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Rasheed, H.

Publications and source records attributed to Rasheed, H..

3 recordsLinked to original sources

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

genomics↗

Within-sibship GWAS improve estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

genetics↗

COVID-19 Variants Database: A repository for Human SARS-CoV-2 Polymorphism Data

COVID-19 is a newly communicable disease with a catastrophe outbreak that affects all over the world. We retrieved about 8,781 nucleotide fragments and complete genomes of SARS-CoV-2 reported from sixty-four countries. The CoV-2 reference genome was obtained from the National Genomics Data Center (NGDC), GISAID, and NCBI Genbank. All the sequences were aligned against reference genomes using Clustal Omega and variants were called using in-house built Python script. We intend to establish a user-friendly online resource to visualize the variants in the viral genome along with the Primer Infopedia. After analyzing and filtering the data globally, it was made available to the public. The detail of data available to the public includes mutations from 5688 SARS-CoV-2 sequences curated from 91 regions. This database incorporated 39920 mutations over 3990 unique positions. According to the translational impact, these mutations include 11829 synonymous mutations including 681 synonymous frameshifts and 21701 nonsynonymous mutations including 10 nonsynonymous frameshifts. Development of SARS-CoV-2 mutation genome browsers is a fundamental step obliging towards the virus surveillance, viral detection, and development of vaccine and therapeutic drugs. The SARS-COV-2 mutation browser is available at http://covid-19.dnageography.com.

genomics↗