A whole-genome reference panel of 14,393 individuals for East Asian populations accelerates discovery of rare functional variants

Machine learning models for pharmacogenomic variant effect predictions - recent developments and future frontiers

Pharmacogenomic variations in genes involved in drug disposition and in drug targets is a major determinant of inter-individual differences in drug response and toxicity. While the effects of common variants are well established, millions of rare variations remain functionally uncharacterized, posing a challenge for the implementation of precision medicine. Recent advances in machine learning (ML) have significantly enhanced the prediction of variant effects by considering DNA as well as protein sequences, as well as their evolutionary conservation and haplotype structures. Emerging deep learning models utilize techniques to capture evolutionary conservation and biophysical properties, and ensemble approaches that integrate multiple predictive models exhibit increased accuracy, robustness, and interpretability. This review explores the current landscape of ML-based variant effect predictors. We discuss key methodological differences and highlight their strengths and limitations for pharmacogenomic applications. We furthermore discuss emerging methodologies for the prediction of substrate-specificity and for consideration of variant epistasis. Combined, these tools improve the functional effect prediction of drug-related variants and offer a viable strategy that could in the foreseeable future translate comprehensive genomic information into pharmacogenetic recommendations.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

SEAD reference panel with 22,134 haplotypes boosts rare variant imputation and genome-wide association analysis in Asian populations

Limited whole genome sequencing (WGS) studies in Asian populations result in a lack of representative reference panels, thus hindering the discovery of ancestry-specific variants. Here, we present the South and East Asian reference Database (SEAD) panel ( https://imputationserver.westlake.edu.cn/ ), which integrates WGS data for 11,067 individuals from various sources across 17 Asian countries. The SEAD panel, comprising 22,134 haplotypes and 88,294,957 variants, demonstrates improved imputation accuracy for South Asian populations compared to 1000 Genomes Project, TOPMed, and ChinaMAP panels, with a higher proportion of well-imputed rare variants. For East Asian populations, SEAD shows concordance comparable to ChinaMAP, but outperforming TOPMed. Additionally, we apply the SEAD panel to conduct a genome-wide association study for total hip (Hip) and femoral neck (FN) bone mineral density (BMD) traits in 5369 genotyped Chinese samples. The single-variant test suggests that rare variants near SNTG1 are associated with Hip BMD (rs60103302, MAF = 0.0092, P = 1.67 × 10-7), and variant-set analysis further supports the association (Pslide_window = 9.08 × 10-9, Pgene_centric = 5.27 × 10-8). This association was not reported previously and can only be detected by using Asian reference panels. Preliminary in vitro experiments for one of the rare variants identified provide evidence that it upregulates SNTG1 expression, which could in turn inhibit the proliferation and differentiation of preosteoblasts.

Genetic Diversity Landscape in African Population: A Review of Implications for Personalized and Precision Medicine

Introduction

Africa, a continent considered to be the cradle of human beings has the largest genetic diversity among its population than other continents. This review discusses the implications of this high African genetic diversity to the development of personalized and precision medicine.

Methodology

A comprehensive search across PubMed, Google Scholar, Science Direct, DOAJ, AJOL, and the Cochrane Library electronic databases and manual Google searches was conducted using key terms "genetics", "genetic diversity", "Africa", "precision medicine", and "personalized medicine". Updated original and review studies focusing on the implications of African high genetic diversity on personalized and precision medicine were included. Included studies were thematically synthesized to elucidate their positive or negative implications for personalized healthcare, aiming to foster informed clinical practice and scientific inquiry.

Results

African populations' high genetic diversity presents opportunities for personalized and precision medicine including improving pharmacogenomics, understanding gene interactions, discovering new variants, mapping disease genes, creating updated genomic reference panels, and validating biomarkers. However, challenges include underrepresentation in studies, scarcity of reference genomes, inaccuracy of genetic testing and interpretation, and ancestry misclassification. Addressing these requires the establishment of genomic research centers, increasing funding, creating biobanks and repositories, education, infrastructure, and international cooperation to enhance healthcare equity and outcomes through personalized and precision medicine.

Conclusion

High African genetic diversity presents both positive and negative implications for personalized and precision medicine. Deep further research is recommended to harness the challenges and use the opportunities to develop customized treatments.

Whole-genome resequencing and RNA-seq analysis implicates GPR75 as a potential genetic basis related to retarded growth in South China carp (Cyprinus carpio rubrofuscus)

The south China carp (Cyprinus carpio rubrofuscus) is an indigenous and important fish species, widely cultured in south China. However, part of individuals experienced retarded growth, the genetic basis of which has yet to be elucidated. In this study, whole-genome resequencing of 35 fast-growing and 35 retarded-growing south China carp were conducted to identify promising genes associated with retarded growth. Twelve candidate SNPs were detected and annotated to the Gpr75 gene, which has been reported to be related with body weight through regulating insulin homeostasis. RNA-seq analysis of muscle suggested that differentially expressed genes were significantly enriched in the insulin signaling pathway. Additionally, the fasting serum insulin level was significantly lower while the blood glucose level was significantly higher in the retarded-growing group. Our preliminary study provides insights into the genetic basis underlying the retarded growth and may facilitate further genetic improvement of south China carp.

Genetic Risk Factors for Bortezomib-induced Neuropathic Pain in an Asian Population: A Genome-wide Association Study in South Korea

Bortezomib-induced neuropathic pain (BINP) poses a challenge in multiple myeloma (MM) treatment. Genetic factors play a key role in BINP susceptibility, but research has predominantly focused on Caucasian populations. This research explored novel genetic risk loci and pathways associated with BINP development in Korean MM patients while evaluating the reproducibility of variants from Caucasians. Clinical data and buffy coat samples from 185 MM patients on bortezomib were collected. The cohort was split into discovery and validation cohorts through random stratification of clinical risk factors for BINP. Genome-wide association study was performed on the discovery cohort (n = 74) with Infinium Global Screening Array-24 v3.0 BeadChip (654,027 single nucleotide polymorphism [SNPs]). Relevant biological pathways were identified using the pathway scoring algorithm. The top 20 SNPs were validated in the validation cohort (n = 111). Previously reported SNPs were validated in the entire cohort (n = 185). Pathway analysis of the genome-wide association study results identified 31 relevant pathways, including immune systems and endosomal vacuolar pathways. Among the top 20 SNPs from the discovery cohort, 16 were replicated, which included intronic variants in ASIC2 and SMOC2, recently implicated in nociception, as well as intergenic variants or long noncoding RNAs. None of the 17 previously reported SNPs remained significant in our cohort (rs2274578, P = .085). This study represents the first investigation of novel genetic loci and biological pathways associated with BINP occurrence. Our findings, in conjunction with existing Caucasian studies, expand the understanding of personalized risk prediction and disease mechanisms. PERSPECTIVE: This article is the first to explore novel genetic loci and pathways linked to BINP in Korean MM patients, offering novel insights beyond the existing research focused on Caucasian populations into personalized risk assessment and therapeutic strategies of BINP.

Genome-wide association study for metabolic syndrome reveals APOA5 single nucleotide polymorphisms with multilayered effects in Koreans

BACKGROUND AND PURPOSE

Genome-wide association studies (GWAS) of metabolic syndrome (MetS) have predominantly focused on non-Asian populations, with limited representation from East Asian cohorts. Moreover, previous GWAS analyses have primarily emphasized the significance of top single nucleotide polymorphisms (SNPs), poorly explaining other SNP signals in linkage disequilibrium. This study aimed to reveal the interaction between rs651821 and rs2266788, the principal variants of apolipoprotein A5 (APOA5), within the most significant loci identified through GWAS on MetS.

METHODS

GWAS on MetS and its components was conducted using the data from the Korean Genome and Epidemiology Study (KoGES) city cohort comprising 58,600 individuals with available biochemical, demographic, lifestyle factors, and the most significant APOA5 locus was analyzed further in depth.

RESULTS

According to GWAS of MetS and its diagnostic components, a significant association between the APOA5 SNPs rs651821/rs2266788 and MetS/triglycerides/high-density lipoprotein phenotypes was revealed. However, a conditional analysis employing rs651821 unveiled a reversal in the odds ratio for rs2266788. Therefore, rs651821 and rs2266788 emerged as independent and opposing signals in the extended GWAS analysis, i.e., the multilayered effects. Further gene-environment interaction analyses regarding lifestyle factors such as smoking, alcohol consumption, and physical activity underscored these multilayered effects.

CONCLUSION

This study unveils the intricate interplay between rs651821 and rs2266788 derived from MetS GWAS. Removing the influence of lead SNP reveals an independent protective signal associated with rs2266788, suggesting a multilayered effect between these SNPs. These findings underline the need for novel perspectives in future MetS GWAS.

A Specialized Reference Panel with Structural Variants Integration for Improving Genotype Imputation in Alzheimer's Disease and Related Dementias (ADRD)

We developed an imputation panel for Alzheimer's disease (AD) and related dementias (ADRD) using whole-genome sequencing (WGS) data from the Alzheimer's Disease Sequencing Project (ADSP). Recognizing the significant associations between structural variants (SVs) and AD, and their underrepresentation in existing public reference panels, our panel uniquely integrates single nucleotide variants (SNVs), short insertions and deletions (indels), and SVs. This panel enhances the imputation of disease susceptibility, including rare AD-associated SNVs, indels, and SVs, onto genotype array data, offering a cost-effective alternative to whole-genome sequencing while significantly augmenting statistical power. Notably, we discovered 10 rare indels nominal significant related to AD that are absent in the TOPMed-r2 panel and identified three suggestive significant (p-value < 1E-05) AD-associated SVs in the genes EXOC3L2 and DMPK, were identified. These findings provide new insights into AD genetics and underscore the critical role of imputation panels in advancing our understanding of complex diseases like ADRD.

Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color

Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.

Imputation accuracy across global human populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of references from non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative improved the imputation of admixed African-ancestry and Hispanic/Latino samples, but imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we imputed the genotypes of over 43,000 individuals across 123 populations around the world and identified numerous populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for variants with minor allele frequencies between 1% and 5% in Saudi Arabians (n = 1,061), Vietnamese (n = 1,264), Thai (n = 2,435), and Papua New Guineans (n = 776) were 0.79, 0.78, 0.76, and 0.62, respectively, compared to 0.90-0.93 for comparable European populations matched in sample size and SNP array content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European-ancestry reference increased, as predicted. Using sequencing data as ground truth, we also showed that Rsq may over-estimate imputation accuracy for non-European populations more than European populations, suggesting further disparity in accuracy between populations. Using 1,496 sequenced individuals from Taiwan Biobank as a second reference panel to TOPMed, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, but this design did not improve accuracy across frequency spectra. Taken together, our analyses suggest that we must ultimately strive to increase diversity and size to promote equity within genetics research.

Placental expression quantitative trait loci in an East Asian population

Expression quantitative trait loci (eQTL) analysis measures the contribution of genetic variation in gene expression on complex traits. Although this methodology has been used to examine gene regulation in numerous human tissues, eQTL research in solid tissues is relatively lacking. We conducted eQTL analysis on placentas collected from an East Asian population in an effort to identify gene regulatory mechanisms in this tissue. Placentas (n = 102) were collected at the time of cesarean delivery. mRNA was extracted, sequenced with NGS, and compared with matched maternal and fetal DNA arrays performed using maternal and neonatal cord blood. Linear regression modeling was performed using tensorQTL. Fine-mapping along with epigenomic annotation was used to select putative functional variants. We identified 2,703 coding genes that contained at least one eQTL with statistical significance (false discovery rate <0.05). After fine-mapping, we found 108 previously unreported eQTL variants with posterior inclusion probability >0.1. Of these, 19% were located in genomic regions with evidence from public placental epigenome suggesting that they may be functionally relevant. For example, variant rs28379289 located in the placenta-specific regulatory region changes the binding affinity of transcription factor leading to higher expression of LGALS3, which is known to affect placental function. This study expands the knowledge base of regulatory elements within the human placenta and identifies 108 previously unreported placenta eQTL signals, which are listed in our publicly available GMI eQTL database. Further studies are needed to identify and characterize genetic regulatory mechanisms that affect placental function in normal pregnancy and placenta-related diseases.

Drug development advances in human genetics-based targets

Drug development is a long and costly process, with a high degree of uncertainty from the identification of a drug target to its market launch. Targeted drugs supported by human genetic evidence are expected to enter phase II/III clinical trials or be approved for marketing more quickly, speeding up the drug development process. Currently, genetic data and technologies such as genome-wide association studies (GWAS), whole-exome sequencing (WES), and whole-genome sequencing (WGS) have identified and validated many potential molecular targets associated with diseases. This review describes the structure, molecular biology, and drug development of human genetics-based validated beneficial loss-of-function (LOF) mutation targets (target mutations that reduce disease incidence) over the past decade. The feasibility of eight beneficial LOF mutation targets (PCSK9, ANGPTL3, ASGR1, HSD17B13, KHK, CIDEB, GPR75, and INHBE) as targets for drug discovery is mainly emphasized, and their research prospects and challenges are discussed. In conclusion, we expect that this review will inspire more researchers to use human genetics and genomics to support the discovery of novel therapeutic drugs and the direction of clinical development, which will contribute to the development of new drug discovery and drug repurposing.