Identifying significant associations between genetic loci and psychiatric disorders is dependent on very large sample sizes. Methods for diagnosing diseases on this scale, such as the use of self-assessment questionnaire...Identifying significant associations between genetic loci and psychiatric disorders is dependent on very large sample sizes. Methods for diagnosing diseases on this scale, such as the use of self-assessment questionnaires and data from electronic health records, incorporate heritable variation unrelated to the disease of interest into the diagnosis. Consequently, genetic mapping will identify loci unrelated to the target disease while missing some that are related, and genetic correlations cannot be used to infer the genetic relationships between diseases and between cohorts. Furthermore, shared biases between different disorders appear as shared etiology. As sample sizes grow, such confounders propagate, and findings based on their presence are replicated and extended. Here, we draw attention to the problem, make suggestions for flagging affected cohorts, and discuss future data collection and machine learning approaches to mitigate the effects of heritable confounders in psychiatric disorders.
Pangenomics is an emerging field that uses collections of genomes, rather than a single reference, to reduce bias and capture intra-species diversity. However, existing pangenomic data formats face challenges in scaling...Pangenomics is an emerging field that uses collections of genomes, rather than a single reference, to reduce bias and capture intra-species diversity. However, existing pangenomic data formats face challenges in scaling to millions of genomes and primarily emphasize variation, often neglecting the underlying mutational events and evolutionary relationships. This work introduces Pangenome Mutation-Annotated Network (PanMAN), a lossless pangenome representation that achieves compression ratios ranging from 3.5-1,391× in file sizes compared to existing variation-preserving formats, with performance generally improving on larger datasets. In addition to compression, PanMAN increases representational capacity by encoding detailed mutational and evolutionary histories inferred across genomes, thereby enabling new biological insights. Using PanMAN, a comprehensive SARS-CoV-2 pangenome was constructed from 8 million publicly available sequences, requiring only 366 MB of disk space. We also present 'panmanUtils', a toolkit that supports common analyses and ensures interoperability with existing software. PanMAN is poised to greatly improve the scale, speed, resolution and scope of pangenomic analysis and data sharing.
Small nuclear RNAs (snRNAs) combine with specific proteins to generate small nuclear ribonucleoproteins (snRNPs), the building blocks of the spliceosome. U4 snRNA forms a duplex with U6 and, together with U5, contributes...Small nuclear RNAs (snRNAs) combine with specific proteins to generate small nuclear ribonucleoproteins (snRNPs), the building blocks of the spliceosome. U4 snRNA forms a duplex with U6 and, together with U5, contributes to the tri-snRNP spliceosomal complex. Variants in RNU4-2, which encodes U4, have recently been implicated in neurodevelopmental disorders. Here we show that heterozygous inherited and de novo variants in RNU4-2 and in four RNU6 paralogs (RNU6-1, RNU6-2, RNU6-8 and RNU6-9), which encode U6, recur in individuals with nonsyndromic retinitis pigmentosa (RP), a genetic disorder causing progressive blindness. These variants cluster within the three-way junction of the U4/U6 duplex, a site that interacts with tri-snRNP splicing factors also known to cause RP (PRPF3, PRPF8, PRPF31), and seem to affect snRNP biogenesis. Based on our cohort, deleterious variants in RNU4-2 and RNU6 paralogs may explain up to ~1.4% of otherwise undiagnosed RP cases. This study highlights the contribution of noncoding RNA genes to Mendelian disease and reveals pleiotropy in RNU4-2, where distinct variants underlie neurodevelopmental disorder and retinal degeneration.
Individuals of African ancestry remain largely underrepresented in genetic and proteomic studies. Here we measure the levels of 2,873 proteins in plasma samples from 163 individuals with type 2 diabetes (T2D) or prediabe...Individuals of African ancestry remain largely underrepresented in genetic and proteomic studies. Here we measure the levels of 2,873 proteins in plasma samples from 163 individuals with type 2 diabetes (T2D) or prediabetes and 362 normoglycemic controls from the Ugandan population. We identify 88 differentially expressed proteins between the two groups. We link genome-wide data to protein expression levels and construct a protein quantitative trait locus (pQTL) map for this population. We identify 399 independent associations with 346 (86.7%) cis-pQTLs and 53 (13.3%) trans-pQTLs; 16.7% of the cis-pQTLs and all of the trans-pQTLs have not been previously reported in individuals of African ancestry. Of these, 37 pQTLs have not been previously reported in any population. We find evidence for colocalization between a pQTL and T2D genetic risk. Our findings reveal proteins causally implicated in the pathogenesis of T2D, which may be leveraged for personalized medicine tailored to individuals of African ancestry.
Most genetic variants influence complex traits by affecting gene regulation. Yet, despite comprehensive catalogs of molecular quantitative trait loci (QTLs), linking trait-associated variants to biological functions rema...Most genetic variants influence complex traits by affecting gene regulation. Yet, despite comprehensive catalogs of molecular quantitative trait loci (QTLs), linking trait-associated variants to biological functions remains difficult. By re-analyzing large maps of protein QTLs (pQTLs), we found that genes with trans-pQTLs but no cis-pQTLs are under strong selective constraints and are particularly informative in interpreting genome-wide association study (GWAS) loci. We observed that trans-pQTLs and their target proteins are frequently involved in protein-protein interactions (PPIs). Notably, trans-pQTLs are enriched in missense variants and at PPI interfaces, suggesting a key role of PPIs in the trans-regulation of proteome. Using PPI annotations to guide trans-pQTL mapping, we identified 17,662 trans-pQTLs affecting 961 PPI clusters after accounting for blood cell composition effects. These trans-pQTLs colocalized with 36% GWAS loci per trait on average for 27 complex traits, helping in many cases to link GWAS loci to cellular function. Finally, we identified trans-pQTL effects at multiple autoimmune GWAS loci that converge to the same PPIs, pinpointing protein complexes and signaling pathways that show promising therapeutic target potential.
Since the discovery of the BRCA1 and BRCA2 (hereafter referred to as BRCA1/2) hereditary breast and ovarian cancer genes three decades ago, genetically engineered and patient-derived mouse models have been instrumental i...Since the discovery of the BRCA1 and BRCA2 (hereafter referred to as BRCA1/2) hereditary breast and ovarian cancer genes three decades ago, genetically engineered and patient-derived mouse models have been instrumental in advancing our understanding of BRCA1/2 biology, particularly their roles in normal development, tumor suppression and therapy response. Brca1/2-mutant mouse models and derivative cell lines have facilitated in vivo dissection of BRCA1/2 functions and identification of the cellular origin and (epi)genetic drivers of BRCA1/2-associated cancer. Genetically engineered and patient-derived mouse tumor models have also been instrumental in developing new (combination) therapies for patients with BRCA1/2-mutated cancers and to study mechanisms of therapy resistance. In this Perspective, we highlight the crucial insights into the complex biology of BRCA1/2 these models have afforded and emphasize those aspects that remain to be elucidated. We also propose next-generation mouse models to further advance our understanding of BRCA1/2 and improve the quality of life of mutation carriers.
Leveraging multi-ancestry data can improve fine-mapping power. We propose MultiSuSiE, an extension of Sum of Single Effects (SuSiE), to multiple ancestries that allows causal effect sizes to vary across ancestries. We ev...Leveraging multi-ancestry data can improve fine-mapping power. We propose MultiSuSiE, an extension of Sum of Single Effects (SuSiE), to multiple ancestries that allows causal effect sizes to vary across ancestries. We evaluated MultiSuSiE using whole-genome sequencing data from 47,000 African-ancestry, 36,000 Latino-ancestry and 116,000 European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr36k + Lat36k + Eur36k was well-calibrated and attained higher power than SuSiE applied to Eur109k; compared to recent multi-ancestry methods (SuSiEx and MESuSiE), MultiSuSiE attained higher power and lower computational cost. In analyses of 14 quantitative traits, MultiSuSiE applied to Afr47k + Lat36k + Eur116k identified 348 fine-mapped variants with posterior inclusion probability (PIP) > 0.9, and MultiSuSiE applied to Afr36k + Lat36k + Eur36k identified 59% more PIP > 0.9 variants than SuSiE applied to Eur109k; MultiSuSiE identified 29% more PIP > 0.9 variants than SuSiEx, and MESuSiE was not included due to its high computational cost. We validated these findings through functional enrichment of fine-mapped variants and highlighted examples implicating biologically plausible fine-mapped variants.
Upland cotton (Gossypium hirsutum), one of the world's major fiber crops, faces challenges from the genetic homogeneity of modern varieties. Here we present 107 gold-standard genome assemblies spanning the wild-to-domest...Upland cotton (Gossypium hirsutum), one of the world's major fiber crops, faces challenges from the genetic homogeneity of modern varieties. Here we present 107 gold-standard genome assemblies spanning the wild-to-domesticated continuum, revealing six large-scale structural variations, including a chromosomal reciprocal translocation and five inversions tracing the evolutionary history of cultivated cotton in the Americas. This history also involved continuous introgression from Gossypium barbadense, shaping the genetic diversity of G. hirsutum landraces and cultivars. Leveraging the graph pan-genome, we capture the sequence and structural diversity of nucleotide-binding site-leucine-rich repeat genes, uncovering pathogen-driven selection signatures and loci associated with disease resistance. A presence-absence variation genome-wide association study (GWAS) identified previously overlooked loci for key fiber traits, complementing single-nucleotide polymorphism-GWAS findings. Additionally, we construct a detailed map of large inversions, offering insights into hybridization dynamics and strategies to mitigate linkage drag. This study enhances our understanding of cotton evolution and domestication while delivering a valuable resource to enhance breeding.
Segmental copy-number gains are major contributors to human genetic variation and disease, but how these alterations arise remains incompletely understood. Here, based on the analyses of both experimental evolution and h...Segmental copy-number gains are major contributors to human genetic variation and disease, but how these alterations arise remains incompletely understood. Here, based on the analyses of both experimental evolution and human disease genomes, we describe a general mechanism of segmental copy-number gain from a rearrangement process termed 'breakage-replication/fusion'. The hallmark genomic feature of breakage-replication/fusion is adjacent parallel breakpoints: two or more rearrangement breakpoints derived from replication of a single ancestral DNA end. We show that adjacent parallel breakpoints are a widespread feature of DNA duplications in human disease genomes and experimental models of chromothripsis. In addition to adjacent parallel breakpoints, breakage-replication/fusion also explains two other patterns of complex rearrangements with unclear provenance: chains of short (≤1 kb) insertions and high-level amplification consisting of inverted segments. Together, these findings revise the mechanistic model for chromothripsis and provide a new conceptual framework for understanding the origin of segmental DNA duplication during genome evolution.
Brain metastasis (BM) carries a poor prognosis, yet the molecular basis of brain tropism remains unclear. Analysis of breast cancer BM (BCBM) revealed pervasive p53 inactivation through mutations and/or aneuploidy, with...Brain metastasis (BM) carries a poor prognosis, yet the molecular basis of brain tropism remains unclear. Analysis of breast cancer BM (BCBM) revealed pervasive p53 inactivation through mutations and/or aneuploidy, with pathway disruption already present in primary tumors. Functionally, p53 inactivation markedly increased BCBM formation and growth in vivo, causally linking p53 perturbation to BM. Mechanistically, p53 inactivation upregulated SCD1 and fatty acid synthesis (FAS), essential for brain-metastasizing cells; SCD1 knockout abolished the p53-dependent growth advantage. Molecularly, p53 suppressed SCD1 directly through promoter binding and indirectly by downregulating its co-activator DEPDC1. Astrocytes further enhanced FAS by secreting factors that were metabolized in a p53-dependent manner, promoting tumor survival, proliferation and migration. Finally, p53-deficient tumors were sensitive to FAS inhibition ex vivo and in vivo. Thus, we identify p53 inactivation as a driver of BCBM, reveal p53-dependent and astrocyte-dependent FAS modulation and highlight FAS as a therapeutically targetable BCBM vulnerability.
BioDIGS Consortium, Alberts T, Albritton CF
… +155 more, Alcazar R, Aljabri Z, Alvarez M, Aradhey A, Ayalew M, Azizian N, Balayah Y, Ball DD, Barragan E, Beshoar C, Best L, Biggane E, Biggane J, Blick J, Blosser M, Brown AK, Campbell MC, Canizares Z, Chanhuhwa FN, Chen Y, Chin DR, Chowdhury K, Collins T, Compton B, Da Silva J, Davis NR, DeCaro N, Delgadillo F, Deng Y, Duncan J, Egwu AC, Ekalle GD, Elnawam N, Enke R, Ewhe N, Ferrel MA, Fierst J, Freymiller G, Fuller K, Fulton-Wright L, Gaysinskaya V, Gill T, Gillespie E, Gonzalez Moreno P, Goodwin S, Graham N, Graham ME, Graves JL, Grob E, Gutierrez R, Hager A, Hakim ST, Harris A, Hoffman AM, Hoffmann T, Horton AM, Hughes A, Humphries EM, Ikechi-Konkwo JS, Ishtiaq A, Jackson R, James JR, James K, Jamison SA, Jimenez A, Johnson R, Kauffman A, Kaur H, Kc K, Keeton A, Kelly OE, Kerr J, Kucher N, Kuehu DL, Larson WA, Lee J, Lee A, Leek JT, Lemaic D, Liburd LE, Lopez AF, Mahmanzar M, Mamae K, Manjikian R, Marone M, Marquez K, Martinson A, Mavruk Eskipehlivan S, Medrano A, Melendrez-Vallard M, Meller R, Méndez LB, Mendez Gonzalez MP, Mesquita N, Miller CM, Mohd-Ibrahim I, Mortensen P, Mosher S, Muja A, Nasrin N, Nasu M, Nguyen MH, Nguyen BT, Nishiguchi M, O'Connor LM, Okie D, Olowookorun T, Ostrovsky A, Ozuna K, Pandey A, Patel SB, Paul G, Pawar S, Pearson A, Petrik D, Platero J, Pontino C, Pratap AP, Pratap S, Qin Y, Rai SK, Ray N, Repesh E, Rhinehardt K, Roche B, Rodriguez A, Roy S, Roy S, Sawa A, Schatz MC, Sen SK, Serikawa R, Smith T, Smith L, Sniezek J, Stewart RD, Suarez-Martinez EB, Taganna J, Tan FJ, Tsotakos N, Udolisa N, Ulbricht K, Veo T, Vessio J, Walker L, Wang O, Wang Q, Wappel R, Wesby K, Whitford M, Wild N, Xie X, Yang H, York S, Zirkle L
The BioDIGS project is a nationwide initiative involving students, researchers and educators across more than 40 research and teaching institutions. Participants lead sample collection, computational analysis and results...The BioDIGS project is a nationwide initiative involving students, researchers and educators across more than 40 research and teaching institutions. Participants lead sample collection, computational analysis and results interpretation to understand the relationships between the soil microbiome, environment and health.