) calling, an interim version of the ATLAS data consisting of 24,318 individuals was used. First, ATLAS data was merged with the 1000 Genome Project , the SimShapeit4 . IBD wasShapeit4  where thShapeit4 . InfoMapShapeit4 . Each cok=4, 5, 6. Specifically, we restrict to only SNPs with only an A, C, G, T allele and with MAF > 0.05 (“--maf 0.05 --snps-only ‘just-acgt’”) within ATLAS. We then merge the data from ATLAS with the 1000 Genomes phase 3 dataset and limit inference to only the subset of the overlapping SNPs. We then perform LD pruning every 2 kb on the merged dataset (“--bp-space 2000”). All filtering steps and LD pruning were performed using PLINK. This resulted in a total of 223,095 SNPs across 36,736 individuals in ATLAS which was then used for ancestry inference using ADMIXTURE. We also found that not restricting to only unrelated individuals does not bias our estimates  maps the specific ICD-9 and ICD-10 codes from each patient’s chart onto a group of >1800 more general and clinically meaningful phenotype terms [https://github.com/PheWAS/PheWAS) creates binary phenotypes. Patients with one or more instances of a phecode were considered cases while patients without any instance of the corresponding phecode were considered controls. We limited analyses to phecodes with at least N-Case > 50 in each GIA group for a total of 1568 phecodes meeting this threshold in the EA GIA, 802 in the AA GIA, 1223 in the HL GIA, and 891 in the EAA GIA group.Billing codes documented in the medical record were used to generate phenotypes for analysis. The previously described pe terms . Mappingpe terms  . For the second definition, we require the presence of an ICD code attached to only encounters from appointment, office, hospital, or procedure visits. This stricter definition attempts to avoid capturing encounters that may be less indicative of a diagnosis . We refer to these two definitions as all-encounter-derived and visit-derived phecodes. Using these two types of definitions, we then vary the number of phecode occurrences required for defining cases and compute the proportion of retained cases compared to the sample sizes if only 1 occurrence was required.To test the differential prevalence of phecodes across genetic ancestry group, we performed a marginal association test for each phecode to compare its prevalence in one of the genetic ancestry groups  with the other three groups using the following logistic regression model:To account for the potential confounding effects of SIRE, we performed additional analyses with the model:p-value<1.12×10−05).Statistical significance was determined after correcting for the number of tested phecodes with Bonferroni correction procedure  for 600 phecodes within each of the seven ATLAS SIRE groups  with the following model:Given the substantial variation of admixture proportion within each SIRE group, we test the association of phecode with admixture proportion .Each model is limited to individuals of one SIRE instead of all ATLAS individuals. Only traits with >10 cases per SIRE were tested. Significance is determined after adjusting for the number of tested phenotypes with Bonferroni correction procedure , African American (N=1995), Hispanic Latino American (N=6073), and East Asian American GIA (N=3331). At this time, we omitted GWAS analyses within the South Asian American GIA group due to the limited sample size (N=625). Individuals who could not be clustered into a specific GIA group (N=2332) were also omitted from GWAS analyses.When performing GWAS, we stratified individuals by GIA groups and then performed an additional level of QC separately within each GIA group. We limited analyses to the 4 largest GIA groups: European American  were removed and SNPs that failed the Hardy-Weinberg equilibrium test  were also removed. Finally, we limited analyses to only SNPs with MAF > 1% within each GIA group, yielding a total of N=22,380 individuals and M=6.0 million SNPs within the European American GIA group, N=1995 individuals and M=5.9 million SNPs within the African American group, N=6073 and M=6.3 million SNPs within the Hispanic Latino American group, and N=3331 individuals and M=4.8 million SNPs within the East Asian American group.For GWAS, we utilized imputed data consisting of 8,048,268 SNPs across N>50 cases. Additional GWAS-specific quality control is performed within each GIA group . We exclude analyses where very few of the SNPs produced a valid (non-NA) p-value which is likely attributed to a small sample size. The meta-analysis for skin cancer consisted of measurements from the EA and HL GIA groups; EA, AA, HL, and EAA GIA groups for ischemic heart disease; EA, AA, HL, and EAA GIA groups for chronic nonalcoholic liver disease; AA and HL GIA groups for uterine leiomyoma; HL and EAA GIA groups for liver/intrahepatic bile duct cancer; EA, AA, and HL GIA groups for chronic kidney disease. We performed each meta-analysis using a fixed effect model as implemented in PLINK . Association statistics computed from the meta-analyses are reported for SNPs that occur in at least two of the GIA groups.We perform meta-analyses for each trait across all GIA groups. First, we run ancestry-specific GWAS see  within ep-value <5×10−8). Only phecodes with at least N-Cases>50 per GIA group were considered, resulting in a total of 1568 phecodes meeting this threshold in the EA GIA and 1223 in the HL GIA. Analyses in the AA and EAA GIA groups were excluded since the top SNPs were not significantly associated in these groups. We additionally stratified individuals by sex for the sex-specific phecodes, which are denoted in the definition of each phecode. This resulted in a total of 24 male- and 113 female-specific phecodes within the EA GIA group, and 12 male- and 87 female-specific phecodes within the HL group after limiting to phecodes with at least N-Cases > 50. We used individuals’ self-identified sex as reported in the EHR for this analysis.We perform a PheWAS on the top SNPs from each ancestry-specific GWAS analysis that met genome-wide significance , thus each GIA group has a specific significance threshold due to the different number of tested phecodes. A more stringent threshold also accounting for genome-wide significance is also computed where p-value <5×10−8/(# phecodes). Both thresholds are denoted in the PheWAS plots.We performed an association test between the top SNP and all phecodes in a given GIA group under a logistic regression model. Age, sex, and PCs 1-10 were used as covariates in the regression where age is defined as the individuals’ current age within the EHR (as of September 2021), and sex is derived from individuals’ EHR. The association test is performed using the logistic regression option implemented in PLINK (“plink --logistic beta”). The PCs used in the regression analysis were re-computed using only on individuals from within each respective GIA group. Phenotype significance was determined as Neff) of each associated phecode, where the effective sample size balances the number of cases and controls when measuring the power of an analysis [Neff= 2 / (1/Ncases + 1/Ncontrols).To assess the power of the PheWas analysis at rs2294915 between the European American (EA) GIA and Hispanic Latino American (HL) GIA groups, we compute the effective sample size  ATLAS individuals within the EA GIA group could not be further clustered into a subcontinental GIA group derived from 1000 Genomes. Using self-identified race groups with N>20 individuals, we repeat the same clustering process described above using individuals’ self-identified race as cluster category labels. Using self-identified race information over information available in 1000 Genomes, we are able to recover two large clusters consisting of individuals with Korean American (N=533) and Filipino American (N=761) GIA as well as identify additional clusters of individuals corresponding to Thai (N=33) and Taiwanese (N=73) GIA .Corresponding analyses were also performed for the African American GIA group in ATLAS, but clear subcontinental clusters could not be constructed from reference panel information  analysis –55. Usink=4, 5, or 6 ancestral populations and visualize groups of individuals by SIRE  , endocrine/metabolic (N=84), and digestive (N=90) system-related categories. Specifically, we recapitulate many known associations such as skin cancer  in the EA GIA group [p-value=4.83×10−97); ischemic heart disease , chronic kidney disease  and uterine leiomyoma  in the AA GIA group [p-value=1.85×10−38) within the EAA GIA group [Understanding how disease prevalence varies across populations is integral to understanding how the interplay of genetic factors and social determinants of health contribute to disease risk. We investigated over 1500 EHR-derived phenotypes (phecodes)  from acrIA group , 37; chrIA group –63, and IA group , 34, 35 To further explore the implications of genetic ancestry for a range of diseases, we focus on 6 phenotypes that were found to be significantly associated with genetically inferred ancestry (GIA) in ATLAS. This set represents a wide variety of diseases: skin cancer, ischemic heart disease, chronic nonalcoholic liver disease, uterine leiomyoma, chronic kidney disease, and liver/intrahepatic bile duct cancer. Our goal was to capture variation across each GIA group: ischemic heart disease, chronic kidney disease, and uterine leiomyoma have the strongest association with the African American GIA group, skin cancer with the European GIA, chronic nonalcoholic liver disease with the Hispanic Latino American GIA, and liver/intrahepatic bile duct cancer with the East Asian American GIA group , we replicate 259 out of 1512 phecode-GIA associations despite the reduced effect magnitude and association significance , Japanese , Filipino , and Chinese  GIA subgroups in ATLAS. Across subgroups, we observe disease associations to varying degrees  but the odds ratio for the Filipino American GIA group is 1.83 . Additionally, the odds ratio estimated for ischemic heart disease in the Filipino American GIA subgroup is 1.81 (p=3.33×10−7), but performing the association at the continental EAA GIA level, a conclusive trend cannot be determined . These results indicate that genetically grouping individuals across subcontinental GIA groups yields meaningful interpretation of disease risk across groups of individuals that might be missed when only grouping individuals at the continental level.We also observe substantial disease risk heterogeneity across subgroups of the same continental GIA group. We perform association tests between subcontinental GIA and phecodes within the East Asian American GIA group in ATLAS for phenotypes with 10−5 see . For exap-value=8.09×10−10) with chronic nonalcoholic liver disease and the proportion of Native American ancestry is significantly positively associated (p=7.68×10−9)  and analyses where most SNPs failed the regression, we have a total of 17 analyses : 77 associations for skin cancer, 1 for ischemic heart disease, and 58 for chronic nonalcoholic liver disease in the EAA GIA group; 1 association for liver/intrahepatic bile duct cancer and 78 for nonalcoholic liver disease in the HL group; and 1 in the EAA group for heart disease .EHR-linked biobanks also offer the opportunity of investigating genetic associations with traits across the genome. These efforts impose special challenges, such as adjusting for population stratification and cryptic relatedness in health systems that serve entire families as well as extracting phenotypes from EHR, namely due to inconsistencies in mapping diagnosis codes (ICD codes) to phenotypes and difficulties in defining appropriate controls for specific phenotypes. We perform GWAS on each of the 6 phenotypes within each GIA group. After filtering out analyses with small sample sizes  in the European American (EA) GIA group  GIA group was not performed due to low sample size (N < 50). We also see significant associations across multiple ancestry groups. For example, in the analyses for nonalcoholic liver disease, we find 58 genome-wide significant SNPs in the EA GIA analysis and 70 in the HL analysis  ancestry group despite the larger sample size in the European ancestry group (N-Case=3177). The lead SNP from both analyses, rs2294915 =2.32×10−16, p-value(EA)=6.73×10−11), is an intronic variant in the PNPLA3 gene and has MAF=0.49 in the HL GIA but only MAF=0.24 in the EA GIA which could contribute to the heightened associations in the HL GIA.First, we observe ancestry-specific associations, such as a strong association at rs12203592 for skin cancer . After performing the meta-analysis, this increases to a total of 28 significant SNPs all within a locus on chromosome 9, with the top SNP having p-value=3.22×10−10 , and all of the SNPs in the HL  and EAA  analyses had failed association tests at all SNPs. Specifically, the association tests resulted in a p-value estimate of NA which is likely due to the small number of cases or difference in allele frequencies across GIA groups. For example, the minor allele frequencies (MAF) of rs12203592, the top SNP for skin cancer in the EA analysis, greatly vary across GIA groups: MAF-EA=0.17, MAF-AA=0.01, MAF-HL=0.07, MAF-EAA=6.010–4. Thus, populations with a lower MAF of associated variants would require larger sample sizes to have sufficiently powered association tests. This demonstrates that in cases where there are large differences in MAF at associated variations, ancestry-specific analyses would be preferred over a meta-analysis which could actually lead to a reduction in power.We are not able to perform a meta-analysis for skin cancer since were are limited to only the statistics computed from the EA GIA group . The analysis in the AA group was omitted to insufficient sample size , making it straightforward and more easily accessible to add valuable information into models without explicit measurements. We recommend deliberately considering the potential harm versus benefit of using SIRE-adjusted prediction models in each use-case. The practice of race/ethnicity-guided algorithms and guidelines inherently reinforces the idea of race-based medicine and embeds the idea that health inequities also stem from biological differences. It is an ongoing discussion about whether or not the inclusion of race/ethnicity information actually re-allocates resources away from racial/ethnic minority patients, causing more harm and an increase in health inequities.There are various limitations within our study, and we describe a few of these in detail as follows. First, the phecodes are based on ICD codes, and due to the nature of billing codes, this form of labeling does not constitute a formal patient diagnosis and may contain individuals who do not have the specific disease. We also only require the presence of one phecode to define a phenotype which is a significant assumption. Although we present exploratory analyses assessing the role of phecode occurrence when defining phenotypes, we underscore that this imprecise phenotyping limits the power of our study. For further investigation into specific phenotypes, we recommend refining each phenotype definition based on additional disease-specific factors and metrics. For example, one could incorporate additional EHR features, such as those described by the algorithms in the PheKB database . AlthougAn open question and potential additional limitation of this work is generalizing these results to broader populations that extend beyond the UCLA Health system. For example, even when assessing self-identified race/ethnicity statistics, there is a discrepancy between the breakdown of SIRE within ATLAS and the city of Los Angeles. For example, it is reported that 48.5% of residents of Los Angeles self-identify as Hispanic or Latino , compareIn this work, we introduce the ATLAS Community Health Initiative, a biobank embedded within the UCLA Health medical system consisting of de-identified EHR-linked genomic data from a diverse patient population. The UCLA Health system serves Los Angeles County, leading to a study population of great demographic, genetic, and phenotypic diversity. We investigate ancestry both on the continental as well as the subcontinental population level and find that genetic ancestry and self-reported demographic information yield distinct subpopulations in the ATLAS biobank. We present a collection of results cataloging the associations between genetically inferred ancestry and EHR-derived phenotypes where we find that disease status is not only associated with continental genetic ancestry but also associated with the specific admixture profile describing an individual. We use multi-ancestry pipelines to recapitulate known associations for chronic nonalcoholic liver disease at the 22q13.31 locus and perform a PheWAS at the lead SNP, where we find associations with neurologic and neoplastic phenotypes exclusively in the HL GIA group. As the sample size increases, the ATLAS Community Health Initiative will enable rigorous genetic and epidemiological studies to further understand the role of genetic ancestry in disease etiology, with a specific aim to accelerate genomic medicine in diverse populations. Already, the ATLAS biobank accounted for 73.4% of the Admixed American samples utilized in the primary analysis from the COVID-19 Host Genetics Initiative .We conclude by discussing directions for future work. Although we investigate admixed populations, such as African American and Hispanic/Latino populations, admixed individuals who do not fall under these groups are excluded from downstream analyses due to concerns over population structure. In the future, we hope to incorporate methods and pipelines that properly control for population structure in all types of admixed populations. Additionally, we plan to compute polygenic risk scores (PRS) across all 5 continental ancestry groups. PRS has already shown modest clinical utility for diseases such as breast cancer  and cardAdditional file 1. Supplementary Materials providing additional graphs and figures.Additional file 2: Table S1. Self-identified race. Table S2. Self-identified ethnicity. Table S3. Self-identified race/ethnicity (SIRE). Table S4. Genetically inferred ancestry. Table S5. Concordance between SIRE and genetically inferred ancestry, Table S6. Average ADMIXTURE proportions stratified by SIRE.Additional file 3: Table S7. Associations between genetically inferred ancestry and phecodes within ATLAS.Additional file 4: Table S8. Associations between genetically inferred ancestry and phecodes within ATLAS while adjusting for SIRE.Additional file 5: Table S9. Associations between genetic ancestry proportions and phecodes within SIREs.Additional file 6: Table S10. Summary of GWAS analyses in ATLAS.Additional file 7: Table S11. Genome-wide significant associations in ATLAS.Additional file 8: Table S12. Effective sample sizes and effect sizes of correlated phenotypes for chronic nonalcoholic liver disease. Table S13. Role of phecode occurrences for defining cases. Table S14. Summary of methods. Table S15. Comparison of GIA clusters when restricting to unrelated individuals. Table S16. Comparison of genetic ancestry proportions within SIREs when using unrelated individuals.


The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1–50 k SNPs ranged from −0.4–11.7 and −0.3–3.8% higher than Fp-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both Fp-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions.The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite “fuzzy” ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. F The availability of high-density (HD) and whole genome sequence (WGS) data has increased in recent years to allow their practical use in genomic evaluations. It was initially expected that prediction accuracies would increase using such data as a result of better tracking of the sources of genetic variation . Howeverp-values [SNP marker preselection has the potential to improve the quality of genomic predictions by reducing multicollinearity and overparameterization and by increasing the biological relevance of markers included in the association model or in the calculation of the genomic relationship matrix. Several published studies have evaluated single preselection criteria, including p-values , estimatp-values , FST scoT scores ,8, and aT scores . In eachThe limited success of single-criteria preselection to improve prediction accuracies is likely to be explained by the limited sensitivity of any single criteria to distinguish between relevant and non-relevant markers. All criteria will be subject to false positives, and the impact of such markers on predictions is unlikely to be null. Ling et al. (2021)  demonstrns. Ref.  proposedIt has been demonstrated that the increase in the accuracy of genomic predictions in the presence of HD marker data requires a balance between the fraction of the genetic variance explained by the prioritized SNPs and the number of preselected markers . IncreasFuzzy logic was initially proposed by Zadeh 1965)  as a str65  as a p-values and FA fuzzy inference system (FIS) may be particularly suitable for genotype data and genomic prediction analyses, as it is generally assumed that the vast majority of markers available are in imperfect linkage disequilibrium (LD) with causative variants rather than they themselves having a causative effect on the trait(s) of interest. While thresholds for assessing the amount of useful LD between a SNP and causative variant have been proposed , these aFuzzy logic is often used in assessing uncertainty associated with a decision-making process. It provides computers with the ability to compare alternatives in a similar manner to the reasoning process of humans. For example, when comparing the daily milk production of two cows producing 40 and 41 kg, a crisp classification system based solely on phenotypic selection for high milk yield will conclude that the daily productions are different and the cow producing 41 kg is a better selection candidate. Realistically, a reasonable farmer would be more likely to use a “soft” approach by balancing the milk production with additional important factors that are not fully assessed  and the final decision would potentially be different from the crisp classification. A FIS is a process that formalizes this strategy and consists of three main components: (1) fuzzification of the inputs, (2) rule evaluation, and (3) defuzzification to achieve a final output. Conceptually, the final fuzzy outputs in this example would not be dissimilar to a selection index where performance across multiple traits is accounted for, though the fuzzy algorithm notably differs from how selection indices are constructed.U is a set that falls within the sample space of a given input, x; and m is a membership function that maps x to membership in U with p-values associated with estimated SNP effects and marker Fm). It is worth noting that a given input value may have partial membership in adjacent classes, hence the fuzziness of the set, under the constraint that the sum of all memberships are less than or equal to one.Fuzzification of the inputs involves constructing for each input class a fuzzy set, i, modeled as a linear combination of the vector of input parameters (m) for that rule, yet only one of these membership values will be associated with the resulting truth output. Which of the membership values is associated with the truth value depends on the choice of the Boolean operator. The minimum (maximum) membership value will be selected using the Boolean operator AND (OR). p-values, respectively.The rule evaluation step maps the underlying association between the input and output of the underlying system. Each set of inputs must be evaluated according to a rule base. The rule base follows a generalized structure of,y) through the aggregation of the output of the different fuzzy sets,p is the number of rules in the rule base. It can be observed here how the construction of the rules and choice of Boolean operator ultimately influence the final output. Since the Boolean operators AND and OR result in y being weighted by the smallest and largest membership values, respectively, for a given rule, the contribution of a particular rule to the final output will vary considerably depending on the choice of operator. It would be reasonable to suggest that alternative strategies to collapsing the input memberships into a membership for the truth value could be used, for example an average, but this was not investigated in the present study.The defuzzification step is the process used to identify the best crisp outcome given the fuzzy set. It consists of generating a single score  by preselected SNPs was quantified as the variance of validation predictions, var selected across preselection criteria. While it is not straightforward how to quantify the relevance of a particular SNP in a scenario where all markers are potentially linked and in LD with a causative variant(s), if the overlap in SNP selected by each criteria is similar to the scenarios where a portion of the SNPs are definitively not linked with any causative variants, then it may be reasonable to expect that the fuzzy scores are robust across genetic architectures for preselection. p-value preselection methods, respectively. The largest advantage for the fuzzy score preselection relative to random and Fp-value preselection did not exceed those of fuzzy preselection for any of the genetic architectures or evaluated panel sizes.p-value preselection; it tended to initially capture more variance in the predictions and increase this variance at a faster rate, but predictions based on p-value preselection are the most over-dispersed. In the case of 300 causative variants, this overdispersion even increased, explaining why under this genetic architecture the p-value accuracy trends downward for panel sizes larger than 3k SNPs. Fp-values were observed under the more realistic scenario where causative variants were distributed across all chromosomes, as shown in p-value preselection by up to 39.7, 7.8 and 3.8, respectively, for 300 causative variants; 24.6, 11.7 and 1.9 for 1 k causative variants; and 6.15, 7.7 and 1.9% for 5 k causative variants. When there were 5 k causative variants, random preselection yielded notably better predictions than the other preselection criteria, outperforming fuzzy preselection for all panels larger than 1 k SNPs by up to 5.3%.Similar trends in accuracies, Much of the optimism around the use of HD marker panels and WGS data was motivated by the expectations that such data would vastly increase the percentage of the genetic variation captured and ultimately the accuracy of genomic selection. However, both simulation and real-data-based results have demonstrated little benefit using such data in genomic predictions. Equation  shows thp-value-based preselection yielded lower genomic prediction accuracies than all other criteria for SNP panels larger than 20 k despite explaining the largest percentage of the genetic variation. In fact, random preselection explained the least genetic variation of all criteria yet yielded the highest accuracies for SNP panels larger than 10 k. These results demonstrate that maximizing the genetic variation captured by a set of SNPs is not necessarily ideal if there is an accumulation of bias in the SNP associations, as p-value preselection has a tendency to do that, given that it consistently yielded the greatest over-dispersion of all criteria evaluated.This is evidenced by the trends of accuracy, GVE, and overdispersion under the 5 k QTL genetic architecture. In such a case, The performance of random preselection relative to the non-random preselection criteria improved as the number of causative variants increased. It would be reasonable to expect that random preselection might capture more genetic variance by more evenly covering the genome, where non-random criteria might instead saturate regions with causative variants with the strongest signals and consequently fail to capture genetic variance from causative variants with a smaller contribution; however, As the complexity of the genetic architecture increases, the maximum contribution to the genetic variance of a single variant is reduced and the sensitivity of non-random preselection criteria to distinguish true from spurious signals becomes more limited.p-values and Fp-values tended to capture a larger portion of the genetic variance but result in more over-dispersed genomic predictions, while Fp-value and FA primary objective of designing a composite preselection score using FIS was to increase accuracies relative to those of the individual preselection criteria used as inputs to the FIS. While fuzzy preselection did not universally yield higher accuracies than Though ultimately none of the preselection criteria yielded better predictions than the use of all 1.3 million SNPs, very similar accuracies were obtained using preselected panels of 50 k SNPs and smaller, which may be useful in analyses when it is not feasible to routinely use SNP panels of such large dimension.Although a HD panel was used in this simulation study (1.3 million markers), the reality of the genomic field in livestock and plant applications is that several million markers/variants are available from whole genome sequencing at different depths. This dramatic increase in the number of variants will undoubtably increase the accumulated bias in variant associations using single criteria for prioritization. The results of this study seem to indicate that an aggregated preselection criterion, such as the FIS approach, may be adequate for such data, especially if further prioritization criteria such as annotation, functional, and expression information associated with the considered SNPs/variants is available.A large amount of WGS data is being generated in several livestock applications largely for potential use in genomic evaluations. Currently, such information has not led to a meaningful increase in accuracy compared to low/moderate commercial marker panels. Several marker prioritization methods were proposed, but their performance were inconsistent across applications with slight to no improve in accuracy. In spite of our understanding of the relationship between the portion of the genetic variance explained by the preselected markers and the bias induced by the prioritization of false positives , we were unable to find the optimum balance between these two opposing forces to maximize accuracy. In this study, a fuzzy logic approach was presented to concatenate two prioritization criteria into a unique score to prioritize relevant markers with the expectation of reducing the number false positives tagged by each individual criteria separately. Under varying marker panel size and genetic complexity , the FIS approach seems to perform generally better than the single criteria approaches. We believe that the performance of the FIS approach could be improved with the inclusion of already available additional prioritization information such as annotation, functional, and expression data. While the results presented here are promising, only simulated data was evaluated, the generation of which necessitates making simplified assumptions regarding the biology of the genome and its role in influencing phenotypes. Future research regarding this methodology will include its application to real data to evaluate the robustness of its performance in SNP selection for genomic predictions as well as the investigation of algorithms for estimation and tuning of FIS model parameters. The FIS approach presents a flexible method to combine noisy/uncertain information to derive reasonable decision-making outputs.


To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on  CellRegMap is a statistical framework to identify and characterise genetic effects on gene expression in single cells. The model has enabled the identification of hundreds of context‐specific eQTL, including variants that are colocalized with human disease variants. These studies have replicated eQTL that had previously been discovered using bulk RNA‐seq profiles , a framework for mapping regulatory variants in an unbiased manner across cell types and cell states as obtained from scRNA‐seq profiles. CellRegMap does not depend on any discretization of cells into cell types, nor is it required to annotate specific cell states aits Fig . The priaits Fig . We valiet al, et al, et al, et al, et al, et al, et al, CellRegMap generalizes the classical linear interaction model for genotype‐environment interactions  as a sum of a conventional—persistent—genetic effect (G), interactions with cellular context (GxC), additive contributions from cell context (C), a relatedness component (rel.) and residual noise  and the cell context covariance, i.e., u∼N0σRC2R⊙Σ , which as expected is best powered to identify variants with primarily association signals Fig . We alsoFinally, we used simulated data to assess the impact of expression level and expression variance on the power to detect genuine GxC effects, finding that our power to identify GxC effects is increased for genes with higher overall expression level mean and lower variance  that spans 125 genetically diverse individuals . We also reassessed these effects using conventional eQTL mapping by defining pseudo‐bulk expression profiles in the bottom and top 20% quantiles of GxC allelic effects, which confirmed 72% of these putative opposite effects  with putatively opposing effects , a linear mixed model for the identification and characterization of context‐specific eQTL that is applicable to cellular states derived from scRNA‐seq. Critically, CellRegMap overcomes the need to define cellular contexts iori Fig  and instet al, Conceptually, CellRegMap is related to and builds on StructLMM, a model that was originally designed to identify genotype‐environment interactions in population cohorts . First, we note that the phenotype (y) now represents single‐cell resolved expression data, so samples are expression levels in cells, not individuals. This introduces additional structure in the data, as typically multiple cells are sampled from the same individual. To account for this repeat structure, an additional random effect component is included in the model. This additional random effect component accounts for relatedness or the repeat structure, which is parameterized as a product kernel between relatedness (R) and the environmental covariance (Σ). Here, R denotes the relatedness matrix of individuals expanded to all cells based on the known assignment of cells to individuals and the covariance Σ again denotes the cell‐level environmental context. Notably, this parametrization extends the classical LMM, which would exclusively consider a relatedness component R. One way to interpret this covariance is to account for polygenic interactions between environment and relatedness, which has previously been considered to estimate the GxE component of heritability :y=gβG+u+c+ey=gβG+g⊙βGxC+u+c+e.The main test implemented in CellRegMap allows users to test for GxC effects for a given eQTL (or gene‐SNP pair). We refer to this as “CellRegMap Interaction test,” or simply “CellRegMap.” The test consists in comparing the following models under the null (H0) and alternative (H1) hypotheses, i.e., we are testing whether et al, et al, In order to evaluate the significant contribution of GxC effect, we use a score test, similar to strategies adopted in StructLMM (Moore βG≠0):y=u+c+e,In addition to the GxC interaction test, it is also possible, within the CellRegMap framework, to test for persistent genetic effects, while appropriately accounting for cell context and multiple cells per donor in the background. CellRegMap‐association essentially draws from the background of the full CellRegMap model and compares the following two models ). To evaluate statistical significance in this model, we employ a likelihood ratio test (LRT), as implemented within the LIMIX  can be then tested for GxC effects using the CellRegMap interaction test described above.βGxC) for each gene‐SNP pair tested. These estimates can be obtained from posterior predictions of the model, which is analogous to the best linear unbiased predictor (BLUP) in a classical LMM  gene expression vector for a given gene in the reference dataset,g is the SNP genotype vector from the reference dataset,ci denotes the i‐th context variable (MOFA factor),βGxCi∼N0σG2ρGxC is the interaction effect size for context i,βG∼N0σG21−ρGxC is the effect size of the persistent genetic effect andσG2 denotes the total genetic variance and ρGxC is the fraction of genetic variance explained by GxC.Synthetic data for 500 gene‐SNP pairs were generated using empirically observed data extracted from , batch effects or context‐specific expression variation present in the observed expression counts We assessed the statistical calibration of the proposed tests, CellRegMap and CellRegMap‐Association, as well as three alternative models Figs  and EV1:StructLMM .MultiEnv‐LRT, a fixed‐effect version of CellRegMap with a multiple‐degree‐of‐freedom likelihood ratio test for GxC effects.ρGxC=0,σG2=0.025) or no genetic effects (σG2=0.025) and testing for GxC effects using either 10 or 20 contexts (MOFA factors). As shown before  for 10 tested and simulated contexts.Varying Varying the number of simulated contexts with GxC , for 20 tested contexts (only CellRegMap and SingleEnv‐LRT).Varying the number of tested contexts  when simulating 20 contexts with GxC (only CellRegMap and SingleEnv‐LRT).P‐values) and background mean gene expression and variance, as well as minor variant allele frequency  from the endoderm differentiation dataset  to definitive endoderm (day3). Compared with the entire dataset considered in the original publication, we discarded two outlying cell sub‐populations .We considered single‐cell expression data from 33,964 cells across 125 individuals from . Significant results were reported at FDR < 5%.Next, we mapped context‐specific effects using either 1 or 10 MOFA factors. In both cases, all 20 MOFA factors were used to construct the background term, i.e., columns in GxC; We estimated both persistent genetic effects (βG) and cell‐level effect sizes due to GxC βGxC;  for eQTLn = 270) and permuting the genotypes across individuals for two random SNPs per gene, finding calibrated test statistics. Additionally, we stratified genes by their correlation with differentiation time (the dominant axis of variation in this dataset) to rule out inflation of CellRegMap's test statistics in cases where the cell context has a strong effect on the outcome variable y  and used the CellRegMap‐Association to identify candidate variants to test for GxC effects. All analyses were based on a cis eindow of 100 kb window flanking the gene body, and SNPs with MAF > 5%. As expected, we observed that this orthogonal filter greatly reduced the number of GxC test compared with an exhaustive analysis . Specifically, we considered 643 expressed genes on chromosomes 20–22 . Next, we applied batch‐correction for the experimental batches using Harmony , identified using CellRegMap based on the leading 10 MOFA factors as cellular contexts. Next, we considered the estimated effect size profiles due to GxC, which were normalized to relative values in the range 0 to 1. We clustered these normalized profiles using SpatialDE . Next, we selected all genes with positive correlation larger than 0.4 and used gprofiler  pathways per cluster  and single‐cell gene expression across all genes (ster Fig .Further statistical details and derivations are provided in Anna SE Cuomo: Conceptualization; data curation; software; formal analysis; investigation; visualization; methodology; writing – original draft; writing – review and editing. Tobias Heinen: Formal analysis; validation; writing – review and editing. Danilo Horta: Software; methodology. John C Marioni: Supervision; writing – original draft. Oliver Stegle: Conceptualization; supervision; funding acquisition; methodology; writing – original draft; project administration; writing – review and editing.CRediT author contributions listed above, the contributions in detail are:In addition to the AC and OS conceived the method. AC and DH implemented the software and methods with input from TH. AC performed computational analyses on real datasets. AC and TH devised the simulation strategy and TH conducted all experiments using simulated data. DV performed some data processing tasks. AC, JM and OS interpreted the results and wrote the manuscript.OS is a paid advisor to Insitro, Inc. All the other authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Table EV5Click here for additional data file.