Mexican Biobank advances population and medical genomics of diverse ancestries.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Correlation-based tests for the formal comparison of polygenic scores in multiple populations.

Polygenic scores (PGS) are measures of genetic risk, derived from the results of genome wide association studies (GWAS). Previous work has proposed the coefficient of determination (R2) as an appropriate measure by which to compare PGS performance in a validation dataset. Here we propose correlation-based methods for evaluating PGS performance by adapting previous work which produced a statistical framework and robust test statistics for the comparison of multiple correlation measures in multiple populations. This flexible framework can be extended to a wider variety of hypothesis tests than currently available methods. We assess our proposed method in simulation and demonstrate its utility with two examples, assessing previously developed PGS for low-density lipoprotein cholesterol and height in multiple populations in the All of Us cohort. Finally, we provide an R package 'coranova' with both parametric and nonparametric implementations of the described methods.

Novel signals and polygenic score for height are associated with pubertal growth traits in Southwestern American Indians.

Most genetic variants associated with adult height have been identified through large genome-wide association studies (GWASs) in European-ancestry cohorts. However, it is unclear how these variants influence linear growth during adolescence. This study uses anthropometric and genotypic data from a longitudinal study conducted in an American Indian community in Arizona between 1965-2007. Growth parameters (i.e. height, velocity, and timing of growth spurt) were derived from the Preece-Baines growth model, a parametric growth curve fitted to longitudinal height data, in 787 participants with height measurements spanning the whole period of growth. Heritability estimates suggested that genetic factors could explain 25% to 71% of the variance of pubertal growth traits. We performed a GWAS of growth parameters, testing their associations with 5 077 595 imputed or directly genotyped variants. Six variants associated with height at peak velocity (P < 5 × 10-8, adjusted for sex, birth year and principal components). Implicated genes include NUDT3, previously associated with adult height, and PACSIN1. Two novel variants associated with duration of growth spurt (P < 5 × 10-8) in LOC105375344, an uncharacterized gene with unknown function. We finally examined the association of growth parameters with a polygenic score for height derived from 9557 single nucleotide polymorphisms (SNPs) identified in the GIANT meta-analysis for which genotypic data were available for the American Indian study population. Height polygenic score was correlated with the magnitude and velocity of height growth that occurred before and at the peak of the adolescent growth spurt, indicating overlapping genetic architecture, with no influence on the timing of adolescent growth.

A positively selected FBN1 missense variant reduces height in Peruvian individuals.

On average, Peruvian individuals are among the shortest in the world1. Here we show that Native American ancestry is associated with reduced height in an ethnically diverse group of Peruvian individuals, and identify a population-specific, missense variant in the FBN1 gene (E1297G) that is significantly associated with lower height. Each copy of the minor allele (frequency of 4.7%) reduces height by 2.2 cm (4.4 cm in homozygous individuals). To our knowledge, this is the largest effect size known for a common height-associated variant. FBN1 encodes the extracellular matrix protein fibrillin 1, which is a major structural component of microfibrils. We observed less densely packed fibrillin-1-rich microfibrils with irregular edges in the skin of individuals who were homozygous for G1297 compared with individuals who were homozygous for E1297. Moreover, we show that the E1297G locus is under positive selection in non-African populations, and that the E1297 variant shows subtle evidence of positive selection specifically within the Peruvian population. This variant is also significantly more frequent in coastal Peruvian populations than in populations from the Andes or the Amazon, which suggests that short stature might be the result of adaptation to factors that are associated with the coastal environment in Peru.

Genetic analyses of diverse populations improves discovery for complex traits.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

An integrative skeletal and paleogenomic analysis of stature variation suggests relatively reduced health for early European farmers.

Human culture, biology, and health were shaped dramatically by the onset of agriculture ∼12,000 y B.P. This shift is hypothesized to have resulted in increased individual fitness and population growth as evidenced by archaeological and population genomic data alongside a decline in physiological health as inferred from skeletal remains. Here, we consider osteological and ancient DNA data from the same prehistoric individuals to study human stature variation as a proxy for health across a transition to agriculture. Specifically, we compared "predicted" genetic contributions to height from paleogenomic data and "achieved" adult osteological height estimated from long bone measurements for 167 individuals across Europe spanning the Upper Paleolithic to Iron Age (∼38,000 to 2,400 B.P.). We found that individuals from the Neolithic were shorter than expected (given their individual polygenic height scores) by an average of −3.82 cm relative to individuals from the Upper Paleolithic and Mesolithic (P = 0.040) and −2.21 cm shorter relative to post-Neolithic individuals (P = 0.068), with osteological vs. expected stature steadily increasing across the Copper (+1.95 cm relative to the Neolithic), Bronze (+2.70 cm), and Iron (+3.27 cm) Ages. These results were attenuated when we additionally accounted for genome-wide genetic ancestry variation: for example, with Neolithic individuals −2.82 cm shorter than expected on average relative to pre-Neolithic individuals (P = 0.120). We also incorporated observations of paleopathological indicators of nonspecific stress that can persist from childhood to adulthood in skeletal remains into our model. Overall, our work highlights the potential of integrating disparate datasets to explore proxies of health in prehistory.

Genetic architecture of microRNA expression and its link to complex diseases in the Japanese population.

Understanding the genetic effects on non-coding RNA (ncRNA) expression facilitates functional characterization of disease-associated genetic loci. Among several classes of ncRNAs, microRNAs (miRNAs) are key post-transcriptional gene regulators. Despite its biological importance, previous studies on the genetic architecture of miRNA expression focused mostly on the European individuals, underrepresented in other populations. Here, we mapped miRNA expression quantitative trait loci (miRNA-eQTL) for 343 miRNAs in 141 Japanese using small RNA sequencing and whole-genome sequencing, identifying 1275 cis-miRNA-eQTL variants for 40 miRNAs (false discovery rate < 0.2). Of these, 25 miRNAs having eQTL were unreported in the European studies, including 5 miRNAs with their lead variant monomorphic in the European populations, which demonstrates the value of miRNA-eQTL analysis in diverse ancestral populations. MiRNAs with eQTL effect showed allele-specific expression (ASE; e.g. miR-146a-3p), and ASE analysis further detected cis-regulatory variants not captured by the conventional miRNA-eQTL mapping (e.g. miR-933). We identified a copy number variation associated with miRNA expression (e.g. miR-570-3p, P = 7.2 × 10-6), which contributes to a more comprehensive landscape of miRNA-eQTLs. To elucidate a post-transcriptional modification in miRNAs, we created a catalog of miRNA-editing sites, including 10 canonical and 6 non-canonical sites. Finally, by integrating the miRNA-eQTLs and Japanese genome-wide association studies of 25 complex traits (mean n = 192 833), we conducted a transcriptome-wide association study, identifying miR-1908-5p as a potential mediator for adult height, colorectal cancer and type 2 diabetes (P < 9.1 × 10-5). Our study broadens the population diversity in ncRNA-eQTL studies and contributes to functional annotation of disease-associated loci found in non-European populations.

The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction.

As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.

Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry.

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations.

A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits.

The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R2 compared to existing PRS approaches. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRSs in the African population, suggesting the generality of our framework across global populations.

Forensic height estimation using polygenic score in Korean population.

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.

Growth reference charts for children with hypochondroplasia.

Hypochondroplasia (HCH) is a rare skeletal dysplasia causing mild short stature. There is a paucity of growth reference charts for this population. Anthropometric data were collected to generate height, weight, and head circumference (HC) growth reference charts for children with a diagnosis of HCH. Mixed longitudinal anthropometric data and genetic analysis results were collected from 14 European specialized skeletal dysplasia centers. Growth charts were generated using Generalized Additive Models for Location, Scale, and Shape. Measurements for height (983), weight (896), and HC (389) were collected from 188 (79 female) children with a diagnosis of HCH aged 0-18 years. Of the 84 children who underwent genetic testing, a pathogenic variant in FGFR3 was identified in 92% (77). The data were used to generate growth references for height, weight, and HC, plotted as charts with seven centiles from 2nd to 98th, for ages 0-4 and 0-16 years. HCH-specific growth charts are important in the clinical care of these children. They help to identify if other comorbidities are present that affect growth and development and serve as an important benchmark for any prospective interventional research studies and trials.

Growth charts in DYRK1A syndrome.

DYRK1A Syndrome (OMIM #614104) is caused by pathogenic variations in the DYRK1A gene located on 21q22. Haploinsufficiency of DYRK1A causes a syndrome with global psychomotor delay and intellectual disability. Low birth weight, growth restriction with feeding difficulties, stature insufficiency, and microcephaly are frequently reported. This study aims to create specific growth charts for individuals with DYRK1A Syndrome and identify parameters for size prognosis. Growth parameters were obtained for 92 individuals with DYRK1A Syndrome (49 males vs. 43 females). The data were obtained from pediatric records, parent reporting, and scientific literature. Growth charts for height, weight, body mass index (BMI), and occipitofrontal circumference (OFC) were generated using generalized additive models through R package gamlss. The growth curves include height, weight, and OFC measurements for patients aged 0-5 years. In accordance with the literature, the charts show that individuals are more likely to present intrauterine growth restriction with low birth weight and microcephaly. The growth is then characterized by severe microcephaly, low weight, and short stature. This study proposes growth charts for widespread use in the management of patients with DYRK1A syndrome.

Combining genotype with height-adjusted kidney length predicts rapid progression of ADPKD.

INTRODUCTION: Our main objective was to identify baseline prognostic factors predictive of rapid disease progression in a large unselected clinical autosomal dominant polycystic kidney disease (ADPKD) cohort. METHODS: A cross-sectional analysis was performed in 618 consecutive ADPKD patients assessed and followed-up for over a decade. A total of 123 patients (19.9%) had reached kidney failure by the study date. Data were available for the following: baseline eGFR (n = 501), genotype (n = 549), baseline ultrasound mean kidney length (MKL, n = 424) and height-adjusted baseline MKL (HtMKL, n = 377). Rapid disease progression was defined as an annualized eGFR decline (∆eGFR) of >2.5 mL/min/year by linear regression over 5 years (n = 158). Patients were further divided into slow, rapid and very rapid ∆eGFR classes for analysis. Genotyped patients were classified into several categories: PKD1 (T, truncating; or NT, non-truncating), PKD2, other genes (non-PKD1 or -PKD2), no mutation detected or variants of uncertain significance. RESULTS: A PKD1-T genotype had the strongest influence on the probability of reduced baseline kidney function by age. A multivariate logistic regression model identified PKD1-T genotype and HtMKL (>9.5 cm/m) as independent predictors for rapid disease progression. The combination of both factors increased the positive predictive value for rapid disease progression over age 40 years and of reaching kidney failure by age 60 years to 100%. Exploratory analysis in a subgroup with available total kidney volumes showed higher positive predictive value (100% vs 80%) and negative predictive value (42% vs 33%) in predicting rapid disease progression compared with the Mayo Imaging Classification (1C-E). CONCLUSION: Real-world longitudinal data confirm the importance of genotype and kidney length as independent variables determining ∆eGFR. Individuals with the highest risk of rapid disease progression can be positively selected for treatment based on this combination.

Common genetic variants contribute to risk of rare severe neurodevelopmental disorders.

There are thousands of rare human disorders that are caused by single deleterious, protein-coding genetic variants1. However, patients with the same genetic defect can have different clinical presentations2-4, and some individuals who carry known disease-causing variants can appear unaffected5. Here, to understand what explains these differences, we study a cohort of 6,987 children assessed by clinical geneticists to have severe neurodevelopmental disorders such as global developmental delay and autism, often in combination with abnormalities of other organ systems. Although the genetic causes of these neurodevelopmental disorders are expected to be almost entirely monogenic, we show that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome-wide common variant burden by showing, in an independent sample of 728 trios (comprising a child plus both parents) from the same cohort, that this burden is over-transmitted from parents to children with neurodevelopmental disorders. Our common-variant signal is significantly positively correlated with genetic predisposition to lower educational attainment, decreased intelligence and risk of schizophrenia. We found that common-variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, which suggests that common-variant risk affects patients both with and without a monogenic diagnosis. In addition, previously published common-variant scores for autism, height, birth weight and intracranial volume were all correlated with these traits within our cohort, which suggests that phenotypic expression in individuals with monogenic disorders is affected by the same variants as in the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in neurodevelopmental disorders that are typically considered to be monogenic.

The UK Biobank resource with deep phenotyping and genomic data.

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

Children With Idiopathic Short Stature: An Expanding Role for Genetic Investigation in Their Medical Evaluation.

Short stature in children is a common reason for referral to a pediatric endocrinologist. Many genetic, nutritional, psychological, illness-related, and hormonal causes must be excluded before labeling as idiopathic. Idiopathic short stature is not a diagnosis, but rather describes a large, heterogeneous group of children, who are short and often slowly growing. As new testing paradigms become available, the pool of patients labeled as idiopathic will shrink, although most will have a polygenic cause. Given that many of the new diagnoses are involved in growth plate biology, physical examination should assess for subtle dysmorphology or disproportion of the skeleton that may indicate a heterozygous mutation that in its homozygous state would be apparent. When laboratory evaluations are negative, one may consider genetic testing, such as targeted gene or gene panel, comparative genomic hybridization, or whole exome or whole genome sequencing (respectively). With a known genetic diagnosis, targeted therapy may be possible rather than recombinant human growth hormone, where response is generally poorer than that for children with growth hormone deficiency, because the variety of diagnoses may have varying growth hormone sensitivity. A firm diagnosis has heuristic value: to truncate further diagnostic evaluation, alert the clinician to other possible comorbidities, inform the family for genetic counseling, and direct appropriate targeted therapy, if available.

Genome-wide association study in the Taiwan Biobank identifies four novel genes for human height: NABP2, RASA2, RNF41 and SLC39A5.

Numerous genome-wide association studies (GWASs) have been conducted for the identification of genetic variants involved with human height. The vast majority of these studies, however, have been conducted in populations of European ancestry. Here, we report the first GWAS of adult height in the Taiwan Biobank using a discovery sample of 14 571 individuals and an independent replication sample of 20 506 individuals. From our analysis, we generalize to the Taiwanese population genome-wide significant associations with height and 18 previously identified genes in European and non-Taiwanese East Asian populations. We also identify and replicate, at the genome-wide significance level, associated variants for height in four novel genes at two loci that have not previously been reported: RASA2 on chromosome 3 and NABP2, RNF41 and SLC39A5 at 12q13.3 on chromosome 12. RASA2 and RNF41 are strong candidates for having a role in height with copy number and loss of function variants in RASA2 previously found to be associated with short stature disorders, and decreased expression of the RNF41 gene resulting in insulin resistance in skeletal muscle. The results from our analysis of the Taiwan Biobank underscore the potential for the identification of novel genetic discoveries in underrepresented worldwide populations, even for traits, such as height, that have been extensively investigated in large-scale studies of European ancestry populations.

Evidence of Polygenic Adaptation in Sardinia at Height-Associated Loci Ascertained from the Biobank Japan.

Adult height is one of the earliest putative examples of polygenic adaptation in humans. However, this conclusion was recently challenged because residual uncorrected stratification from large-scale consortium studies was considered responsible for the previously noted genetic difference. It thus remains an open question whether height loci exhibit signals of polygenic adaptation in any human population. We re-examined this question, focusing on one of the shortest European populations, the Sardinians, in addition to mainland European populations. We utilized height-associated loci from the Biobank Japan (BBJ) dataset to further alleviate concerns of biased ascertainment of GWAS loci and showed that the Sardinians remain significantly shorter than expected under neutrality (∼0.22 standard deviation shorter than Utah residents with ancestry from northern and western Europe [CEU] on the basis of polygenic height scores, p = 3.89 × 10-4). We also found the trajectory of polygenic height scores between the Sardinian and the British populations diverged over at least the last 10,000 years (p = 0.0082), consistent with a signature of polygenic adaptation driven primarily by the Sardinian population. Although the polygenic score-based analysis showed a much subtler signature in mainland European populations, we found a clear and robust adaptive signature in the UK population by using a haplotype-based statistic, the trait singleton density score (tSDS), driven by the height-increasing alleles (p = 9.1 × 10-4). In summary, by ascertaining height loci in a distant East Asian population, we further supported the evidence of polygenic adaptation at height-associated loci among the Sardinians. In mainland Europeans, the adaptive signature was detected in haplotype-based analysis but not in polygenic score-based analysis.