An integrative framework to prioritize genes in more than 500 loci associated with body mass index.

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.

Associations Between Phenotypes of Childhood and Adolescent Obesity and Incident Hypertension in Young Adulthood.

Objectives: We investigated whether empirically derived childhood obesity phenotypes were differentially associated with risk of hypertension in young adulthood, and whether these associations differed by sex. Methods: Data came from 11,404 participants in the Growing Up Today Study, a prospective cohort study in the US established in 1996. We used a childhood obesity phenotype variable that was previously empirically derived using latent class analysis. The childhood obesity phenotypes included an early puberty phenotype (females only), a mothers with obesity phenotype, a high weight concerns phenotype, and a mixed phenotype. Participants without overweight or obesity in childhood or adolescence were the reference group. We then used logistic regression models with generalized estimating equations to examine associations of childhood obesity phenotypes with incident hypertension between ages 20-35 years. All analyses were stratified by sex. Results: Among females, participants in all of the empirically derived childhood obesity phenotypes were more likely than their peers without childhood overweight/obesity to develop hypertension in young adulthood (early puberty subtype odds ratio (OR) = 2.52; 95% confidence interval (CI) = 1.75, 3.62; mothers with obesity (MO) subtype OR = 2.98; 95% CI = 1.93, 4.59; high weight concerns (WC) subtype OR = 2.33; 95% CI = 1.65, 3.28; mixed subtype OR = 1.66; 95% CI = 1.25, 2.20). Among males, the childhood obesity phenotypes were associated with a higher risk of developing hypertension, although males in the MO (OR = 2.65; 95% CI = 1.82, 3.87) and WC phenotypes (OR = 3.52; 95% CI = 2.38, 5.20) had a greater risk of developing hypertension than the mixed subtype (OR = 1.51; 95% CI = 1.23, 1.86) (p = 0.004). Conclusion: Risk for incident hypertension in young adulthood varied by childhood obesity phenotypes, as well as by biological sex. If replicated, these results may suggest that increased surveillance of specific childhood obesity phenotypes might help in targeting those at highest risk for hypertension.

Contributions of common genetic variants to constitutional delay of puberty and idiopathic hypogonadotropic hypogonadism.

CONTEXT: Constitutional delay of puberty (CDP) is highly heritable, but the genetic basis for CDP is largely unknown. Idiopathic hypogonadotropic hypogonadism (IHH) can be caused by rare genetic variants, but in about half of cases, no rare-variant cause is found. OBJECTIVE: To determine whether common genetic variants that influence pubertal timing contribute to CDP and IHH. DESIGN: Case-control study. PARTICIPANTS: 80 individuals with CDP; 301 with normosmic IHH, and 348 with Kallmann syndrome; control genotyping data from unrelated studies. MAIN OUTCOME MEASURES: Polygenic scores (PGS) based on genome-wide association studies for timing of male pubertal hallmarks and age at menarche (AAM). RESULTS: The CDP cohort had higher PGS for male pubertal hallmarks and for AAM compared to controls (for male hallmarks, Cohen's d = 0.85, p = 1 × 10-16; for AAM, d = 0.67, p = 1 × 10-10). The normosmic IHH cohort also had higher PGS for male hallmarks compared to controls, but the difference was smaller (male hallmarks d = 0.20, p = 0.003; AAM d = 0.10, p = 0.055). No differences were seen for the KS cohort compared to controls (male hallmarks d = 0.04, p = 0.45; AAM d = -0.03, p = 0.86). CONCLUSIONS: Common genetic variants that influence pubertal timing in the general population contribute strongly to the genetics of CDP, weakly to normosmic IHH, and potentially not at all to KS. These findings demonstrate that the common-variant genetics of CDP and normosmic IHH are largely but not entirely distinct.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Pediatric Features of Genetic Predisposition to Polycystic Ovary Syndrome.

CONTEXT: Polycystic ovary syndrome (PCOS) has historically been conceptualized as a disorder of the reproductive system in women. However, offspring of women with PCOS begin to show metabolic features of PCOS in childhood, suggestive of childhood manifestations. OBJECTIVE: To identify childhood manifestations of genetic risk for PCOS. METHODS: We calculated a PCOS polygenic risk score (PRS) for 12 350 girls and boys in 4 pediatric cohorts-ALSPAC (UK), COPSAC (Denmark), Project Viva (USA), and The HOLBÆK Study (Denmark). We tested for association of the PRS with PCOS-related phenotypes throughout childhood and with age at pubarche and age at peak height velocity and meta-analyzed effects across cohorts using fixed-effect models. RESULTS: Higher PRS for PCOS was associated with higher body mass index in midchildhood (0.05 kg/m2 increase per 1 SD of PRS, 95% CI 0.03, 0.07, P = 3 × 10-5) and higher risk of obesity in early childhood (OR 1.34, 95% CI 1.13, 1.59, P = .0009); both persisted through late adolescence (P all ≤.03). Higher PCOS PRS was associated with earlier age at pubarche (0.85-month decrease per 1 SD of PRS, 95% CI -1.44, -0.26, P = .005) and younger age at peak height velocity (0.64-month decrease per 1 SD of PRS, 95% CI -0.94, -0.33, P = 4 × 10-5). CONCLUSION: Genetic risk factors for PCOS are associated with alterations in metabolic, growth, and developmental traits in childhood. Thus, PCOS may not simply be a condition that affects women of reproductive age but, rather, a possible manifestation of an underlying condition that affects both sexes starting in early life.

Inferring compound heterozygosity from large-scale exome sequencing data.

Recessive diseases arise when both copies of a gene are impacted by a damaging genetic variant. When a patient carries two potentially causal variants in a gene, accurate diagnosis requires determining that these variants occur on different copies of the chromosome (that is, are in trans) rather than on the same copy (that is, in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. Here we developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in the Genome Aggregation Database (v2, n = 125,748 exomes). Our approach estimates phase with 96% accuracy, both in trio data and in patients with Mendelian conditions and presumed causal compound heterozygous variants. We provide a public resource of phasing estimates for coding variants and counts per gene of rare variants in trans that can aid interpretation of rare co-occurring variants in the context of recessive disease.

Phenotyping children and adolescents with obesity using behavioral, psychological, and familial data.

OBJECTIVE: This prospective cohort study aimed to empirically derive phenotypes of children and adolescents with overweight and obesity. METHODS: Latent class analyses using Mplus were carried out in the Growing Up Today Study. Information on participants' weight status, disordered eating behaviors, body image and weight concerns, depressive symptoms, and pubertal timing, as well as and maternal weight status, were included in the latent class analyses, which were stratified by sex. Mixed-effects regression was used to examine associations of the obesity phenotypes with adult weight gain, between age 20 and 35 years, independent of weight at beginning of follow-up and duration of follow-up. RESULTS: Among the girls, four obesity phenotypes were identified: 1) "early puberty"; 2) "mothers with obesity"; 3) "high weight concerns"; and 4) "mixed." Only three phenotypes were identified among the boys: 1) "high weight concerns"; 2) "mothers with obesity"; and 3) "mixed." Participants who had overweight or obesity in childhood or adolescence gained more weight in young adulthood than their leaner peers, but the patterns of weight gain in young adulthood varied by phenotype of obesity in childhood and adolescence. CONCLUSIONS: These results support examining risk factors for and treatment outcomes by obesity phenotypes.

Identification and analysis of individuals who deviate from their genetically-predicted phenotype.

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations.

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Prevalence of Deleterious Variants in MC3R in Patients With Constitutional Delay of Growth and Puberty.

CONTEXT: The melanocortin 3 receptor (MC3R) has recently emerged as a critical regulator of pubertal timing, linear growth, and the acquisition of lean mass in humans and mice. In population-based studies, heterozygous carriers of deleterious variants in MC3R report a later onset of puberty than noncarriers. However, the frequency of such variants in patients who present with clinical disorders of pubertal development is currently unknown. OBJECTIVE: This work aimed to determine whether deleterious MC3R variants are more frequently found in patients clinically presenting with constitutional delay of growth and puberty (CDGP) or normosmic idiopathic hypogonadotropic hypogonadism (nIHH). METHODS: We examined the sequence of MC3R in 362 adolescents with a clinical diagnosis of CDGP and 657 patients with nIHH, experimentally characterized the signaling properties of all nonsynonymous variants found and compared their frequency to that in 5774 controls from a population-based cohort. Additionally, we established the relative frequency of predicted deleterious variants in individuals with self-reported delayed vs normally timed menarche/voice-breaking in the UK Biobank cohort. RESULTS: MC3R loss-of-function variants were infrequent but overrepresented in patients with CDGP (8/362 [2.2%]; OR = 4.17; P = .001). There was no strong evidence of overrepresentation in patients with nIHH (4/657 [0.6%]; OR = 1.15; P = .779). In 246 328 women from the UK Biobank, predicted deleterious variants were more frequently found in those self-reporting delayed (aged ≥16 years) vs normal age at menarche (OR = 1.66; P = 3.90E-07). CONCLUSION: We have found evidence that functionally damaging variants in MC3R are overrepresented in individuals with CDGP but are not a common cause of this phenotype.

Genetics of skeletal proportions in two different populations.

Human height can be divided into sitting height and leg length, reflecting growth of different parts of the skeleton whose relative proportions are captured by the ratio of sitting to total height (as sitting height ratio, SHR). Height is a highly heritable trait, and its genetic basis has been well-studied. However, the genetic determinants of skeletal proportion are much less well-characterized. Expanding substantially on past work, we performed a genome-wide association study (GWAS) of SHR in ∼450,000 individuals with European ancestry and ∼100,000 individuals with East Asian ancestry from the UK and China Kadoorie Biobanks. We identified 565 loci independently associated with SHR, including all genomic regions implicated in prior GWAS in these ancestries. While SHR loci largely overlap height-associated loci (P < 0.001), the fine-mapped SHR signals were often distinct from height. We additionally used fine-mapped signals to identify 36 credible sets with heterogeneous effects across ancestries. Lastly, we used SHR, sitting height, and leg length to identify genetic variation acting on specific body regions rather than on overall human height.

Genome-wide CRISPR screening of chondrocyte maturation newly implicates genes in skeletal growth and height-associated GWAS loci.

Alterations in the growth and maturation of chondrocytes can lead to variation in human height, including monogenic disorders of skeletal growth. We aimed to identify genes and pathways relevant to human growth by pairing human height genome-wide association studies (GWASs) with genome-wide knockout (KO) screens of growth-plate chondrocyte proliferation and maturation in vitro. We identified 145 genes that alter chondrocyte proliferation and maturation at early and/or late time points in culture, with 90% of genes validating in secondary screening. These genes are enriched in monogenic growth disorder genes and in KEGG pathways critical for skeletal growth and endochondral ossification. Further, common variants near these genes capture height heritability independent of genes computationally prioritized from GWASs. Our study emphasizes the value of functional studies in biologically relevant tissues as orthogonal datasets to refine likely causal genes from GWASs and implicates new genetic regulators of chondrocyte proliferation and maturation.

Corrigendum: Genetic heritability as a tool to evaluate the precision of 24-hour recall dietary questionnaire variables in UK Biobank.

[This corrects the article DOI: 10.3389/fgene.2022.1070511.].

Inferring compound heterozygosity from large-scale exome sequencing data.

Recessive diseases arise when both the maternal and the paternal copies of a gene are impacted by a damaging genetic variant in the affected individual. When a patient carries two different potentially causal variants in a gene for a given disorder, accurate diagnosis requires determining that these two variants occur on different copies of the chromosome (i.e., are in trans) rather than on the same copy (i.e. in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. We developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in exome sequencing data from the Genome Aggregation Database (gnomAD v2, n=125,748). When applied to trio data where phase can be determined by transmission, our approach estimates phase with 95.7% accuracy and remains accurate even for very rare variants (allele frequency < 1×10-4). We also correctly phase 95.9% of variant pairs in a set of 293 patients with Mendelian conditions carrying presumed causal compound heterozygous variants. We provide a public resource of phasing estimates from gnomAD, including phasing estimates for coding variants across the genome and counts per gene of rare variants in trans, that can aid interpretation of rare co-occurring variants in the context of recessive disease.

Identification and analysis of individuals who deviate from their genetically-predicted phenotype.

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Author Summary: Human genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that approximately 0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes.

Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease.

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes.

Post-translational control of beige fat biogenesis by PRDM16 stabilization.

Compelling evidence shows that brown and beige adipose tissue are protective against metabolic diseases1,2. PR domain-containing 16 (PRDM16) is a dominant activator of the biogenesis of beige adipocytes by forming a complex with transcriptional and epigenetic factors and is therefore an attractive target for improving metabolic health3-8. However, a lack of knowledge surrounding the regulation of PRDM16 protein expression hampered us from selectively targeting this transcriptional pathway. Here we identify CUL2-APPBP2 as the ubiquitin E3 ligase that determines PRDM16 protein stability by catalysing its polyubiquitination. Inhibition of CUL2-APPBP2 sufficiently extended the half-life of PRDM16 protein and promoted beige adipocyte biogenesis. By contrast, elevated CUL2-APPBP2 expression was found in aged adipose tissues and repressed adipocyte thermogenesis by degrading PRDM16 protein. Importantly, extended PRDM16 protein stability by adipocyte-specific deletion of CUL2-APPBP2 counteracted diet-induced obesity, glucose intolerance, insulin resistance and dyslipidaemia in mice. These results offer a cell-autonomous route to selectively activate the PRDM16 pathway in adipose tissues.

Endocrine and behavioural features of Lowe syndrome and their potential molecular mechanisms.

BACKGROUND: Lowe syndrome (LS) is an X linked disease caused by pathogenic variants in the OCRL gene that impacts approximately 1 in 500 000 children. Classic features include congenital cataract, cognitive/behavioural impairment and renal tubulopathy. METHODS: This study is a retrospective review of clinical features reported by family based survey conducted by Lowe Syndrome Association. Frequency of non-ocular clinical feature(s) of LS and their age of onset was summarised. An LS-specific therapy effectiveness scale was used to assess the response to the administered treatment. Expression of OCRL and relevant neuropeptides was measured in postmortem human brain by qPCR. Gene expression in the mouse brain was determined by reanalysis of publicly available bulk and single cell RNA sequencing. RESULTS: A total of 137 individuals (1 female, 89.1% white, median age 14 years (range 0.8-56)) were included in the study. Short stature (height <3rd percentile) was noted in 81% (n=111) individuals, and 15% (n=20) received growth hormone therapy. Undescended testis was reported in 47% (n=64), and median age of onset of puberty was 15 years. Additional features were dental problems (n=77, 56%), bone fractures (n=63, 46%), hypophosphataemia (n=60, 44%), developmental delay and behavioural issues. OCRL is expressed in human and mouse hypothalami, and in hypothalamic cell clusters expressing Ghrh, Sst, Oxt, Pomc and pituitary cells expressing Gh and Prl. CONCLUSIONS: There is a wide spectrum of the clinical phenotype of LS. Some of the features may be partly driven by the loss of function of OCRL in the hypothalamus and the pituitary.

Orgo-Seq integrates single-cell and bulk transcriptomic data to identify cell type specific-driver genes associated with autism spectrum disorder.

Cerebral organoids can be used to gain insights into cell type specific processes perturbed by genetic variants associated with neuropsychiatric disorders. However, robust and scalable phenotyping of organoids remains challenging. Here, we perform RNA sequencing on 71 samples comprising 1,420 cerebral organoids from 25 donors, and describe a framework (Orgo-Seq) to integrate bulk RNA and single-cell RNA sequence data. We apply Orgo-Seq to 16p11.2 deletions and 15q11-13 duplications, two loci associated with autism spectrum disorder, to identify immature neurons and intermediate progenitor cells as critical cell types for 16p11.2 deletions. We further applied Orgo-Seq to identify cell type-specific driver genes. Our work presents a quantitative phenotyping framework to integrate multi-transcriptomic datasets for the identification of cell types and cell type-specific co-expressed driver genes associated with neuropsychiatric disorders.