Deciphering the genomic architecture of the stickleback brain with a novel multilocus gene‐mapping approach

Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small‐effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine‐spined sticklebacks (Pungitius pungitius) with the aid of novel multilocus QTL‐mapping approaches based on a de‐biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL‐mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single‐locus analyses of an F₂ interpopulation cross with 239 individuals and 15 198, fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multilocus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multilocus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1–6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small‐effect loci. Apart from suggesting moderately heritable (h² ≈ 0.15–0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL‐mapping approach developed here has distinctive advantages over the traditional QTL‐mapping methods in analyses of dense marker panels.     

Genome‐wide association studies based on sequence‐derived genotypes reveal new QTL associated with conformation and performance traits in the Franches–Montagnes horse breed

To identify novel quantitative trait loci (QTL) within horses, we performed genome‐wide association studies (GWAS) based on sequence‐level genotypes for conformation and performance traits in the Franches–Montagnes (FM) horse breed. Sequence‐level genotypes of FM horses were derived by re‐sequencing 30 key founders and imputing 50K data of genotyped horses. In total, we included 1077 FM horses genotyped for ~4 million SNPs and their respective de‐regressed breeding values of the traits in the analysis. Based on this dataset, we identified a total of 14 QTL associated with 18 conformation traits and one performance trait. Therefore, our results suggest that the application of sequence‐derived genotypes increases the power to identify novel QTL which were not identified previously based on 50K SNP chip data.     

Genetic associations with coronary heart disease: Meta-analyses of 12 candidate genetic variants

Aims

The aim of this study was to evaluate the combined contribution of 12 genetic variants to the risk of coronary heart disease (CHD).

Methods

Through a comprehensive literature search for genetic variants involved in the CHD association study, we harvested a total of 10 genes (12 variants) for the current meta-analyses. These genes consisted of GPX1 (rs1050450), PPARD (rs2016520), ALOX15 (rs34210653), SELPLG (rs2228315), FCGR2A (rs1801274), CCL5 (rs2107538), CYP1A1 (rs4646903), TP53 (rs1042522), CX37 (rs1764391), and PECAM1 (rs668, rs12953, and rs1131012).

Results

A total of 45 studies among 23,314 cases and 28,430 controls were retrieved for the meta-analyses of 12 genetic variants. The results showed a significant association between the GPX1 rs1050450 polymorphism and CHD (odd ratio (OR) = 1.61, 95% confidence interval (CI) = 1.25–2.07, P = 0.0002). Other meta-analyses of the rest 11 variants suggested a lack of association with the risk of CHD.

Conclusion

Our results confirmed that GPX1 rs1050450 was associated with susceptibility to CHD in Chinese and Indian populations.     

Association of the variant rs2243421 of human DOC-2/DAB2 interactive protein gene (hDAB2IP) with gastric cancer in the Chinese Han population

Human DOC-2/DAB2 interactive protein (hDAB2IP) gene is a novel member of the Ras GTPase-activating family and has been demonstrated to be a tumor-suppressor gene that inhibits cell survival and proliferation and induces cell apoptosis. It was reported that the expression level of hDAB2IP in gastric cancer tissue was highly correlated with tumor progression, however, whether hDAB2IP genetic variants are associated with the risk of gastric cancer remains yet unknown. In this case–control study, we conducted a genetic analysis for hDAB2IP variants in 311 patients with gastric cancer and 425 controls from the Chinese Han population. We found that the SNP rs2243421 of hDAB2IP gene with the minor allele C significantly revealed strong association with decreased gastric cancer susceptibility (P = 0.007, adjusted odds ratio [OR] = 0.734, 95%CI = 0.586–0.919). Haplotypes rs2243421 and rs10985332 (HaploType: CC, P = 0.012, aOR = 0.760) and haplotypes rs2243421 and rs555996 (HaploType: CG, P = 0.034, aOR = 0.788) represented the decreased risk of gastric cancer, respectively. On the contrary, rs2243421 and rs555996 showed an elevated susceptibility (HaploType: TG, P = 0.010, aOR = 1.320). Our results for the first time provided new insight into susceptibility factors of hDAB2IP gene variants in carcinogenesis of gastric cancer.     

Common variant in FUT2 gene is associated with levels of vitamin B12 in Indian population

Vitamin B₁₂ is an essential micronutrient synthesized by microorganisms. Mammals including humans have evolved ways for transport and absorption of this vitamin. Deficiency of vitamin B₁₂ (either due to low intake or polymorphism in genes involved in absorption and intracellular transport of this vitamin) has been associated with various complex diseases. Genome-wide association studies have recently identified several common single nucleotide polymorphisms (SNPs) in fucosyl transferase 2 gene (FUT2) to be associated with levels of vitamin B₁₂—the strongest association was with a non-synonymous SNP rs602662 in this gene. In the present study, we attempted to replicate the association of this SNP (rs602662) in an Indian population since a significant proportion has been reported to have low levels of vitamin B₁₂ in this population. A total of 1146 individuals were genotyped for this SNP using a single base extension method and association with levels of vitamin B₁₂ was assessed in these individuals. Regression analysis was performed to analyze the association considering various confounding factors like for age, sex, diet, hypertension, diabetes mellitus and coronary artery disease status. We found that the SNP rs602662 was significantly associated with the levels of vitamin B₁₂ (p value < 0.0001). We also found that individuals adhering to a vegetarian diet with GG (homozygous major genotype) have significantly lower levels of vitamin B₁₂ in these individuals. Thus, our study reveals that vegetarian diet along with polymorphism in the FUT2 gene may contribute significantly to the high prevalence of vitamin B₁₂ deficiency in India.     

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.     

A new method for post Genome-Wide Association Study (GWAS) analysis of colorectal cancer in Taiwan

Recently, single nucleotide polymorphisms (SNPs) located in specific loci or genes have been identified associated with susceptibility to colorectal cancer (CRC) in Genome-Wide Association Studies (GWAS). However, in different ethnicities and regions, the genetic variations and the environmental factors can widely vary. Therefore, here we propose a post-GWAS analysis method to investigate the CRC susceptibility SNPs in Taiwan by conducting a replication analysis and bioinformatics analysis. One hundred and forty-four significant SNPs from published GWAS results were collected by a literature survey, and two hundred and eighteen CRC samples and 385 normal samples were collected for post-GWAS analysis. Finally, twenty-six significant SNPs were identified and reported as associated with susceptibility to colorectal cancer, other cancers, obesity, and celiac disease in a previous GWAS study. Functional analysis results of 26 SNPs indicate that most biological processes identified are involved in regulating immune responses and apoptosis. In addition, an efficient prediction model was constructed by applying Jackknife feature selection and ANOVA testing. As compared to another risk prediction model of CRC for European Caucasians population, which performs 0.616 of AUC by using 54 SNPs, the proposed model shows good performance in predicting CRC risk within the Taiwanese population, i.e., 0.724 AUC by using 16 SNPs. We believe that the proposed risk prediction model is highly promising for predicting CRC risk within the Taiwanese population. In addition, the functional analysis results could be helpful to explore the potential associated regulatory mechanisms that may be involved in CRC development.