Replicability and Prediction: Lessons and Challenges from GWAS

Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry

Background

Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts.

Methods

Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations.

Results

Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained.

Conclusion

This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.

Genetic Association and Transferability for Urinary Albumin-Creatinine Ratio as a Marker of Kidney Disease in four Sub-Saharan African Populations and non-continental Individuals of African Ancestry

BACKGROUND

Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts.

METHODS

Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations.

RESULTS

Two genome-wide significant (P<5x10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained.

CONCLUSION

This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.

METHODS

Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations.

RESULTS

Two genome-wide significant (P<5x10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained.

CONCLUSION

This study contributes novel insights into the genetic architecture of kidney function in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations.

GWAS meta-analysis of kidney function traits in Japanese populations

BACKGROUND

Genetic epidemiological evidence for the kidney function traits in East Asian population including Japanese remain still relatively unclarified. Especially, the number of GWASs for kidney traits reported still remains limited, and the sample size of each independent study is relatively small. Given the genetic variability between ancestries/ethnicities, implementation of GWAS with sufficiently large sample sizes in specific population of Japanese is considered meaningful.

METHODS

We conducted the GWAS meta-analyses of kidney traits by leveraging the GWAS summary data of the representative large genome cohort studies with about 200,000 Japanese participants (n = 202,406 for estimated glomerular filtration rate [eGFR] and n = 200,845 for serum creatinine [SCr]).

RESULTS

In the present GWAS meta-analysis, we identified 110 loci with 169 variants significantly associated with eGFR (on chromosomes 1-13 and 15-22; p < 5×10-8), whereas we also identified 112 loci with 176 variants significantly associated with SCr (on chromosomes 1-22; p < 5×10-8), of which one locus (more than 1Mb distant from known loci) with one variant (CD36 rs146148222 on chromosome 7) for SCr was considered as the truly novel finding.

CONCLUSIONS

The present GWAS meta-analysis of largest genome cohort studies in Japanese provided some original genomic loci associated with kidney function in Japanese, which may contribute to the possible development of personalized prevention of kidney diseases based on genomic information in the near future.

Identification of novel loci in obstructive sleep apnea in European American and African American children

STUDY OBJECTIVES

To identify genetic susceptibility variants in pediatric obstructive sleep apnea in European American and African American children.

METHODS

A phenotyping algorithm using electronic medical records was developed to recruit cases with OSA and control subjects from the Center for Applied Genomics at Children's Hospital of Philadelphia (CHOP). Genome-wide association studies (GWAS) were performed in pediatric OSA cases and control subjects with European American (EA) and African American (AA) ancestry followed by meta-analysis and sex stratification.

RESULTS

The algorithm accrued 1486 subjects (46.3% European American, 53.7% African American). We identified genomic loci at 1p36.22 and 15q26.1 that associated with OSA risk in EA and AA, respectively. We also revealed a shared risk locus at 18p11.32 (rs114124196, p = 1.72 × 10-8) across EA and AA populations. Additionally, association at 1q43 (rs12754698) and 2p25.1 (rs72775219) was identified in the male-only analysis of EA children with OSA, while association at 8q21.11 (rs6472959), 11q24.3 (rs4370952) and 15q21.1 (rs149936782) was detected in the female-only analysis of EA children and association at 18p11.23 (rs9964029) was identified in the female-only analysis of African-American children. Moreover, the 18p11.32 locus was replicated in an EA cohort (rs114124196, p = 8.8 × 10-3).

CONCLUSIONS

We report the first GWAS for pediatric OSA in European Americans and African Americans. Our results provide novel insights to the genetic underpins of pediatric OSA.

Multiple long-term conditions in people with psoriasis: a latent class and bidirectional Mendelian randomization analysis

BACKGROUND

Coexisting long-term conditions (LTCs) in psoriasis and their potential causal associations with the disease are not well -established.

OBJECTIVES

To determine distinct clusters of LTCs in people with psoriasis and the potential bidirectional causal association between these LTCs and psoriasis.

METHODS

Using latent class analysis, cross-sectional data from people with psoriasis from the UK Biobank were analysed to identify distinct psoriasis-related comorbidity profiles. Linkage disequilibrium score regression (LDSR) was applied to compute the genetic correlation between psoriasis and LTCs. Two-sample bidirectional Mendelian randomization (MR) analysis assessed the potential causal direction using independent genetic variants that reached genome-wide significance (P < 5 × 10-8).

RESULTS

Five comorbidity clusters were identified in a population of 10 873 people with psoriasis. LDSR revealed that psoriasis was positively genetically correlated with heart failure [genetic correlation (rg) = 0.23, P = 8.8 × 10-8], depression (rg = 0.12, P = 2.7 × 10-5), coronary artery disease (CAD; rg = 0.15, P = 2 × 10-4) and type 2 diabetes (rg = 0.19, P = 3 × 10-3). Genetic liability to CAD was associated with an increased risk of psoriasis [inverse variance weighted (IVW) odds ratio (ORIVW) 1.159, 95% confidence interval (CI) 1.055-1.274; P = 2 × 10-3]. The MR pleiotropy residual sum and outlier (MR-PRESSO; ORMR-PRESSO 1.13, 95% CI 1.042-1.228; P = 6 × 10-3) and the MR-robust adjusted profile score (RAPS) (ORMR-RAPS 1.149, 95% CI 1.062-1.242; P = 5 × 10-4) approaches corroborate the IVW findings. The weighted median (WM) generated similar and consistent effect estimates but was not statistically significant (ORWM 1.076, 95% CI 0.949-1.221; P = 0.25). Evidence for a suggestive increased risk was detected for CAD (ORIVW 1.031, 95% CI 1.003-1.059; P = 0.03) and heart failure (ORIVW 1.019, 95% CI 1.005-1.033; P = 9 × 10-3) in those with a genetic liability to psoriasis; however, MR sensitivity analyses did not reach statistical significance.

CONCLUSIONS

Five distinct clusters of psoriasis comorbidities were observed with these findings to offer opportunities for an integrated approach to comorbidity prevention and treatment. Coexisting LTCs share with psoriasis common genetic and nongenetic risk factors, and aggressive lifestyle modification in these people is anticipated to have an impact beyond psoriasis risk. Genetically predicted CAD is possibly associated with an increased risk of psoriasis, altering our prior knowledge.

Genetics and the Evolution of Prezygotic Isolation

The significance of prezygotic isolation for speciation has been recognized at least since the Modern Synthesis. However, fundamental questions remain. For example, how are genetic associations between traits that contribute to prezygotic isolation maintained? What is the source of genetic variation underlying the evolution of these traits? And how do prezygotic barriers affect patterns of gene flow? We address these questions by reviewing genetic features shared across plants and animals that influence prezygotic isolation. Emerging technologies increasingly enable the identification and functional characterization of the genes involved, allowing us to test established theoretical expectations. Embedding these genes in their developmental context will allow further predictions about what constrains the evolution of prezygotic isolation. Ongoing improvements in statistical and computational tools will reveal how pre- and postzygotic isolation may differ in how they influence gene flow across the genome. Finally, we highlight opportunities for progress by combining theory with appropriate data.

Association of GNB3, ACE polymorphisms with POAG and NTG

PURPOSE

Primary open-angle glaucoma (POAG) represents the most prevalent form of glaucoma and stands as a foremost contributor to irreversible vision impairment on a global scale. Despite notable strides made in comprehending the genetic underpinnings of POAG, investigations within the context of Russia remain constrained.

METHODS

The study cohort comprised a total of 235 individuals, with 135 of them exhibiting various forms of glaucoma encompassing both POAG and (NTG, while the remaining 100 individuals served as control subjects. Each participant underwent a comprehensive ocular examination to ascertain their ocular health status. Genotyping of the relevant single nucleotide polymorphisms (SNPs) was carried out using the Taq Man genotyping assay. Specifically, the two SNPs under scrutiny were GNB3 rs5443 gene and ACE rs4646994. Statistical analysis was performed to evaluate the association of these SNPs with glaucoma risk.

RESULTS

The presence of the T allele of rs5443 was found to be associated with NTG (p = .004). However, no statistically significant correlation was identified between this SNP and POAG (p = .88).

CONCLUSION

This study provides evidence of an association between the T allele of rs5443 and a reduced susceptibility NTG within the Russian population. These observations augment the comprehension of the genetic underpinnings of glaucoma and hold potential implications for the prospective development of targeted therapeutic interventions.

Considerations, Caveats, and Suggestions for the Use of Polygenic Scores for Social and Behavioral Traits

Polygenic scores (PGS) are increasingly being used for prediction of social and behavioral traits, but suffer from many methodological, theoretical, and ethical concerns that profoundly limit their value. Primarily, these scores are derived from statistical correlations, carrying no inherent biological meaning, and thus may capture indirect effects. Further, the performance of these scores depends upon the diversity of the reference populations and the genomic panels from which they were derived, which consistently underrepresent minoritized populations, leading to poor fit when applied to diverse groups. There is also inherent danger of eugenic applications for the information gained from these scores, and general risk of misunderstandings that could lead to stigmatization for underrepresented groups. We urge extreme caution in use of PGS particularly for social/behavioral outcomes fraught for misinterpretation, with potential harm for the minoritized groups least likely to benefit from their use.

Open Science Practices in Psychiatric Genetics: A Primer

Open science ensures that research is transparently reported and freely accessible for all to assess and collaboratively build on. Psychiatric genetics has led among the health sciences in implementing some open science practices in common study designs, such as replication as part of genome-wide association studies. However, thorough open science implementation guidelines are limited and largely not specific to data, privacy, and research conduct challenges in psychiatric genetics. Here, we present a primer of open science practices, including selection of a research topic with patients/nonacademic collaborators, equitable authorship and citation practices, design of replicable, reproducible studies, preregistrations, open data, and privacy issues. We provide tips for informative figures and inclusive, precise reporting. We discuss considerations in working with nonacademic collaborators and distributing research through preprints, blogs, social media, and accessible lecture materials. Finally, we provide extra resources to support every step of the research process.

A statistical genetic investigation of psychiatric resilience

Background: Although trauma exposure (TE) is a transdiagnostic risk factor for many psychiatric disorders, not everyone who experiences TE develops a psychiatric disorder. Resilience may explain this heterogeneity; thus, it is critical to understand the etiologic underpinnings of resilience.Objective: The present study sought to examine the genetic underpinnings of psychiatric resilience using genome-wide association studies (GWAS), genome-wide complex trait analysis (GCTA), and polygenic risk score (PRS) analyses.Method: Participants were 6,634 trauma exposed college students attending a diverse, public university in the Mid Atlantic. GWAS and GCTA analyses were conducted, and using GWAS summary statistics from large genetic consortia, PRS analyses examined the shared genetic risk between resilience and various phenotypes.Results: Results demonstrate that nine single-nucleotide polymorphisms (SNPs) met the suggestive of significance threshold, heritability estimates for resilience were non-significant, and that there is genetic overlap between resilience and AD, as well as resilience and PTSD.Conclusion: Mixed findings from the present study suggest additional research to elucidate the etiological underpinnings of resilience, ideally with larger samples less biased by variables such as heterogeneity (i.e. clinical vs. population based) and population stratification. Genetic investigations of resilience have the potential to elucidate the molecular bases of stress-related psychopathology, suggesting new avenues for prevention and intervention efforts.

Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records

BACKGROUND

Recent advances in machine learning combined with the growing availability of digitized health records offer new opportunities for improving early diagnosis of depression. An emerging body of research shows that Electronic Health Records can be used to accurately predict cases of depression on the basis of individual's primary care records. The successes of these studies are undeniable, but there is a growing concern that their results may not be replicable, which could cast doubt on their clinical usefulness.

METHODS

To address this issue in the present paper, we set out to reproduce and replicate the work by Nichols et al. (2018), who trained predictive models of depression among young adults using Electronic Healthcare Records. Our contribution consists of three parts. First, we attempt to replicate the methodology used by the original authors, acquiring a more up-to-date set of primary health care records to the same specification and reproducing their data processing and analysis. Second, we test models presented in the original paper on our own data, thus providing out-of-sample prediction of the predictive models. Third, we extend past work by considering several novel machine-learning approaches in an attempt to improve the predictive accuracy achieved in the original work.

RESULTS

In summary, our results demonstrate that the work of Nichols et al. is largely reproducible and replicable. This was the case both for the replication of the original model and the out-of-sample replication applying NRCBM coefficients to our new EHRs data. Although alternative predictive models did not improve model performance over standard logistic regression, our results indicate that stepwise variable selection is not stable even in the case of large data sets.

CONCLUSION

We discuss the challenges associated with the research on mental health and Electronic Health Records, including the need to produce interpretable and robust models. We demonstrated some potential issues associated with the reliance on EHRs, including changes in the regulations and guidelines (such as the QOF guidelines in the UK) and reliance on visits to GP as a predictor of specific disorders.

Genetic variants associated with weight loss and metabolic outcomes after bariatric surgery: A systematic review

The extent to which genetic variations contribute to interindividual differences in weight loss and metabolic outcomes after bariatric surgery is unknown. Identifying genetic variants that impact surgery outcomes may contribute to clinical decision making. This review evaluates current evidence addressing the association of genetic variants with weight loss and changes in metabolic parameters after bariatric surgery. A search was conducted using Medline, Embase, Scopus, Web of Science, and Cochrane Library. Fifty-two eligible studies were identified. Single nucleotide polymorphisms (SNPs) at ADIPOQ (rs226729, rs1501299, rs3774261, and rs17300539) showed a positive association with postoperative change in measures of glucose homeostasis and lipid profiles (n = 4), but not with weight loss after surgery (n = 6). SNPs at FTO (rs11075986, rs16952482, rs8050136, rs9939609, rs9930506, and rs16945088) (n = 10) and MC4R (rs11152213, rs476828, rs2229616, rs9947255, rs17773430, rs5282087, and rs17782313) (n = 9) were inconsistently associated with weight loss and metabolic improvement. Four studies examining the UCP2 SNP rs660339 reported associations with postsurgical weight loss. In summary, there is limited evidence supporting a role for specific genetic variants in surgical outcomes after bariatric surgery. Most studies have adopted a candidate gene approach, limiting the scope for discovery, suggesting that the absence of compelling evidence is not evidence of absence.

The role of admixture in the rare variant contribution to inflammatory bowel disease

BACKGROUND

Identification of rare variants involved in complex, polygenic diseases like Crohn's disease (CD) has accelerated with the introduction of whole exome/genome sequencing association studies. Rare variants can be used in both diagnostic and therapeutic assessments; however, since they are likely to be restricted to specific ancestry groups, their contributions to risk assessment need to be evaluated outside the discovery population. Prior studies implied that the three known rare variants in NOD2 are absent in West African and Asian populations and only contribute in African Americans via admixture.

METHODS

Whole genome sequencing (WGS) data from 3418 African American individuals, 1774 inflammatory bowel disease (IBD) cases, and 1644 controls were used to assess odds ratios and allele frequencies (AF), as well as haplotype-specific ancestral origins of European-derived CD variants discovered in a large exome-wide association study. Local and global ancestry was performed to assess the contribution of admixture to IBD contrasting European and African American cohorts.

RESULTS

Twenty-five rare variants associated with CD in European discovery cohorts are typically five-fold lower frequency in African Americans. Correspondingly, where comparisons could be made, the rare variants were found to have a predicted four-fold reduced burden for IBD in African Americans, when compared to European individuals. Almost all of the rare CD European variants were found on European haplotypes in the African American cohort, implying that they contribute to disease risk in African Americans primarily due to recent admixture. In addition, proportion of European ancestry correlates the number of rare CD European variants each African American individual carry, as well as their polygenic risk of disease. Similar findings were observed for 23 mutations affecting 10 other common complex diseases for which the rare variants were discovered in European cohorts.

CONCLUSIONS

European-derived Crohn's disease rare variants are even more rare in African Americans and contribute to disease risk mainly due to admixture, which needs to be accounted for when performing cross-ancestry genetic assessments.

Clustering of colon, lung, and other cancer susceptibility genes with protein tyrosine phosphatases and protein kinases in multiple short genomic regions

Interactions of large gene families are poorly understood. We found that human, mouse, and rat colon and lung cancer susceptibility genes, presently considered as separate gene families, were frequently pairwise linked. The orthologous mouse map positions of 142 of 159 early discovered colon and lung cancer susceptibility genes formed 41 genomic clusters conserved >70 million years. These linked gene pairs concordantly affected both tumors and their majority was linked with two other gene families - protein tyrosine phosphatases and cancer driver protein kinases. 25% of both protein tyrosine phosphatases and protein kinases mapped <1 cM from a colon or lung cancer susceptibility gene, and 50% in <3 cM. Similar linkage was detected with most other human susceptibility genes that controlled 29 different cancer types. This concentration of tumor susceptibility genes with protein tyrosine phosphatases and driver protein kinases in multiple relatively short genomic regions suggests their possible functional diversity.

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Impact of Genetic Variations on Thromboembolic Risk in Saudis with Sickle Cell Disease

BACKGROUND

Sickle cell disease (SCD) is a Mendelian disease characterized by multigenic phenotypes. Previous reports indicated a higher rate of thromboembolic events (TEEs) in SCD patients. A number of candidate polymorphisms in certain genes (e.g., FVL, PRT, and MTHFR) were previously reported as risk factors for TEEs in different clinical conditions. This study aimed to genotype these genes and other loci predicted to underlie TEEs in SCD patients.

METHODOLOGY

A multi-center genome-wide association study (GWAS) involving Saudi SCD adult patients with a history of TEEs (n = 65) and control patients without TEE history (n = 285) was performed. Genotyping used the 10× Affymetrix Axiom array, which includes 683,030 markers. Fisher's exact test was used to generate p-values of TEE associations with each single-nucleotide polymorphism (SNP). The haplotype analysis software tool version 1.05, designed by the University of Göttingen, Germany, was used to identify the common inherited haplotypes.

RESULTS

No association was identified between the targeted single-nucleotide polymorphism rs1801133 in MTHFR and TEEs in SCD (p = 0.79). The allele frequency of rs6025 in FVL and rs1799963 in PRT in our cohort was extremely low (<0.01); thus, both variants were excluded from the analysis as no meaningful comparison was possible. In contrast, the GWAS analysis showed novel genome-wide associations (p < 5 × 10-8) with seven signals; five of them were located on Chr 11 (rs35390334, rs331532, rs317777, rs147062602, and rs372091), one SNP on Chr 20 (rs139341092), and another on Chr 9 (rs76076035). The other 34 SNPs located on known genes were also detected at a signal threshold of p < 5 × 10-6. Seven of the identified variants are located in olfactory receptor family 51 genes (OR51B5, OR51V1, OR51A1P, and OR51E2), and five variants were related to family 52 genes (OR52A5, OR52K1, OR52K2, and OR52T1P). The previously reported association between rs5006884-A in OR51B5 and fetal hemoglobin (HbF) levels was confirmed in our study, which showed significantly lower levels of HbF (p = 0.002) and less allele frequency (p = 0.003) in the TEE cases than in the controls. The assessment of the haplotype inheritance pattern involved the top ten significant markers with no LD (rs353988334, rs317777, rs14788626882, rs49188823, rs139349992, rs76076035, rs73395847, rs1368823, rs8888834548, and rs1455957). A haplotype analysis revealed significant associations between two haplotypes (a risk, TT-AA-del-AA-ins-CT-TT-CC-CC-AA, and a reverse protective, CC-GG-ins-GG-del-TT-CC-TT-GG-GG) and TEEs in SCD (p = 0.024, OR = 6.16, CI = 1.34-28.24, and p = 0.019, OR = 0.33, CI = 0.13-0.85, respectively).

CONCLUSIONS

Seven markers showed novel genome-wide associations; two of them were exonic variants (rs317777 in OLFM5P and rs147062602 in OR51B5), and less significant associations (p < 5 × 10-6) were identified for 34 other variants in known genes with TEEs in SCD. Moreover, two 10-SNP common haplotypes were determined with contradictory effects. Further replication of these findings is needed.

The phenotype-genotype reference map: Improving biobank data science through replication

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

A Multibreed Genome-Wide Association Study for Cattle Leukocyte Telomere Length

Telomeres are terminal DNA regions of chromosomes that prevent chromosomal fusion and degradation during cell division. In cattle, leukocyte telomere length (LTL) is associated with longevity, productive lifespan, and disease susceptibility. However, the genetic basis of LTL in this species is less studied than in humans. In this study, we utilized the whole-genome resequencing data of 239 animals from 17 cattle breeds for computational leukocyte telomere length estimation and subsequent genome-wide association study of LTL. As a result, we identified 42 significant SNPs, of which eight were found in seven genes (EXOC6B, PTPRD, RPS6KC1, NSL1, AGBL1, ENSBTAG00000052188, and GPC1) when using covariates for two major breed groups (Turano-Mongolian and European). Association analysis with covariates for breed effect detected 63 SNPs, including 13 in five genes (EXOC6B, PTPRD, RPS6KC1, ENSBTAG00000040318, and NELL1). The PTPRD gene, demonstrating the top signal in analysis with breed effect, was previously associated with leukocyte telomere length in cattle and likely is involved in the mechanism of alternative lengthening of telomeres. The single nucleotide variants found could be tested for marker-assisted selection to improve telomere-length-associated traits.

Replicable brain-phenotype associations require large-scale neuroimaging data

Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.

Complexities of recapitulating polygenic effects in natural populations: replication of genetic effects on wing shape in artificially selected and wild-caught populations of Drosophila melanogaster

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a genome-wide association studies in three genes: dachsous, extra-macrochaete, and neuralized, using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the dachsous shape change vector, we observe genetic differentiation of variants in dachsous and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the extra-macrochaete shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of dachsous, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals

BACKGROUND

DNA methylation is a dynamic epigenetic mechanism that occurs at cytosine-phosphate-guanine dinucleotide (CpG) sites. Epigenome-wide association studies (EWAS) investigate the strength of association between methylation at individual CpG sites and health outcomes. Although blood methylation may act as a peripheral marker of common disease states, previous EWAS have typically focused only on individual conditions and have had limited power to discover disease-associated loci. This study examined the association of blood DNA methylation with the prevalence of 14 disease states and the incidence of 19 disease states in a single population of over 18,000 Scottish individuals.

METHODS AND FINDINGS

DNA methylation was assayed at 752,722 CpG sites in whole-blood samples from 18,413 volunteers in the family-structured, population-based cohort study Generation Scotland (age range 18 to 99 years). EWAS tested for cross-sectional associations between baseline CpG methylation and 14 prevalent disease states, and for longitudinal associations between baseline CpG methylation and 19 incident disease states. Prevalent cases were self-reported on health questionnaires at the baseline. Incident cases were identified using linkage to Scottish primary (Read 2) and secondary (ICD-10) care records, and the censoring date was set to October 2020. The mean time-to-diagnosis ranged from 5.0 years (for chronic pain) to 11.7 years (for Coronavirus Disease 2019 (COVID-19) hospitalisation). The 19 disease states considered in this study were selected if they were present on the World Health Organisation's 10 leading causes of death and disease burden or included in baseline self-report questionnaires. EWAS models were adjusted for age at methylation typing, sex, estimated white blood cell composition, population structure, and 5 common lifestyle risk factors. A structured literature review was also conducted to identify existing EWAS for all 19 disease states tested. The MEDLINE, Embase, Web of Science, and preprint servers were searched to retrieve relevant articles indexed as of March 27, 2023. Fifty-four of approximately 2,000 indexed articles met our inclusion criteria: assayed blood-based DNA methylation, had >20 individuals in each comparison group, and examined one of the 19 conditions considered. First, we assessed whether the associations identified in our study were reported in previous studies. We identified 69 associations between CpGs and the prevalence of 4 conditions, of which 58 were newly described. The conditions were breast cancer, chronic kidney disease, ischemic heart disease, and type 2 diabetes mellitus. We also uncovered 64 CpGs that associated with the incidence of 2 disease states (COPD and type 2 diabetes), of which 56 were not reported in the surveyed literature. Second, we assessed replication across existing studies, which was defined as the reporting of at least 1 common site in >2 studies that examined the same condition. Only 6/19 disease states had evidence of such replication. The limitations of this study include the nonconsideration of medication data and a potential lack of generalizability to individuals that are not of Scottish and European ancestry.

CONCLUSIONS

We discovered over 100 associations between blood methylation sites and common disease states, independently of major confounding risk factors, and a need for greater standardisation among EWAS on human disease.

Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs

The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations-500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1-which has been linked to weight loss in mice and is potentially influential for AGE100-and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes-namely, PDZRN4, LIPM, and ANKRD22-which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.

A Polygenic Risk Score Enhances Risk Prediction for Adolescents' Antisocial Behavior over the Combined Effect of 22 Extra-familial, Familial, and Individual Risk Factors in the Context of the Family Check-Up

Possessing informative tools to predict who is most at risk for antisocial behavior in adolescence is important to help identify families most in need of early intervention. Polygenic risk scores (PRSs) have been shown to predict antisocial behavior, but it remains unclear whether PRSs provide additional benefit above more conventional tools to early risk detection for antisocial behavior. This study examined the utility of a PRS in predicting adolescents' antisocial behavior after accounting for a broad index of children's contextual and individual risk factors for antisocial behavior. Participants were drawn from a longitudinal family-based prevention study (N = 463; Ncontrol = 224; 48.8% girls; 45.1% White; 30.2% Black; 12.7% Hispanic/Latino, 10.4% biracial; 0.2% Native American). Participants were recruited from US-based Women, Infants, and Children Nutritional Supplement programs. A risk tolerance PRS was created from a genome-wide association study. We created a robust measure capturing additive effects of 22 conventional measures of a risk of antisocial behavior assessed at child age 2 (before intervention). A latent variable capturing antisocial behavior (ages 10.5-16) was created. After accounting for intervention status and the conventional risk index, the risk tolerance PRS predicted independent variance in antisocial behavior. A PRS-by-conventional risk interaction showed that the conventional risk measure only predicted antisocial behavior at high levels of the PRS. Thus, the risk tolerance PRS provides unique predictive information above conventional screening tools and, when combined with them, identified a higher-risk subgroup of children. Integrating PRSs could facilitate risk identification and, ultimately, prevention screening, particularly in settings unable to serve all individuals in need.

Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk

The abundance of Lp(a) protein holds significant implications for the risk of cardiovascular disease (CVD), which is directly impacted by the copy number (CN) of KIV-2, a 5.5 kbp sub-region. KIV-2 is highly polymorphic in the population and accurate analysis is challenging. In this study, we present the DRAGEN KIV-2 CN caller, which utilizes short reads. Data across 166 WGS show that the caller has high accuracy, compared to optical mapping and can further phase ~50% of the samples. We compared KIV-2 CN numbers to 24 previously postulated KIV-2 relevant SNVs, revealing that many are ineffective predictors of KIV-2 copy number. Population studies, including USA-based cohorts, showed distinct KIV-2 CN, distributions for European-, African-, and Hispanic-American populations and further underscored the limitations of SNV predictors. We demonstrate that the CN estimates correlate significantly with the available Lp(a) protein levels and that phasing is highly important.

Leveraging GWAS data derived from a large cooperative group trial to assess the risk of taxane-induced peripheral neuropathy (TIPN) in patients being treated for breast cancer: Part 2-functional implications of a SNP cluster associated with TIPN risk in patients being treated for breast cancer

INTRODUCTION

Using GWAS data derived from a large collaborative trial (ECOG-5103), we identified a cluster of 267 SNPs which predicted CIPN in treatment-naive patients as reported in Part 1 of this study. To assess the functional and pathological implications of this set, we identified collective gene signatures were and evaluated the informational value of those signatures in defining CIPN's pathogenesis.

METHODS

In Part 1, we analyzed GWAS data derived from ECOG-5103, first identifying those SNPs that were most strongly associated with CIPN using Fisher's ratio. After identifying those SNPs which differentiated CIPN-positive from CIPN-negative phenotypes, we ranked them in order of their discriminatory power to produce a cluster of SNPs which provided the highest predictive accuracy using leave-one-out cross validation (LOOCV). An uncertainty analysis was included. Using the best predictive SNP cluster, we performed gene attribution for each SNP using NCBI Phenotype Genotype Integrator and then assessed functionality by applying GeneAnalytics, Gene Set Enrichment Analysis, and PCViz.

RESULTS

Using aggregate data derived from the GWAS, we identified a 267 SNP cluster which was associated with a CIPN+ phenotype with an accuracy of 96.1%. We could attribute 173 genes to the 267 SNP cluster. Six long intergenic non-protein coding genes were excluded. Ultimately, the functional analysis was based on 138 genes. Of the 17 pathways identified by Gene Analytics (GA) software, the irinotecan pharmacokinetic pathway had the highest score. Highly matching gene ontology attributions included flavone metabolic process, flavonoid glucuronidation, xenobiotic glucuronidation, nervous system development, UDP glycosyltransferase activity, retinoic acid binding, protein kinase C binding, and glucoronosyl transferase activity. Gene Set Enrichment Analysis (GSEA) GO terms identified neuron-associated genes as most significant (p = 5.45e-10). Consistent with the GA's output, flavone, and flavonoid associated terms, glucuronidation were noted as were GO terms associated with neurogenesis.

CONCLUSION

The application of functional analyses to phenotype-associated SNP clusters provides an independent validation step in assessing the clinical meaningfulness of GWAS-derived data. Functional analyses following gene attribution of a CIPN-predictive SNP cluster identified pathways, gene ontology terms, and a network which were consistent with a neuropathic phenotype.

Identification of a SNP cluster associated with taxane-induced peripheral neuropathy risk in patients being treated for breast cancer using GWAS data derived from a large cooperative group trial

BACKGROUND

Chemotherapy-induced peripheral neuropathy (CIPN) is a common toxicity of taxanes for which there is no effective intervention. Genomic CIPN risk determination has yielded promising, but inconsistent results. The present study assessed the utility of a collective SNP cluster identified using novel analytics to describe taxane-associated CIPN risk.

METHODS

We analyzed GWAS data derived from ECOG-5103, first identifying SNPs that were most strongly associated with CIPN using Fisher's ratio (FR). We then ranked ordered those SNPs which discriminated CIPN-positive (CIPN +) from CIPN-negative phenotypes based on their discriminatory power and developed the cluster of SNPs which provided the highest predictive accuracy using leave-one-out cross-validation (LOOCV).

RESULTS

Using aggregated genotype data obtained from the previously reported ECOG-5103 clinical trial (in which two different arrays were used, HumanOmniExpress (727,227 SNPs) and HumanOmni1-Quad1 (1,131,857 SNPs)), we identified a 267 SNP cluster which was associated with a CIPN + phenotype with an accuracy of 96.1%.

CONCLUSIONS

A cluster of SNPs was identified which prospectively discriminated patients most likely to develop symptomatic CIPN following taxane exposure as part of a breast cancer chemotherapy regimen. Validation using an independent patient cohort should be performed.

APOE ɛ4 allele and TOMM40-APOC1 variants jointly contribute to survival to older ages

Age-related diseases characteristic of post-reproductive life, aging, and life span are the examples of polygenic non-Mendelian traits with intricate genetic architectures. Polygenicity of these traits implies that multiple variants can impact their risks independently or jointly as combinations of specific variants. Here, we examined chances to live to older ages, 85 years and older, for carriers of compound genotypes comprised of combinations of genotypes of rs429358 (APOE ɛ4 encoding polymorphism), rs2075650 (TOMM40), and rs12721046 (APOC1) polymorphisms using data from four human studies. The choice of these polymorphisms was motivated by our prior results showing that the ɛ4 carriers having minor alleles of the other two polymorphisms were at exceptionally high risk of Alzheimer's disease (AD), compared with non-carriers of the minor alleles. Consistent with our prior findings for AD, we show here that the adverse effect of the ɛ4 allele on survival to older ages is significantly higher in carriers of minor alleles of rs2075650 and/or rs12721046 polymorphisms compared with their non-carriers. The exclusion of AD cases made this effect stronger. Our results provide compelling evidence that AD does not mediate the associations of the same compound genotypes with chances to survive until older ages, indicating the existence of genetically heterogeneous mechanisms. The survival chances can be mainly associated with lipid- and immunity-related mechanisms, whereas the AD risk, can be driven by the AD-biomarker-related mechanism, among others. Targeting heterogeneous polygenic profiles of individuals at high risks of complex traits is promising for the translation of genetic discoveries to health care.

Integrating genome-wide association studies and population genomics analysis reveals the genetic architecture of growth and backfat traits in pigs

Growth and fat deposition are complex traits, which can affect economical income in the pig industry. Due to the intensive artificial selection, a significant genetic improvement has been observed for growth and fat deposition in pigs. Here, we first investigated genomic-wide association studies (GWAS) and population genomics (e.g., selection signature) to explore the genetic basis of such complex traits in two Large White pig lines (n = 3,727) with the GeneSeek GGP Porcine HD array (n = 50,915 SNPs). Ten genetic variants were identified to be associated with growth and fatness traits in two Large White pig lines from different genetic backgrounds by performing both within-population GWAS and cross-population GWAS analyses. These ten significant loci represented eight candidate genes, i.e., NRG4, BATF3, IRS2, ANO1, ANO9, RNF152, KCNQ5, and EYA2. One of them, ANO1 gene was simultaneously identified for both two lines in BF100 trait. Compared to single-population GWAS, cross-population GWAS was less effective for identifying SNPs with population-specific effect, but more powerful for detecting SNPs with population-shared effects. We further detected genomic regions specifically selected in each of two populations, but did not observe a significant enrichment for the heritability of growth and backfat traits in such regions. In summary, the candidate genes will provide an insight into the understanding of the genetic architecture of growth-related traits and backfat thickness, and may have a potential use in the genomic breeding programs in pigs.

Accurate modeling of replication rates in genome-wide association studies by accounting for Winner's Curse and study-specific heterogeneity

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in genome-wide association studies has been well-studied in the context of Winner's Curse, which is the inflation of effect size estimates for significant variants due to statistical chance. However, Winner's Curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes genome-wide association studies and replication studies to jointly model Winner's Curse and study-specific heterogeneity due to confounding factors. We apply this framework to 100 genome-wide association studies from the Human Genome-Wide Association Studies Catalog and observe that there is a large range in the level of estimated confounding. We demonstrate how this framework can be used to distinguish when studies fail to replicate due to statistical noise and when they fail due to confounding.

Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer

Epigenetic modifications are known to regulate cell phenotype during cancer progression, including breast cancer. Unlike genetic alterations, changes in the epigenome are reversible, thus potentially reversed by epi-drugs. Breast cancer, the most common cause of cancer death worldwide in women, encompasses multiple histopathological and molecular subtypes. Several lines of evidence demonstrated distortion of the epigenetic landscape in breast cancer. Interestingly, mammary cells isolated from breast cancer patients and cultured ex vivo maintained the tumorigenic phenotype and exhibited aberrant epigenetic modifications. Recent studies indicated that the therapeutic efficiency for breast cancer regimens has increased over time, resulting in reduced mortality. Future medical treatment for breast cancer patients, however, will likely depend upon a better understanding of epigenetic modifications. The present review aims to outline different epigenetic mechanisms including DNA methylation, histone modifications, and ncRNAs with their impact on breast cancer, as well as to discuss studies highlighting the central role of epigenetic mechanisms in breast cancer pathogenesis. We propose new research areas that may facilitate locus-specific epigenome editing as breast cancer therapeutics.

Fine-mapping and association analysis of candidate genes for papilla number in sea cucumber, Apostichopus japonicus

The papilla number is one of the most economically important traits of sea cucumber in the China marketing trade. However, the genetic basis for papilla number diversity in holothurians is still scarce. In the present study, we conducted genome-wide association studies (GWAS) for the trait papilla number of sea cucumbers utilizing a set of 400,186 high-quality SNPs derived from 200 sea cucumbers. Two significant trait-associated SNPs that passed Bonferroni correction (P < 1.25E-7) were located in the intergenic region near PATS1 and the genic region of EIF4G, which were reported to play a pivotal role in cell growth and proliferation. The fine-mapping regions around the top two lead SNPs provided precise causative loci/genes related to papilla formation and cellular activity, including PPP2R3C, GBP1, and BCAS3. Potential SNPs with P < 1E-4 were acquired for the following GO and KEGG enrichment analysis. Moreover, the two lead SNPs were verified in another population of sea cucumber, and the expressive detection of three potential candidate genes PATS1, PPP2R3C, and EIF4G that near or cover the two lead SNPs was conducted in papilla tissue of TG (Top papilla number group) and BG (Bottom papilla number group) by qRT-PCR. We found the significantly higher expression profile of PATS1 (3.34-fold), PPP2R3C (4.90-fold), and EIF4G (4.23-fold) in TG, implying their potential function in papilla polymorphism. The present results provide valuable information to decipher the phenotype differences of the papilla trait and will provide a scientific basis for selective breeding in sea cucumbers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-022-00139-w.

Replication of gene polymorphisms associated with periodontitis-related traits in an elderly cohort: the Washington Heights/Inwood Community Aging Project Ancillary Study of Oral Health

AIM

We sought to replicate findings from published genome-wide association studies (GWAS), linking specific candidate gene loci with periodontitis-related clinical/microbial traits.

MATERIALS AND METHODS

In the published GWAS, a total of 2196 single nucleotide polymorphisms associated with periodontitis-related traits at a p ≤ 5 × 10^-6 and mapped to 136 gene loci. The replication cohort included 1124 individuals, 65-98 years old (67% female, 45% Hispanic, 30% Black, 23% White) with available genome-wide genotypes and full-mouth periodontal status. Microbial profiles using checkerboard DNA-DNA hybridization and 16SrRNA sequencing were available from 912 and 739 participants, respectively.

RESULTS

Using gene-specific p-values after linkage disequilibrium pruning, the following gene/phenotype associations replicated successfully: CLEC19A with edentulism and %teeth with pocket depth (PD) ≥4 mm; IL37, HPVC1, TRPS1, ABHD12B, LDLRAD4 (C180rF1), TGM3, and GRK5 with %teeth with PD ≥4 mm; DAB2IP with presence of PD ≥6 mm; KIAA1715(LNPK), ROBO2, RAB28, LINC01017, NELL1, LDLRAD4(C18orF1), and CRYBB2P1 with %teeth with clinical attachment level (CAL) ≥3 mm; RUNX2 and LAMA2 with %teeth with CAL ≥5 mm; and KIAA1715(LNPK) with high colonization by Aggregatibacter actinomycetemcomitans. In addition, CLEC19A, IQSEC1, and EMR1 associated with microbial abundance based on checkerboard data, LBP and NCR2 with abundance based on sequencing data, and NCR2 with microbial diversity based on sequencing data.

CONCLUSIONS

Several gene loci identified in published GWAS as associated with periodontitis-related phenotypes replicated successfully in an elderly cohort.

Venom gene sequence diversity and expression jointly shape diet adaptation in pitvipers

Understanding the joint roles of protein sequence variation and differential expression during adaptive evolution is a fundamental, yet largely unrealized goal of evolutionary biology. Here, we use phylogenetic path analysis to analyze a comprehensive venom-gland transcriptome dataset spanning three genera of pitvipers to identify the functional genetic basis of a key adaptation (venom complexity) linked to diet breadth (DB). The analysis of gene-family-specific patterns reveals that, for genes encoding two of the most important venom proteins (snake venom metalloproteases and snake venom serine proteases), there are direct, positive relationships between sequence diversity (SD), expression diversity (ED), and increased DB. Further analysis of gene-family diversification for these proteins showed no constraint on how individual lineages achieved toxin gene SD in terms of the patterns of paralog diversification. In contrast, another major venom protein family (PLA₂s) showed no relationship between venom molecular diversity and DB. Additional analyses suggest that other molecular mechanisms—such as higher absolute levels of expression—are responsible for diet adaptation involving these venom proteins. Broadly, our findings argue that functional diversity generated through sequence and expression variations jointly determine adaptation in the key components of pitviper venoms, which mediate complex molecular interactions between the snakes and their prey.

NELL-1 in Genome-Wide Association Studies across Human Diseases

Neural epidermal growth factor-like (EGFL)-like protein (NELL)-1 is a potent and key osteogenic factor in the development and regeneration of skeletal tissues. Intriguingly, accumulative data from genome-wide association studies (GWASs) have started unveiling potential broader roles of NELL-1 beyond its functions in bone and cartilage. With exploration of the genetic variants of the entire genome in large-scale disease cohorts, GWASs have been used for establishing the connection between specific single-nucleotide polymorphisms of NELL1, in addition to osteoporosis, metabolic diseases, inflammatory conditions, neuropsychiatric diseases, neurodegenerative disorders, and malignant tumors. This review summarizes the findings from GWASs on the manifestation, significance level, implications on function, and correlation of specific NELL1 single-nucleotide polymorphisms in various disorders in humans. By offering a unique and comprehensive correlation between genetic variants and plausible functions of NELL1 in GWASs, this review illustrates the wide range of potential effects of a single gene on the pathogenesis of multiple disorders in humans.

SNP characteristics and validation success in genome wide association studies

Genome wide association studies (GWASs) have identified tens of thousands of single nucleotide polymorphisms (SNPs) associated with human diseases and characteristics. A significant fraction of GWAS findings can be false positives. The gold standard for true positives is an independent validation. The goal of this study was to identify SNP features associated with validation success. Summary statistics from the Catalog of Published GWASs were used in the analysis. Since our goal was an analysis of reproducibility, we focused on the diseases/phenotypes targeted by at least 10 GWASs. GWASs were arranged in discovery-validation pairs based on the time of publication, with the discovery GWAS published before validation. We used four definitions of the validation success that differ by stringency. Associations of SNP features with validation success were consistent across the definitions. The strongest predictor of SNP validation was the level of statistical significance in the discovery GWAS. The magnitude of the effect size was associated with validation success in a non-linear manner. SNPs with risk allele frequencies in the range 30-70% showed a higher validation success rate compared to rarer or more common SNPs. Missense, 5'UTR, stop gained, and SNPs located in transcription factor binding sites had a higher validation success rate compared to intergenic, intronic and synonymous SNPs. There was a positive association between validation success and the level of evolutionary conservation of the sites. In addition, validation success was higher when discovery and validation GWASs targeted the same ethnicity. All predictors of validation success remained significant in a multivariate logistic regression model indicating their independent contribution. To conclude, we identified SNP features predicting validation success of GWAS hits. These features can be used to select SNPs for validation and downstream functional studies.

Definitive roles of TOMM40-APOE-APOC1 variants in the Alzheimer's risk

Despite advances, the roles of genetic variants from the APOE-harboring 19q13.32 region in Alzheimer's disease (AD) remain controversial. We leverage a comprehensive approach to gain insights into a more homogeneous genetic architecture of AD in this region. We use a sample of 2,673 AD-affected and 16,246 unaffected subjects from 4 studies and validate our main findings in the landmark Alzheimer's Disease Genetics Consortium cohort (3,662 AD-cases and 1,541 controls). We report the remarkably high excesses of the AD risk for carriers of the ε4 allele who also carry minor alleles of rs2075650 (TOMM40) and rs12721046 (APOC1) polymorphisms compared to carriers of their major alleles. The exceptionally high 4.37-fold (p=1.34 × 10^-3) excess was particularly identified for the minor allele homozygotes. The beneficial and adverse variants were significantly depleted and enriched, respectively, in the AD-affected families. This study provides compelling evidence for the definitive roles of the APOE-TOMM40-APOC1 variants in the AD risk.

Polygenic risk prediction models for colorectal cancer: a systematic review

BACKGROUND

Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.

METHODS

We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.

RESULTS

We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.

CONCLUSIONS

Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide

Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genomewide association study (GWAS) of psoriasis in South Asians (SAS), consisting of 2,590 cases and 1,720 controls. Comparison with our existing European-origin (EUR) GWAS showed that effect sizes of known psoriasis signals were highly correlated in SAS and EUR (Spearman ρ = 0.78; p < 2 × 10-14). Transethnic meta-analysis identified two non-MHC psoriasis loci (1p36.22 and 1q24.2) not previously identified in EUR, which may have regulatory roles. For these two loci, the transethnic GWAS provided higher genetic resolution and reduced the number of potential causal variants compared to using the EUR sample alone. We then explored multiple strategies to develop reference panels for accurately imputing MHC genotypes in both SAS and EUR populations and conducted a fine-mapping of MHC psoriasis associations in SAS and the largest such effort for EUR. HLA-C*06 was the top-ranking MHC locus in both populations but was even more prominent in SAS based on odds ratio, disease liability, model fit and predictive power. Transethnic modeling also substantially boosted the probability that the HLA-C*06 protein variant is causal. Secondary MHC signals included coding variants of HLA-C and HLA-B, but also potential regulatory variants of these two genes as well as HLA-A and several HLA class II genes, with effects on both chromatin accessibility and gene expression. This study highlights the shared genetic basis of psoriasis in SAS and EUR populations and the value of transethnic meta-analysis for discovery and fine-mapping of susceptibility loci.

Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores

Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.

Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility

Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6, and GM11545 with bone mineral density, and Psmb9 with weight. However, replication at a nominal threshold of 0.05 between the two component studies was low, with less than one-third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner's Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations, we integrated information about replication rates, study-specific heterogeneity, and Winner's Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.

Preparation and Curation of Omics Data for Genome-Wide Association Studies

With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.

A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.

Polygenic Risk Score Prediction for Endometriosis

Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10⁻⁷] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10⁻¹¹), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10⁻⁵), infiltrating (OR = 1.66, p = 2.7× 10⁻⁹), and peritoneal (OR = 1.51, p = 2.6 × 10⁻³). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p < 2.2× 10⁻¹⁶). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.

Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention

Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

Bench Research Informed by GWAS Results

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

TIGA: target illumination GWAS analytics

MOTIVATION

Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

RESULTS

Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

AVAILABILITY AND IMPLEMENTATION

Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-Disease Outcome and Response to Therapy

Inflammatory bowel diseases [IBD] are a heterogeneous spectrum with two extreme phenotypes, Crohn's disease [CD] and ulcerative colitis [UC], which both represent numerous phenotypical variations. Hence, we should no longer approach all IBD patients similarly, but rather aim to rethink clinical classifications and modify treatment algorithms to usher in a new era of precision medicine in IBD. This scientific ECCO workshop aims to provide a state-of-the-art overview on prognostic and predictive markers, shed light on key questions in biomarker development, propose best practices in IBD biomarker development [including trial design], and discuss the potential for multi-omic data integration to help drive further advances to make precision medicine a reality in IBD.