Fine-mapping the HOXB region detects common variants tagging a rare coding allele: evidence for synthetic association in prostate cancer

Evolutionary conservation in noncoding genomic regions

Humans may share more genomic commonalities with other species than previously thought. According to current estimates, ~5% of the human genome is functionally constrained, which is a much larger fraction than the ~1.5% occupied by annotated protein-coding genes. Hence, ~3.5% of the human genome comprises likely functional conserved noncoding elements (CNEs) preserved among organisms, whose common ancestors existed throughout hundreds of millions of years of evolution. As whole-genome sequencing emerges as a standard procedure in genetic analyses, interpretation of variations in CNEs, including the elucidation of mechanistic and functional roles, becomes a necessity. Here, we discuss the phenomenon of noncoding conservation via four dimensions (sequence, regulatory conservation, spatiotemporal expression, and structure) and the potential significance of CNEs in phenotype variation and disease.

Dissecting the heritable risk of breast cancer: From statistical methods to susceptibility genes

Decades of research have shown that rare highly penetrant mutations can promote tumorigenesis, but it is still unclear whether variants observed at high-frequency in the broader population could modulate the risk of developing cancer. Genome-wide Association Studies (GWAS) have generated a wealth of data linking single nucleotide polymorphisms (SNPs) to increased cancer risk, but the effect of these mutations are usually subtle, leaving most of cancer heritability unexplained. Understanding the role of high-frequency mutations in cancer can provide new intervention points for early diagnostics, patient stratification and treatment in malignancies with high prevalence, such as breast cancer. Here we review state-of-the-art methods to study cancer heritability using GWAS data and provide an updated map of breast cancer susceptibility loci at the SNP and gene level.

Die Rolle seltener Varianten bei häufigen Krankheiten

Häufige Krankheiten, die sog. Volkskrankheiten, sind in der Regel multifaktoriell verursacht, d. h. zu ihrer Entwicklung tragen sowohl genetische Faktoren als auch nicht-genetische Umgebungseinflüsse bei. Die geschätzte Gesamterblichkeit (‑heritabilität) reicht von moderat bis vergleichsweise hoch. Die genetische Architektur ist komplex und kann das gesamte allelische Spektrum, von häufigen Varianten mit niedriger Penetranz bis hin zu seltenen Varianten mit höherer Penetranz, sowie alle möglichen Kombinationen umfassen. Während häufige Varianten seit mehreren Jahren mit großem Erfolg durch genomweite Assoziationsstudien (GWAS) identifiziert werden, war bisher die Identifizierung seltener Varianten, insbesondere aufgrund der großen Zahl beitragender Gene, nur begrenzt erfolgreich. Dies ändert sich derzeit dank der Anwendung von Hochdurchsatz-Sequenziertechnologien („next-generation sequencing“, NGS) und der daraus resultierenden zunehmenden Verfügbarkeit von exom- und genomweiten Sequenzdaten großer Kollektive. In diesem Artikel geben wir einen Überblick über die Bedeutung seltener Varianten bei häufigen Erkrankungen sowie den aktuellen Stand in Bezug auf deren Identifizierung mittels NGS. Wir betrachten insbesondere die folgenden Fragen: Bei welchen häufigen Krankheiten ist ein Beitrag seltener Varianten zu erwarten, wie können diese Varianten identifiziert werden, und welches Potenzial bieten seltene Varianten für das Verständnis biologischer Prozesse bzw. für die Translation in die klinische Praxis?

Post-GWAS in prostate cancer: from genetic association to biological contribution

Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.

A Review of Prostate Cancer Genome-Wide Association Studies (GWAS)

Prostate cancer is the most common cancer in men in Europe and the United States. The genetic heritability of prostate cancer is contributed to by both rarely occurring genetic variants with higher penetrance and moderate to commonly occurring variants conferring lower risks. The number of identified variants belonging to the latter category has increased dramatically in the last 10 years with the development of the genome-wide association study (GWAS) and the collaboration of international consortia that have led to the sharing of large-scale genotyping data. Over 40 prostate cancer GWAS have been reported, with approximately 170 common variants now identified. Clinical utility of these variants could include strategies for population-based risk stratification to target prostate cancer screening to men with an increased genetic risk of disease development, while for those who develop prostate cancer, identifying genetic variants could allow treatment to be tailored based on a genetic profile in the early disease setting. Functional studies of identified variants are needed to fully understand underlying mechanisms of disease and identify novel targets for treatment. This review will outline the GWAS carried out in prostate cancer and the common variants identified so far, and how these may be utilized clinically in the screening for and management of prostate cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 845-57. ©2018 AACR.

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

Genotype Imputation from Large Reference Panels

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

Whole exome sequencing: a state-of-the-art approach for defining (and exploring!) genetic landscapes in pediatric nephrology

The genesis of whole exome sequencing as a powerful tool for detailing the protein coding sequence of the human genome was conceptualized based on the availability of next-generation sequencing technology and knowledge of the human reference genome. The field of pediatric nephrology enriched with molecularly unsolved phenotypes is allowing the clinical and research application of whole exome sequencing to enable novel gene discovery and provide amendment of phenotypic misclassification. Recent studies in the field have informed us that newer high-throughput sequencing techniques are likely to be of high yield when applied in conjunction with conventional genomic approaches such as linkage analysis and other strategies used to focus subsequent analysis. They have also emphasized the need for the validation of novel genetic findings in large collaborative cohorts and the production of robust corroborative biological data. The well-structured application of comprehensive genomic testing in clinical and research arenas will hopefully continue to advance patient care and precision medicine, but does call for attention to be paid to its integrated challenges.

Improving imputation in disease-relevant regions: lessons from cystic fibrosis

Does genotype imputation with public reference panels identify variants contributing to disease? Genotype imputation using the 1000 Genomes Project (1KG; 2504 individuals) displayed poor coverage at the causal cystic fibrosis (CF) transmembrane conductance regulator (CFTR) locus for the International CF Gene Modifier Consortium. Imputation with the larger Haplotype Reference Consortium (HRC; 32,470 individuals) displayed improved coverage but low sensitivity of variants clinically relevant for CF. A hybrid reference that combined whole genome sequencing (WGS) from 101 CF individuals with the 1KG imputed a greater number of single-nucleotide variants (SNVs) that would be analyzed in a genetic association study (r² ≥ 0.3 and MAF ≥ 0.5%) than imputation with the HRC, while the HRC excelled in the lower frequency spectrum. Using the 1KG or HRC as reference panels missed the most common CF-causing variants or displayed low imputation accuracy. Designs that incorporate population-specific WGS can improve imputation accuracy at disease-specific loci, while imputation using public data sets can omit disease-relevant genotypes.

Trans-ethnic follow-up of breast cancer GWAS hits using the preferential linkage disequilibrium approach

Leveraging population-distinct linkage equilibrium (LD) patterns, trans-ethnic follow-up of variants discovered from genome-wide association studies (GWAS) has proved to be useful in facilitating the identification of bona fide causal variants. We previously developed the preferential LD approach, a novel method that successfully identified causal variants driving the GWAS signals within European-descent populations even when the causal variants were only weakly linked with the GWAS-discovered variants. To evaluate the performance of our approach in a trans-ethnic setting, we applied it to follow up breast cancer GWAS hits identified mostly from populations of European ancestry in African Americans (AA). We evaluated 74 breast cancer GWAS variants in 8,315 AA women from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Only 27% of them were associated with breast cancer risk at significance level α=0.05, suggesting race-specificity of the identified breast cancer risk loci. We followed up on those replicated GWAS hits in the AMBER consortium utilizing the preferential LD approach, to search for causal variants or better breast cancer markers from the 1000 Genomes variant catalog. Our approach identified stronger breast cancer markers for 80% of the GWAS hits with at least nominal breast cancer association, and in 81% of these cases, the marker identified was among the top 10 of all 1000 Genomes variants in the corresponding locus. The results support trans-ethnic application of the preferential LD approach in search for candidate causal variants, and may have implications for future genetic research of breast cancer in AA women.

Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities

Background

Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants.

Methods

Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry.

Results

After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing.

Conclusions

This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.

Anticancer effect of deoxypodophyllotoxin induces apoptosis of human prostate cancer cells

Deoxypodophyllotoxin (DPPT) is extracted and separated from citrus-related plants, including Podophyllum (P.) peltatum, P. pleianthum, P. emodi (also called P. hexandrum) and Diphylleia grayi. DPPT has significant antitumor and antiviral activity. However, due to its strong toxicity and side effects, its use is limited in practical applications. The in vitro antitumor efficacy of DPPT on human prostate cancer (PCa) cells remains to be determined. The present study investigated the anticancer effect of DPPT on human PCa cells and its potential mechanism. The data revealed that DPPT markedly reduced cell proliferation and activated the caspase-3 expression level by an increase in apoptotic cell death in DU-145 cells. In addition, treatment with DPPT markedly downregulated the levels of phosphorylated Akt and activated the p53/B-cell lymphoma 2 associated X protein (Bax)/phosphatase and tensin homolog (PTEN) signaling pathway in DU-145 cells, suggesting that caspase-mediated pathways were involved in DPPT-induced apoptosis. The present study suggested the role of DPPT as a novel chemotherapeutic drug for human PCa, which may function through the Akt/p53/Bax/PTEN signaling pathway.

Recurrent HOXB13 mutations in the Dutch population do not associate with increased breast cancer risk

The HOXB13 p.G84E mutation has been firmly established as a prostate cancer susceptibility allele. Although HOXB13 also plays a role in breast tumor progression, the association of HOXB13 p.G84E with breast cancer risk is less evident. Therefore, we comprehensively interrogated the entire HOXB13 coding sequence for mutations in 1,250 non-BRCA1/2 familial breast cancer cases and 800 controls. We identified two predicted deleterious missense mutations, p.G84E and p.R217C, that were recurrent among breast cancer cases and further evaluated their association with breast cancer risk in a larger study. Taken together, 4,520 familial non-BRCA1/2 breast cancer cases and 3,127 controls were genotyped including the cases and controls of the whole gene screen. The concordance rate for the genotyping assays compared with Sanger sequencing was 100%. The prostate cancer risk allele p.G84E was identified in 18 (0.56%) of 3,187 cases and 16 (0.70%) of 2,300 controls (OR = 0.81, 95% CI = 0.41–1.59, P = 0.54). Additionally, p.R217C was identified in 10 (0.31%) of 3,208 cases and 2 (0.087%) of 2,288 controls (OR = 3.57, 95% CI = 0.76–33.57, P = 0.14). These results imply that none of the recurrent HOXB13 mutations in the Dutch population are associated with breast cancer risk, although it may be worthwhile to evaluate p.R217C in a larger study.

Strategies for Imputing and Analyzing Rare Variants in Association Studies

Rare genetic variants may be responsible for a significant amount of the uncharacterized genetic risk underlying many diseases. An efficient approach to characterizing the disease burden of rare variants may be to impute them into existing large datasets. It is well known that the ability to impute a rare variant is dependent both on the array choice and number of individuals in the reference panel carrying that variant, although it is still unclear exactly how well imputation will work for rare variants. Here, we review the additional challenges that arise when imputing rare variants, looking at studies that have been able to impute rare variants, methods behind merging reference panels, approaches for imputing rare variants, and methods for analyzing rare variants.

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation

Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.

Rare ATAD5 missense variants in breast and ovarian cancer patients

ATAD5/ELG1 is a protein crucially involved in replication and maintenance of genome stability. ATAD5 has recently been identified as a genomic risk locus for both breast and ovarian cancer through genome-wide association studies. We aimed to investigate the spectrum of coding ATAD5 germ-line mutations in hospital-based series of patients with triple-negative breast cancer or serous ovarian cancer compared with healthy controls. The ATAD5 coding and adjacent splice site regions were analyzed by targeted next-generation sequencing of DNA samples from 273 cancer patients, including 114 patients with triple-negative breast cancer and 159 patients with serous epithelial ovarian cancer, and from 276 healthy females. Among 42 different variants identified, twenty-two were rare missense substitutions, of which 14 were classified as pathogenic by at least one in silico prediction tool. Three of four novel missense substitutions (p.S354I, p.H974R and p.K1466N) were predicted to be pathogenic and were all identified in ovarian cancer patients. Overall, rare missense variants with predicted pathogenicity tended to be enriched in ovarian cancer patients (14/159) versus controls (11/276) (p = 0.05, 2df). While truncating germ-line variants in ATAD5 were not detected, it remains possible that several rare missense variants contribute to genetic susceptibility toward epithelial ovarian carcinomas.

Rare high-impact disease variants: properties and identifications

Although many genome-wide association studies have been performed, the identification of disease polymorphisms remains important. It is now suspected that many rare disease variants induce the association signal of common variants in linkage disequilibrium (LD). Based on recent development of genetic models, the current study provides explanations of the existence of rare variants with high impacts and common variants with low impacts. Disease variants are neither necessary nor sufficient due to gene–gene or gene–environment interactions. A new method was developed based on theoretical aspects to identify both rare and common disease variants by their genotypes. Common disease variants were identified with relatively small odds ratios and relatively small sample sizes, except for specific situations in which the disease variants were in strong LD with a variant with a higher frequency. Rare disease variants with small impacts were difficult to identify without increasing sample sizes; however, the method was reasonably accurate for rare disease variants with high impacts. For rare variants, dominant variants generally showed better Type II error rates than recessive variants; however, the trend was reversed for common variants. Type II error rates increased in gene regions containing more than two disease variants because the more common variant, rather than both disease variants, was usually identified. The proposed method would be useful for identifying common disease variants with small impacts and rare disease variants with large impacts when disease variants have the same effects on disease presentation.

Brief report: association of CCR1, KLRC4, IL12A-AS1, STAT4, and ERAP1 With Behçet's disease in Iranians

OBJECTIVE

To independently replicate the top findings from 4 published genome-wide association studies (GWAS) of susceptibility genes in Behçet's disease (BD).

METHODS

We tested 14 single-nucleotide polymorphisms (SNPs) in 13 genomic loci (excluding the major histocompatibility complex [MHC], IL10, and IL23R-IL12RB2, which have already been associated with BD in Iranians) for allelic and genotypic associations with BD in 973 patients and 828 controls from Iran and performed meta-analyses of the significantly associated markers.

RESULTS

Six SNPs (in decreasing order of significance, rs7616215 located 38 kb downstream of CCR1, rs2617170 [p.Asn104Ser] in KLRC4, rs17810546 in IL12A-AS1, rs7574070 in STAT4, and rs10050860 [p.Asp575Asn] and rs13154629 in ERAP1) were nominally associated with BD in both allelic association tests (5.05 × 10(-9) ≤ Pallele ≤ 7.55 × 10(-3) ) and sex-adjusted genotypic association tests (6.01 × 10(-9) ≤ adjusted P value ≤ 1.30 × 10(-2) ). For all 6 SNPs tested by meta-analysis (Pmeta ), the association with BD was strengthened, because the direction and magnitude of association were similar across populations (e.g., for rs7574070, odds ratio [OR] for A allele 1.29 [95% confidence interval (95% CI) 1.21-1.37], Pmeta  = 2.34 × 10(-16) ; for rs7616215, OR for C allele 0.70 [95% CI 0.65-0.76], Pmeta  = 1.54 × 10(-19) ; for rs17810546, OR for A allele 0.60 [95% CI 0.52-0.70], Pmeta  = 6.34 × 10(-11) ; for rs2617170, OR for T allele 0.76 [95% CI 0.70-0.81], Pmeta  = 2.75 × 10(-14) ; for rs13154629, OR for TT genotype 2.76 [95% CI 2.01-3.80], Pmeta  = 3.57 × 10(-10) ).

CONCLUSION

This study reinforces the notion that CCR1, KLRC4, IL12A-AS1, STAT4, and ERAP1 are bona fide susceptibility genes for BD, in addition to the MHC, IL10, and IL23R-IL12RB2 loci. Future genetic and functional studies are now warranted to uncover the roles of these genes in the pathogenesis of BD.

Biomarkers for prostate cancer: present challenges and future opportunities

Prostate cancer (PCa) has variable biological potential with multiple treatment options. A more personalized approach, therefore, is needed to better define men at higher risk of developing PCa, discriminate indolent from aggressive disease and improve risk stratification after treatment by predicting the likelihood of progression. This may improve clinical decision-making regarding management, improve selection for active surveillance protocols and minimize morbidity from treatment. Discovery of new biomarkers associated with prostate carcinogenesis present an opportunity to provide patients with novel genetic signatures to better understand their risk of developing PCa and help forecast their clinical course. In this review, we examine the current literature evaluating biomarkers in PCa. We also address current limitations and present several ideas for future studies.

Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle

The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01<MAF<0.05), and 83,856 rare variants (MAF<0.01). We describe a novel method for estimating the proportion of the rare variants that are sequencing errors using parent-progeny duos. We then used mixed model methodology to estimate the proportion of variance captured by these different classes of variants for fat, milk and protein yields, as well as for fertility. Common sequence variants captured 83%, 77%, 76% and 84% of the total genetic variance for fat, milk, and protein yields and fertility, respectively. This was between 2 and 5% more variance than that captured from 600k SNPs on a high density chip, although the difference was not significant. Rare variants captured 3%, 0%, 1% and 14% of the genetic variance for fat, milk and protein yields, and fertility respectively, whereas pedigree explained the remaining amount of genetic variance (none for fertility). The proportion of variation explained by rare variants is likely to be under-estimated due to reduced accuracies of imputation for this class of variants. Using common sequence variants slightly improved accuracy of genomic predictions for fat and milk yield, compared to high density SNP array genotypes. However, including rare variants from transcript regions did not increase the accuracy of genomic predictions. These results suggest that rare variants recover a small percentage of the missing heritability for complex traits, however very large reference sets will be required to exploit this to improve the accuracy of genomic predictions. Our results do suggest the contribution of rare variants to genetic variation may be greater for fitness traits.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Genetic diversity and association mapping of iron and zinc concentrations in chickpea (Cicer arietinum L.)

Chickpea (Cicer arietinum L.) is the world's second most important pulse crop after common bean. Chickpea has historically been an important daily staple in the diet of millions of people, especially in the developing countries. Current chickpea breeding programs have mainly been directed toward high yield, biotic and abiotic stress resilience that has increased global production, but less attention has been directed toward improving micronutrient concentrations in seeds. In an effort to develop micronutrient-dense chickpea lines, a study to examine the variability and to identify SNP alleles associated with seed iron and zinc concentrations was conducted using 94 diverse accessions of chickpea. The results indicated that there is substantial variability present in chickpea germplasm for seed iron and zinc concentrations. In the current set of germplasm, zinc is negatively correlated with grain yield across all locations and years; whereas the negative correlation between iron and grain yield was only significant at the Elrose locality. Eight SNP loci associated with iron and (or) zinc concentrations in chickpea seeds were identified. One SNP located on chromosome 1 (chr1) is associated with both iron and zinc concentrations. On chr4, three SNPs associated with zinc concentration and two SNPs for iron concentration were identified. Two additional SNP loci, one on chr6 and the other on chr7, were also found to be associated with iron and zinc concentrations, respectively. The results show potential opportunity for molecular breeding for improvement of seed iron and zinc concentrations in chickpea.

Genetic Variants Underlying Risk of Intracranial Aneurysms: Insights from a GWAS in Portugal

Subarachnoid hemorrhage (SAH) is a life-threatening event that most frequently leads to severe disability and death. Its most frequent cause is the rupture of a saccular intracranial aneurysm (IA), which is a blood vessel dilation caused by disease or weakening of the vessel wall. Although the genetic contribution to IA is well established, to date no single gene has been unequivocally identified as responsible for IA formation or rupture. We aimed to identify IA susceptibility genes in the Portuguese population through a pool-based multistage genome-wide association study. Replicate pools were allelotyped in triplicate in a discovery dataset (100 IA cases and 92 gender-matched controls) using the Affymetrix Human SNP Array 6.0. Top SNPs (absolute value of the relative allele score difference between cases and controls |RAS_diff|≥13.0%) were selected for technical validation by individual genotyping in the discovery dataset. From the 101 SNPs successfully genotyped, 99 SNPs were nominally associated with IA. Replication of technically validated SNPs was conducted in an independent replication dataset (100 Portuguese IA cases and 407 controls). rs4667622 (between UBR3 and MYO3B), rs6599001 (between SCN11A and WDR48), rs3932338 (214 kilobases downstream of PRDM9), and rs10943471 (96 kilobases upstream of HTR1B) were associated with IA (unadjusted allelic chi-square tests) in the datasets tested (discovery: 6.84E-04≤P≤1.92E-02, replication: 2.66E-04≤P≤2.28E-02, and combined datasets: 6.05E-05≤P≤5.50E-04). Additionally, we confirmed the known association with IA of rs1333040 at the 9p21.3 genomic region, thus validating our dataset. These novel findings in the Portuguese population warrant further replication in additional independent studies, and provide additional candidates to more comprehensively understand IA etiopathogenesis.

Imputation of the rare HOXB13 G84E mutation and cancer risk in a large population-based cohort

An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into large well-phenotyped cohorts with existing genome-wide genotype data using large sequenced referenced panels. The success of this approach hinges on the accuracy of rare variant imputation, which remains controversial. For example, a recent study suggested that one cannot adequately impute the HOXB13 G84E mutation associated with prostate cancer risk (carrier frequency of 0.0034 in European ancestry participants in the 1000 Genomes Project). We show that by utilizing the 1000 Genomes Project data plus an enriched reference panel of mutation carriers we were able to accurately impute the G84E mutation into a large cohort of 83,285 non-Hispanic White participants from the Kaiser Permanente Research Program on Genes, Environment and Health Genetic Epidemiology Research on Adult Health and Aging cohort. Imputation authenticity was confirmed via a novel classification and regression tree method, and then empirically validated analyzing a subset of these subjects plus an additional 1,789 men from Kaiser specifically genotyped for the G84E mutation (r² = 0.57, 95% CI = 0.37−0.77). We then show the value of this approach by using the imputed data to investigate the impact of the G84E mutation on age-specific prostate cancer risk and on risk of fourteen other cancers in the cohort. The age-specific risk of prostate cancer among G84E mutation carriers was higher than among non-carriers. Risk estimates from Kaplan-Meier curves were 36.7% versus 13.6% by age 72, and 64.2% versus 24.2% by age 80, for G84E mutation carriers and non-carriers, respectively (p = 3.4×10⁻¹²). The G84E mutation was also associated with an increase in risk for the fourteen other most common cancers considered collectively (p = 5.8×10⁻⁴) and more so in cases diagnosed with multiple cancer types, both those including and not including prostate cancer, strongly suggesting pleiotropic effects.

An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into existing well-phenotyped cohorts with genome-wide data by using large sequenced reference panels; however, the efficacy of this approach remains controversial. A recent study suggested that it is not possible to impute the rare HOXB13 G84E variant using neighboring SNP markers. We show that by using an enriched reference sequenced sample of 22 mutation carriers, we were able to impute this mutation into a large cohort of 83,285 non-Hispanic White individuals from the Kaiser Permanente Research Program on Genes, Environment, and Health Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. The imputation was confirmed via a novel classification and regression tree method, and then empirically validated by direct mutation genotyping of a subset of 1,673 of these individuals in addition to 1,789 other men from Kaiser. Using the same GERA cohort, we then confirmed that the G84E mutation is associated with increased risk of prostate cancer, and estimated the age-specific risk for carriers of the mutation. Finally, we obtained evidence that the mutation is associated with additional types of cancer in the GERA cohort.

Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations

Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.