Volume 56 Issue 2, February 2024

Understanding clinical heterogeneity in attention deficit hyperactivity disorder (ADHD) is important for improving personalized care and long-term outcomes. A study exploits the large scale and breadth of phenotyping of the iPSYCH cohort to link clinical heterogeneity to genetic heterogeneity in ADHD.

An open-source automated algorithm called DeepFlow enables large-scale derivation of aortic flow measurements, and genetic analysis of aortic flow, structural and functional traits demonstrates a causal relationship between aortic size and aortic valve regurgitation.

Skin color is highly variable in Africans, but the underlying molecular mechanisms remain poorly understood. Using population genetics and functional genomics, we identified key genetic variants, regulatory elements and genes that affect skin pigmentation, an adaptive trait, which provides valuable insights into the mechanisms underlying human skin color diversity and evolution.

The genetic background of pediatric acute myeloid leukemia (AML) does not fit with classification systems developed for adult AML. This study investigates the genetic background of pediatric AML and proposes a genomic framework for improved classification and risk stratification based on the driver alterations.

This Review explores mechanistic theories of aging, discusses challenges in establishing causality of these mechanisms and suggests that genetically informed investigation will help address this gap.

Genome-wide association analysis of triglycerides to high-density lipoprotein cholesterol (TG:HDL-C) ratio within the UK Biobank identifies candidate insulin resistance-associated loci linked to metabolic pathways and insulin biology. A polygenic risk score derived from these results shows an association with multiple cardiometabolic traits.

Multi-ancestry genome-wide association meta-analysis of major depression identifies new risk loci, assesses the transferability of risk loci across ancestry groups, and improves fine-mapping resolution and prioritization of candidate effector genes.

Genome-wide analyses yield insights into the polygenic effects contributing to clinical heterogeneity in attention deficit hyperactivity disorder, advancing understanding of its genetic etiology and serving as a model for future studies in other complex disorders.

DeepFlow uses deep learning to extract cardiac flow phenotypes from phase-contrast magnetic resonance images. Genome-wide analyses of cardiac flow traits in UK Biobank highlight the contribution of connective tissue genes to aortic valve function.

Massively parallel reporter assays identify 165 functional variants associated with skin pigmentation in ethnically diverse Africans. Functional characterization of eight variants demonstrates their impact in regulating melanin levels and validates CYB561A3 as a novel gene involved in melanogenesis and pigmentation.

Inherited polygenic scores for blood cell traits are associated with an increased risk of JAK2^V617F clonal expansion and influence clinical phenotypes in individuals with myeloproliferative neoplasms.

Genomic profiling of 887 pediatric AML samples highlights 23 groups with distinct molecular and clinical features.

Multi-omic analysis of single nuclei from 12 human placentas collected during early-stage and late-stage pregnancy characterizes syncytiotrophoblast diversity. Gene regulatory network analysis implicates candidate lineage regulators such as STAT5A and CEBPB.

A combination of single-cell imaging and dynamic polymer simulation shows that stacked boundary conformation facilitates cis-regulatory elements communication across topologically associating domain (TAD) borders at the Pitx1 locus in developing mouse limbs.

ZmWAKL, which encodes a cell-wall-associated receptor kinase-like protein, regulates quantitative disease resistance to gray leaf spot in maize through the ZmWAKL–ZmWIK–ZmBLK1–ZmRBOH4 module.

A new method allows selection of matched controls from an external pool of samples without genotype sharing. This method has been implemented in an online repository containing 39,472 exome sequencing controls that can be used for association analyses.

Causal-TWAS (cTWAS) is a statistical framework that adjusts for genetic confounders in transcriptome-wide association studies. Application of cTWAS on common traits leads to reliable detection of candidate causal genes.

GIFT fine-maps candidate causal genes in a transcription-wide association study by conditioning on predicted expression of nearby genes, leading to improved statistical power and enhanced mapping resolution when applied to complex traits.