Improving GWAS discovery and genomic prediction accuracy in biobank data

Risk Assessment for Gastrointestinal Diseases via Clinical Dimension and Genome-Wide Polygenic Risk Scores of Type 2 Diabetes: A Population-Based Cohort Study

OBJECTIVE

We aimed to evaluate whether individuals with type 2 diabetes (T2D) were at higher risk of developing a wide range of gastrointestinal diseases based on a population-based cohort study.

RESEARCH DESIGN AND METHODS

This study included 374,125 participants free of gastrointestinal disorders at baseline; of them, 19,719 (5.27%) with T2D were followed-up by linking to multiple medical records to record gastrointestinal disease diagnoses. Multivariable Cox models were used to estimate the hazard ratios (HRs) and CIs. Logistic models were used to examine the associations between polygenic risk scores (PRS) and clinical gastrointestinal phenotypes.

RESULTS

During a median follow-up of 12.0 years, we observed the new onset of 15 gastrointestinal diseases. Compared with nondiabetes, participants with T2D had an increased risk of gastritis and duodenitis (HR 1.58, 95% CI 1.51-1.65), peptic ulcer (HR 1.56, 95% CI 1.43-1.71), diverticular disease (HR 1.19, 95% CI 1.14-1.24), pancreatitis (HR 1.45, 95% CI 1.24-1.71), nonalcoholic fatty liver disease (HR 2.46, 95% CI 2.25-2.69), liver cirrhosis (HR 2.92, 95% CI 2.58-3.30), biliary disease (HR 1.18, 95% CI 1.10-1.26), gastrointestinal tract cancers (HR 1.28, 95% CI 1.17-1.40), and hepatobiliary and pancreatic cancer (HR 2.32, 95% CI 2.01-2.67). Positive associations of PRS of T2D with gastritis, duodenitis, and nonalcoholic fatty liver disease were also observed.

CONCLUSIONS

In this large cohort study, we found that T2D was associated with increased risks of a wide range of gastrointestinal outcomes. We suggest the importance of early detection and prevention of gastrointestinal disorders among patients with T2D.

Single nucleotide polymorphism SNP19140160 A > C is a potential breeding locus for fast-growth largemouth bass (Micropterus salmoides)

BACKGROUND

Largemouth bass (Micropterus salmoides) has significant economic value as a high-yielding fish species in China's freshwater aquaculture industry. Determining the major genes related to growth traits and identifying molecular markers associated with these traits serve as the foundation for breeding strategies involving gene pyramiding. In this study, we screened restriction-site associated DNA sequencing (RAD-seq) data to identify single nucleotide polymorphism (SNP) loci potentially associated with extreme growth differences between fast-growth and slow-growth groups in the F1 generation of a largemouth bass population.

RESULTS

We subsequently identified associations between these loci and specific candidate genes related to four key growth traits (body weight, body length, body height, and body thickness) based on SNP genotyping. In total, 4,196,486 high-quality SNPs were distributed across 23 chromosomes. Using a population-specific genotype frequency threshold of 0.7, we identified 30 potential SNPs associated with growth traits. Among the 30 SNPs, SNP19140160, SNP9639603, SNP9639605, and SNP23355498 showed significant associations; three of them (SNP9639603, SNP9639605, and SNP23355498) were significantly associated with one trait, body length, in the F1 generation, and one (SNP19140160) was significantly linked with four traits (body weight, height, length, and thickness) in the F1 generation. The markers SNP19140160 and SNP23355498 were located near two growth candidate genes, fam174b and ppip5k1b, respectively, and these candidate genes were closely linked with growth, development, and feeding. The average body weight of the group with four dominant genotypes at these SNP loci in the F1 generation population (703.86 g) was 19.63% higher than that of the group without dominant genotypes at these loci (588.36 g).

CONCLUSIONS

Thus, these four markers could be used to construct a population with dominant genotypes at loci related to fast growth. These findings demonstrate how markers can be used to identify genes related to fast growth, and will be useful for molecular marker-assisted selection in the breeding of high-quality largemouth bass.

CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops

The explosive amount of multi-omics data has brought a paradigm shift both in academic research and further application in life science. However, managing and reusing the growing resources of genomic and phenotype data points presents considerable challenges for the research community. There is an urgent need for an integrated database that combines genome-wide association studies (GWAS) with genomic selection (GS). Here, we present CropGS-Hub, a comprehensive database comprising genotype, phenotype, and GWAS signals, as well as a one-stop platform with built-in algorithms for genomic prediction and crossing design. This database encompasses a comprehensive collection of over 224 billion genotype data and 434 thousand phenotype data generated from >30 000 individuals in 14 representative populations belonging to 7 major crop species. Moreover, the platform implemented three complete functional genomic selection related modules including phenotype prediction, user model training and crossing design, as well as a fast SNP genotyper plugin-in called SNPGT specifically built for CropGS-Hub, aiming to assist crop scientists and breeders without necessitating coding skills. CropGS-Hub can be accessed at https://iagr.genomics.cn/CropGS/.

Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using large correlation matrices, but this approach is inefficient, especially in ancestrally diverse studies. In the present study, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We published LDGMs and ancestry-specific LDGM precision matrices for 18 million common variants (minor allele frequency >1%) in five ancestry groups, validated their accuracy and demonstrated order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implemented an extremely fast multiancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally diverse genetic association data across millions of variants and individuals.

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients

The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.

Genetic association studies using disease liabilities from deep neural networks

The case-control study is a widely used method for investigating the genetic landscape of binary traits. However, the health-related outcome or disease status of participants in long-term, prospective cohort studies such as the UK Biobank are subject to change. Here, we develop an approach for the genetic association study leveraging disease liabilities computed from a deep patient phenotyping framework (AI-based liability). Analyzing 44 common traits in 261,807 participants from the UK Biobank, we identified novel loci compared to the conventional case-control (CC) association studies. Our results showed that combining liability scores with CC status was more powerful than the CC-GWAS in detecting independent genetic loci across different diseases. This boost in statistical power was further reflected in increased SNP-based heritability estimates. Moreover, polygenic risk scores calculated from AI-based liabilities better identified newly diagnosed cases in the 2022 release of the UK Biobank that served as controls in the 2019 version (6.2% percentile rank increase on average). These findings demonstrate the utility of deep neural networks that are able to model disease liabilities from high-dimensional phenotypic data in large-scale population cohorts. Our pipeline of genome-wide association studies with disease liabilities can be applied to other biobanks with rich phenotype and genotype data.

Liability-scale heritability estimation for biobank studies of low-prevalence disease

Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare, and understand results generated from biological studies. In particular, through estimation of the liability-scale heritability (LSH), liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes. Increasingly, large-scale biobank studies that link genetic information to electronic health records, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample, are becoming available. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower mean square error (MSE) and is less sensitive to prevalence misspecification as compared to previous transformations for diseases with ≤2% population prevalence and LSH of ≤0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of ≤3% prevalence in the UK Biobank study revealed important differences in LSH obtained from the different theoretical expressions that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low-prevalence disease outcomes and facilitates improved inference of the underlying genetic basis of ≤2% population prevalence diseases, especially where biobank sample ascertainment results in a healthier sample population.