Similarity-based multimarker association tests for continuous traits

Associations between single nucleotide polymorphisms in 33 candidate genes and meat quality traits in commercial pigs

This study aimed to evaluate the effects of single nucleotide polymorphisms (SNPs) in candidate genes for meat quality using a custom 96-SNP panel (Illumina Vera Code GoldenGate Assay) on 15 traits collected from 400 commercial pigs. Meat quality measurements included muscle pH, color (L*, a* and b*), drip loss, cooking loss, peak shear force and six sensory traits including appearance (outside and inside), tenderness, juiciness, flavor and overall liking as well as carcass weight and probe yield. Thirty-five SNPs with minor allele frequencies > 0.10 remained for the multimarker association using the GLM procedure of sas 9.2. Results showed that 20 SNPs were significantly associated with at least one of the traits with either additive or dominance or both effects (P < 0.05). Among these significant SNPs, five of them in ADIPOQ, FTO, TNF, LEPR and AMPD1 had an effect on more than three traits simultaneously; those in MC4R, CAST, DGAT1 and MYF6 had an effect on two traits, while the others were associated with one trait. The results suggest that these markers could be incorporated into commercial pigs for marker-assisted selection and breeding programs for carcass and meat quality trait improvement.

Genetic association analysis of 30 genes related to obesity in a European American population

OBJECTIVE

Obesity, which is frequently associated with diabetes, hypertension and cardiovascular diseases, is primarily the result of a net excess of caloric intake over energy expenditure. Human obesity is highly heritable, but the specific genes mediating susceptibility in non-syndromic obesity remain unclear. We tested candidate genes in pathways related to food intake and energy expenditure for association with body mass index (BMI).

METHODS

We reanalyzed 355 common genetic variants of 30 candidate genes in seven molecular pathways related to obesity in 1982 unrelated European Americans from the New York Cancer Project. Data were analyzed by using a Bayesian hierarchical generalized linear model. The BMIs were log-transformed and then adjusted for covariates, including age, age(2), gender and diabetes status. The single-nucleotide polymorphisms (SNPs) were modeled as additive effects.

RESULTS

With the stipulated adjustments, nine SNPs in eight genes were significantly associated with BMI: ghrelin (GHRL; rs35683), agouti-related peptide (AGRP; rs5030980), carboxypeptidase E (CPE; rs1946816 and rs4481204), glucagon-like peptide-1 receptor (GLP1R; rs2268641), serotonin receptors (HTR2A; rs912127), neuropeptide Y receptor (NPY5R;Y5R1c52), suppressor of cytokine signaling 3 (SOCS3; rs4969170) and signal transducer and activator of transcription 3 (STAT3; rs4796793). We also found a gender-by-SNP interaction (rs1745837 in HTR2A), which indicated that variants in the gene HTR2A had a stronger association with BMI in males. In addition, NPY1R was detected as having a significant gene effect even though none of the SNPs in this gene was significant.

CONCLUSION

Variations in genes AGRP, CPE, GHRL, GLP1R, HTR2A, NPY1R, NPY5R, SOCS3 and STAT3 showed modest associations with BMI in European Americans. The pathways in which these genes participate regulate energy intake, and thus these associations are mechanistically plausible in this context.

Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants

For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.

Haplotype-based methods for detecting uncommon causal variants with common SNPs

Detecting uncommon causal variants (minor allele frequency [MAF] < 5%) is difficult with commercial single-nucleotide polymorphism (SNP) arrays that are designed to capture common variants (MAF > 5%). Haplotypes can provide insights into underlying linkage disequilibrium (LD) structure and can tag uncommon variants that are not well tagged by common variants. In this work, we propose a wei-SIMc-matching test that inversely weights haplotype similarities with the estimated standard deviation of haplotype counts to boost the power of similarity-based approaches for detecting uncommon causal variants. We then compare the power of the wei-SIMc-matching test with that of several popular haplotype-based tests, including four other similarity-based tests, a global score test for haplotypes (global), a test based on the maximum score statistic over all haplotypes (max), and two newly proposed haplotype-based tests for rare variant detection. With systematic simulations under a wide range of LD patterns, the results show that wei-SIMc-matching and global are the two most powerful tests. Among these two tests, wei-SIMc-matching has reliable asymptotic P-values, whereas global needs permutations to obtain reliable P-values when the frequencies of some haplotype categories are low or when the trait is skewed. Therefore, we recommend wei-SIMc-matching for detecting uncommon causal variants with surrounding common SNPs, in light of its power and computational feasibility.