GLIMPSE is a new method for haplotype phasing and genotype imputation of low-coverage sequencing datasets from large reference panels. GLIMPSE shows remarkable performance across different coverages and human populations.

PolyGembler, a method for grouping and ordering contigs into complete pseudomolecules by combining long-read sequencing and genotype information from an outbred mapping population, improves the accuracy for assembly of polyploidy plant genomes.

CHESS is an algorithm that compares chromatin contact maps and identifies differential features. It can analyze interspecies syntenic regions and three-dimensional changes caused by genetic perturbation.

MR-JTI, a unified framework for joint-tissue imputation and Mendelian randomization, improves prediction performance in a tissue-dependent manner when applied to large-scale biobanks and meta-analysis data.

STAAR is a powerful rare variant association test that incorporates variant functional categories and complementary functional annotations using a dynamic weighting scheme based on annotation principal components. STAAR accounts for population structure and relatedness and is scalable for analyzing large whole-genome sequencing studies.

The HDL method improves the precision in genetic correlation estimation over LD score regression when applied to GWAS summary statistics of complex traits from the UK Biobank.

SAIGE-GENE is a scalable generalized mixed-model region-based association test that can analyze large datasets while accounting for sample relatedness and unbalanced case–control ratios for binary traits.

A new association method using both case–control status and family history (LT-FH) greatly increases association power in analyses of 12 diseases from the UK Biobank.

fastGWA is a mixed linear model–based approach for performing genome-wide association analyses at biobank scale, while controlling for population stratification and relatedness.

The authors present a high-throughput single-cell ChIP-seq method with coverage of up to 10,000 loci per cell. They identify diverse chromatin landscapes in breast cancer cells characterized by dynamic H3K27me3 levels.

Signature Multivariate Analysis is a new computational tool that detects the mutational signature of homologous-recombination deficiency in clinical samples sequenced with targeted panels, enabling the identification of patients who are responsive to poly (ADP-ribose) polymerase inhibition therapy.

Linked-read analysis is a method for analyzing single-cell DNA-sequencing data that accurately identifies somatic single-nucleotide variants by using read-level phasing with nearby germline variants, enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.

UTMOST (unified test for molecular signatures) is a method for cross-tissue gene expression imputation for transcriptome-wide association analyses. Cross-tissue TWAS using UTMOST identifies new candidate genes for late-onset Alzheimer’s disease.

S-CAP is an RNA-splicing pathogenicity–prediction tool that can eliminate 41% of variants of unknown significance at 95% sensitivity.

GARFIELD is a new approach that classifies genomic features related to phenotypes on the basis of integrating GWAS signals with functional annotations. GARFIELD is used to characterize enrichment patterns for 29 traits integrated with ENCODE and Roadmap Epigenomics annotations.

Graph Genome Pipeline is a read-alignment and variant-calling pipeline based on graph genomes that offers improved read-mapping and variant-calling accuracy while achieving speed comparable to those of linear reference genome pipelines.

FitCons2 is a new framework that simultaneously clusters genomic sites by epigenomic features and evaluates the strength of natural selection on these sites. FitCons2 scores are used to generate fitness–consequence maps for 115 human cell types.

Multivariate adaptive shrinkage (mash) is a method for estimating and testing multiple effects in multiple conditions. When applied to GTEx data, mash can be used to analyze sharing of eQTL effects by examining variation in effect sizes.

StructLMM is a new method to identify genotype–environment interactions (G×E) that involve multiple exposures or environments. When applied to UK Biobank and eQTL data, StructLMM discovers new G×E signals.

A machine learning approach for refinement of somatic variant calls automates this process and reduces bias stemming from inter-reviewer variability.

Tri-C is a new 3C approach to identify concurrent chromatin interactions at individual alleles. The authors observe specific higher-order structures involving simultaneous interactions between multiple enhancers and promoters, called regulatory hubs.

This study presents a new latent causal variable (LCV) model that distinguishes between genetic correlation and causation. Applying LCV to genome-wide association summary statistics for 52 traits identified genetically causal effects for 59 pairs of traits.

The heteroskedastic linear mixed model is a new framework for testing both mean and variance effects on quantitative traits. Applying the heteroskedastic linear mixed model to body mass index in the UK Biobank shows that the approach increases the power to detect associated loci.

BayesTyper is a new probabilistic genotyping algorithm that offers superior sensitivity and accuracy relative to existing methods by using exact alignment of read k-mers to a graph representation of the reference and candidate variants.

DLO Hi-C is a new method to investigate the 3D genome. It requires only two rounds of digestion and ligation and removes non-ligated DNA in a cost-effective step by purifying specific linker-ligated DNA fragments.

LeafCutter is a new tool that identifies variable intron splicing events from RNA-seq data for analysis of complex alternative splicing. The method does not require transcript annotation and can be used to map splicing quantitative trait loci.

Covariates for multiphenotype studies (CMS), a new approach for testing for associations from large-scale datasets, leverages genetic and environmental factors shared between correlated variables measured on the same samples. Applying CMS to real and simulated data demonstrates a large increase in power equivalent to that gained by doubling the sample size.

Graphtyper is a fast and scalable method for variant genotyping that aligns short-read sequence data to a pangenome. Graphtyper was able to accurately genotype ∼90 million sequence variants in the whole genomes of ∼28,000 Icelanders, including those in six HLA genes.

Adam Siepel and colleagues report a new computational method, LINSIGHT, that combines evolutionary conservation and functional genomic information to predict the fitness consequences of noncoding mutations in the human genome. They use LINSIGHT to show that fitness consequences of enhancer mutations depend on tissue and cell type specificity and promoter constraints.

Kun Zhang and colleagues present a metric called methylation haplotype load (MHL) that quantifies methylation patterns within blocks of tightly linked CpG dinucleotides. They show that the MHL can distinguish samples from different human somatic tissues and that it can be used to improve detection of cancer-derived circulating DNA and identify its tissue of origin.

Adam Phillippy, Curtis Van Tassell, Timothy Smith and colleagues present a new reference genome assembly for the domestic goat using a pipeline that improves contiguity of the assembly by more than 250-fold. The pipeline uses a combination of short- and long-read sequencing, optical mapping, and chromatin interaction mapping.

Stuart Orkin, Daniel Bauer and colleagues present DNA Striker, a computational tool to design variant-aware saturating-mutagenesis screens with multiple CRISPR-associated nucleases. They apply their methodology to the HBS1L-MYB intergenic region, which is associated with red-blood-cell traits, and identify putative regulatory elements that control MYB expression.

James Liley, John Todd and Chris Wallace present a statistical method for determining whether disease-associated variants have different effect sizes in phenotypically defined subgroups of disease cases. The test can be combined with existing methods to determine whether genetic heterogeneity is driven by population stratification or by different mechanisms of disease pathology.

Yun Song and colleagues present SMC++, a statistical method for population history inference capable of analyzing unphased whole genomes and sample sizes much larger than can be analyzed by current methods. The authors apply SMC++ to sequence data from human, Drosophila and finch populations.

John Storey, David Blei and colleagues present a method, TeraStructure, for estimating population structure from human genomic data sets on a scale not possible with current methods. TeraStructure is able to analyze data from the Human Genome Diversity Panel and the 1000 Genomes Project in less than three hours.

Gill Bejerano and colleagues present M-CAP, a classifier that estimates variant pathogenicity in clinical exome data sets. They show that M-CAP outperforms other existing methods at all thresholds and correctly dismisses 60% of rare missense variants of uncertain significance at 95% sensitivity.

Po-Ru Loh, Alkes Price and colleagues present Eagle2, a reference-based phasing algorithm that allows for highly accurate and efficient phasing of genotypes across a broad range of cohort sizes. They demonstrate an approximately 10% improvement in accuracy and 20% improvement in speed compared to a competing method, SHAPEIT2.

Runjun Kumar, S. Joshua Swamidass and Ron Bose present an unsupervised parsimony-guided method, ParsSNP, for prioritizing candidate cancer driver mutations. They apply ParsSNP to a gastric cancer data set and predict potential driver mutations not detected by other methods, including truncations in known tumor-suppressor genes and previously confirmed drivers.

Victoria Hore, Jonathan Marchini and colleagues present a method for multiple-tissue gene expression studies aimed at uncovering gene networks linked to genetic variation. They apply their method to RNA sequencing data from adipose, skin and lymphoblastoid cell lines and identify several biologically relevant gene networks with a genetic basis.

Richard Mott, Simon Myers and colleagues present a new imputation method, STITCH, which does not require genotyping arrays or high-quality reference panels. They use STITCH to accurately impute genotypes in both outbred laboratory mice and a sample human population directly from low-coverage (<2×) sequencing data.

Po-Ru Loh, Pier Francesco Palamara and Alkes Price develop a new long-range phasing method, Eagle, that harnesses long, shared identical-by-descent tracts and can be applied to large outbred populations. They use Eagle to phase samples from the UK Biobank and find that it is faster and has better accuracy than existing methods.

Jonathan Marchini and colleagues develop a new method for haplotype phasing, SHAPEIT3, capable of handling large data sets from biobanks containing >100,000 genotyped samples. They find that their method is fast and accurate, with a low switch error rate, and can be scaled to data sets from increasingly larger cohorts.

Soumya Raychaudhuri, Buhm Han and colleagues present a statistical method to distinguish whether shared genetic risk variants among complex traits are driven by whole-group pleiotropy or a subset of individuals who constitute a genetically heterogeneous subgroup. They use the method to examine genetic sharing among autoimmune diseases and between major depressive disorder and schizophrenia and find that most genetic sharing cannot be explained by subgroup heterogeneity but that, in contrast, seronegative rheumatoid arthritis is a heterogeneous condition.

Andy Dahl and colleagues present a method for imputing missing phenotype data in genetic studies with multiple correlated phenotypes where samples can have any level of relatedness. They apply their method to simulated and real data sets and show that it improves the sensitivity to detect association signals.

Iuliana Ionita-Laza, Kenneth McCallum and colleagues developed an unsupervised statistical approach, Eigen, that integrates different functional annotations into a single measure of functional importance for coding and noncoding variants. Their meta-score can outperform the recently proposed CADD score and can be applied to fine-mapping studies.

Natsuhiko Kumasaka, Andrew Knights and Daniel Gaffney develop a new statistical approach for association mapping that models genetic effects and accounts for biases in sequencing data in a single probabilistic framework. They apply this method to generate a map of chromatin accessibility QTLs and show how it can be used to fine-map regulatory variants and link distal regulatory elements with genes.

Matthew Stephens and colleagues present a method for visualizing geographic patterns in genetic population structure. They apply this method to data from elephant, human and Arabidopsis thaliana populations and illustrate its potential to highlight barriers and corridors to gene flow.

Kornelia Polyak, Franziska Michor and colleagues report a novel method, STAR-FISH, for combined in situ single-cell analysis of point mutations and copy number alterations in archived tissue samples. They apply STAR-FISH to clinically relevant PIK3CA mutations and HER2 amplifications and observe associations between intratumoral diversity and clinical outcome.

Hae Kyung Im and colleagues report a method for predicting gene expression perturbations from genotype data after training on reference transcriptome data sets. Association of predicted gene expression with disease traits identifies known and new candidate disease genes.

Michael Beer and colleagues report a metric based on a regulatory region annotation method, gkm-SVM, and use this to predict the effects of regulatory variants from sequencing and DNase I–hypersensitive site data. They apply their method to autoimmune disease GWAS data and report several new predictions for causal SNPs.