Nathan Morris

Codon optimality is a major determinant of mRNA stability

mRNA degradation represents a critical regulated step in gene expression. Although the major pathways in turnover have been identified, accounting for disparate half-lives has been elusive. We show that codon optimality is one feature that contributes greatly to mRNA stability. Genome-wide RNA decay analysis revealed that stable mRNAs are enriched in codons designated optimal, whereas unstable mRNAs contain predominately non-optimal codons. Substitution of optimal codons with synonymous, non-optimal codons results in dramatic mRNA destabilization, whereas the converse substitution significantly increases stability. Further, we demonstrate that codon optimality impacts ribosome translocation, connecting the processes of translation elongation and decay through codon optimality. Finally, we show that optimal codon content accounts for the similar stabilities observed in mRNAs encoding proteins with coordinated physiological function. This work demonstrates that codon optimization exists as a mechanism to finely tune levels of mRNAs and, ultimately, proteins.

Choosing an optimal method to combine P-values.

Fisher (1925) was the first to suggest a method of combining the p-values obtained from several statistics and many other methods have been proposed since then. However, there is no agreement about what is the best method. Motivated by a situation that now often arises in genetic epidemiology, we consider the problem when it is possible to define a simple alternative hypothesis of interest for which the expected effect size of each test statistic is known and we determine the most powerful test for this simple alternative hypothesis. Based on the proposed method, we show that information about the effect sizes can be used to obtain the best weights for Liptaks method of combining p-values. We present extensive simulation results comparing methods of combining p-values and illustrate for a real example in genetic epidemiology how information about effect sizes can be deduced.

Genome-wide association study identifies three novel loci in Fuchs endothelial corneal dystrophy

The structure of the cornea is vital to its transparency, and dystrophies that disrupt corneal organization are highly heritable. To understand the genetic aetiology of Fuchs endothelial corneal dystrophy (FECD), the most prevalent corneal disorder requiring transplantation, we conducted a genome-wide association study (GWAS) on 1,404 FECD cases and 2,564 controls of European ancestry, followed by replication and meta-analysis, for a total of 2,075 cases and 3,342 controls. We identify three novel loci meeting genome-wide significance (P<5 × 10−8): KANK4 rs79742895, LAMC1 rs3768617 and LINC00970/ATP1B1 rs1200114. We also observe an overwhelming effect of the established TCF4 locus. Interestingly, we detect differential sex-specific association at LAMC1, with greater risk in women, and TCF4, with greater risk in men. Combining GWAS results with biological evidence we expand the knowledge of common FECD loci from one to four, and provide a deeper understanding of the underlying pathogenic basis of FECD.

Single-Marker and Two-Marker Association Tests for Unphased Case-Control Genotype Data, with a Power Comparison

In case‐control single nucleotide polymorphism (SNP) data, the allele frequency, Hardy Weinberg Disequilibrium, and linkage disequilibrium (LD) contrast tests are three distinct sources of information about genetic association. While all three tests are typically developed in a retrospective context, we show that prospective logistic regression models may be developed that correspond conceptually to the retrospective tests. This approach provides a flexible framework for conducting a systematic series of association analyses using unphased genotype data and any number of covariates. For a single stage study, two single‐marker tests and four two‐marker tests are discussed. The true association models are derived and they allow us to understand why a model with only a linear term will generally fit well for a SNP in weak LD with a causal SNP, whatever the disease model, but not for a SNP in high LD with a non‐additive disease SNP. We investigate the power of the association tests using real LD parameters from chromosome 11 in the HapMap CEU population data. Among the single‐marker tests, the allelic test has on average the most power in the case of an additive disease, but for dominant, recessive, and heterozygote disadvantage diseases, the genotypic test has the most power. Among the four two‐marker tests, the Allelic‐LD contrast test, which incorporates linear terms for two markers and their interaction term, provides the most reliable power overall for the cases studied. Therefore, our result supports incorporating an interaction term as well as linear terms in multi‐marker tests. Genet. Epidemiol. 34:67–77, 2010.

How meaningful are heritability estimates of liability

It is commonly acknowledged that estimates of heritability from classical twin studies have many potential shortcomings. Despite this, in the post-GWAS era, these heritability estimates have come to be a continual source of interest and controversy. While the heritability estimates of a quantitative trait are subject to a number of biases, in this article we will argue that the standard statistical approach to estimating the heritability of a binary trait relies on some additional untestable assumptions which, if violated, can lead to badly biased estimates. The ACE liability threshold model assumes at its heart that each individual has an underlying liability or propensity to acquire the binary trait (e.g., disease), and that this unobservable liability is multivariate normally distributed. We investigated a number of different scenarios violating this assumption such as the existence of a single causal diallelic gene and the existence of a dichotomous exposure. For each scenario, we found that substantial asymptotic biases can occur, which no increase in sample size can remove. Asymptotic biases as much as four times larger than the true value were observed, and numerous cases also showed large negative biases. Additionally, regions of low bias occurred for specific parameter combinations. Using simulations, we also investigated the situation where all of the assumptions of the ACE liability model are met. We found that commonly used sample sizes can lead to biased heritability estimates. Thus, even if we are willing to accept the meaningfulness of the liability construct, heritability estimates under the ACE liability threshold model may not accurately reflect the heritability of this construct. The points made in this paper should be kept in mind when considering the meaningfulness of a reported heritability estimate for any specific disease.

A variance component based multi-marker association test using family and unrelated data.

BackgroundIncorporating family data in genetic association studies has become increasingly appreciated, especially for its potential value in testing rare variants. We introduce here a variance-component based association test that can test multiple common or rare variants jointly using both family and unrelated samples.ResultsThe proposed approach implemented in our R package aggregates or collapses the information across a region based on genetic similarity instead of genotype scores, which avoids the power loss when the effects are in different directions or have different association strengths. The method is also able to effectively leverage the LD information in a region and it can produce a test statistic with an adaptively estimated number of degrees of freedom. Our method can readily allow for the adjustment of non-genetic contributions to the familial similarity, as well as multiple covariates.ConclusionsWe demonstrate through simulations that the proposed method achieves good performance in terms of Type I error control and statistical power. The method is implemented in the R package “fassoc”, which provides a useful tool for data analysis and exploration.

Induction of KIAA1199/CEMIP is associated with colon cancer phenotype and poor patient survival

Genes induced in colon cancer provide novel candidate biomarkers of tumor phenotype and aggressiveness. We originally identified KIAA1199 (now officially called CEMIP) as a transcript highly induced in colon cancer: initially designating the transcript as Colon Cancer Secreted Protein 1. We molecularly characterized CEMIP expression both at the mRNA and protein level and found it is a secreted protein induced an average of 54-fold in colon cancer. Knockout of CEMIPreduced the ability of human colon cancer cells to form xenograft tumors in athymic mice. Tumors that did grow had increased deposition of hyaluronan, linking CEMIP participation in hyaluronan degradation to the modulation of tumor phenotype. We find CEMIP mRNA overexpression correlates with poorer patient survival. In stage III only (n = 31) or in combined stage II plus stage III colon cancer cases (n = 73), 5-year overall survival was significantly better (p = 0.004 and p = 0.0003, respectively) among patients with low CEMIP expressing tumors than those with high CEMIP expressing tumors. These results demonstrate that CEMIP directly facilitates colon tumor growth, and high CEMIP expression correlates with poor outcome in stage III and in stages II+III combined cohorts. We present CEMIP as a candidate prognostic marker for colon cancer and a potential therapeutic target.

IV-treated pulmonary exacerbations in the prior year: An important independent risk factor for future pulmonary exacerbation in cystic fibrosis

BACKGROUND Single-center analyses have suggested that the number of CF pulmonary exacerbations (PEx) treated with intravenous antibiotics an individual has experienced in the prior year is significantly associated with their future PEx hazard. METHODS We studied Prior-year PEx association with future PEx hazard by Cox proportional hazards regression among CF Foundation Patient Registry patients who experienced PEx after Jan 1, 2010. RESULTS Among 13,579 patients, those with 1, 2, 3, or ≥4 Prior-year PEx treated with intravenous antibiotics were at 1.8, 2.9, 4.8, and 8.7 higher PEx hazard vs those without (P<.0001). Adjustment with significant demographic and clinical covariates (univariate P≤.0001) reduced Prior-year PEx hazard ratios to 1.6, 2.4, 3.6, and 6.0 (P<.0001). No other covariates had adjusted hazard ratios of >1.7. CONCLUSIONS Prior-year PEx strongly associate with future PEx hazard and should be accounted for in prospective trials where treatment-associated change in PEx hazard is an efficacy outcome.

What Is the Significance of Difference in Phenotypic Variability across SNP Genotypes

We studied the general problem of interpreting and detecting differences in phenotypic variability among the genotypes at a locus, from both a biological and a statistical point of view. The scales on which we measure interval-scale quantitative traits are man-made and have little intrinsic biological relevance. Before claiming a biological interpretation for genotype differences in variance, we should be sure that no monotonic transformation of the data can reduce or eliminate these differences. We show theoretically that for an autosomal diallelic SNP, when the three corresponding means are distinct so that the variance can be expressed as a quadratic function of the mean, there implicitly exists a transformation that will tend to equalize the three variances; we also demonstrate how to find a transformation that will do this. We investigate the validity of Bartletts test, Boxs modification of it, and a modified Levenes test to test for differences in variances when normality does not hold. We find that, although they may detect differences in variability, these tests do not necessarily detect differences in variance. The same is true for permutation tests that use these three statistics.

Disentangling Multiple Sclerosis and depression: an adjusted depression screening score for patient-centered care

Screening for depression can be challenging in Multiple Sclerosis (MS) patients due to the overlap of depressive symptoms with other symptoms, such as fatigue, cognitive impairment and functional impairment, for MS patients. The aim of this study was to understand these overlapping symptoms and subsequently develop an adjusted depression screening tool for better clinical assessment of depressive symptoms in MS patients. We evaluated 3,507 MS patients with a self-reported depression screening (PHQ-9) score using a multiple indicator multiple cause modeling approach. Our models showed significant differential item functioning effects denoting significant overlap of depressive symptoms with all MS symptoms under study and good model fit. The magnitude of the overlap was especially large for fatigue. Adjusted depression screening scales were formed based on factor scores and loadings that will allow clinicians to understand the depressive symptoms separate from other symptoms for MS patients for improved patient care.