A map of human genome variation from population-scale sequencing.
paper
Cited
Public
- Authors
- 1000 Genomes Project Consortium; Abecasis, Gonçalo R; Altshuler, David; Auton, Adam; Brooks, Lisa D; Durbin, Richard M; Gibbs, Richard A; Hurles, Matt E; McVean, Gil A
- Year
- 2010
- Journal
- Nature
- PMID
- 20981092
- DOI
- 10.1038/nature09534
- PMCID
- PMC3042601
The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.
Properties of the variants founda, Venn diagrams showing the numbers of SNPs identified in each pilot project in each population or analysis panel, subdivided according to whether the SNP was present in dbSNP release 129 (βKnownβ) or not (βNovelβ). Exon analysis panel AFR is YRI+LWK, ASN is CHB+CHD+JPT, and EUR is CEU+TSI. Note that the scale for the exon project column is much larger than for the other pilots. b, The number of variants per Mb at different allele frequencies divided by the expectation under the neutral coalescent (1/i, where i is the variant allele count), thus giving an estimate of theta per megabase. Blue: low coverage SNPs, red: low coverage indels, black: low coverage genotyped large deletions, green: exon SNPs. The spikes at the right ends of the lines correspond to excess variants for which all samples differed from the reference (approximately 1 per 30 kb), consistent with errors in the reference sequence. c, Fraction of variants in each allele frequency class that were novel. Novelty was determined by comparison to dbSNP release 129 for SNPs and small indels, dbVar (June 2010) for deletions, and two published genomes10, 11 for larger indels. d, Size distribution and novelty of variants discovered in the low coverage project. SNPs are shown in blue, deletions with respect to the reference sequence in red, and insertions or duplications with respect to the reference in green. The fraction of variants in each size bin that were novel is shown by the purple line, and is defined relative to dbSNP (SNPs and indels), dbVar (deletions, duplications, mobile element insertions), dbRIP and other studies49 (mobile element insertions), Venter and Watson genomes10, 11 (indels and deletions), and indels from split capillary reads50 (indels and deletions). To account for ambiguous placement of many indels, discovered indels were deemed to match known indels if they were within 25 bp of a known indel of the same size. To account for imprecise knowledge of the location of most deletions and duplications, discovered variants were deemed to match known variants if they had > 50% reciprocal overlap.
LLM interpretation
This figure consists of four panels analyzing genetic variants across different projects and populations. Panel **a** uses Venn diagrams to show the overlap of known and novel SNPs among CEU, YRI, and other populations across Trio, Low coverage, and Exon projects. Panel **b** is a line plot showing observed theta per Mb across variant allele frequencies for SNPs, indels, and deletions, while panel **c** shows the fraction of novel variants decreasing as allele frequency increases. Panel **d** is a histogram showing the size distribution of deletions (red), SNPs (blue), and insertions (green), with a purple line indicating the proportion of novel variants across size bins.
Variant discovery rates and genotype accuracy in the low coverage projecta, Rates of low coverage variant detection by allele frequency in CEU. Lines show the fraction of variants seen in overlapping samples in independent studies, that were also found to be polymorphic in the low coverage project (in the same overlapping samples), as a function of allele count in the 60 low coverage samples. Note that we plot power against expected allele count in 60 samples, e.g. a variant present in, say, 2 copies in an overlap of 30 samples is expected to be present 4 times in 60 samples. The crosses on the right represent the average discovery fraction for all variants having more than 10 copies in the sample. Colours correspond to: (red) HapMap II sites, excluding sites also in HapMap 3 (43 overlapping samples); (blue) exon project sites (57 overlapping samples); (green) deletions from Conrad et al.20 (60 overlapping samples; deletions were classified as βfoundβ if there was any overlap). b, Estimated rates of discovery of variants at different frequencies in the CEU (blue), a population related to the CEU with Fst = 1% (green) and across Europe as a whole (light blue). The insert shows a cartoon of the statistical model for population history and thus allele frequencies in related populations where an ancestral population gave rise to many equally related populations, one of which (blue circle) has samples sequenced. c, SNP genotype accuracy by allele frequency in the CEU low coverage project, measured by comparison to HapMap II genotypes at sites present in both call sets, excluding sites that were also in HapMap 3. Lines represent the average accuracy of homozygote reference (red), heterozygote (green) and homozygote alternative calls (blue) as a function of the alternative allele count in the overlapping set of 43 samples, and the overall genotype error rate (grey, at bottom of plot). The inset shows the number of each genotype class as a function of alternative allele count. d, Coverage and accuracy for the low coverage and exon projects as a function of depth threshold. For 41 CEU samples sequenced in both the exon and low coverage projects, on the x axis is shown the number of non-reference SNP genotype calls at HapMap II sites not in HapMap 3 that were called in the exon project target region, and on the y axis is shown the number of these calls that were not variant (i.e., are reference homozygote and thus incorrectly were called as variant) according to HapMap II. Each point plotted corresponds to a minimum depth threshold for called sites. Grey lines show constant error rates. The exon project calls (red) were made independently per sample, whereas the low coverage calls (blue), which were only slightly less accurate, were made using LD information that combined partial information across samples and sites in an imputation-based algorithm. The additional data added from point β1β to point β0β (upper right in the figure) for the low coverage project were completely imputed.
LLM interpretation
This figure consists of four panels (a-d) analyzing variant discovery and genotype accuracy in a low-coverage sequencing project. Panel **a** uses line plots to show that detection power increases with variant allele count for HapMap II SNPs, exon project SNPs, and large deletions. Panel **b** displays the fraction of variants discovered as a function of allele frequency for three population groups, including a schematic of the population history model. Panel **c** shows genotype accuracy for reference homozygotes, heterozygotes, and alternative homozygotes across allele counts, with an inset showing genotype counts. Panel **d** is a scatter plot comparing the number of incorrect variant genotype calls to the total number of calls for the exon project (red) and low-coverage project (blue) across different depth thresholds.
The value of additional samples for variant discoveryThe fraction of variants present in an individual that would not have been found in a sequenced reference panel, as a function of reference panel size and the sequencing strategy. The lines represent predictions for Synonymous (Syn), Nonsynonymous (NonSyn), and Loss of function (LOF) variant classes, broken down by sequencing category: full sequencing as for exons (Full) and low coverage sequencing (LowCov). The values were calculated from observed distributions of variants of each class in 321 East Asian samples (CHB, CHD and JPT populations) in the exon data, and power to detect variants at low allele counts in the reference panel from Figure 2a.
LLM interpretation
This line graph shows the percentage of variants in a single individual not discovered as a function of the number of samples sequenced (x-axis). The data is categorized by variant class (Synonymous, Nonsynonymous, and Loss of Function) and sequencing strategy (Full vs. Low Coverage/LC). Across all categories, the fraction of undiscovered variants decreases as the reference panel size increases, with low-coverage sequencing (dashed lines) consistently resulting in a higher percentage of undiscovered variants compared to full sequencing (solid lines).
Imputation from the low coverage dataa, Accuracy of imputing variant genotypes using HapMap 3 sites to impute sites from the low coverage (LC) project into the trio fathers as a function of allele frequency. Accuracy of imputing genotypes from the HapMap II reference panels4 is also shown. Imputation accuracy for common variants was generally a few percent worse from the low coverage project than from HapMap, although error rates increase for less common variants. b, An example of imputation in a cis-eQTL for TIMM22, for which the original Ilumina 300K genotype data gave a weak signal30. Imputation using HapMap data made a small improvement, and imputation using low coverage haplotypes provided a much stronger signal.
LLM interpretation
**Figure a** is a line graph showing imputation accuracy on the y-axis versus minor allele frequency (0.0 to 0.5) on the x-axis for CEU and YRI populations. Accuracy increases as allele frequency increases, with HapMap II (dashed lines) generally performing slightly better than low coverage (LC, solid lines) data. **Figure b** is a Manhattan plot showing $-\log_{10}(\text{P-value})$ against the position on chromosome 17 (Mb). The 1000Genomes imputation (dark blue dots) shows a significantly stronger signal at rs1054083 compared to HapMap II imputation (light blue) and Illumina 300K genotype data (red).
Variation around genesa, Diversity in genes calculated from the CEU low coverage genotype calls (upper) and diversity divided by divergence between humans and rhesus macaque (lower). Within each element averaged diversity is shown for the first and last 25 base pairs, with the remaining 150 positions sampled at fixed distances across the element (elements shorter than 150 base pairs were not considered). Note that estimates of diversity will be reduced compared to the true population value due to the reduced power for rare variants, but relative values should be little affected. b, Average autosomal diversity divided by divergence, as a function of genetic distance from coding transcripts, calculated at putatively neutral sites, i.e., excluding phastcons conserved noncoding sequences and all sites in coding exons but four-fold degenerate sites. c, Numbers of SNPs showing increasingly high levels of differentiation in allele frequency between the CEU and CHB+JPT (red), CEU and YRI (green) and CHB+JPT and YRI (blue). Lines indicate synonymous variants (dashed), nonsynonymous variants (dotted) and other variants (solid). The most highly differentiated genic SNPs were enriched for nonsynonymous variants, indicating local adaptation. d, The decay of population differentiation around genic SNPs showing extreme allele frequency differences between populations (difference in frequency of at least 0.8 between populations, thinned so there is no more than one per gene considered). For all such SNPs the highest allele frequency difference in bins of 0.01 cM away from the variant was recorded and averaged.
LLM interpretation
This figure consists of four panels analyzing genetic diversity and differentiation. Panel **a** shows scatter plots with mean lines (red) of diversity and diversity/divergence across different gene regions (e.g., exons, introns), while panel **b** displays diversity/divergence as a function of genetic distance (cM) from transcription start/stop sites for YRI, CEU, and CHBJPT populations. Panel **c** is a log-linear plot showing the number of SNPs relative to the absolute difference in allele frequency for three population comparisons, categorized by variant type (non-coding, synonymous, nonsynonymous). Panel **d** shows the mean maximum frequency difference in bins as a function of genetic distance (cM) from highly differentiated SNPs for the same three population comparisons.
Recombinationa, Improved resolution of hotspot boundaries. The average recombination rate estimated from low coverage project data around recombination hotspots detected in HapMap II. Recombination hotspots were narrower, and in CEU (orange) and CHB+JPT (purple) more intense than previously estimated. b, The concentration of recombination in a small fraction of the genome, one line per chromosome. If recombination were uniformly distributed throughout the genome, then the lines on this figure would appear along the diagonal. Instead, most recombination occurs in a small fraction of the genome. Recombination rates in YRI (green) appeared to be less concentrated in recombination hotspots than CEU (orange) or CHB+JPT (purple). HapMap II estimates are shown in black. c, The relationship between genetic variation and recombination rates in the YRI population. The top plot shows average levels of diversity, measured as mean number of segregating sites per base, surrounding occurrences of the previously described hotspot motif 43 (CCTCCCTNNCCAC, red line) and a closely related, but not recombinogenic DNA sequence (CTTCCCTNNCCAC, green line). The lighter red and green shaded areas give 95% confidence intervals on diversity levels. The bottom plot shows estimated mean recombination rates surrounding motif occurrences, with colours defined as in the top plot.
LLM interpretation
This figure consists of three panels analyzing recombination rates across different populations. Panel **a** is a line graph showing that recombination rates peak at the hotspot center, with CEU (orange) and CHB+JPT (purple) showing higher, narrower peaks than the HapMap II estimate (black). Panel **b** uses Lorenz-style curves to show that recombination is concentrated in a small fraction of the genome, with YRI (green) showing less concentration than CEU or CHB+JPT. Panel **c** contains two line plots comparing a recombinogenic motif (red) to a non-recombinogenic sequence (green), showing that while SNP density remains similar between the two, the recombinogenic motif is associated with a significant peak in the recombination rate.
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| Dissecting single-cell genomes through the clonal organoid technique. | Youk J et al. | β | 2021 | β |
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| Diverse Genetic Landscape of Suspected Retinitis Pigmentosa in a Large Korean Cohort. | Kim YJ et al. | β | 2021 | β |
| Effect of semaphorin 3C gene variants in multifactorial Hirschsprung disease. | Gunadi et al. | β | 2021 | β |
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| Evaluation of OpenArrayβ’ as a Genotyping Method for Forensic DNA Phenotyping and Human Identification. | Ragazzo M et al. | β | 2021 | β |
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| Genetic analysis by targeted next-generation sequencing and novel variation identification of maple syrup urine disease in Chinese Han population. | Fang X et al. | β | 2021 | β |
| Genetic ancestry and skeletal toxicities among childhood acute lymphoblastic leukemia patients in the DFCI 05-001 cohort. | Yao S et al. | β | 2021 | β |
| Genetic Associations with Weight Gain among South Africans who Initiated Dolutegravir-Containing and Tenofovir-Containing Regimens. | Cindi Z et al. | β | 2021 | β |
| Genetic Diagnosis in Hereditary Hemochromatosis: Discovering and Understanding the Biological Relevance of Variants. | Baas FS et al. | β | 2021 | β |
| Genetic Profile and Associated Characteristics of 150 Korean Patients with Retinitis Pigmentosa. | Kim YN et al. | β | 2021 | β |
| Genetic Screening Revealed Latent Keratoconus in Asymptomatic Individuals. | Chen S et al. | β | 2021 | β |
| Genetic Structure and Forensic Feature of 38 X-Chromosome InDels in the Henan Han Chinese Population. | Zhang L et al. | β | 2021 | β |
| Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability. | Long X et al. | β | 2021 | β |
| Genetic Variation and the Distribution of Variant Types in the Horse. | Durward-Akhurst SA et al. | β | 2021 | β |
| Genome-wide analyses of smoking behaviors in schizophrenia: Findings from the Psychiatric Genomics Consortium. | Peterson RE et al. | β | 2021 | β |
| Genome-Wide Association Analysis of Neonatal White Matter Microstructure. | Zhang J et al. | β | 2021 | β |
| Genome-wide association studies identify two novel loci conferring susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. | Imamura M et al. | β | 2021 | β |
| Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color. | Simcoe M et al. | β | 2021 | β |
| Genome-wide copy number variations in a large cohort of bantu African children. | Yilmaz F et al. | β | 2021 | β |
| Genome-Wide Linkage and Association Study of Childhood Gender Nonconformity in Males. | Sanders AR et al. | β | 2021 | β |
| Genome-wide methylation data improves dissection of the effect of smoking on body mass index. | Amador C et al. | β | 2021 | β |
| Genome-wide survival study identifies a novel synaptic locus and polygenic score for cognitive progression in Parkinson's disease. | Liu G et al. | β | 2021 | β |
| Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping. | Ahmed Z et al. | β | 2021 | β |
| GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs. | Kretzschmar GC et al. | β | 2021 | β |
| Haplotype-resolved diverse human genomes and integrated analysis of structural variation. | Ebert P et al. | β | 2021 | β |
| Haplotype tagging reveals parallel formation of hybrid races in two butterfly species. | Meier JI et al. | β | 2021 | β |
| High Response Rate and Durability Driven by HLA Genetic Diversity in Patients with Kidney Cancer Treated with Lenvatinib and Pembrolizumab. | Lee CH et al. | β | 2021 | β |
| High-throughput phenotyping methods for quantifying hair fiber morphology. | Lasisi T et al. | β | 2021 | β |
| Human-lineage-specific genomic elements are associated with neurodegenerative disease and APOE transcript usage. | Chen Z et al. | β | 2021 | β |
| HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework. | Widanage C et al. | β | 2021 | β |
| Identification of Functional Genetic Determinants of Cardiac Troponin T and I in a Multiethnic Population and Causal Associations With Atrial Fibrillation. | Yang Y et al. | β | 2021 | β |
| Identification of Key Regions Mediating Human Melatonin Type 1 Receptor Functional Selectivity Revealed by Natural Variants. | Hegron A et al. | β | 2021 | β |
| Identification of novel functional CpG-SNPs associated with Type 2 diabetes and birth weight. | Liu RK et al. | β | 2021 | β |
| Identifying disease-causing mutations with privacy protection. | AkgΓΌn M et al. | β | 2021 | β |
| <i>In Silico</i> Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review. | Yazar M et al. | β | 2021 | β |
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| Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel. | Chatzinakos C et al. | β | 2021 | β |
| Inferring recombination patterns in African populations. | van Eeden G et al. | β | 2021 | β |
| Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways. | Damena D et al. | β | 2021 | β |
| Integrating brain imaging endophenotypes with GWAS for Alzheimer's disease. | Knutson KA et al. | β | 2021 | β |
| Integration of Multiple-Omics Data to Analyze the Population-Specific Differences for Coronary Artery Disease. | Hu Y et al. | β | 2021 | β |
| Inter-Individual Variation and Cardioprotection in Anthracycline-Induced Heart Failure. | Norton N et al. | β | 2021 | β |
| Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs. | Sakurai-Yageta M et al. | β | 2021 | β |
| JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping. | Ahmed Z et al. | β | 2021 | β |
| Knowledge graphs and their applications in drug discovery. | MacLean F | β | 2021 | β |
| Large-scale genome-wide study reveals climate adaptive variability in a cosmopolitan pest. | Chen Y et al. | β | 2021 | β |
| LncRNA MALAT1 promotes breast cancer progression and doxorubicin resistance via regulating miR-570-3p. | Yue X et al. | β | 2021 | β |
| Long-read technologies identify a hidden inverted duplication in a family with choroideremia. | Fadaie Z et al. | β | 2021 | β |
| Low Diversity of Human Variation Despite Mostly Mild Functional Impact of De Novo Variants. | Mahlich Y et al. | β | 2021 | β |
| Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning. | Baptiste M et al. | β | 2021 | β |
| Management of Women With Breast Cancer and Pathogenic Variants in Genes Other Than <i>BRCA1</i> or <i>BRCA2</i>. | Robson M | β | 2021 | β |
| Mapping leprosy-associated coding variants of interleukin genes by targeted sequencing. | Zhang DF et al. | β | 2021 | β |
| Mapping the human genetic architecture of COVID-19. | COVID-19 Host Genetics Initiative | β | 2021 | β |
| MARS: leveraging allelic heterogeneity to increase power of association testing. | Hormozdiari F et al. | β | 2021 | β |
| Megabase-scale presence-absence variation with Tripsacum origin was under selection during maize domestication and adaptation. | Huang Y et al. | β | 2021 | β |
| Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. | Galano-Frutos JJ et al. | β | 2021 | β |
| Multiple, independent, common variants at RET, SEMA3 and NRG1 gut enhancers specify Hirschsprung disease risk in European ancestry subjects. | Kapoor A et al. | β | 2021 | β |
| Mutational bias in spermatogonia impacts the anatomy of regulatory sites in the human genome. | Kaiser VB et al. | β | 2021 | β |
| Nephroplex: a kidney-focused NGS panel highlights the challenges of PKD1 sequencing and identifies a founder BBS4 mutation. | Zacchia M et al. | β | 2021 | β |
| New neural network classification method for individuals ancestry prediction from SNPs data. | Soumare H et al. | β | 2021 | β |
| Novel compound heterozygous missense variants (c.G955A and c.A1822C) of <i>CACNA2D4</i> likely causing autosomal recessive retinitis pigmentosa in a Chinese patient. | Cheng J et al. | β | 2021 | β |
| Novel In-Frame Deletion in <i>HTRA1</i> Gene, Responsible for Stroke at a Young Age and Dementia-A Case Study. | GrigaitΔ J et al. | β | 2021 | β |
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| PanSVR: Pan-Genome Augmented Short Read Realignment for Sensitive Detection of Structural Variations. | Li G et al. | β | 2021 | β |
| Paths and timings of the peopling of Polynesia inferred from genomic networks. | Ioannidis AG et al. | β | 2021 | β |
| Pearls & Oy-sters: Hickam's Dictum in Genetic Myopathies: When a Proven Pathogenic Mutation Does Not Explain the Phenotype. | SΓ‘nchez-Tejerina D et al. | β | 2021 | β |
| PedMiner: a tool for linkage analysis-based identification of disease-associated variants using family based whole-exome sequencing data. | Zhou J et al. | β | 2021 | β |
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| Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation. | Khurshid S et al. | β | 2021 | β |
| Promise and Peril of Population Genomics for the Development of Genome-First Approaches in Mendelian Cardiovascular Disease. | Parikh VN | β | 2021 | β |
| Proteogenomic and metabolomic characterization of human glioblastoma. | Wang LB et al. | β | 2021 | β |
| Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry. | Wenric S et al. | β | 2021 | β |
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| Resource profile and user guide of the Polygenic Index Repository. | Becker J et al. | β | 2021 | β |
| Response and Toxicity to Cytarabine Therapy in Leukemia and Lymphoma: From Dose Puzzle to Pharmacogenomic Biomarkers. | Di Francia R et al. | β | 2021 | β |
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| Role of Sex on the Genetic Susceptibility to Childhood Asthma in Latinos and African Americans. | Espuela-Ortiz A et al. | β | 2021 | β |
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| Structural and functional analysis of somatic coding and UTR indels in breast and lung cancer genomes. | Chen J et al. | β | 2021 | β |
| Susceptibility loci and polygenic architecture highlight population specific and common genetic features in inguinal hernias: genetics in inguinal hernias. | Hikino K et al. | β | 2021 | β |
| Synonymous and non-synonymous polymorphisms in toll-like receptor 2 (TLR2) gene among complicated measles cases at a tertiary care hospital, Peshawar, Pakistan. | Ilyas M et al. | β | 2021 | β |
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| Targeted genomic analysis of 364 adrenocortical carcinomas. | Pozdeyev N et al. | β | 2021 | β |
| The Application of Control Materials for Ongoing Quality Management of Next-Generation Sequencing in a Clinical Genetic Laboratory. | Min YK et al. | β | 2021 | β |
| The Complexity of the Ovine and Caprine Keratin-Associated Protein Genes. | Zhou H et al. | β | 2021 | β |
| The Extent and Impact of Variation in ADME Genes in Sub-Saharan African Populations. | da Rocha JEB et al. | β | 2021 | β |
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| The Need for a Human Pangenome Reference Sequence. | Miga KH et al. | β | 2021 | β |
| The need for precision nutrition, genetic variation and resolution in Covid-19 patients. | Simopoulos AP et al. | β | 2021 | β |
| The Role of Structural Variation in Adaptation and Evolution of Yeast and Other Fungi. | Gorkovskiy A et al. | β | 2021 | β |
| The transcription factors CREBH, PPARa, and FOXO1 as critical hepatic mediators of diet-induced metabolic dysregulation. | Yang Z et al. | β | 2021 | β |
| Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults. | Li B et al. | β | 2021 | β |
| Twin studies to GWAS: there and back again. | Friedman NP et al. | β | 2021 | β |
| Two-Sample Mendelian Randomization Analysis of Associations Between Periodontal Disease and Risk of Cancer. | Corlin L et al. | β | 2021 | β |
| Type 2 Diabetes and Cognitive Status in the Health and Retirement Study: A Mendelian Randomization Approach. | Ware EB et al. | β | 2021 | β |
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| VariantStore: an index for large-scale genomic variant search. | Pandey P et al. | β | 2021 | β |
| Whole-exome sequencing reveals novel vacuolar ATPase genes' variants and variants in genes involved in lysosomal biology and autophagosomal formation in oral granular cell tumors. | FranΓ§a JA et al. | β | 2021 | β |
| Whole Exome Sequencing Study in a Family with Type 2 Diabetes Mellitus. | Zhou X et al. | β | 2021 | β |
| Whole genome copy number analyses reveal a highly aberrant genome in TP53 mutant lung adenocarcinoma tumors. | BjaanΓ¦s MM et al. | β | 2021 | β |
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| Whole genome sequencing in the Middle Eastern Qatari population identifies genetic associations with 45 clinically relevant traits. | Thareja G et al. | β | 2021 | β |
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| Candidate Gene Analysis Reveals Strong Association of <i>CETP</i> Variants With High Density Lipoprotein Cholesterol and <i>PCSK9</i> Variants With Low Density Lipoprotein Cholesterol in Ghanaian Adults: An AWI-Gen Sub-Study. | Agongo G et al. | β | 2020 | β |
| Clinical characteristics and disease progression of retinitis pigmentosa associated with PDE6B mutations in Korean patients. | Kim YN et al. | β | 2020 | β |
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| COG5 variants lead to complex early onset retinal degeneration, upregulation of PERK and DNA damage. | Tabbarah S et al. | β | 2020 | β |
| Data Sanitization to Reduce Private Information Leakage from Functional Genomics. | GΓΌrsoy G et al. | β | 2020 | β |
| Differential admixture in Latin American populations and its impact on the study of colorectal cancer. | Colistro V et al. | β | 2020 | β |
| Effect of <i>EGLN1</i> Genetic Polymorphisms on Hemoglobin Concentration in Andean Highlanders. | Yasukochi Y et al. | β | 2020 | β |
| Genome-wide association studies of antidepressant class response and treatment-resistant depression. | Li QS et al. | β | 2020 | β |
| Genomic Mutations of Primary and Metastatic Lung Adenocarcinoma in Chinese Patients. | Chen X et al. | β | 2020 | β |
| <i>GJA1</i> Gene Polymorphisms and Topographic Distribution of Cranial MRI Lesions in Cerebral Small Vessel Disease. | Zhang J et al. | β | 2020 | β |
| INDEL detection, the 'Achilles heel' of precise genome editing: a survey of methods for accurate profiling of gene editing induced indels. | Bennett EP et al. | β | 2020 | β |
| Meta-analysis investigating the role of interleukin-6 mediated inflammation in type 2 diabetes. | Bowker N et al. | β | 2020 | β |
| Modeling of variables in cellularΒ infection reveals CXCL10 levels are regulated by human genetic variation and the Chlamydia-encoded CPAF protease. | Schott BH et al. | β | 2020 | β |
| NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering. | Zamani F et al. | β | 2020 | β |
| ncVarDB: a manually curated database for pathogenic non-coding variants and benign controls. | Biggs H et al. | β | 2020 | β |
| Primate phylogenomics uncovers multiple rapid radiations and ancient interspecific introgression. | Vanderpool D et al. | β | 2020 | β |
| Red panda: a novel method for detecting variants in single-cell RNA sequencing. | Cornish A et al. | β | 2020 | β |
| Resource conservation manifests in the genetic code. | Shenhav L et al. | β | 2020 | β |
| Spectrum of Disease Severity in Patients With X-Linked Retinitis Pigmentosa Due to RPGR Mutations. | Di Iorio V et al. | β | 2020 | β |
| Stairway Plot 2: demographic history inference with folded SNP frequency spectra. | Liu X et al. | β | 2020 | β |
| The association of clinical phenotypes to known AD/FTD genetic risk loci and their inter-relationship. | Li QS et al. | β | 2020 | β |
| The association of genetic polymorphisms with nonalcoholic fatty liver disease in a longitudinal study. | Chung GE et al. | β | 2020 | β |
| Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis. | Cui Z et al. | β | 2020 | β |
| Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity. | Newell F et al. | β | 2020 | β |