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Nilanjan Chatterjee and colleagues report an analysis of the number and effect size distribution of susceptibility variants identified from current genome-wide association studies. They estimate the number of susceptibility loci expected to be discovered by GWAS over a range of sample sizes and compare to recent findings from GWAS for height, Crohn's disease and several cancers.

In this Viewpoint, we asked six experts to give their opinions on the utility of polygenic scores, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging. The authors review how the choice of analytical methods used to process, analyse and interpret genomic data can influence genomic research, as well as existing methodological approaches to promote equity and fairness in genomics.

Genome-wide association studies (GWAS) have improved our understanding of the genetic basis of lung adenocarcinoma but known susceptibility variants explain only a small fraction of the familial risk. Here, the authors perform a two-stage GWAS and report 12 novel genetic loci associated with lung adenocarcinoma in East Asians.

Cherian et al. discuss the ICMR—Phase 1 Clinical Trial Network that has been established in India. The network envisages to build capacity for early phase clinical trials in the country focusing on the clinical development of leads which are of national health priority.

Nilanjan Chatterjee and colleagues report a theoretical framework to assess the predictive performance of polygenic models for risk prediction, based on analysis of genome-wide association study data sets. Across a range of common diseases and quantitative traits, they examine how predictive performance depends on the sample size, the total heritability and the underlying effect-size distributions.

Here, the authors develop fastASSET, a method for efficient detection of variant-level pleiotropic association across many traits. Using this method, they characterize genome-wide pleiotropy and links to genomic features, identifying 21 trait-specific SNPs.

Large-scale genetic association studies have identified many trait-associated variants that influence gene expression. Here, the authors present ARCHIE, a tool for identifying sets of genes whose regulation may be related to specific complex traits.

In cancer many gene variants may contribute to disease etiology, but the impact of a given gene variant may have varied effect size. Here, the authors analyse summary statistics of genome-wide association studies from fourteen cancers, and show the utility of polygenic risk scores may vary depending on cancer type.

Montserrat Garcia-Closas and colleagues report a genome-wide association study for bladder cancer. They identify three new susceptibility loci on chromosomes 22q13.1, 19q12 and 2q37.1.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. Here the authors propose a new multi-ancestry PRS method, PROSPER, to reduce disparity of PRS performance across ancestry groups.

Analysis of summary statistics from 32 GWAS datasets using a new likelihood-based approach evaluates polygenicity across different traits. Effect-size distributions predict the sample sizes needed to explain the SNP-based heritability of traits.

Mendelian randomization (MR) is a powerful and widely used method for causal inference leveraging genetic information. Here, the authors develop MRMix, an MR method using mixture models for more robust and efficient estimation of causal effects.

Analyses of cis-genetic regulation of the plasma proteome in European and African American populations lead to the identification of shared and unique cis-protein quantitative trait loci and models for proteome-wide association studies of complex traits.

Genome-wide analysis identifies 32 loci associated with breast cancer susceptibility, accounting for estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade.

Kevin Jacobs and colleagues report a new test statistic for detection of membership of an individual within a genome-wide association study, based on reporting of study genotype frequencies.

Meredith Yeager and colleagues with the Cancer Genetics Markers of Susceptibility (CGEMS) initiative report a new association to prostate cancer at chromosome 8q24. This defines a new locus, region 4, which shows association to prostate cancer susceptibility independent of previously reported associations at 8q24.

Ruth Loos and colleagues report results of a large genome-wide association study for body mass index. They identify 18 new loci associated with this trait, some of which map near key hypothalamic regulators of energy balance.

It is unclear how often genetic mosaicism of chromosome X arises. Here, the authors examine women with cancer and cancer-free controls and show that X chromosome mosaicism occurs more frequently than on autosomes, especially on the inactive X chromosome, but is not linked to non-haematologic cancer risk

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

Qing Lan and colleagues report the results of a genome-wide association study of lung cancer in never-smoking women from Asia. They identify three new susceptibility loci and confirm three other previously reported associations.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

Some cancer patients first present with metastases where the ___location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

Heart failure has a heterogeneous etiology and the genetic underpinnings are not well understood. Here, Arvanitis et al. perform GWAS meta-analysis including 10,976 heart failure cases and 437,573 controls, identify new loci near ABO and ACTN2 and show that deletion of a ACTN2 enhancer leads to reduced ACTN2 expression in differentiating cardiomyocytes.

The effects of genetic variation on DNA methylation patterns are poorly understood. Here, Shi et al.systematically map methylation-quantitative trait loci in lung, breast and kidney tissue to reveal the impact of inherited variation on the human methylome, which also affects cancer risk.

Luis Pérez-Jurado, Stephen Chanock and colleagues detect clonal chromosomal abnormalities in peripheral blood or buccal samples from individuals in the general population. They show that the frequency of such events increases with age and is associated with elevated risk of developing subsequent hematological cancers.

Genetic variants at multiple loci of chr5p15.33 have been associated with susceptibility to numerous cancers. Here the authors show that the association of one of these loci may be explained by a variant, rs36115365, influencing telomerase reverse transcriptase (TERT) expression via ZNF148.