Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.
Abstract. Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years to interrogate a broad range of traits, across diverse populations.These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power for trait mapping, and will enable studies of genome biology, population genetics and genome function at unprecedented scale. A central challenge for joint analysis is that different WGS data processing and analysis pipelines cause substantial batch effects in combined datasets, necessitating computationally expensive reprocessing and harmonization prior to variant calling. This approach is no longer tenable given the scale of current studies and data volumes.Here, in a collaboration across multiple genome centers and NIH programs, we define WGS data processing standards that allow different groups to produce "functionally equivalent" (FE) results suitable for joint variant calling with minimal batch effects. Our approach promotes broad harmonization of upstream data processing steps, while allowing for diverse variant callers. Importantly, it allows each group to continue innovating on data processing pipelines, as long as results remain compatible. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results -including single nucleotide (SNV), insertion/deletion (indel) and structural variation (SV) -and produce significantly less variability than sequencing replicates. Residual inter-pipeline variability is concentrated at low quality sites and repetitive genomic regions prone to stochastic effects. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide "big-data" human genetics studies. Main textOver the past few years, a wave of large-scale WGS-based human genetics studies have been launched by various institutes and funding programs worldwide, aimed at elucidating the genetic basis of a variety of human traits. These projects will generate hundreds of thousands of publicly available deep (>20x) WGS datasets from diverse human populations. Indeed, at the time of writing, >150,000 human genomes have already been sequenced by three NIH programs: NHGRI Centers for Common Disease Genomics 1 (CCDG), NHLBI Trans-Omics for Precision Medicine 2 (TOPMed), and NIMH Whole Genome Sequencing in Psychiatric Disorders 3 (WGSPD). Systematic aggregation and co-analysis of these (and other) genomic datasets will enable increasingly well-powered studies of human traits, population history and genome evolution, and will provide population-scale reference databases that expand upon the groundbreaking efforts of the 1000 Genomes Project 4,5 , Haplotype Reference Consortium 6 , ExAC 7 and GnomAD 8 .Our ability as a field to harness these collective data to their full analytic potential depends on the availability of high quality variant calls from large populations of in...
Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large ( n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in Admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study (GWAS) data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide binding groove, explaining 12.9% of trait variance.
Targeted therapies in endometrial cancer (EC) using kinase inhibitors rarely result in complete tumor remission and are frequently challenged by the appearance of refractory cell clones, eventually resulting in disease relapse. Dissecting adaptive mechanisms is of vital importance to circumvent clinical drug resistance and improve the efficacy of targeted agents in EC. Sorafenib is an FDA-approved multitarget tyrosine and serine/threonine kinase inhibitor currently used to treat hepatocellular carcinoma, advanced renal carcinoma and radioactive iodine-resistant thyroid carcinoma. Unfortunately, sorafenib showed very modest effects in a multi-institutional phase II trial in advanced uterine carcinoma patients. Here, by leveraging RNA-sequencing data from the Cancer Cell Line Encyclopedia and cell survival studies from compound-based high-throughput screenings we have identified the lysosomal pathway as a potential compartment involved in the resistance to sorafenib. By performing additional functional biology studies we have demonstrated that this resistance could be related to macroautophagy/autophagy. Specifically, our results indicate that sorafenib triggers a mechanistic MAPK/JNK-dependent early protective autophagic response in EC cells, providing an adaptive response to therapeutic stress. By generating in vivo subcutaneous EC cell line tumors, lung metastatic assays and primary EC orthoxenografts experiments, we demonstrate that targeting autophagy enhances sorafenib cytotoxicity and suppresses tumor growth and pulmonary metastasis progression. In conclusion, sorafenib induces the activation of a protective autophagic response in EC cells. These results provide insights into the unopposed resistance of advanced EC to sorafenib and highlight a new strategy for therapeutic intervention in recurrent EC.
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