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Although emerging sequencing technologies can characterize all genetic variants, the cost is still high. Illumina released the HumanOmni5M-4v1 (Omni5) genotype array with ~4.3M assayed SNPs, a much denser array compared with other available arrays. The Omni5 balances both cost and array density. In this article, we illustrate the power of Omni5 to detect genetic associations. The Omni5 includes variants with a wide range of minor allele frequencies down to

Background Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. Results When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth’s logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. Conclusions We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth’s logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1498-y) contains supplementary material, which is available to authorized users.

We carried out a genome-wide association study of genetic predictors of anti-cyclic citrullinated peptide antibody (anti-CCP) level in 531 self-reported non-Hispanic Caucasian Rheumatoid Arthritis (RA) patients enrolled in the Brigham Rheumatoid Arthritis Sequential Study (BRASS). For replication, we then analyzed 289 single nucleotide polymorphisms (SNPs) with P0.001 in BRASS in an independent population of 849 RA patients from the North American Rheumatoid Arthritis Consortium (NARAC). BRASS and NARAC samples were genotyped using the Affymetrix 100K and Illumina 550K platforms respectively. Association between SNPs and anti-CCP titer was tested using general linear models. The five most significant SNPs from BRASS all were within the major histocompatibility complex (MHC) region (Por = 3.5 x 10(-6)). After controlling for the human leukocyte antigen shared epitope (HLA-SE), the top SNPs still yielded P values0.0002. In NARAC, a single SNP from the MHC region near BTNL2 and HLA-DRA, rs1980493 (r(2) = 0.85 with the top five SNPs from BRASS), was associated significantly with CCP titer (P = 6.1 x 10(-5)) even after adjustment for the HLA-SE (P = 0.0002). The top SNPs found in BRASS and NARAC had r(2) = 0.46 and 0.64, respectively, to HLA-DRB1 DR3 alleles. These results confirm that the most significant genome region affecting anti-CCP titers in RA is the MHC region. We identified a SNP in moderate linkage disequilibrium (LD) with HLA-DR3, which may influence anti-CCP titer independently of the HLA-SE.

A mutation in DFNA5 leads to a type of hearing loss that closely resembles the frequently observed age-related hearing impairment (ARHI). The hearing loss is sensorineural, progressive and starts at the high frequencies. As DFNA5 was considered an excellent candidate ARHI susceptibility gene, we performed linkage analysis to a quantitive measure of high frequency hearing loss. However, no significant linkage between ARHI and microsatellite markers from the DFNA5 region could be detected. Subsequently, the DFNA5 coding region was analysed for single nucleotide polymorphisms (SNPs). Two SNPs leading to amino-acid substitutions (P142H and V207M) were selected for further analysis. Using these SNPs, an association study based on a collection of random individuals, and a case-control association study were performed. No significant differences in genotypes between good hearing and hearing impaired individuals could be detected in either study design. We conclude that there exists no strong association between DFNA5 and ARHI.

Waardenburg syndrome (WS) type 1 is an autosomal dominant disorder characterized by sensorineural hearing loss, pigmentary abnormalities of the eye, hair, and skin, and dystopia canthorum. The phenotype is variable and affected individuals may exhibit only one or a combination of several of the associated features. To assess the relationship between phenotype and gene defect, clinical and genotype data on 48 families (271 WS individuals) collected by members of the Waardenburg Consortium were pooled. Forty-two unique mutations in the PAX3 gene, previously identified in these families, were grouped in five mutation categories: amino acid (AA) substitution in the paired domain, AA substitution in the homeodomain, deletion of the Ser-Thr-Pro-rich region, deletion of the homeodomain and the Ser-Thr-Pro-rich region, and deletion of the entire gene. These mutation classes are based on the structure of the PAX3 gene and were chosen to group mutations predicted to have similar defects in the gene product. Association between mutation class and the presence of hearing loss, eye pigment abnormality, skin hypopigmentation, or white forelock was evaluated using generalized estimating equations, which allowed for incorporation of a correlation structure that accounts for potential similarity among members of the same family. Odds for the presence of eye pigment abnormality, white forelock, and skin hypopigmentation were 2, 8, and 5 times greater, respectively, for individuals with deletions of the homeodomain and the Pro-Ser-Thr-rich region compared to individuals with an AA substitution in the homeodomain. Odds ratios that differ significantly from 1.0 for these traits may indicate that the gene products resulting from different classes of mutations act differently in the expression of WS. Although a suggestive association was detected for hearing loss with an odds ratio of 2.6 for AA substitution in the paired domain compared with AA substitution in the homeodomain, this odds ratio did not differ significantly from 1.0.

The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies.Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests.The participants were white of European descent and from the FHS Original and Offspring Cohorts (examination 1 Offspring mean age 32 +/- 9 years, 54% women). This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequencyor + 10%, genotype call rateor = 80%, Hardy-Weinberg equilibrium p-valueor = 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication.

Background We explored three approaches to heritability and linkage analyses of longitudinal total cholesterol levels (CHOL) in the Genetic Analysis Workshop 13 simulated data without knowing the answers. The first two were univariate approaches and used 1) baseline measure at exam one or 2) summary measures such as mean and slope from multiple exams. The third method was a multivariate approach that directly models multiple measurements on a subject. A variance components model (SOLAR) was employed in the univariate approaches. A mixed regression model with polynomials was employed in the multivariate approach and implemented in SAS/IML. Results Using the baseline measure at exam 1, we detected all baseline or slope genes contributing a substantial amount (0.08) of variance (LOD > 3). Compared to the baseline measure, the mean measures yielded slightly higher LOD at the slope genes, and a lower LOD at the baseline genes. The slope measure produced a somewhat lower LOD for the slope gene than did the mean measure. Descriptive information on the pattern of changes in gene effects with age was estimated for three linked loci by the third approach. Conclusion We found simple univariate methods may be effective to detect genes affecting longitudinal phenotypes but may not fully reveal temporal trends in gene effects. The relative efficiency of the univariate methods to detect genes depends heavily on the underlying model. Compared with the univariate approaches, the multivariate approach provided more information on temporal trends in gene effects at the cost of more complicated modelling and more intense computations.