As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1  and CEP112  with delayed recall as well as SMOC2  with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 . While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.Alzheimer’s disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from  APP) and presenilins 1 and 2 (PSEN1/PSEN2), which only make up a small fraction (<<5%) of all AD cases , subjects with mild cognitive impairment (MCI) and subjects with a clinical diagnosis of AD. A diagnosis of AD was based on National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association criteria (NINCDS-ADRDA)   while MCS-ADRDA) . An overS-ADRDA) . The locS-ADRDA) .3, left and right hemisphere, and sum of both; all adjusted for intracranial volume), whole brain cortical thickness (in mm), and white matter lesions   . The Fazz-transformation, so that the data were comparable within a cognitive domain despite representing partially different tests across centers. For the cross-sectional GWAS analyses, the z-scores derived from baseline data were used. The number of subjects used for each test can be found in Cross-sectional (and follow-up) data were available for the following seven neuropsychological domains within the EMIF-AD MBD dataset: global cognition , attention, executive function, language, memory (immediate and delayed) and visuoconstruction  threshold of 0.05 and were excluded from all subsequent analyses. Only the most frequently used tests per cognitive domain were included in the final phenotypes  and subsequent data processing can be found in 1 using the extended HRC reference panel (P < 1.0E-4) which was performed on all samples combined as recommended previously  using allele dosages (to account for imputation uncertainty) in PLINK2 . The modTo explore associations on the X chromosome that were potentially driven by genetic sex, we additionally conducted the analyses separately in females and males. We then combined these two additional sets of results in a meta-analysis using Stouffer’s method as implemented in METAL . As we f2 .In FUMA, both the SNP annotation and the Combined Annotation Dependent Depletion (CADD) score  are provP-values per respective phenotype, were examined in more detail using additional tools. First, the Variant Effect Predictor on Ensembl  to estimate the extent of genetic correlation with the GWAS results for three other phenotypes. To this end, we used the summary statistics of a GWAS on AD risk  as compaP < 5.0E-08) signals but observed 385 variants with at least suggestively significant (P < 1.0E-05) evidence of association  and SCHIP1 (protein: schwannomin-interacting protein 1). In the GTEx database  using the MRI traits analyzed. The genomic inflation factor λ ranged between 0.984 and 1.060 in these five gene-based analyses.The genomic inflation factor λ ranged between 1.004 and 1.012 in all five SNP-based analyses, indicating that the results of the MRI GWAS analyses were not affected by substantial inflation of the test statistics. In the actual association analyses of the five quantitative MRI phenotypes, we identified no genome-wide significant  for rs73045828, which attained P = 5.65E-09 in the longitudinal GWAS for immediate memory  and rs7412 . In relation to the common genotype ε3/ε3, the risk to develop AD is increased by a factor of ∼3.2 for genotype ε3/ε4, while two ε4 alleles (genotype ε4/ε4) show ORs around 10–12 when compared to normal controls  hippocampus, while no association was observed with the corresponding data of the right hemisphere (P = 0.2956). We note that for both traits, i.e., delayed memory and left hippocampal volume, the effect direction the corresponding β coefficient is consistent with the deleterious effect of the minor (T/ε4) allele at rs429358 known from the literature  levels  levels . Interes) levels , showcasP = 7.50E-11; SMOC2, encodes secreted modular calcium-binding protein 2. SMOC2 is an extracellular matrix protein from the secreted protein, acidic and rich in cysteine (SPARC) family . While variants in this gene did not show strong evidence of genetic association with the traits analyzed here, it is noteworthy that SMOC1 was recently nominated as a novel AD biomarker in proteomic screens of AD CSF and brain samples in various studies  family . In the ) family . Interes studies . In summP = 1.06E-09; IL1RAPL1. This gene encodes interleukin 1 receptor accessory protein−like 1, which belongs to a class of molecules that regulate synapse formation. IL1RAPL1 is mostly expressed in brain areas that are involved in memory development, such as hippocampus, dentate gyrus, and entorhinal cortex, suggesting that the protein may have a specialized role in physiological processes underlying memory and learning abilities  on the X chromosome  polymerases and is involved in various processes in the body, such as telomere regulation, Wnt/β-catenin signaling pathway or glucose transport (TNKS is highly expressed in brain (mostly in cerebellum). Moreover, SNPs annotated to TNKS were associated with brain white matter hyperintensity (WMH) measurements  on chromosome 8p23.1  to the lead variant identified here. Notwithstanding, given that variants in this gene have shown genetic links to both cognitive function and structural brain imaging, we consider this finding as plausible and highly interesting.The last highlighted finding from the longitudinal analyses relates to the genome-wide significant association observed between SNP rs9652864 and the delayed recall memory phenotype , which acts as transcription factor and plays a role in brain and sensory organ development in Drosophila and vertebrates, including humans  in any of the 19 GWAS investigated here. The strongest associations with this allele were seen with MRI-based hippocampus volume , and it was not reported to show genome-wide significant evidence of association (P = 4.1E-07) with hippocampal volume in the GWAS by APOE ε4’s role in contributing to AD risk and Aβ42-related phenotypes, the same allele does not appear to show a comparably strong influence on variation of other AD-related traits, including the ones studied here.In addition to searching for novel genetic determinants of the neuroimaging and neurocognitive traits analyzed in this study, we also investigated the overlap with known GWAS findings. First and foremost, this relates to two commonly studied alleles in the Extending the comparison to additional genetic variants associated with AD risk in the GWAS by While our study has several noteworthy strengths , it may also have been negatively affected by some limitations. First and foremost, we note that the sample size used for the present analyses is comparatively small for “GWAS standards” and was well under 1,000 in some instances . AccordiIn conclusion, our study delivers an entirely novel set of GWAS results from participants of the EMIF-AD MBD dataset. We nominate several novel and functionally interesting genetic association signals with phenotypes related to neurocognitive function and structural brain imaging. Even though independent replication is still needed, our results may prove informative to better understand the genetic forces underlying AD and related phenotypes.P-value < 1E-05) results are listed in the Supplementary Tables. Full GWAS summary statistics are available from the authors upon request. Clinical data and genome-wide genotyping data are stored on an online data platform using the “tranSMART” data warehouse framework. Access to the genome-wide genotyping data can be requested from the corresponding author of this study who will forward each request to the EMIF-AD data access team.GWAS summary statistics for the top , which was a part of the GU Ventures Incubator Program. FB is supported by the NIHR biomedical research centre at UCLH. JP received consultation honoraria from Nestle Institute of Health Sciences, Ono Pharma, OM Pharma, and Fujirebio, unrelated to the submitted work. CT has a collaboration contract with ADx Neurosciences, Quanterix and Eli Lilly, performed contract research or received grants from AC-Immune, Axon Neurosciences, Biogen, Brainstorm Therapeutics, Celgene, EIP Pharma, Eisai, PeopleBio, Roche, Toyama, Vivoryon. She serves on editorial boards of Medidact Neurologie/Springer, Alzheimer Research and Therapy, Neurology: Neuroimmunology and Neuroinflammation, and was editor of a Neuromethods book Springer. CT also holds a speaker’s contract with Roche, Inc. KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Pharmatrophix, Prothena, Roche Diagnostics, and Siemens Healthineers, and was a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which was a part of the GU Ventures Incubator Program, outside the work presented in this paper. SL is currently an employee at Janssen Medical UK. JS was an employee of Janssen R&D, LLC., and is currently an employee and chief medical officer of AC Immune SA. JR was an employee of Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sturnus vulgaris) within Australia appear to be highly heritable (presumably genetic) or appear to vary with environmental gradients despite underlying genetics (presumably non-heritable plasticity). We also sought to determine which environmental variables, if any, play the strongest role shaping genetic and phenotypic patterns. We determined that environmental variables—particularly elevation—play an important role in shaping allelic trends in Australian starlings and may also reinforce neutral genetic patterns resulting from historic introduction regime. We examined a range of phenotypic traits that appear to be heritable (body mass and spleen mass) or negligibly heritable (e.g. beak surface area and wing length) across the starlings’ Australian range. Using SNP variants associated with each of these phenotypes, we identify key environmental variables that correlate with genetic patterns, specifically that temperature and precipitation putatively play important roles shaping phenotype in this species. Finally, we determine that overall phenotypic variation is correlated with underlying genetic variation, and that these interact positively with the level of vegetation variation within a region, suggesting that ground cover plays an important role in shaping selection and plasticity of phenotypic traits within the starlings of Australia.Patterns of covariation between phenotype and environment are presumed to be reflective of local adaptation, and therefore translate to a meaningful influence on an individual’s overall fitness within that specific environment. However, these environmentally driven patterns may be the result of numerous and interacting processes, such as genetic variation, epigenetic variation, or plastic non-heritable variation. Understanding the relative importance of different environmental variables on underlying genetic patterns and resulting phenotypes is fundamental to understanding adaptation. Invasive systems are excellent models for such investigations, given their propensity for rapid evolution. This study uses reduced representation sequencing data paired with phenotypic data to examine whether important phenotypic traits in invasive starlings ( Phenotypic trends within a species are often driven by the environment. Patterns of covariation between phenotype and environment are presumed to be reflective of local adaptation or plasticity, and translate to a meaningful influence on an individual’s overall fitness within that specific environment . Across While the relationship between phenotype and environment may be hard coded in the genome of an organism, it may also be a result of plasticity. Plasticity, the ability of an organism to produce different phenotypes in response to environmental variation but with the same genetic basis, is fundamental to explain phenotypic diversity within a species . OverallInvasive species are excellent model systems in which to investigate questions regarding heritable and non-heritable environmental effects on phenotype. Invasive species’ persistence relies in part on rapid adaptation, and plasticity acts as an immediate means through which an invasive species may be able to survive in their novel environment . UnderstSturnus vulgaris, hereafter the starling) is a near-globally invasive passerine, and was introduced to Australia in the mid-late 19th century from their native range in Eurasia  at least some aspects of the novel invaded environment would have impacted the genetics of this species. It is important here to note that by studying morphological variation within the invaded Australian range, we may observe how original genetic diversity from the native range  was acted upon to elicit adaptive phenotypic change, or alternatively which phenotypic traits were altered irrespective of the genetics, shedding light on plastic developmental responses in this species. With concern over plummeting starling numbers within the native range , we may The present study uses reduced representation sequencing data with paired phenotypic, ecological, and spatial data to examine important phenotypic traits in Australian starlings. Specifically, we seek to answer four key aims. Firstly, we ask what environmental variables appear to be shaping the overall allelic frequency landscape. We expect that temperature and precipitation play a vital role due to major differences in these variables between Australia and the native range. Next, we examine the heritability of different phenotypic traits and how this is partitioned across the chromosomes. We predict that few traits will be heritable, and that many of these heritable traits will be polygenic and so will show a linear relationship between explained variance per chromosome and chromosome size. We then ask which SNPs are correlated with phenotype, and then examine the amount of genetic variance in these SNPs that is explained by key environmental predictors. Finally, we assess genetic, phenotype, and environmental relationships more broadly by examining patterns in variance. We investigate whether phenotypic variance correlates with genetic variance and/or environmental variance, and if there are any range-edge effects. We predict that phenotypic variance would be lower at the range edge due to genetic drift in these smaller populations, and that phenotype and genetic variance would be positively correlated.S. vulgaris vAU1.0 , a minor allele frequency of 0.05 , a minimum allele depth of 2 (--min-meanDP 2), a maximum allele depth of 50 (--max-meanDP 50), an allele number of 2 , and a minimum genotype quality score of 15 (--minGQ 15). We then pruned the SNP data set for linkage using BCFtools prune for SNPs with linkage values of less than 0.6 in 1,000 bp sliding windows . We assessed the per-individual missingness levels, and any individual that was missing sequencing data in more than 50% of SNP sites was excluded from the final data set. This produced a data set with 111,037 SNPs from a total of 321 individuals across 24 sample sites  section; Admixture v1.3.0 . This produced a data set with 128,217 SNPs from a total of 212 individuals across 14 sample sites , for use in analysis that involved phenotype , bio3 , bio4 , bio8 (mean temperature of wettest quarter), bio9 (mean temperature of driest quarter), bio12 , bio14 (precipitation of driest month), bio15 , bio18 (precipitation of warmest quarter), bio19 (precipitation of coldest quarter), elevation, and mnNDVI .To quantitatively assess the contributions of spatial and environmental variables to the overall genetic variation within the Australian range : aim 1, Vegan  to conduVegan . EnvironVegan , obtaine package . To creagradientforest v 0.1–24 ), and considered traits that had a lower 65% confidence interval above zero to be heritable according to this analysis.We calculated overall heritability of the phenotypic axis using seven models, one for each phenotypic trait. We fitted the 29 autosomes of the starling genome to the seven models simultaneously, and all seven traits reached convergence. We estimated the proportion of variance explained by each chromosome, we ran each of the 29 autosomes (using the option—autosome to create the GRM) and Z chromosome (using the option—make-grm-xchr to create the GRM) individually, with one separate model of each phenotype trait. We calculated the 65% confidence interval (CI) for each trait  and the auxiliary covariate model (BFmc) to evaluate the association of SNP frequencies with phenotype. For each SNP, the BF is expressed in deciban units (dB 10 log10(BFis/mc)). Following Jeffrey’s rule, SNPs with a BFis/mc ≥ 20 were retained as indicating a SNP under “decisive” selection. From this analysis we obtained seven separate lists of SNPs, capturing which genomic sites were associated with each of the seven phenotypic measures.To identify SNPs that were strongly associated with phenotypic traits : aim 3, Environmental and Spatial Associations With Genome Wide Genetic Variation section  of 20,000 SNPs, and a 1% empirical threshold was calculated for the observed XtX. Through core model and POD analysis, we identified which SNPs were divergent between sample sites (XtX>22.628) and were associated with phenotype (BFis/mc ≥ 20). We generated a list of candidate genes under selection associated with each of the phenotypic traits using Bedtoolsintersect (S. vulgaris vAU1.0 annotation (vcftools—site-pi) using the aov function in R, to see if there was any relationship occurrence of an correlated SNP under selection and the degree of site polymorphism across the population.We also sought to determine if any of the identified phenotype-associated SNPs appeared to be under selection. A selection subset of the above phenotype-associated SNPs was created by retaining only the SNPs that were also flagged as highly divergent across sample sites by further ntersect , using tnotation . The catTo examine if phenotypic variation was dependent on either genetic variation, environmental variation, distance from introduction site, or an interaction of some or all these factors : aim 4, We calculated summaries of phenotypic variation as well as genetic variation, termed “phenotypic dispersion” and “genetic dispersion” respectively. These measures, obtained through an ordination approach see , captureEnvironmental and Spatial Associations With Genome Wide Genetic Variation section) that captured climate variability: bio3 , bio4 , and bio15 . Additionally, we included variation in normalised difference vegetation index (NDVI) values obtained for the same genetic sample sites in InDaPCA  within each of the two genetic subpopulations  using g package .lmer in the package lme4 v1.1-26  analysis confirmed that the spatial variables each had a higher weighted importance in explaining genetic patterns than did any of the environmental variables considered. Of the environmental variables, the most important was altitude and bio04 , followed by bio09 (mean temperature of driest quarter), bio18 (precipitation of warmest quarter), bio1 , and bio08 (mean temperature of wettest quarter). Of the variables used in the analysis, the 10 spatial variables explained 81% and environmental variables explained 19% of the genetic variation . There wsnp 65% confidence interval that does not span 0; spleen mass (hsnp lower 65% CI = 0.311) and body mass (hsnp lower 65% CI = 0.193)  . The remanning 0 .1,28 = 12.41, p-value = 0.001483, Adjusted R2 = 0.2824)  . We foun 0.2824) .Chromosomal variance partitioning revealed that there was no single chromosome that explained a high amount of variance for all traits, with macrochromosomes (1–12), microchromosomes (13–27), and the sex chromosome (Z) explaining different amounts of the variance across the assessed traits . GeneralBaypass, with the latter model detecting the fewest number of loci , tarsus-associated Protein transport protein SEC61 (Sec61), wing-associated Phosphatidylinositol glycan anchor biosynthesis class U protein (PIGU), spleen-associated Sodium channel protein type 5 subunit alpha (Scn5a), and heart-associated LHFPL tetraspan subfamily member 3 protein (LHFPL3) and CUB and sushi domain-containing protein 2 (CSMD2)   . We did  0.0716) .1,12 = 12.7, p-value = 0.00389, Adjusted R2 = 0.4738)  .Using our linear mixed effect model to assess the effects of genetic and environmental variance on phenotypic variance, we found only one significant second level interaction, which was the interaction of genetic dispersion and env axis 2  . This inOur results demonstrate that environmental variables (particularly elevation) play important roles in shaping spatial trends Australian starling genetics. The relationship between allelic frequencies and environmental variables across the starlings’ range may be indicative that strong spatial patterns associated with historic introductory regimes could be reinforced by differing environmental gradients during colonisation and expansion. Further, we identified that both genetics and plasticity explain previously identified ecogeographical phenotype trends across the Australian range. We examined a range of phenotypic traits that appear to be heritable (body mass and spleen mass) or negligibly heritable (e.g. beak surface area and wing length) across the starlings’ Australian range. For SNP variants associated with each of these phenotypes, we identify key environmental variables that correlate with genetic patterns, identifying temperature and precipitation variables that putatively play important roles shaping phenotype in this invasive population. Finally, we illustrate that overall phenotypic dispersion is correlated with underlying genetic variability, and that these interact positively with the level of vegetation variation within a region, suggesting that an interaction between ground cover and genetic variation plays an important role in shaping plastic development in Australian starlings.S. vulgaris allelic patterns across the Australian range, it is unsurprising that the spatial variables explain such a large proportion of the overall trends. This confirms previous isolation-by-distance analysis that identifies physical distance between sites as the largest determinant of genetic patterns  would be biologically unlikely, and thus we may expect the true hsnp values to be towards the lower end of the confidence interval. Nevertheless, it is apparent that trends in body and spleen mass across Australia appear to be correlated with genetic relatedness, an interesting result considering that both these traits may be strongly influenced by non-genetic factors (Passer domesticus)  . Likely esticus) . Hence eOut of all the phenotypic traits assessed in this study, only the degree of variance in body mass was found to be heritable and significantly associated with chromosome size . Body masnp values such as beak surface may vary only with environmental gradients, irrespective of broad underlying genetics. Considering these results within the context of previous findings in this species  and beak surface area-associated SNPs. Previous research using maximal temperature data concluded that beak surface measurements adhere to Allen’s rule , reflectGALK1, Scn5a, LHFPL3, and CSMD2) were flagged as under selection across these three traits and may play important adaptive roles in responding to precipitation regimes across Australia, and other environmental factors that may covary with precipitation (e.g. via CSMD2 mediated pathogen responses (In agreement with previous findings regarding heart mass not adhering to Hesse’s rule (organisms will have larger hearts in colder environments) , we see esponses ). DespitWithin the novel invasive Australian range, we found that adherence to biologically significant ecogeographical patterns within starlings may be accomplished through a complex sum of heritable and non-heritable components. Heritable components of selection may be driven by only a small number of biologically significant SNPs, for which oftentimes temperature variability or precipitation during warmer, dryer months play a vital role. A better understanding of environmental and phenotypic interaction is invaluable for helping predict climate shift responses . Further


Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies. Due to the increasing prevalence, and high costs of renal replacement therapies, CKD already represents one of the most expensive health problems in developed countries3. In the United States, an estimated 13.6% of adults have CKD1 and more than 726,331 Americans have end-stage kidney disease (ESKD), being dialysis-dependent or having received a kidney transplant4. ESKD prevalence is about 3.7 times greater in African Americans, 1.4 times greater in Native Americans, and 1.5 times greater in Asian Americans than in Whites/Europeans. Inherited factors, such as APOL1 polymorphisms6 and other genetic factors8, are likely contributing to these disparities.Chronic Kidney Disease (CKD) is associated with a high burden of comorbidities and increased mortality10. Unlike most other disease states, the onset of kidney disease is often asymptomatic, and the diagnosis is based solely on blood and/or urine tests. As a result, early CKD is frequently under-recognized and under-treated11. While several measures, such as dietary interventions, hyperlipidemia management with statins12, blood pressure control13, glycemic control14, and use of angiotensin system blockade15 or sodium-glucose cotransporter-2 inhibitors17 can slow down the progression of early disease or reduce complications, advanced CKD is irreversible and associated with accelerated cardiovascular disease and increased mortality18. Thus, early detection and improved awareness of CKD is of paramount importance.CKD is defined as an abnormality of kidney structure or function present for longer than 90 days and can occur due to many heterogeneous disorders affecting the kidney19 and the Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for the Evaluation and Management of CKD10. We designed our algorithm to detect CKD in its earliest stages by calculating two orthogonal measures of CKD severity: albuminuria (used for A-staging of CKD) and estimated glomerular filtration rate .Electronic health records (EHR) provide a rich source of clinical data that can be used reliably to establish a CKD diagnosis. With increased reliance on the EHR for pragmatic implementation of clinical and genetic studies, there is an unmet need for a standardized portable electronic definition of CKD and its severity. To address this need, we designed a comprehensive electronic CKD phenotype that combines expert domain knowledge and the consensus definitions of the National Kidney Foundation’s (NKF) Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines20. To assure transferability across different EHR systems, our algorithm was developed using training and validation datasets across several institutions, including Columbia University (CU), University of Minnesota (UMN), Vanderbilt University (VU), and Mayo Clinic (MC). To show scalability and portability, the algorithm was applied to the Columbia Clinical Data Warehouse (CDW) of over 1.3 million patients, as well as across the entire Electronic Medical Records and Genomics-III (eMERGE-III) network of eight centers with genetic and EHR data for 105,108 individuals21.Our electronic phenotyping approach provides pragmatic means to enhance broad and proactive CKD screening, risk stratification, and timely initiation of treatment to reduce the global burden of kidney disease, as recommended by the 2019 consensus statement of the KDIGO Conference on “Early Identification and Intervention in CKD”22. Using thousands of reconstructed pedigrees of diverse ancestries, we demonstrated significant heritability of eGFR, albuminuria, and CKD at a scale previously unobtainable for family-based studies. Third, we performed genome-wide association studies for CKD across the eMERGE network, demonstrating that our algorithmic phenotype definition recovers known genome-wide significant risk loci. Finally, we analyzed 19,853 distinct ICD codes mapped to 1804 phecodes in all 105,108 eMERGE participants to comprehensively define pleiotropic associations of the top CKD risk loci using phenome-wide association approach23.We demonstrated several powerful applications of the algorithm. First, we performed large scale observational analyses of common comorbidities across the A-by-G grid to define independent associations for A and G-stage, including several comorbidity patterns that have not been previously recognized. Second, we applied our recently published Relationship Inference From The Electronic Health Record (RIFTEHR) method to computationally infer familial relationships from EHR data and estimate pedigree-based observational heritability of kidney diseaseIn summary, we created an accurate, portable, and scalable electronic phenotype for CKD diagnosis and staging. We performed extensive validations of the algorithm and demonstrated its broad clinical and research applications, from enabling automated detection of patients that would benefit from renoprotective therapies, to empowering “big data” genetic and observational studies of CKD at a scale unobtainable using traditional phenotyping methods.19, the KDIGO Clinical Practice Guideline for the Evaluation and Management of CKD10, and domain expert knowledge in nephrology to define CKD cases and controls using laboratory measurements in combination with diagnosis and procedure codes  of the algorithmic diagnoses by performing 451 blinded manual chart reviews of algorithm-derived diagnostic labels across three major US medical centers Table . The oveTo perform additional testing of the algorithm and to enable estimation of the overall diagnostic sensitivity and specificity, we constructed a large case-control dataset of 2350 patients. We defined 1136 cases as patients seen by a nephrologist in the Columbia CKD clinic, and 1214 controls as healthy women without a known CKD diagnosis undergoing a prenatal visit at Columbia University during the same time interval as the cases. In this dataset, the sensitivity, specificity, PPV and NPV of the algorithm were 87%, 97%, 97%, and 89%, respectively (Supplementary Table https://phekb.org/phenotype/chronic-kidney-disease)25. The PheKB documentation also includes a detailed list of all ICD-9-CM, ICD-10-CM, SNOMED, lab LOINC, and procedure CPT codes used by the algorithm.We provide an open-source implementation of a parameterized and modularized version of this algorithm through the publicly accessible Phenotype Knowledgebase  patients, and 19,515 ESRD patients on dialysis or after kidney transplant. Notably, only 45,405 (19%) of algorithm-diagnosed CKD cases had a diagnostic or procedure code compatible with CKD, demonstrating superior sensitivity of our phenotyping approach. The counts by the NKF stage and KDIGO A-by-G grid are provided in Supplementary Table Next, we calculated the prevalence of comorbidities for each NKF stage  as previously recognized28 but were also negatively correlated with A-stage (P = 4.2 × 10−5) after accounting for G-stage, highlighting a new protective association that should be studied. Conversely, alcohol abuse was positively correlated with A-stage (P < 1 × 10−16) but appeared progressively less common with increasing G-stage (P = 9.2 × 10−5).We also observed several new or unexpected trends that exceeded our Bonferroni-corrected significance threshold. For example, valvular diseases were positively correlated with G-stage , were considerably more prevalent among patients with mild CKD (G2) than patients with normal renal function in age and sex-independent manner. The relationship between CKD and neuropsychiatric diseases has previously been observed in advanced CKD22. We intersected these data with our CKD algorithm’s output to estimate pedigree-based observational heritability (ho2) of renal function. We note that ho2 is an estimate of the narrow-sense heritability based on observational data. Because observational data are subject to confounding by physician and patient behaviors that may affect the probability that a particular trait is ascertained, we used repeated subsampling with SOLARStrap to produce heritability estimates that are more robust to this bias, as previously described22. We also control for age, sex, race/ethnicity, and household effects (see Methods).Using emergency contact information, we have previously inferred 3,244,380 unique familial relationships that were used to reconstruct 223,307 pedigrees among patients with EHR records at CUIMCP = 4.3 × 10−5). After stratifying by self-reported ancestry, the ho2 was 0.244  for African Americans and 0.197  for White/Europeans. We also estimated ho2 for eGFR by restricting the pedigrees to those with at least one member with CKD Stage 3 (moderate CKD or greater) and those with at least one member with CKD Stage 4 (advanced CKD or greater). With this ascertainment, the eGFR ho2 increased to 0.237  for moderate CKD, and 0.455  for advanced CKD.Our analysis strongly supported significant genetic contributions to renal function Fig. . Based o30, we next analyzed a dichotomous CKD phenotype (any stage) as defined by our algorithm. In the analysis of 3460 pedigrees, we confirmed significant CKD ho2 at 0.290 . Additional ho2 estimates stratified by stage and race/ethnicity are provided in Table Using the liability threshold modelo2 of albuminuria  was estimated at 0.236 . For a subset with heavy albuminuria (A3), the ho2 was 0.490 . Because of a smaller number of A-staged pedigrees, we could not further sub-stratify this analysis by ancestry.The algorithmic A-staging also provided us with an opportunity to estimate hria Fig. . Based o35, but we also observed higher heritability of kidney disease in African Americans, potentially contributing to the known racial disparities in CKD risk.Taken together, these analyses provide the largest and most comprehensive pedigree-based analysis of heritability for kidney function, albuminuria, and CKD presently. They are based on a multiethnic urban cohort of the size that was previously unobtainable for family-based analyses. The results are generally consistent with prior estimates based on much smaller pedigree-based studieso2 compared to milder disease. From these, we derived a cohort of 25,377 European participants, consisting of 7536 CKD cases and 17,841 controls matched by platform and genetic ancestry. We performed GWAS under additive genotype coding with adjustment for age, sex, site/platform, and six significant principal components of ancestry , a known GWAS risk locus in Europeans  stratified by ancestry. To define GWAS cases, we selected CKD Stage 3 or greater (G3-5 and ESRD) based on the observation that moderate CKD had higher htry Fig. . We achians Fig. .Fig. 4CoAPOL1 locus  . Neither G1 nor G2 variants were imputed at high confidence in the eMERGE dataset.We performed a similar analysis among African ancestry, with 2731 algorithmically defined participants (702 cases and 2029 controls). GWAS was performed under an additive model with adjustment for age, sex, site/platform, and three significant principal components of ancestry, with adequate control of genomic inflation (lambda = 1.03). We detected a genome-wide significant signal at the known 10) Fig. , d. The UMOD and APOL1 loci. The PheWAS for UMOD was performed in all 78,638 available European-ancestry eMERGE participants . Moreover, we have recovered the previously reported protective associations of the CKD risk variant with nephrolithiasis, including “calculus of kidney” , “urinary calculus” , as well as a suggestive protective association for “acute cystitis”  (Supplementary Data 1). The mechanisms underlying these protective effects are currently not known.We next performed phenome-wide association studies (PheWAS) for both nts Fig.  and cleaAPOL1 risk variants was performed in 16,976 individuals of genetically-defined African ancestry and uncovered a broad spectrum of effects with relatively large effect sizes despite simple additive coding used in PheWAS. These associations included kidney transplantation , end-stage renal disease , dialysis , glomerulonephritis , as well as numerous complications of kidney disease, including anemia , renal osteodystrophy  and transplant comorbidities  or disease-related (e.g. acute kidney injury) fluctuations of single time point serum Cr values. Our algorithm includes a judicious criterion for chronicity, requiring CKD duration for over 90 days as documented by repeat blood or urine tests, or documentation by a prior diagnostic code. We also carefully define qualifying eGFR as the one that does not co-occur with acute kidney injury, volume depletion, or critical illness.49. We used a supervised machine-learning approach to design accurate classifiers translating the most commonly used urinalysis tests to the KDIGO-defined A-stages. We also demonstrated that our urinalysis-based A-stage classifier outperforms the alternative methods published while our study was under review24. This improved performance is most likely due to the fact that our classifiers include urine specific gravity in addition to DSP, reducing misclassification due to variations in urine concentration. Our convenient classifiers are fully automated and can be used to improve phenotype definitions in large scale genetic, epidemiologic, and interventional studies of CKD.One of the greatest obstacles for developing our algorithm was the fact that the A-staging requires accurate estimation of daily urine albumin excretion. Estimating albuminuria using EHR data is not straightforward, mainly because an array of urine protein tests is used in clinical practice. Current guidelines recommend spot UACR as an optimal method to quantify albuminuria. However, recent studies using EHR have shown that even patients at high risk for CKD frequently receive only DSP tests50. The CKD-EPI formula utilizes age, sex, and race in addition to serum Cr to determine eGFR. The race information is problematic, since it is based on self-report and this information is frequently inaccurate in medical records51. Additionally, Cr-based GFR estimation in individuals of diverse or admixed ancestries may be inaccurate, since CKD-EPI was derived on a cohort composed of only White and Black Americans. Although CKD-EPI equation presently provides the most accurate means for GFR estimation, it could be easily replaced by any future equations that replace the race variable without other major changes to the algorithm52. Similarly, estimation of GFR in pediatric patients may be less accurate compared to adults, but the formula used by the algorithm could be updated once more accurate equations become available. Third, our algorithm is currently designed for detection and staging of all cause CKD along the two axes of A-by-G grid. Adding the third axis of CKD subtype (i.e. primary disease subtype) could substantially enhance our phenotyping. However, automated determination of primary kidney diagnoses using medical records proves to be challenging for a number of reasons, including large etiologic heterogeneity, long time period of CKD progression that may not be well covered by EHRs, inadequate classification of kidney disease subtypes by older billing codes (e.g. ICD-9-CM), and the fact that a kidney biopsy (the gold standard for primary kidney disease diagnosis) is underutilized in clinical practice53. As a result, the primary cause of kidney disease is often difficult to establish with certainty, even by manual review of medical records.Several limitations of our approach should be noted. First, the successful detection and staging of CKD using our approach is dependent on the availability of relevant blood and urine tests in medical records. Because serum Cr is usually determined as part of routine health screening tests, most patients have serum Cr available for G-staging. However, urine tests are performed less frequently, and the A-staging could therefore be incomplete due to missing data. Second, our algorithm performs G-staging using the CKD-EPI formula for GFR estimation in adults11. Given that CKD progression is often irreversible and early interventions are considered to be most effective, our algorithm could address this issue by flagging CKD diagnoses in medical records, alerting both clinicians and patients. The clinical benefit may be greatest for the detection of mild (G1A2-3) disease that may benefit most from early therapeutic interventions, such as renin-angiotensin system blockade15 or the use of sodium-glucose cotransporter-2 inhibitors16.Despite these limitations, we demonstrate that our electronic phenotype provides effective means for clinical detection and staging of CKD. There are several ways in which automated diagnosis of CKD could substantially enhance clinical care. First, algorithmic diagnosis could enhance physician and patient awareness of the disease. Recent studies show that less than 10% of patients with early CKD (stages 1–3), and only half (52%) of those with severe CKD (stage 4) are aware of having a kidney problem54. Once confirmed, additional tests may be needed to define the cause of renal damage, including renal imaging, diabetes screening, blood pressure monitoring, and possibly a renal biopsy. Second, our algorithm also enables the implementation of stage-specific recommendations for the management of common complications of more advanced CKD. For example, the management of CKD-associated anemia and renal osteodystrophy are complex, stage-specific, and expensive55. Our staging algorithm could be easily incorporated into a clinical decision support system that helps treating physicians to achieve target levels of hemoglobin or parathyroid hormone by suggesting appropriate stage-specific use of oral and injectable medications56.We recommend a confirmation of A-staging by UACR, which represents the gold standard underutilized in clinical screening for CKDTo assure the algorithm’s interoperability and portability, we used a parameterized and modularized implementation which can be easily customized to different data models , commonly used data elements available in different EHR systems , and data dictionaries which are fully compatible with commonly used coding schemes . We provide open-source software on PheKB website (https://phekb.org/phenotype/chronic-kidney-disease) for a straightforward local implementation of the CKD phenotype, indicating parts of the code that require local customization. This code can be used as a starting point for building CKD alert systems or related clinical decision support applications.UMOD locus originally discovered at comparable significance in the analysis of 19,877 Europeans ascertained using traditional methods57. This suggests that our algorithm’s applications to large biobanks with genetic data linked to medical records could empower new genetic discoveries for CKD.In addition to clinical utility, we demonstrate that our algorithm has multiple research applications, from observational inference to genetics and any other population health research based on EHR, as demonstrated by our large-scale comorbidity, heritability, and GWAS analyses. Our GWAS involving 25,377 Europeans recapitulates genome-wide significant CKD associations at the The studies using our electronic phenotype have already provided valuable insights into the genetic architecture of CKD. Our pedigree-based analyses support a substantial hereditary component to CKD and highlight ancestral disparities in genetic susceptibility to kidney diseases. Despite high heritability in pedigree-based analyses, the SNP-based heritability of CKD was estimated at only ~1% based on the largest available studies performed predominantly in European-ancestry cohorts. Although SNP-based heritability of eGFR is higher (estimated at up to 8%) compared to CKD, this estimate is more likely to be confounded by inherited differences in muscle mass, Cr production, and Cr metabolism in population-based studies, and thus may be less reflective of the true heritability of CKD as defined by reduced Cr clearance. The low estimates of SNP-based heritability of CKD  are likely due to the etiologic heterogeneity of CKD, and the fact that the existing GWAS for CKD are still of limited sample size. Widespread application of our algorithm to big biobanks should empower larger GWAS for CKD, providing more accurate estimates.58. There are several potential explanations for this observation. First, the estimates of pedigree-based heritability could be partially inflated by shared environment and epigenetic effects. Although we make an attempt to control for shared household in our heritability estimates, environmental effects are generally difficult to adjust for in family-based studies. Second, the heritability gap may be due to additive modeling not accounting for non-additive SNP effects, such as recessive, dominant, gene-gene, or gene-environment interaction effects. For example, APOL1 risk genotype effects are contributing to family-based heritability in African Americans, but are not captured by SNP-based heritability, because the genetic risk model is recessive and APOL1 locus is missed in GWAS dominated by Europeans. Third, the heritability gap could be explained by rare Mendelian or structural variants that are not accounted for in the estimation of SNP-based heritability. This may indeed represent the most likely explanation given that recent exome sequencing studies in CKD demonstrate that up to 1 in 10 adult cases may be attributable to a monogenic disease variant59. Moreover, up to 7% of pediatric CKD and up to 6% of congenital kidney defects could be attributable to genomic disorders62.The wide gap between pedigree-based and SNP-based heritability is not unique to CKD, and has been reported for other complex traits63. Future improvements of e-phenotyping for CKD are likely to involve automated CKD subtype determination, and more accurate methods for estimation of GFR in adult and pediatric patients of diverse ancestral backgrounds.Taken together, the applications of our electronic phenotype to GWAS and pedigree-based analysis provide support for a strong genetic predisposition to CKD, but low SNP-based heritability. Our findings are consistent with the notion that CKD may not represent a single phenotype but rather a collection of genetically and phenotypically heterogeneous diseases encompassing multiple Mendelian subtypes, as well as disorders of more complex genetic determination. These observations have important implications for the implementation of kidney precision medicine. For example, the approaches that combine diagnostic sequencing with polygenic risk scores for specific subtypes of kidney diseases may be better suited for clinical risk stratification compared to polygenic risk scores based on GWAS for eGFR alone19, (2) the KDIGO Clinical Practice Guideline for the Evaluation and Management of CKD10, and (3) domain expert knowledge in nephrology to define CKD cases and controls using lab measurements in combination with diagnosis and procedure codes  the NKF’s KDOQI guidelines the NKF’50 and Bedside Schwartz equation in pediatric patients (age < 18 years old)65. Since the Bedside Schwartz equation requires height concurrent with serum Cr, and the height data do not always coincide with Cr data, we used a simple height extrapolation method , 24-h urine collection for albumin (A24), Urine Protein-to-Cr-Ratio (UPCR), 24-h urine collection for protein (P24) and urine dipstick protein test (DSP). For the purpose of our algorithm, we assume that P24 [mg/24 h] and UPCR [mg/g Cr] are numerically equivalent. While A-stages can be derived directly from UACR and A24 using KDIGO-recommended cut-offs, our algorithm aimed to perform A-staging using UPCR, P24, or DSP. For this purpose, we designed two separate supervised machine-learning approaches.n = 4641 paired measurements). We used UACR as the gold standard to define the A-stage . Using this training set, we next applied an ordinal regression-based approach to construct the A-stage classifier. Feature selection was performed with a goal to minimize mean squatted error of the model. The features tested included log-transformed UPCR, age, sex, diabetes, race, ethnicity. For each model, we estimated model coefficients, used them to compute probabilities of A1, A2, and A3, and maximized over these probabilities to predict the most likely stage for any given set of predictor values. We then used a 10-fold cross-validation approach, enabling calculation of mean squared error (MSE) for our ordinal classifier (Eq. n = 8688) and VU (n = 5770). The MSE, accuracy, sensitivity, and specificity metrics were remarkably similar between internal and external validation datasets, thus we build a final predictive model that was derived from the entire dataset of 19,099 paired observations across all three institutions (Supplementary Tables 1–2). This final model was used in our algorithm. This classifier had 86.7%, 80.0%, and 92.3% accuracy for A1, A2, and A3, respectively. The specificity was high for all A-stages (87.1–94.8%), but the sensitivity was lower for A2 (63.5%) compared to A1 and A3 (86.2–86.7%). This is consistent with the UPCR method being less accurate at a lower level of albuminuria.The first approach aimed to build an ordinal classifier which maps UPCR or P24 values to individual A-stages. For our training set, we identified all same day paired urine tests for UPCR and UACR within the Columbia EHR  measured at the same time as DSP as an additional input feature. For the second problem, we identified two most common urinalysis scales and developed an A-stage classifier using two separate training sets for these scales: Scale 1  and Scale 2 .To develop a Scale 1-based classifier, we used 12,185 simultaneous DSP and UACR measurements from the CUIMC EHR system  to derive the final classifier. The performance of the Stage 2-classifier was tested by 10-fold cross-validation  we demonstrated that the performance of our A-stage classifiers was consistent between the testing and discovery datasets, and generally comparable to the newly published methods. Similar to our study, the CKD Prognosis Consortium models additionally adjusted for sex, diabetes, and hypertension did not perform better over simple models based on UPCR or DSP alone .To enable portable implementation, a parameterized and modularized algorithm query was developedWe determined the PPV of the algorithm by conducting 451 blinded manual chart reviews as a gold standard across three large US institutions. For internal validation at CUIMC, we selected 251 charts  with adequate data within the EHR. Two blinded nephrologists were asked to make the CKD diagnosis and stage the disease based on the latest lab values and clinical chart data; a third expert blinded to the algorithm results resolved any discrepancies. For external validation, we performed manual review of additional 200 charts  within the VU and Mayo Clinic EHR system. We calculated overall PPVs as well as PPVs by case/control status, by institution, and by CKD stage  and 1214 controls . The algorithm had specificity of 97%, sensitivity of 87%, PPV of 97%, NPV of 89%, and F1 measure of 92% for discriminating CKD patients from healthy controls . The association screen for CKD comorbidities was performed by evaluating co-occurrence of CKD with all other diagnostic and procedure codes by A and G stage. We tested for significant additive patterns in age and sex-adjusted comorbidities across the A-by-G grid using logistic regression; each comorbidity was used as an outcome, and A and G stages were used as ordinal predictors with age and sex as covariates in the model. Using these models, we tested for an independent additive effect of A and G stages on each comorbid condition using Wald test. Given a total of 40 independent comorbidities tested with two tests per each comorbidity, we used a Bonferroni-adjusted alpha of 0.05/80 = 6.25 × 10−4 to declare statistical significance.We applied our algorithm to the entire Columbia CDW covering data from 1997 to 2017. Among 1,365,098 patients with at least one serum creatinine value available, the algorithm had sufficient data to stage 672,858 individuals. We used the AHRQ Elixhauser Comorbidity Index that defines 40 comorbidity measures from ICD-9-CM and ICD-10-CM codes for comorbidity analysis22 to infer familial relationships among individuals with inpatient EHR records at CUIMC. Briefly, a total of 3,244,380 unique relationships have been identified at Columbia based on emergency contact information combined with relationship inference as described previously22. We grouped individuals into families by identifying disconnected relationship sub-graphs and found 223,307 families ranging from 2 to 134 members per family. We next intersected the pedigree dataset with the output of the CKD algorithm applied to the CUIMC EHR. This allowed us to estimate observational heritability for our electronic CKD phenotypes, including eGFR, any albuminuria (A2 or A3), heavy albuminuria (A3), the diagnosis of any CKD, moderate CKD (stage 3 or greater), and advanced CKD (stage 4 or greater). We modeled heritability under additive genetic model with phenotype adjusted for age, sex, race/ethnicity, and common environment (approximated by a term that used the mother ID as the household ID). We used SOLARStrap, a repeated subsampling procedure in which each subsampled set of families is used to estimate heritability using SOLAR30. These estimates are then averaged to produce a robust heritability estimate that is less prone to ascertainment bias22.We used the RIFTEHR algorithm21. Briefly, GWAS datasets were imputed using the latest multiethnic Haplotype Reference Consortium (HRC) panel. The imputation was performed in 81 individual batches across the 12 contributing medical centers participating in eMERGE-I, II, and III. For post-imputation analyses, we included only markers with MAF ≥ 0.01 and R2 ≥ 0.8 in ≥75% of batches. These quality control analyses were performed using a combination of VCFtools, PLINK, and custom scripts in PYTHON and R70. To assess population stratification and remove population outliers, we applied a principal component analysis using FlashPCA71. We applied k-means clustering algorithm to the PCA data to split the overall cohort into the three major ancestral clusters based on similarity to reference populations from the 1000 Genomes Project . All genome-wide association analyses were subsequently performed within each major ancestral group, after adjustment for age, sex, site, and significant principal components re-derived for each ancestral cluster . Each site participating in eMERGE-III implemented our electronic phenotype and provided the algorithm output for linkage with the genetic data. The association analyses of binary traits (CKD vs. control) were performed using logistic regression. We used a dosage method under additive genotype coding to account for imputation uncertainty. For each SNP, we derived pooled effect estimates, their standard errors, and 95% confidence intervals. Genome-wide distributions of P values were examined visually using quantile-quantile plots and we estimated genomic inflation factors for each genome-wide scan72. We used the generally accepted alpha = 5 × 10−8 to declare genome-wide significance73. To estimate the fraction of additive genetic variance contributed by genome-wide SNP data and to derive pairwise genetic correlations between phenotypes, we used the linkage disequilibrium score regression (LDSC) method74.For the purpose of genetic studies, we implemented the CKD phenotype across the entire eMERGE-III network. The network provides access to EHR information linked to GWAS data for 105,108 individuals. Detailed pre-imputation quality control pipelines for genetic data of the eMERGE-III consortium have previously been described23. The package uses pre-defined “control” groups for each phecode “case” grouping. In total 1804 phecodes were tested using age, sex, center, and principal component-adjusted logistic regression model with each phecode case-control status as an outcome. The genotype predictors were coded under additive model for risk alleles. We set the Bonferroni-corrected statistical significance threshold at 2 × 10−5 (0.05/1804) to control for the number of phecodes tested.We used the latest release of eMERGE-III data for PheWAS. The phenotype data consisted of 19,853 distinct ICD-9-CM codes for 105,108 individuals with genotype data. The ICD-9-CM codes were mapped to phecodes and PheWAS was performed using the PheWAS R packageThe study was approved by the Columbia University Institutional Review Board (IRB protocol numbers IRB-AAAP7926 and IRB-AAAO4154) and individual IRBs at all eMERGE-III network sites contributing human genetic and clinical data. Our large scale heritability and comorbidity analyses based on the Columbia Data Warehouse were performed under an approved waiver of consent. BioVU operated on an opt-out basis until January 2015 and on an opt-in basis since. The phenotypic data in BioVU are all de-identified and the study was designated “non-human subjects” research by the Vanderbilt Institutional Review Board. All other eMERGE participants provided informed consent to participate in genetic studies.Further information on experimental design is available in the Reporting SummarySupplementary InformationSupplementary Data 1Supplementary Data 2


Strongylocentrotus purpuratus) sourced from rapidly-declining bull kelp (Nereocystis leutkeana) forests and nearby barrens and maintained on habitat-specific diets. Kelp forest urchins were of superior condition to barrens urchins, with ~ 6–9 times more gonad per body mass. Grazing and condition in kelp forest urchins were more negatively affected by distant-future and extreme upwelling conditions, whereas grazing and survival in urchins from barrens were sensitive to both current-day and all future-predicted upwelling, and to increases in acidity, hypoxia, and temperature regardless of upwelling. We conclude that urchin barren urchins are more susceptible to increases in the magnitude and duration of upwelling-related stressors than kelp forest urchins. These findings have important implications for urchin population dynamics and their interaction with kelp.Globally, kelp forests are threatened by multiple stressors, including increasing grazing by sea urchins. With coastal upwelling predicted to increase in intensity and duration in the future, understanding whether kelp forest and urchin barren urchins are differentially affected by upwelling-related stressors will give insight into how future conditions may affect the transition between kelp forests and barrens. We assessed how current and future-predicted changes in the duration and magnitude of upwelling-associated stressors  affected the performance of purple sea urchins ( In some regions, kelp decline can be attributed to sea urchins, the major grazers of kelp worldwide5. Disruption to the interplay between sea urchin grazing and kelp forest productivity can tip the balance between stable states that alternate between diverse kelp forests and species-depauperate urchin barrens and the transition from one state to the other can be initiated by several factors, including the abundance of algal food, predators, storm intensities, and incidence of disease6. For example, when kelp forests are productive and healthy, sea urchins remain relatively immobile, preferring to feed on the drift kelp that is produced and sloughed off in large quantities by standing kelp8. However, when large-scale disturbance  remove large amounts of standing kelp biomass, sea urchins mobilize and graze on young kelp recruits  a ‘perfect storm’ of multiple factors has led to historic increases in purple sea urchin (Strongylocentrotus purpuratus) abundance and dramatic reductions (> 90% loss in canopy cover) in bull kelp (Nereocystis leutkeana) forests along the northern California coast, many of which have been converted to urchin barrens5. The kelp loss has had cascading and detrimental effects on other organisms and the vital ecosystem services they provide11.Further, recruitment peaks can cause sea urchin densities to spike5, it is critical that we gain a greater understanding of how current and future environmental conditions will shape the performance of this key grazer. One way that sea urchins and their interaction with kelp may be affected is via coastal upwelling, a process that brings cold, nutrient-rich, hypoxic and acidic water to nearshore coastal environments seasonally. Upwelling thus simultaneously changes multiple environmental factors that can affect species performance and their interactions with other species12. For example, slight changes in water temperature associated with upwelling have been shown to dramatically alter the consumptive effects of predatory sea stars (Pisaster ochraceus) on rocky shore mussels . Likewise, in estuarine food webs, low, but nonlethal, dissolved oxygen (DO) concentrations can modulate the consumption rates of predatory invertebrates on fish eggs and larvae13. Further, in some regions, present-day upwelling can deliver water as acidic as that predicted for global ocean averages 50–100 years from now14, conditions that can alter competitive interactions between turf algae and kelp, shifting rocky reefs to kelp-dominated habitats15. Alternatively, upwelling-driven increases in nutrients can lead to increases in algal primary productivity, that can outpace consumption by grazers16.Because urchins can play a major role in the destruction of kelp forests18, few have examined the effects of these stressors in concert19. Even fewer have examined how future predicted and temporal variability in these stressors will influence species interactions (but see21). However, upwelling in coastal systems is an inherently multi-stressor phenomenon with highly correlated changes in temperature, DO, and pH occurring intermittently, over scales of days to weeks23. Thus, to understand how current and future upwelling conditions will affect key species interactions, it is necessary to examine multiple stressors acting concurrently under natural temporal scales of variability.Whereas the majority of previous studies have focused on separating out the effects of individual stressors associated with upwelling on species performance26, dominating during the spring and early summer months and dissipating in the late summer and fall, before the start of winter storms27. The magnitude of current day upwelling conditions varies regionally along the CCLME25. Off the coast of southern Oregon and northern California, between Cape Blanco and Cape Mendocino, upwelling can last as few as three days and as long as 12, with high variability28. On average, upwelling periods are shorter than non-upwelling periods in this region and in 2018, Trinidad Harbor (located roughly midway between Capes Blanco and Mendocino) experienced pH levels below 7.8 49% of the time, DO below 8.0 mg L−1 44% of the time, and temperature below 10 °C 23% of the time, during upwelling season29. Although greenhouse warning has been proposed to intensify alongshore wind stress on the ocean surface, leading to acceleration of coastal upwelling30, subsequent analyses have not reached consensus on historical and projected trends in coastal upwelling34. Despite this, most models and historical trends predict that nearshore coastal regions in northern California will experience either more frequent35, prolonged36, or intense38 periods of upwelling. For example, Iles et al.28 showed that annual mean duration of upwelling increased by 26–86% from 1967 to 2010 in regions of Oregon and northern California, and recent models predict that upwelling will increase in this region . Along with changes in the duration of upwelling, current models predict that by 2100, global ocean pH will decrease to 7.742, DO will decrease between 0.6 and 2.0% from current levels42, and sea surface temperatures will increase by 2–3 °C42 under current anthropogenic climate change44. Further, anomalous marine heat waves have already become more frequent, longer lasting, and more intense in the past few decades46. Taken together, coastal habitats along the CCLME are therefore predicted to experience an increase in the duration and magnitude of stressors associated with coastal upwelling and marine heat waves, with potentially important consequences for kelp-urchin interactions.Upwelling plays a defining role in the biogeography, ecology, and productivity of the CCLME48), several studies have hypothesized, and found, that these negative effects can be reduced in situations where energy  is more readily available51. Kelp forest and urchin barrens urchins provide an excellent model to test this hypothesis. In kelp forests, high-quality food is abundant and sea urchins are well-nourished, moving little and feeding on the drift kelp. In urchin barrens, drift kelp is sparse, and sea urchins are poorly nourished, actively grazing the substratum for poor-quality food. The high densities of urchins and lack of kelp food resources in urchin barrens can result in urchins in poorer condition and with reduced gonad production (indicative of an energetic tradeoff) relative to kelp forest urchins52. We therefore hypothesized that better-condition kelp forest urchins would be less negatively affected by upwelling-related stressors than poorer-condition barrens urchins.With the deforestation of kelp forests in many regions of the world, it is crucial to begin to predict how both current and future upwelling will impact the performance of sea urchins, arguably one of the largest threats to kelp globally. Further, the rapid decline in kelp forest habitat and concomitant increase in urchin barrens in northern California provides a unique opportunity to test whether organismal condition may play a role in mediating the effects of upwelling stressors on their performance. Because the negative effects of environmental stressors on organisms often result from energetic deficiencies due to increased metabolic demand  on the grazing rates, survival, and gonad development of purple sea urchins from kelp forest and urchin barren habitats. By designing a mesocosm experiment that provides a realistic, temporally variable, and multiple-stressor habitat-specific context, we aimed to provide better insight into how purple sea urchins, bull kelp’s major grazer, will be affected under current day and future climate scenarios.F3,26 = 19.32, P < 0.0001; see Supplementary Fig. 2 dosing system succeeded in achieving the pH conditions very close to the targets for each upwelling severity treatment  on urchin grazing rates Table , indicatDuring initial non-upwelling conditions, kelp forest urchins grazed at similar rates in all treatments except in Future 2, where urchins grazed almost 3 × faster than urchins in Future 1 and 3 , but urchin habitat did , with urchin barren urchins suffering ~ 18 times (55 to 3) the mortality of kelp forest urchins .High mortality in barrens urchins led to only four urchins surviving the duration of the experiment, thus treatment effects on the gonad indices of barrens urchins could not be assessed. Upwelling severity did affect the condition of kelp forest urchins that survived the duration of the experiment  from different habitats (kelp forests and urchin barrens) and maintained on habitat-specific diets exhibit differential susceptibility to both current day upwelling, and future-predicted increases in the magnitude and duration of abiotic stressors associated with upwelling. Kelp forest urchins, which were more resilient, survived well under all upwelling severity treatments. This was in sharp contrast to urchin barren urchin mortality, which was high across all upwelling severity treatments. These results predict potentially important population- and ecosystem-level consequences, as kelp forest urchins, so long as they have abundant and high-quality kelp food resources, will be more resilient to current- and near-future upwelling stress than urchin barren urchins limited to poorer quality algal diets. Although there is currently an overabundance of purple sea urchins on the northern California coast because of low mortality for certain age classes, owing to a lack of natural predators and anomalously strong recruitment5, our results suggest that as urchin barrens become more common in these areas, purple urchins may experience increasingly higher rates of mortality owing to increased exposure to and magnitude of upwelling stress.Our results indicate that purple sea urchins  that were ~ 11–12 times lower than the kelp forest urchins in our experiment. The poor condition of urchin barren urchins prior to the study, and their lower overall feeding rates relative to the kelp forest urchins during the experiment, thus likely led to their increased mortality.We hypothesize that mortality differences in kelp forest and urchin barren urchins were due to in part to the effects of diet during the study and to the condition status previous to the study. Our goal was not to separate out whether initial urchin condition , or food quality in the experiment (which was intended to maintain condition differences throughout the experiment) was responsible for condition-dependent sensitivity to upwelling-stressors. Rather our goal was to determine whether poor-condition urchins with a low-quality food source, as is the case in urchin barrens habitats, would be more susceptible to upwelling-associated stressors than good-condition urchins with a high-quality food source, as is the case in kelp forests. In urchin barrens, high quality food is scarce or absent, and urchins must rely on poor-quality coralline algae as opposed to in healthy bull kelp forest habitats, where abundant 56, and the coralline algae we used in our study has articulated pinnate branching with cylindrical stipes, which may be more difficult for urchins to grasp, bite or scrape than broad, smooth, and flat bull kelp blades. Further, a diet of kelp has been shown to accelerate reproductive maturation and growth rate in sea urchins relative to a diet of coralline algae57, indicating its nutritional superiority. Regardless of the precise mechanism underlying the observed grazing rates, the proportional changes in grazing from the initial non-upwelling conditions to the first upwelling events were of similar magnitude for both kelp forest urchins and urchin barrens urchins, indicating that initial exposure to treatment conditions affected grazing rates similarly, regardless of food quality.Absolute differences between the grazing rates of kelp forest urchins and urchin barren urchins over time and across treatments are also unlikely to simply reflect differences in urchin condition. Differences in the nutritional quality between kelp and coralline algae are also likely to have affected consumption and digestion rates. Algal shapes or textures that increase handling time by herbivores can influence feeding rates58). In this regard, kelp forest urchins were also more resilient than urchin barren urchins, with their grazing rates rebounding to a larger degree under non-upwelling events. These differences in kelp forest and urchin barrens urchin performance highlight that environmental stress often imposes energetic trade-offs on organisms and that an organism’s condition, which is a product of their resource environment, will strongly influence how well it can balance those tradeoffs51.Despite that urchin grazing rates were lower when exposed to conditions that simulated upwelling overall, one nuance was the observed temporal effect, where urchin grazing increased during the second upwelling event compared to the first. This could be explained by urchins experiencing the first upwelling event as a physiological “shock” to which the urchins acclimated once the second upwelling event occurred . In general, it has been shown that increasing temperatures increase metabolic activity, thus leading to higher grazing rates in many organisms59. We hypothesize that increased temperatures during non-upwelling events led to higher grazing rates for both kelp forest and barren urchins during these times compared to upwelling events. The four degree increase from 12 °C during current day non-upwelling events to 16 °C in Future 2 non-upwelling events may also explain associated increases in urchin grazing between these treatments. Recent experimental work has shown that when purple urchins were exposed to low temperatures, low pH, and low DO separately, low temperature and DO had significant negative effects on respiration and grazing rates, but low pH did not; however, when combined, pH, temperature and DO can have interactive effects19. For example, low temperatures and low DO together had larger combined effects on urchin respiration and grazing than either did alone19. In contrast, when low DO or low temperature was combined with low pH, low pH seemed to lessen the effects of temperature and DO on urchin respiration and grazing19. When all three were combined, low pH was not enough to compensate for combined negative effects of low temperature and low DO20. Because we saw a large reduction in grazing during upwelling events, it is reasonable to believe that low DO in our future treatments was mainly responsible for a decrease in grazing for both kelp forest urchins and urchin barren urchins. It should be noted, however, that our study reached low levels of pH and high temperatures for longer periods of time than in19. Further, much of what we simulate with our experimental treatments could also be considered marine heatwave conditions60. Specifically, some of our future “low” temperatures are higher than current low or high temperatures and are associated with low dissolved oxygen—such higher temperatures can exacerbate low oxygen effects by increasing metabolic rates. It is therefore possible that pH and temperature played direct or indirect roles in reducing urchin grazing here as well. Further studies independently altering pH and temperature to the levels reported here for a similar exposure time could elucidate the relative importance of each.Although the integrated nature of our treatments did not allow us to precisely indicate which upwelling-related stressor had the largest effect on urchin grazing, the observed pattern of increased grazing during non-upwelling events and reduced grazing during upwelling events are consistent with previous work . Increased metabolism might explain the lower gonad indices in urchins in this treatment, as higher metabolic costs would leave less energy available for gonad production and energy storage. Metabolic rates increase with temperature across a wide range of organisms63, especially at lower urchin densities64, and gonads are a main target for predators due to their nutritional value65, so effects on gonad production could also reduce trophic energy transfer. Further, urchin grazing is critical to shallow subtidal ecosystems, and its effects could influence kelp forest dynamics and ‘tipping points’ between alternate community states66. Therefore, present-day spatial differences in the intensity and duration of upwelling and future changes in upwelling exposure could have population- and ecosystem-level effects via direct impacts on one or two sensitive organism-level responses, such as urchin grazing and gonad development. However, because increased nutrient concentrations from upwelling and temperature increases from ocean warming can both directly affect kelp68, understanding how future conditions will affect the kelp-urchin interaction will require determining whether upwelling-driven increases in nutrients will lead to increases in kelp growth that will surpass consumption by stressed urchin grazers16, and whether ocean warming with continue to deplete already-diminishing kelp forests69. Further, future studies should incorporate how changes in upwelling duration and magnitude might affect early life stages of urchins, whose rapid growth may show differences in response to environmental stressors more quickly than adults71.Although complexity in our experimental design did not allow us to isolate the effects of upwelling intensity vs. duration or the individual effects of stressors on urchins, our results are novel and important as individual stressors associated with upwelling are never independent in nature and upwelling intensity and duration are predicted to increase together in the future in some regions. Varying exposure to multiple upwelling stressors could have important implications for sea urchin population dynamics and kelp forest ecosystem structure. Fewer gonads in sea urchins reduce reproductive capacity73 It is therefore necessary to examine the effects of different exposures under realistic patterns of variability, and how energetic context mediates these effects. We showed that exposure to increased intensity and duration of upwelling (as predicted by some climate models) has negative impacts on ecologically important purple sea urchins, and that these impacts are mediated by urchin diet and condition (a result of their habitat). Further, the responses of S. purpuratus to multiple stressors could not have been predicted from studies of single stressors, which would have produced results opposite to what we observed under conditions more likely to occur in the future . This highlights the importance of understanding the combined effects of multiple stressors on marine organisms74 now, and in the future.As climate change alters the magnitude and duration of physiological stressors in ecosystems, subpopulations of organisms will be differentially affected75. In urchin barrens, dense urchins and lack of kelp lead to poorly nourished individuals with reduced gonads relative to kelp forests, indicative of a food-mediated energetic tradeoff52. Thus, we chose urchins of this size to minimally detect habitat-specific differences and maximize potential for gonad development during our experiment. To ensure urchins from different habitats differed in condition, we collected n = 15 from each habitat  and calculated gonad indices ((blotted wet mass gonad)/ * 100), which are useful for comparing the condition of similarly-sized individuals in a population from different habitats through time75. Urchins were placed in individual chambers, starved for 24 h, and acclimated to experimental conditions for at least 24 h. Kelp forest urchins were fed bull kelp (Nereocystis luetkeana) and urchin barren urchins were fed articulated coralline algae , ad libitum.We collected urchins via SCUBA from urchin barrens at Baker Beach , and kelp forests in Trinidad Harbor  in northern California in July 2019. Urchins were transported to the Telonicher Marine Laboratory, weighed (blotted wet mass) and measured (test diameter). We used urchins averaging 43.23 ± 3.62 mm in test diameter because in nearby populations all individuals > 40 mm produce mature gametes with the ratio of gonad to body size peaking between 40 and 50 mm29. Future 1, 2, and 3 treatments were created by increasing the duration of upwelling events by 25, 100, and 150%, respectively; and by increasing upwelling temperature by 1 °C and decreasing upwelling pH and DO by 0.1 unit and 1.0 mg L−1, respectively, for each treatment. Non-upwelling temperatures were increased by 2 °C for each treatment. Non-upwelling DO was allowed to reach equilibrium with the atmosphere based on the given treatment temperature and pH. Increases in the duration of upwelling and the magnitudes of associated stressors were within range of historical patterns of change42 and model predictions44.We established four upwelling severity treatments where we manipulated the duration of upwelling and non-upwelling events, and the magnitude of pH, temperature, and dissolved oxygen to represent current day and future-predicted conditions Fig. . TreatmeTreatments were established in separate 208L insulated reservoirs, supplied with flow-through seawater and maintained via float valves. Treatments were replicated 5 times in 80L insulated tanks drawing from their corresponding reservoir. Each tank had four cylindrical, perforated PVC columns (41.4 cm × 15.2 cm) housing one urchin each, totaling two urchin barren and two kelp forest urchins per tank and 10 per treatment  through which pH and temperature were monitored and logged (Loligo Systems CapCTRL software) at 1 s intervals. Hysteresis for each pH treatment was set to ± 0.01 pH units, so if reservoir water deviated from the desired setpoint by 0.01, gas was added or stopped. pH probes were calibrated regularly using three-point calibration. DO was manipulated by bubbling pure N2 gas through 60 mm fine-pore diffusers. Each reservoir had one ocular DO probe (Vernier Software and Technology) controlled by an Arduino that monitored and recorded DO in mg L−1 every second. Temperature was controlled using aquarium chillers (JBJ Arctica Aquarium Chiller), heaters (titanium 1000-W) and heat pumps .To lower pH, CO2) and processed to measure pCO2 and DIC via gas equilibration and stripping, respectively; followed by infrared detection76 modified for discrete samples77. To ensure accuracy, gas and liquid standards included the complete range of values for ocean seawater, and the instrument was calibrated with Certified Reference Materials provided by A. Dickson (Scripps Inst. Oceanogr.). To calculate complete carbonate chemistry  we used the seacarb package in R79.Discrete water samples were collected in 350 mL amber glass bottles roughly every week (n = 5–6/treatment) to verify treatment conditions and calculate carbonate chemistry. Samples were immediately poisoned with 100 µL of saturated mercuric chloride  ad libitum. Algae were fastened at the top of each column to force urchins to expend energy to graze and create a realistic scenario where urchins in declining kelp forests, where our urchins were sourced80, have to shift from sheltering-in-place to active foraging, including climbing the stipes of kelp7. To control for autogenic changes in kelp and coralline algae in the absence of urchins, a separate perforated container with each alga was placed within each replicate tank, which we used as our control algae. Both kelp and coralline algae were dried via manual centrifuge (spun 5 times) and weighed to the nearest 0.01 g before fed to urchins. To capture grazing differences between upwelling and non-upwelling events, we measured grazing rates  every 3–4 days, as kelp degrades in the laboratory after ~ 4 days. The number of grazing rate measurements was proportional to the duration of each upwelling/non-upwelling event . When multiple grazing rate measurements were taken per event, we averaged them to estimate grazing during the event. Final algal weights were obtained as with initial weights. To account for autogenic change, we subtracted the weight change in control algae from that in grazed algae.To assess whether grazing by kelp forest and urchin barren urchins were differentially affected by upwelling severity, we measured grazing rates throughout the experiment. To maintain the natural condition and diet of urchins from each habitat, kelp forest urchins were fed bull kelp and urchin barren urchins were fed coralline algae (Urchins were measured for test diameter (mm) and total wet mass (g) at the beginning and end of the experiment. Throughout, we recorded urchin mortality and replaced dead urchins immediately, recording final diameter and mass. We dissected each urchin at the end of the experiment and calculated its gonad index. We used the subset of 30 urchins destructively sampled at the beginning of the experiment to obtain habitat-specific initial gonad indices, allowing us to calculate the difference in gonad indices due to experimental treatment effects.post-hoc comparisons among treatment means. Replacements for dead urchins were excluded from this analysis. All analyses were done in R81.Residuals were checked for normality and homoscedasticity via Shapiro Wilk and Levene’s test, respectively. All data, except for mortality, met assumptions and so were not transformed. To assess differences in gonad wet mass between kelp forest and urchin barrens urchins we used ANCOVA with urchin habitat as a fixed factor and total wet mass as a covariate. Because urchin grazing rates were measured during each upwelling and non-upwelling event, and so were not independent, we used a mixed effects model with repeated measures to analyze them, with upwelling severity treatment, urchin habitat, time (upwelling event), and their interactions as fixed factors, and tank as a random factor. To compare across groups, including among interactions of factors, we used least square means separation tests, adjusting P-values for multiple comparisons with the Tukey method. We assessed differences in urchin mortality with a generalized linear mixed model with a Poisson family distribution and a log-link function with upwelling severity treatment and urchin habitat as fixed factors, and tank as a random factor. Because of the high mortality in urchin barren urchins, we only assessed treatment differences in gonad change for kelp forest urchins, using mixed model ANOVA with treatment as a fixed factor, and tank as a random factor. We used Tukey’s HSD test to make Supplementary Information.