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Cerebral organoids are comprised of diverse cell types found in the developing human brain, and can be leveraged for the identification of critical cell types perturbed by genetic risk variants in common, neuropsychiatric disorders. There is great interest in developing high-throughput, multiplexed technologies to associate genetic variants with cell types. Here, we describe a multiplexed, quantitative approach (oFlowSeq) by utilizing CRISPR-Cas9, FACS sorting and next-generation sequencing. Using oFlowSeq, we found that deleterious mutations in autism-associated gene KCTD13 resulted in increased proportions of Nestin+ cells and decreased proportions of TRA-1- 60+ cells within mosaic cerebral organoids. We further identified that a locus-wide CRISPR-Cas9 survey of another 19 genes in the 16p11.2 locus resulted in >2.5% maximum editing efficiencies for short and long indels, suggesting a high feasibility for an unbiased, locus-wide experiment using oFlowSeq. Our approach presents a novel method to identify genotype-to-cell type imbalances in an unbiased, multiplexed, quantitative manner.

Inducing somatic cells into pluripotent stem cells (iPSCs) provides an excellent model for studying systems in-vitro. Understanding the impact of individual donor genetic backgrounds on reprogramming ability would allow researchers to harness these genetic differences and increase the efficiency of the reprogramming process. To better understand the genetic basis of reprogramming cells into iPSCs, we present Induction of Pluripotency from Pooled Cells (iPPC) - an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs low-coverage pooled sequencing and single nucleotide polymorphisms (SNPs), we estimate individual donor proportions of cell lines within large cohorts. With iPPC, we concurrently reprogrammed over one hundred donor LCLs into iPSCs and found strong correlations of individual donors’ reprogramming ability across multiple experiments. We note that individual donors’ reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and that the expression of certain immunoglobulin precursor genes (IGLV10-54, IGLV3-9, IGLV1-17, IGLV1-6, and IGLV3-1) may impact reprogramming ability. Our process enables a multiplex framework to study the reprogramming ability of different donor cells into iPSCs and also provides a reliable method along with a pooled library of donor iPSCs for downstream research and investigation of other in-vitro phenotypes.

Human-derived cerebral organoids demonstrate great promise for identifying cell types and cell type specific molecular processes perturbed by genetic variants associated with neuropsychiatric and neurodevelopmental disorders, which are notoriously challenging to study using animal models. However, considerable challenges remain in achieving robust, scalable and generalizable phenotyping of organoids to discover cell types and cell type specific genes. 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 from human post-mortem brains and cerebral organoids, for the identification of cell types and cell type specific individual genes. We apply Orgo-Seq for two autism-associated loci: 16p11.2 deletions and 15q11-13 duplications, and identify neuroepithelial cells as critical cell types for 16p11.2 deletions, and discover novel and previously reported cell type specific driver genes. Finally, we validated our results that mutations in the KCTD13 gene in the 16p11.2 locus lead to imbalances in the proportion of neuroepithelial cells, using CRISPR/Cas9-edited mosaic organoids. Our work presents a quantitative technological framework to integrate multiple transcriptomics datasets to identify cell types and cell type specific driver genes associated with complex diseases using cerebral organoids.

Neuropsychiatric and neurodevelopmental disorders have been particularly challenging to study using animal models, and recently, human-derived cerebral organoids demonstrate great promise for discovering molecular processes that are important in these disorders. However, several challenges remain in achieving robust phenotyping to discover cell type specific genes. We perform RNA sequencing on 71 samples comprising of 1,420 cerebral organoids from 25 donors, and describe a framework (Orgo-Seq) to identify cell type specific driver genes, for 16p11.2 deletions and 15q11-13 duplications. We identify neuroepithelial cells as critical cell types for 16p11.2 deletions, and discover novel and previously reported cell type specific driver genes. Finally, we validated our results that mutations in KCTD13 in the 16p11.2 locus lead to imbalances in the proportion of neuroepithelial cells, using CRISPR/Cas9-edited mosaic organoids. Our work presents a quantitative discovery and validation framework for identifying cell type specific driver genes associated with complex diseases using cerebral organoids.

The RNA-guided endonuclease Cas9 can be converted into a programmable transcriptional repressor, but inefficiencies in target-gene silencing have limited its utility. Here we describe an improved Cas9 repressor based on the C-terminal fusion of a rationally designed bipartite repressor domain, KRAB–MeCP2, to nuclease-dead Cas9. We demonstrate the system’s superiority in silencing coding and noncoding genes, simultaneously repressing a series of target genes, improving the results of single and dual guide RNA library screens, and enabling new architectures of synthetic genetic circuits.

We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcodes as markers of donor identity. Instead, we take advantage of common single nucleotide polymorphisms, whole-genome sequencing, and an algorithm to calculate the proportions from the sequencing data. By testing using simulated and real data, we showed that our method robustly predicts the individual proportions from a mixed-pool of numerous donors, thus enabling the multiplexed testing of diverse donor cells en masse., National Human Genome Research Institute (U.S.) (Grant RM1HG008525), Robert Wood Johnson Foundation (Grant 74178)

Construction of genetic variant libraries with phenotypic measurement is central to advancing today’s functional genomics, and remains a grand challenge. Here, we introduce a Cas9-based approach for generating pools of mutants with defined genetic alterations (deletions, substitutions and insertions), along with methods for tracking their fitnessen masse. We demonstrate the utility of our approach in performing focused analysis of hundreds of mutants of a single protein and in investigating the biological function of an entire family of poorly characterized genetic elements. Our platform allows fundamental biology questions to be investigated in a quick, easy and affordable manner.

Construction and characterization of large genetic variant libraries is essential for understanding genome function, but remains challenging. Here, we introduce a Cas9-based approach for generating pools of mutants with defined genetic alterations (deletions, substitutions, and insertions) with an efficiency of 80-100% in yeast, along with methods for tracking their fitness en masse. We demonstrate the utility of our approach by characterizing the DNA helicase SGS1 with small tiling deletion mutants that span the length of the protein and a series of point mutations against highly conserved residues in the protein. In addition, we created a genome-wide library targeting 315 poorly characterized small open reading frames (smORFs

The Personal Genome Project (PGP) is an effort to enroll many participants to create an open-access repository of genome, health and trait data for research. However, PGP participants are not enrolled for studying any specific traits and participants choose the phenotypes to disclose. To measure the extent and willingness and to encourage and guide participants to contribute phenotypes, we developed an algorithm to score and rank the phenotypes and participants of the PGP. The scoring algorithm calculates the participation index (P-index) for every participant, where 0 indicates no reported phenotypes and 100 indicate complete phenotype reporting. We calculated the P-index for all 5,015 participants in the PGP and they ranged from 0 to 96.7. We found that participants mainly have either high scores (P-index > 90, 29.5%) or low scores (P-index P = 0.015). We also reported the P-indexes of participants based on demographics and states like Missouri and Massachusetts have better P-indexes than states like Utah and Minnesota. The P-index can therefore be used as an unbiased way to measure and rank participant’s phenotypic contribution towards the PGP.

Previous meta-analysis of genome-wide association (GWA) studies has identified 180 loci that influence adult height. However, each GWA locus typically comprises a set of contiguous genes, only one of which presumably modulates height. We reasoned that many of the causative genes within these loci influence height because they are expressed in and function in the growth plate, a cartilaginous structure that causes bone elongation and thus determines stature. Therefore, we used expression microarray studies of mouse and rat growth plate, human disease databases and a mouse knockout phenotype database to identify genes within the GWAS loci that are likely required for normal growth plate function. Each of these approaches identified significantly more genes within the GWA height loci than at random genomic locations (P < 0.0001 each), supporting the validity of the approach. The combined analysis strongly implicates 78 genes in growth plate function, including multiple genes that participate in PTHrP-IHH, BMP and CNP signaling, and many genes that have not previously been implicated in the growth plate. Thus, this analysis reveals a large number of novel genes that regulate human growth plate chondrogenesis and thereby contribute to the normal variations in human adult height. The analytic approach developed for this study may be applied to GWA studies for other common polygenic traits and diseases, thus providing a new general strategy to identify causative genes within GWA loci and to translate genetic associations into mechanistic biological insights.

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p, National Institutes of Health (U.S.) (NIH RC2 HL-102925)

In most complex diseases, much of the heritability remains unaccounted for by common variants. It has been postulated that lower-frequency variants contribute to the remaining heritability. Here, we describe a method to test for polygenic inheritance from lower-frequency variants by using GWAS summary association statistics. We explored scenarios with many causal low-frequency variants and showed that there is more power to detect risk variants than to detect protective variants, resulting in an increase in the ratio of detected risk to protective variants (R/P ratio). Such an excess can also occur if risk variants are present and kept at lower frequencies because of negative selection. The R/P ratio can be falsely elevated because of reasons unrelated to polygenic inheritance, such as uneven sample sizes or asymmetric population stratification, so precautions to correct for these confounders are essential. We tested our method on published GWAS results and observed a strong signal in some diseases (schizophrenia and type 2 diabetes) but not others. We also explored the shared genetic component in overlapping phenotypes related to inflammatory bowel disease (Crohn disease [CD] and ulcerative colitis [UC]) and diabetic nephropathy (macroalbuminuria and end-stage renal disease [ESRD]). Although the signal was still present when both CD and UC were jointly analyzed, the signal was lost when macroalbuminuria and ESRD were jointly analyzed, suggesting that these phenotypes should best be studied separately. Thus, our method may also help guide the design of future genetic studies of various traits and diseases.

Rare-variant association studies in common, complex diseases are customarily conducted under an additive risk model in both single-variant and burden testing. Here, we describe a method to improve detection of rare recessive variants in complex diseases termed RAFT (recessive-allele-frequency-based test). We found that RAFT outperforms existing approaches when the variant influences disease risk in a recessive manner on simulated data. We then applied our method to 1,791 Finnish individuals with type 2 diabetes (T2D) and 2,657 matched control subjects. In BBS10, we discovered a rare variant (c.1189AG [p.Ile397Val]; rs202042386) that confers risk of T2D in a recessive state (p = 1.38 × 10(-6)) and would be missed by conventional methods. Testing of this variant in an established in vivo zebrafish model confirmed the variant to be pathogenic. Taken together, these data suggest that RAFT can effectively reveal rare recessive contributions to complex diseases overlooked by conventional association tests.

Note S1â 2. working example of the method and comparison of the method against PRISM. (PDF 278Â kb)