High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

Waples

2022

Preprint

Local ancestry is the source ancestry at each point in the genome of an admixed individual. Inferred local ancestry is used for admixture mapping and population genetic analyses. We present FLARE (Fast Local Ancestry Estimation), a new method for local ancestry inference. FLARE achieves high accuracy through the use of an extended Li and Stephens model, and it achieves exceptional computational performance through incorporation of computational techniques developed for genotype imputation. Memory requirements are reduced through on-the-fly compression of reference haplotypes and stored checkpoints. Computation time is reduced through the use of composite reference haplotypes. These techniques allow FLARE to scale to data sets with hundreds of thousands of sequenced individuals and to provide superior accuracy on large-scale data. FLARE is freely available at https://github.com/browning-lab/flare.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast, accurate local ancestry inference with FLARE

Waples

2022

Preprint

“…The OFC cohort was sequenced, aligned, and SNVs and small insertions/deletions (indels) were called using the procedures detailed by Bishop et al 12 . The control cohort was sequenced as described in Byrska-Bishop et al 15 . The same quality control procedures were performed on the separate case and control VCF files.…”

Section: Sequencing and Quality Controlmentioning

confidence: 99%

Rare variants found in clinical gene panels illuminate the genetic and allelic architecture of orofacial clefting

Perez¹,

Curtis²,

Sanchis-Juan³

et al. 2022

Preprint

Self Cite

Orofacial clefts (OFCs) are common craniofacial birth defects including cleft lip (CL), cleft lip with cleft palate (CLP), and cleft palate (CP). The etiological heterogeneity of OFCs complicates clinical diagnostics as it is not always readily apparent if the cause is Mendelian, environmental, or multifactorial. Although sequencing could aid diagnosis, it is not commonly used for the 60-70% of OFC cases that appear isolated or lack a strong family history. We aimed to estimate the diagnostic yield evaluating 503 genes using whole-genome sequencing data from 841 cases and 294 controls. After curating variants, we evaluated them according to the American College of Medical Genetics pathogenicity criteria blinded to case-control status. We found 'likely pathogenic' variants in 9.04% of cases and 1.36% of controls (p<0.0001), which was almost exclusively driven by dominant variants in autosomal genes. The yield was highest in CP (17.6%) and CLP (9.09%) cases, while CL (2.80%) cases were not significantly different from controls. We identified 'likely pathogenic' variants in cases in 39 genes, underscoring the genetic heterogeneity of OFCs. Notably, nine genes, including CTNND1, IRF6, and COL2A1, were recurrently mutated, accounting for more than half of the yield (occurring in 4.64% of OFC cases). Most reviewed variants (60.6%) were 'variants of uncertain significance' (VUS) and were more frequent in cases (p=7.31 x 10-4), but no individual gene showed a significant excess of VUS. Cumulatively, these results underscore the etiological heterogeneity of OFCs and suggest clinical sequencing could help reduce the diagnostic gap in OFC etiology.

“…The High Coverage dataset was generated using the 30x High Coverage samples from the New York Genome Center (NYGC) Byrska-Bishop et al (2021). Please refer to Github.com/sugolov/GWAS-Workshop/Notebooks/ High_Coverage.Rmd to generate a set of High Coverage data.…”

Section: Phenotype Extraction and Dataset Generationmentioning

confidence: 99%

“…Example phenotype files for ERAP2 are provided for the CEU and YRI populations both separately and together at Github.com/sugolov/GWAS-Workshop/Datasets. Please refer to Github.com/sugolov/GWAS-Workshop/Notebooks/YRI_Analysis.Rmd to combine the phenotype files with the 1KG dataset from The Centre for Applied Genomics Auton et al (2015), Roslin et al (2016)B High Coverage DatasetThe High Coverage dataset was generated using the 30x High Coverage samples from the New York Genome Center (NYGC)Byrska-Bishop et al (2021). Please refer to Github.com/sugolov/GWAS-Workshop/Notebooks/ High_Coverage.Rmd to generate a set of High Coverage data.…”

mentioning

confidence: 99%

Statistical learning of large-scale genetic data: How to run a genome-wide association study of gene-expression data using the 1000 Genomes Project data

Sugolov

Emmenegger

Sun

et al. 2022

Preprint

Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end, we developed a hands-on, week-long workshop for senior high school or junior undergraduate students, without prior knowledge in statistical genetics but with some basic knowledge in data science, to conduct their own genome-wide association studies (GWAS). The GWAS was performed for open source gene expression data, using publicly available human genetics data. Assisted by a detailed instruction manual, students were able to obtain ∼1.4 million p-values from a real scientific study, within several days. This early motivation kept students engaged in learning the theories that support their results, including regression, data visualization, results interpretation, and large-scale multiple hypothesis testing. To further their learning motivation by emphasizing the personal connection to this type of data analysis, students were encouraged to make short presentations about how GWAS has provided insights into the genetic basis of diseases that are present in their friends and/or families. The appended open source, step by-step instruction manual includes descriptions of the datasets used, the software needed, and results from the workshop. Additionally, scripts used in the workshop are archived on Zenodo to further enhance reproducible research and training.