Quantitative methods for evaluating association between multiple rare genetic variants and complex human traits

First, we propose two methods for aggregation of rare variants in data from Genome-wide Association Studies (GWAS), a weighted haplotype-based approach and an imputation-based approach, to test for the effect of rare variants with GWAS data. Both methods can incorporate external sequencing data when available. Our methods clearly show enhanced statistical power over existing methods for a wide range of population-attributable risk, percentage of disease-contributing rare variants, and proportion of rare alleles working in different directions. We thus demonstrate that the evaluation of rare variants with GWAS data is possible, particularly when public sequencing data are incorporated. Second, we present a systematic evaluation of multiple weighting schemes through a series of simulations intended to mimic large sequencing studies of a quantitative trait. We evaluate existing phenotype-independent and phenotype-dependent methods, as well as weights estimated by penalized regression. We find that the difference in power between phenotype-dependent schemes is negligible when high-quality functional annotations are available. When functional annotations are unavailable or incomplete, all methods lose power; however, the variable selection methods outperform the others at a cost of increased computational time. In the absence of highly accurate annotation, we recommend variable selection methods (which can be viewed as statistical annotation) on top of regions implicated by a phenotype-independent weighting scheme. Finally, we propose a method to apply the Sequence Kernel Association Test (SKAT), a similarity-based approach for rare variant association, to data from admixed populations by first estimating local ancestry for each variant. In simulations, we find that when the true causal alleles are causal only from only one ancestral population, our proposed approaches show a marked improvement in power over the original SKAT method. In real data, our results support the previously reported European-specific association and illustrate the increased statistical power of the proposed methods to find such associations.Doctor of Philosoph

Gene Expression in Peripheral Blood Leukocytes in Monozygotic Twins Discordant for Chronic Fatigue: No Evidence of a Biomarker

Background: Chronic fatiguing illness remains a poorly understood syndrome of unknown pathogenesis. We attempted to identify biomarkers for chronic fatiguing illness using microarrays to query the transcriptome in peripheral blood leukocytes. Methods: Cases were 44 individuals who were clinically evaluated and found to meet standard international criteria for chronic fatigue syndrome or idiopathic chronic fatigue, and controls were their monozygotic co-twins who were clinically evaluated and never had even one month of impairing fatigue. Biological sampling conditions were standardized and RNA stabilizing media were used. These methodological features provide rigorous control for bias resulting from case-control mismatched ancestry and experimental error. Individual gene expression profiles were assessed using Affymetrix Human Genome U133 Plus 2.0 arrays. Findings: There were no significant differences in gene expression for any transcript. Conclusions: Contrary to our expectations, we were unable to identify a biomarker for chronic fatiguing illness in the transcriptome of peripheral blood leukocytes suggesting that positive findings in prior studies may have resulted fro

The Value of Statistical or Bioinformatics Annotation for Rare Variant Association with Quantitative Trait

In the past few years, a plethora of methods for rare variant association with phenotype have been proposed. These methods aggregate information from multiple rare variants across genomic region(s), but there is little consensus as to which method is most effective. The weighting scheme adopted when aggregating information across variants is one of the primary determinants of effectiveness. Here we present a systematic evaluation of multiple weighting schemes through a series of simulations intended to mimic large sequencing studies of a quantitative trait. We evaluate existing phenotype-independent and -dependent methods, as well as weights estimated by penalized regression approaches including Lasso, Elastic Net and SCAD. We find that the difference in power between phenotype-dependent schemes is negligible when high quality functional annotations are available. When functional annotations are unavailable or incomplete, all methods suffer from power loss; however, the variable selection methods outperform the others at the cost of increased computational time. Therefore, in the absence of good annotation, we recommend variable selection methods (which can be viewed as “statistical annotation”) on top regions implicated by a phenotype independent weighting scheme. Further, once a region is implicated, variable selection can help to identify potential causal SNPs for biological validation. These findings are supported by an analysis of a high coverage targeted sequencing study of 1898 individuals

A geometric bound on F-term inflation

We discuss a general bound on the possibility to realise inflation in any minimal supergravity with F-terms. The derivation crucially depends on the sGoldstini, the scalar field directions that are singled out by spontaneous supersymmetry breaking. The resulting bound involves both slow-roll parameters and the geometry of the K\"ahler manifold of the chiral scalars. We analyse the inflationary implications of this bound, and in particular discuss to what extent the requirements of single field and slow-roll can both be met in F-term inflation.Comment: 14 pages, improved analysis, references added, matches published versio

Likelihood-based complex trait association testing for arbitrary depth sequencing data

Summary: In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ∼60% when the causal variant is of low frequency (MAF < 0.01)

The effects of a family-centered psychosocial-based nutrition intervention in patients with advanced cancer: the PiCNIC2 pilot randomised controlled trial

BACKGROUND: Malnutrition in advanced cancer patients is common but limited and inconclusive data exists on the effectiveness of nutrition interventions. Feasibility and acceptability of a novel family-based nutritional psychosocial intervention were established recently. The aims of this present study were to assess the feasibility of undertaking a randomised controlled trial of the latter intervention, to pilot test outcome measures and to explore preliminary outcomes.METHODS: Pilot randomised controlled trial recruiting advanced cancer patients and family caregivers in Australia and Hong Kong. Participants were randomised and assigned to one of two groups, either a family-centered nutritional intervention or the control group receiving usual care only. The intervention provided 2-3 h of direct dietitian contact time with patients and family members over a 4-6-week period. During the intervention, issues with nutrition impact symptoms and food or eating-related psychosocial concerns were addressed through nutrition counselling, with a focus on improving nutrition-related communication between the dyads and setting nutritional goals. Feasibility assessment included recruitment, consent rate, retention rate, and acceptability of assessment tools. Validated nutritional and quality of life self-reported measures were used to collect patient and caregiver outcome data, including the 3-day food diary, the Patient-Generated Subjective Global Assessment Short Form, the Functional Assessment Anorexia/Cachexia scale, Eating-related Distress or Enjoyment, and measures of self-efficacy, carers' distress, anxiety and depression.RESULTS: Seventy-four patients and 54 family caregivers participated in the study. Recruitment was challenging, and for every patient agreeing to participate, 14-31 patients had to be screened. The consent rate was 44% in patients and 55% in caregivers. Only half the participants completed the trial's final assessment. The data showed promise for some patient outcomes in the intervention group, particularly with improvements in eating-related distress (p = 0.046 in the Australian data; p = 0.07 in the Hong Kong data), eating-related enjoyment (p = 0.024, Hong Kong data) and quality of life (p = 0.045, Australian data). Energy and protein intake also increased in a clinically meaningful way. Caregiver data on eating-related distress, anxiety, depression and caregiving burden, however, showed little or no change.CONCLUSIONS: Despite challenges with participant recruitment, the intervention demonstrates good potential to have positive effects on patients' nutritional status and eating-related distress. The results of this trial warrant a larger and fully-powered trial to ascertain the effectiveness of this intervention.TRIAL REGISTRATION: The trial was registered with the Australian &amp; New Zealand Clinical Trials Registry, registration number ACTRN12618001352291 .</p

Inertial Sensor-Based Gait and Attractor Analysis as Clinical Measurement Tool: Functionality and Sensitivity in Healthy Subjects and Patients With Symptomatic Lumbar Spinal Stenosis

Objective: To determine if the attractor for acceleration gait data is similar among healthy persons defining a reference attractor; if exercise-induced changes in the attractor in patients with symptomatic lumbar spinal stenosis (sLSS) are greater than in healthy persons; and if the exercise-induced changes in the attractor are affected by surgical treatment.Methods: Twenty-four healthy subjects and 19 patients with sLSS completed a 6-min walk test (6MWT) on a 30-m walkway. Gait data were collected using inertial sensors (RehaGait®;) capturing 3-dimensional foot accelerations. Attractor analysis was used to quantify changes in low-pass filtered acceleration pattern (δM) and variability (δD) and their combination as attractor-based index (δF = δM* δD) between the first and last 30 m of walking. These parameters were compared within healthy persons and patients with sLSS (preoperatively and 10 weeks and 12 months postoperatively) and between healthy persons and patients with sLSS. The variability in the attractor pattern among healthy persons was assessed as the standard deviation of the individual attractors.Results: The attractor pattern differed greatly among healthy persons. The variability in the attractor between subjects was about three times higher than the variability around the attractor within subject. The change in gait pattern and variability during the 6MWT did not differ significantly in patients with sLSS between baseline and follow-up but differed significantly compared to healthy persons.Discussion: The attractor for acceleration data varied largely among healthy subjects, and hence a reference attractor could not be generated. Moreover, the change in the attractor and its variability during the 6MWT differed between patients and elderly healthy persons but not between repeated assessments. Hence, the attractor based on low-pass filtered signals as used in this study may reflect pathology specific differences in gait characteristics but does not appear to be sufficiently sensitive to serve as outcome parameter of decompression surgery in patients with sLSS

Biodiversity increases and decreases ecosystem stability

International audienc

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals

Risk attitudes and personality traits predict perceptions of benefits and risks for medicinal products: a field study of European medical assessors

Background: Risk attitudes and personality traits are known predictors of decision making among laypersons, but very little is known of their influence among experts participating in organizational decision making. Methods: Seventy-five European medical assessors were assessed in a field study using the Domain Specific Risk Taking scale and the Big Five Inventory scale. Assessors rated the risks and benefits for a mock “clinical dossier” specific to their area of expertise, and ordinal regression models were used to assess the odds of risk attitude or personality traits in predicting either the benefit or the risk ratings. Results: An increase in the “conscientiousness” score predicted an increase in the perception of the drug’s benefit, and male assessors gave higher scores for the drug’s benefit ratings than did female assessors. Extraverted assessors saw fewer risks, and assessors with a perceived neutral-averse or averse risk profile saw greater risks. Conclusions: Medical assessors perceive the benefits and risks of medicines via a complex interplay of the medical situation, their personality traits and even their gender. Further research in this area is needed to determine how these potential biases are managed within the regulatory setting