Latest Articles
Genome-wide association studies
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Nature
Estimation and mapping of the missing heritability of human phenotypes
Rare coding variants shape inter-individual differences in human phenotypes1. However, the contribution of rare non-coding variants to those differences remains poorly characterized. Here we analyse whole-genome sequence (WGS) data from 347,630 individuals with European ancestry in the UK Biobank2,3to quantify the relative contribution of 40 million single-nucleotide and short indel variants (with a minor allele frequency (MAF) larger than 0.01%) to the heritability of 34 complex traits and diseases. On average across phenotypes, we find that WGS captures approximately 88% of the pedigree-based narrow sense heritability: that is, 20% from rare variants (MAF < 1%) and 68% from common variants (MAF ≥ 1%). We show that coding and non-coding genetic variants account for 21% and 79% of the rare-variant WGS-based heritability, respectively. We identified 15 traits with no significant difference between WGS-based and pedigree-based heritability estimates, suggesting their heritability is fully accounted for by WGS data. Finally, we performed genome-wide association analyses of all 34 phenotypes and, overall, identified 11,243 common-variant associations and 886 rare-variant associations. Altogether, our study provides high-precision estimates of rare-variant heritability, explains the heritability of many phenotypes and demonstrates for lipid traits that more than 25% of rare-variant heritability can be mapped to specific loci using fewer than 500,000 fully sequenced genomes.
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Nature
Population-specific polygenic risk scores for people of Han Chinese ancestry
Predicting complex disease risks on the basis of individual genomic profiles is an advancing field in human genetics1,2. However, most genetic studies have focused on populations of European ancestry, creating a global imbalance in precision medicine and underscoring the need for genomic research in non-European groups3,4. The Taiwan Precision Medicine Initiative recruited more than half a million Taiwanese residents, providing a large dataset of genetic profiles and electronic medical record data for people with Han Chinese ancestry. Using extensive phenotypic data, we conducted comprehensive genomic analyses across the medical phenome with individuals genetically similar to Han Chinese reference populations. These analyses identified population-specific genetic risk variants and new findings for various complex traits. We developed polygenic risk scores, demonstrating strong predictive performance for conditions such as cardiometabolic diseases, autoimmune disorders, cancers and infectious diseases. We observed consistent findings in an independent dataset, Taiwan Biobank, and among people of East Asian ancestry in the UK Biobank and the All of Us Project. The identified genetic risks accounted for up to 10.3% of the overall health variation in the Taiwan Precision Medicine Initiative cohort. Our approach of characterizing the phenome-wide genomic landscape, developing population-specific risk-prediction models, assessing their performance and identifying the genetic effect on health serves as a model for similar studies in other diverse study populations.
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Nature
The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies
Han Chinese comprise nearly 20% of the global population yet remain underrepresented in genetic studies1,2, creating an urgent need for large-scale cohorts to advance precision medicine. Here, we present the Taiwan Precision Medicine Initiative (TPMI), initiated by Academia Sinica in collaboration with 16 major medical centers around Taiwan, which has recruited 565,390 participants who consented to provide DNA samples for genetic profiling and grant access to their electronic medical records (EMR) for research. EMR access is both retrospective and prospective, allowing longitudinal studies. Genetic profiling is done with population-optimized SNP arrays for those of Han Chinese ancestry that enable genome-wide association3,4, phenome-wide association5,6, and polygenic risk score7,8studies to evaluate common disease risk and pharmacogenetic response. Participants also agree to be recontacted for future research and receive personalized genetic risk profiles with health management recommendations. TPMI has established the TPMI Data Access Platform (TDAP), a central database and analysis platform that both safeguards the security of the data and facilitates academic research. As a large non-European ancestry cohort that merges genetic profiles with EMR and enables longitudinal follow-up, TPMI provides a unique resource to validate genetic risk prediction models, conduct clinical trials of risk-based health management, and inform health policies. Ultimately, the TPMI cohort will contribute to global genetic research and serve as a model for population-based precision medicine.
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Nature
Polygenic and developmental profiles of autism differ by age at diagnosis
Although autism has historically been conceptualized as a condition that emerges in early childhood1,2, many autistic people are diagnosed later in life3,4,5. It is unknown whether earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Using longitudinal data from four independent birth cohorts, we demonstrate that two different socioemotional and behavioural trajectories are associated with age at diagnosis. In independent cohorts of autistic individuals, common genetic variants account for approximately 11% of the variance in age at autism diagnosis, similar to the contribution of individual sociodemographic and clinical factors, which typically explain less than 15% of this variance. We further demonstrate that the polygenic architecture of autism can be broken down into two modestly genetically correlated (rg= 0.38, s.e. = 0.07) autism polygenic factors. One of these factors is associated with earlier autism diagnosis and lower social and communication abilities in early childhood, but is only moderately genetically correlated with attention deficit–hyperactivity disorder (ADHD) and mental-health conditions. Conversely, the second factor is associated with later autism diagnosis and increased socioemotional and behavioural difficulties in adolescence, and has moderate to high positive genetic correlations with ADHD and mental-health conditions. These findings indicate that earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Our findings have important implications for how we conceptualize autism and provide a model to explain some of the diversity found in autism.
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Nature
SLC45A4is a pain gene encoding a neuronal polyamine transporter
Polyamines are regulatory metabolites with key roles in transcription, translation, cell signalling and autophagy1. They are implicated in multiple neurological disorders, including stroke, epilepsy and neurodegeneration, and can regulate neuronal excitability through interactions with ion channels2. Polyamines have been linked to pain, showing altered levels in human persistent pain states and modulation of pain behaviour in animal models3. However, the systems governing polyamine transport within the nervous system remain unclear. Here, undertaking a genome-wide association study (GWAS) of chronic pain intensity in the UK Biobank (UKB), we found a significant association between pain intensity and variants mapping to theSLC45A4gene locus. In the mouse nervous system,Slc45a4expression is enriched in all sensory neuron subtypes within the dorsal root ganglion, including nociceptors. Cell-based assays show that SLC45A4 is a selective plasma membrane polyamine transporter, and the cryo-electron microscopy (cryo-EM) structure reveals a regulatory domain and basis for polyamine recognition. Mice lacking SLC45A4 show normal mechanosensitivity but reduced sensitivity to noxious heat- and algogen-induced tonic pain that is associated with reduced excitability of C-polymodal nociceptors. Our findings therefore establish a role for neuronal polyamine transport in pain perception and identify a target for therapeutic intervention in pain treatment.