A genomic mutational constraint map using variation in 76,156 human genomes.

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.

A harmonized public resource of deeply sequenced diverse human genomes.

Underrepresented populations are often excluded from genomic studies due in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high quality set of 4,094 whole genomes from HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also demonstrate substantial added value from this dataset compared to the prior versions of the component resources, typically combined via liftover and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared to previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.

Genetic structure correlates with ethnolinguistic diversity in eastern and southern Africa.

African populations are the most diverse in the world yet are sorely underrepresented in medical genetics research. Here, we examine the structure of African populations using genetic and comprehensive multi-generational ethnolinguistic data from the Neuropsychiatric Genetics of African Populations-Psychosis study (NeuroGAP-Psychosis) consisting of 900 individuals from Ethiopia, Kenya, South Africa, and Uganda. We find that self-reported language classifications meaningfully tag underlying genetic variation that would be missed with consideration of geography alone, highlighting the importance of culture in shaping genetic diversity. Leveraging our uniquely rich multi-generational ethnolinguistic metadata, we track language transmission through the pedigree, observing the disappearance of several languages in our cohort as well as notable shifts in frequency over three generations. We find suggestive evidence for the rate of language transmission in matrilineal groups having been higher than that for patrilineal ones. We highlight both the diversity of variation within Africa as well as how within-Africa variation can be informative for broader variant interpretation; many variants that are rare elsewhere are common in parts of Africa. The work presented here improves the understanding of the spectrum of genetic variation in African populations and highlights the enormous and complex genetic and ethnolinguistic diversity across Africa.

Neuropsychiatric Genetics of Psychosis in the Mexican Population: A Genome-Wide Association Study Protocol for Schizophrenia, Schizoaffective, and Bipolar Disorder Patients and Controls.

No large-scale genome-wide association studies (GWASs) of psychosis have been conducted in Mexico or Latin America to date. Schizophrenia and bipolar disorder in particular have been found to be highly heritable and genetically influenced. However, understanding of the biological basis of psychosis in Latin American populations is limited as previous genomic studies have almost exclusively relied on participants of Northern European ancestry. With the goal of expanding knowledge on the genomic basis of psychotic disorders within the Mexican population, the National Institute of Psychiatry Ramón de la Fuente Muñiz (INPRFM), the Harvard T.H. Chan School of Public Health, and the Broad Institute's Stanley Center for Psychiatric Research launched the Neuropsychiatric Genetics Research of Psychosis in Mexican Populations (NeuroMex) project to collect and analyze case-control psychosis samples from 5 states across Mexico. This article describes the planned sample collection and GWAS protocol for the NeuroMex study. The 4-year study will span from April 2018 to 2022 and aims to recruit 9,208 participants: 4,604 cases and 4,604 controls. Study sites across Mexico were selected to ensure collected samples capture the genomic diversity within the Mexican population. Blood samples and phenotypic data will be collected during the participant interview process and will contribute to the development of a local biobank in Mexico. DNA extraction will be done locally and genetic analysis will take place at the Broad Institute in Cambridge, MA. We will collect extensive phenotypic information using several clinical scales. All study materials including phenotypic instruments utilized are openly available in Spanish and English. The described study represents a long-term collaboration of a number of institutions from across Mexico and the Boston area, including clinical psychiatrists, clinical researchers, computational biologists, and managers at the 3 collaborating institutions. The development of relevant data management, quality assurance, and analysis plans are the primary considerations in this protocol article. Extensive management and analysis processes were developed for both the phenotypic and genetic data collected. Capacity building, partnerships, and training between and among the collaborating institutions are intrinsic components to this study and its long-term success.