Effects of sample size on estimates of population growth rates calculated with matrix models

Background

Matrix models are widely used to study the dynamics and demography of populations. An important but overlooked issue is how the number of individuals sampled influences estimates of the population growth rate (λ) calculated with matrix models. Even unbiased estimates of vital rates do not ensure unbiased estimates of λ–Jensen''s Inequality implies that even when the estimates of the vital rates are accurate, small sample sizes lead to biased estimates of λ due to increased sampling variance. We investigated if sampling variability and the distribution of sampling effort among size classes lead to biases in estimates of λ.

Methodology/Principal Findings

Using data from a long-term field study of plant demography, we simulated the effects of sampling variance by drawing vital rates and calculating λ for increasingly larger populations drawn from a total population of 3842 plants. We then compared these estimates of λ with those based on the entire population and calculated the resulting bias. Finally, we conducted a review of the literature to determine the sample sizes typically used when parameterizing matrix models used to study plant demography.

Conclusions/Significance

We found significant bias at small sample sizes when survival was low (survival = 0.5), and that sampling with a more-realistic inverse J-shaped population structure exacerbated this bias. However our simulations also demonstrate that these biases rapidly become negligible with increasing sample sizes or as survival increases. For many of the sample sizes used in demographic studies, matrix models are probably robust to the biases resulting from sampling variance of vital rates. However, this conclusion may depend on the structure of populations or the distribution of sampling effort in ways that are unexplored. We suggest more intensive sampling of populations when individual survival is low and greater sampling of stages with high elasticities.     

Effects of plant size and weather on the flowering phenology of the organ pipe cactus (Stenocereus thurberi)

Background and Aims

Flowering phenology is a critical life-history trait that influences reproductive success. It has been shown that genetic, climatic and other factors such as plant size affect the timing of flowering and its duration. The spatial and temporal variation in the reproductive phenology of the columnar cactus Stenocereus thurberi and its association with plant size and environmental cues was studied.

Methods

Flowering was monitored during 3 years in three populations of S. thurberi along a latitudinal gradient. Plant size was related to phenological parameters. The actual and past weather were used for each site and year to investigate the environmental correlates of flowering.

Key Results

There was significant variation in the timing of flowering within and among populations. Flowering lasted 4 months in the southern population and only 2 months in the northern population. A single flowering peak was evident in each population, but ocurred at different times. Large plants produced more flowers, and bloomed earlier and for a longer period than small plants. Population synchrony increased as the mean duration of flowering per individual decreased. The onset of flowering is primarily related to the variance in winter minimum temperatures and the duration to the autumn–winter mean maximum temperature, whereas spring mean maximum temperature is best correlated with synchrony.

Conclusions

Plant size affects individual plant fecundity as well as flowering time. Thus the population structure strongly affects flowering phenology. Indications of clinal variation in the timing of flowering and reproductive effort suggest selection pressures related to the arrival of migrating pollinators, climate and resource economy in a desert environment. These pressures are likely to be relaxed in populations where individual plants can attain large sizes.Key words: Flowering phenology, optimal timing, plant size, Sonoran Desert, Stenocereus thurberi, temperature     

Generalized Linear Mixed Models for Binary Data: Are Matching Results from Penalized Quasi-Likelihood and Numerical Integration Less Biased?

Background

Over time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching results from PQL and QUAD indicate less bias in estimated regression coefficients and variance parameters via simulation.

Methods

We performed a simulation study in which we varied the size of the data set, probability of the outcome, variance of the random effect, number of clusters and number of subjects per cluster, etc. We estimated bias in the regression coefficients, odds ratios and variance parameters as estimated via PQL and QUAD. We ascertained if similarity of estimated regression coefficients, odds ratios and variance parameters predicted less bias.

Results

Overall, we found that the absolute percent bias of the odds ratio estimated via PQL or QUAD increased as the PQL- and QUAD-estimated odds ratios became more discrepant, though results varied markedly depending on the characteristics of the dataset

Conclusions

Given how markedly results varied depending on data set characteristics, specifying a rule above which indicated biased results proved impossible.This work suggests that comparing results from generalized linear mixed models estimated via PQL and QUAD is a worthwhile exercise for regression coefficients and variance components obtained via QUAD, in situations where PQL is known to give reasonable results.     

Area disease estimation based on sentinel hospital records

Background

Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased.

Methods and Findings

A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospital''s records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators.

Conclusions

The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Kriging''s inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimator''s limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.     

Implementation of Web-Based Respondent-Driven Sampling among Men Who Have Sex with Men in Vietnam

Objective

Lack of representative data about hidden groups, like men who have sex with men (MSM), hinders an evidence-based response to the HIV epidemics. Respondent-driven sampling (RDS) was developed to overcome sampling challenges in studies of populations like MSM for which sampling frames are absent. Internet-based RDS (webRDS) can potentially circumvent limitations of the original RDS method. We aimed to implement and evaluate webRDS among a hidden population.

Methods and Design

This cross-sectional study took place 18 February to 12 April, 2011 among MSM in Vietnam. Inclusion criteria were men, aged 18 and above, who had ever had sex with another man and were living in Vietnam. Participants were invited by an MSM friend, logged in, and answered a survey. Participants could recruit up to four MSM friends. We evaluated the system by its success in generating sustained recruitment and the degree to which the sample compositions stabilized with increasing sample size.

Results

Twenty starting participants generated 676 participants over 24 recruitment waves. Analyses did not show evidence of bias due to ineligible participation. Estimated mean age was 22 years and 82% came from the two large metropolitan areas. 32 out of 63 provinces were represented. The median number of sexual partners during the last six months was two. The sample composition stabilized well for 16 out of 17 variables.

Conclusion

Results indicate that webRDS could be implemented at a low cost among Internet-using MSM in Vietnam. WebRDS may be a promising method for sampling of Internet-using MSM and other hidden groups.     

Breeding experience and the heritability of female mate choice in collared flycatchers

Background

Heritability in mate preferences is assumed by models of sexual selection, and preference evolution may contribute to adaptation to changing environments. However, mate preference is difficult to measure in natural populations as detailed data on mate availability and mate sampling are usually missing. Often the only available information is the ornamentation of the actual mate. The single long-term quantitative genetic study of a wild population found low heritability in female mate ornamentation in Swedish collared flycatchers. One potentially important cause of low heritability in mate ornamentation at the population level is reduced mate preference expression among inexperienced individuals.

Methodology/Principal Findings

Applying animal model analyses to 21 years of data from a Hungarian collared flycatcher population, we found that additive genetic variance was 50 percent and significant for ornament expression in males, but less than 5 percent and non-significant for mate ornamentation treated as a female trait. Female breeding experience predicted breeding date and clutch size, but mate ornamentation and its variance components were unrelated to experience. Although we detected significant area and year effects on mate ornamentation, more than 85 percent of variance in this trait remained unexplained. Moreover, the effects of area and year on mate ornamentation were also highly positively correlated between inexperienced and experienced females, thereby acting to remove difference between the two groups.

Conclusions/Significance

The low heritability of mate ornamentation was apparently not explained by the presence of inexperienced individuals. Our results further indicate that the expression of mate ornamentation is dominated by temporal and spatial constraints and unmeasured background factors. Future studies should reduce unexplained variance or use alternative measures of mate preference. The heritability of mate preference in the wild remains a principal but unresolved question in evolutionary ecology.     

Performance of penalized maximum likelihood in estimation of genetic covariances matrices

Background

Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that "borrow strength" from the phenotypic covariance matrix are considered.

Methods

An extensive simulation study was carried out to investigate the reduction in average ''loss'', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored.

Results

It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy.

Conclusions

Penalized maximum likelihood estimation provides the means to ''make the most'' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should become part of our everyday toolkit for multivariate estimation in quantitative genetics.     

On estimation of genetic variance within families using genome-wide identity-by-descent sharing

Background

Traditionally, heritability and other genetic parameters are estimated from between-family variation. With the advent of dense genotyping, it is now possible to compute the proportion of the genome that is shared by pairs of sibs and thus undertake the estimation within families, thereby avoiding environmental covariances of family members. Formulae for the sampling variance of estimates have been derived previously for families with two sibs, which are relevant for humans, but sampling errors are large. In livestock and plants much larger families can be obtained, and simulation has shown sampling variances are then much smaller.

Methods

Based on the assumptions that realised relationship of sibs can be obtained from genomic data and that data are analyzed by restricted maximum likelihood, formulae were derived for the sampling variance of the estimates of genetic variance for arbitrary family sizes. The analysis used statistical differentiation, assuming the variance of relationships is small.

Results

The variance of the estimate of the additive genetic variance was approximately proportional to 1/ (fn²σR2), for f families of size n and variance of relationships σR2.

Conclusions

Because the standard error of the estimate of heritability decreased in proportion to family size, the use of within-family information becomes increasingly efficient as the family size increases. There are however, limitations, such as near complete confounding of additive and dominance variances in full sib families.     

Respondent Driven Sampling: Determinants of Recruitment and a Method to Improve Point Estimation

Introduction

Respondent-driven sampling (RDS) is a variant of a link-tracing design intended for generating unbiased estimates of the composition of hidden populations that typically involves giving participants several coupons to recruit their peers into the study. RDS may generate biased estimates if coupons are distributed non-randomly or if potential recruits present for interview non-randomly. We explore if biases detected in an RDS study were due to either of these mechanisms, and propose and apply weights to reduce bias due to non-random presentation for interview.

Methods

Using data from the total population, and the population to whom recruiters offered their coupons, we explored how age and socioeconomic status were associated with being offered a coupon, and, if offered a coupon, with presenting for interview. Population proportions were estimated by weighting by the assumed inverse probabilities of being offered a coupon (as in existing RDS methods), and also of presentation for interview if offered a coupon by age and socioeconomic status group.

Results

Younger men were under-recruited primarily because they were less likely to be offered coupons. The under-recruitment of higher socioeconomic status men was due in part to them being less likely to present for interview. Consistent with these findings, weighting for non-random presentation for interview by age and socioeconomic status group greatly improved the estimate of the proportion of men in the lowest socioeconomic group, reducing the root-mean-squared error of RDS estimates of socioeconomic status by 38%, but had little effect on estimates for age. The weighting also improved estimates for tribe and religion (reducing root-mean-squared-errors by 19–29%), but had little effect for sexual activity or HIV status.

Conclusions

Data collected from recruiters on the characteristics of men to whom they offered coupons may be used to reduce bias in RDS studies. Further evaluation of this new method is required.     

Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

Background

The purpose of this work was to study the impact of both the size of genomic reference populations and the inclusion of a residual polygenic effect on dairy cattle genetic evaluations enhanced with genomic information.

Methods

Direct genomic values were estimated for German Holstein cattle with a genomic BLUP model including a residual polygenic effect. A total of 17,429 genotyped Holstein bulls were evaluated using the phenotypes of 44 traits. The Interbull genomic validation test was implemented to investigate how the inclusion of a residual polygenic effect impacted genomic estimated breeding values.

Results

As the number of reference bulls increased, both the variance of the estimates of single nucleotide polymorphism effects and the reliability of the direct genomic values of selection candidates increased. Fitting a residual polygenic effect in the model resulted in less biased genome-enhanced breeding values and decreased the correlation between direct genomic values and estimated breeding values of sires in the reference population.

Conclusions

Genetic evaluation of dairy cattle enhanced with genomic information is highly effective in increasing reliability, as well as using large genomic reference populations. We found that fitting a residual polygenic effect reduced the bias in genome-enhanced breeding values, decreased the correlation between direct genomic values and sire''s estimated breeding values and made genome-enhanced breeding values more consistent in mean and variance as is the case for pedigree-based estimated breeding values.     

Power and Sample Size Determination for the Group Comparison of Patient-Reported Outcomes with Rasch Family Models

Background

Patient-reported outcomes (PRO) that comprise all self-reported measures by the patient are important as endpoint in clinical trials and epidemiological studies. Models from the Item Response Theory (IRT) are increasingly used to analyze these particular outcomes that bring into play a latent variable as these outcomes cannot be directly observed. Preliminary developments have been proposed for sample size and power determination for the comparison of PRO in cross-sectional studies comparing two groups of patients when an IRT model is intended to be used for analysis. The objective of this work was to validate these developments in a large number of situations reflecting real-life studies.

Methodology

The method to determine the power relies on the characteristics of the latent trait and of the questionnaire (distribution of the items), the difference between the latent variable mean in each group and the variance of this difference estimated using Cramer-Rao bound. Different scenarios were considered to evaluate the impact of the characteristics of the questionnaire and of the variance of the latent trait on performances of the Cramer-Rao method. The power obtained using Cramer-Rao method was compared to simulations.

Principal Findings

Powers achieved with the Cramer-Rao method were close to powers obtained from simulations when the questionnaire was suitable for the studied population. Nevertheless, we have shown an underestimation of power with the Cramer-Rao method when the questionnaire was less suitable for the population. Besides, the Cramer-Rao method stays valid whatever the values of the variance of the latent trait.

Conclusions

The Cramer-Rao method is adequate to determine the power of a test of group effect at design stage for two-group comparison studies including patient-reported outcomes in health sciences. At the design stage, the questionnaire used to measure the intended PRO should be carefully chosen in relation to the studied population.     

Estimating the Sizes of Populations at High Risk for HIV: A Comparison Study

Objectives

Behavioral interventions are effective strategies for HIV/AIDS prevention and control. However, implementation of such strategies relies heavily on the accurate estimation of the high-risk population size. The multiplier method and generalized network scale-up method were recommended to estimate the population size of those at high risk for HIV by UNAIDS/WHO in 2003 and 2010, respectively. This study aims to assess and compare the two methods for estimating the size of populations at high risk for HIV, and to provide practical guidelines and suggestions for implementing the two methods.

Methods

Studies of the multiplier method used to estimate the population prevalence of men who have sex with men in China published between July 1, 2003 and July 1, 2013 were reviewed. The generalized network scale-up method was applied to estimate the population prevalence of men who have sex with men in the urban district of Taiyuan, China.

Results

The median of studies using the multiplier method to estimate the population prevalence of men who have sex with men in China was 4–8 times lower than the national level estimate. Meanwhile, the estimate of the generalized network scale-up method fell within the range of national level estimate.

Conclusions

When high-quality existing data are not readily available, the multiplier method frequently yields underestimated results. We thus suggest that the generalized network scale-up method is preferred when sampling frames for the general population and accurate demographic information are available.     

Decreased Rate of Evolution in Y Chromosome STR Loci of Increased Size of the Repeat Unit

Background

Polymorphic Y chromosome short tandem repeats (STRs) have been widely used in population genetic and evolutionary studies. Compared to di-, tri-, and tetranucleotide repeats, STRs with longer repeat units occur more rarely and are far less commonly used.

Principal Findings

In order to study the evolutionary dynamics of STRs according to repeat unit size, we analysed variation at 24 Y chromosome repeat loci: 1 tri-, 14 tetra-, 7 penta-, and 2 hexanucleotide loci. According to our results, penta- and hexanucleotide repeats have approximately two times lower repeat variance and diversity than tri- and tetranucleotide repeats, indicating that their mutation rate is about half of that of tri- and tetranucleotide repeats. Thus, STR markers with longer repeat units are more robust in distinguishing Y chromosome haplogroups and, in some cases, phylogenetic splits within established haplogroups.

Conclusions

Our findings suggest that Y chromosome STRs of increased repeat unit size have a lower rate of evolution, which has significant relevance in population genetic and evolutionary studies.     

Evaluation of two methods to estimate and monitor bird populations

Background

Effective management depends upon accurately estimating trends in abundance of bird populations over time, and in some cases estimating abundance. Two population estimation methods, double observer (DO) and double sampling (DS), have been advocated for avian population studies and the relative merits and short-comings of these methods remain an area of debate.

Methodology/Principal Findings

We used simulations to evaluate the performances of these two population estimation methods under a range of realistic scenarios. For three hypothetical populations with different levels of clustering, we generated DO and DS population size estimates for a range of detection probabilities and survey proportions. Population estimates for both methods were centered on the true population size for all levels of population clustering and survey proportions when detection probabilities were greater than 20%. The DO method underestimated the population at detection probabilities less than 30% whereas the DS method remained essentially unbiased. The coverage probability of 95% confidence intervals for population estimates was slightly less than the nominal level for the DS method but was substantially below the nominal level for the DO method at high detection probabilities. Differences in observer detection probabilities did not affect the accuracy and precision of population estimates of the DO method. Population estimates for the DS method remained unbiased as the proportion of units intensively surveyed changed, but the variance of the estimates decreased with increasing proportion intensively surveyed.

Conclusions/Significance

The DO and DS methods can be applied in many different settings and our evaluations provide important information on the performance of these two methods that can assist researchers in selecting the method most appropriate for their particular needs.     

Efficacy of Muscle Exercise in Patients with Muscular Dystrophy: A Systematic Review Showing a Missed Opportunity to Improve Outcomes

Background

Although muscular dystrophy causes muscle weakness and muscle loss, the role of exercise in the management of this disease remains controversial.

Objective

The purpose of this systematic review is to evaluate the role of exercise interventions on muscle strength in patients with muscular dystrophy.

Methods

We performed systematic electronic searches in Medline, Embase, Web of Science, Scopus and Pedro as well as a list of reference literature. We included trials assessing muscle exercise in patients with muscular dystrophy. Two reviewers independently abstracted data and appraised risk of bias.

Results

We identified five small (two controlled and three randomized clinical) trials comprising 242 patients and two ongoing randomized controlled trials. We were able to perform two meta-analyses. We found an absence of evidence for a difference in muscle strength (MD 4.18, 95% CIs - 2.03 to 10.39; p = 0.91) and in endurance (MD −0.53, 95% CIs –1.11 to 0.05; p = 0.26). In both, the direction of effects favored muscle exercise.

Conclusions

The first included trial about the efficacy of muscular exercise was published in 1978. Even though some benefits of muscle exercise were consistently reported across studies, the benefits might be due to the small size of studies and other biases. Detrimental effects are still possible. After several decades of research, doctors cannot give advice and patients are, thus, denied basic information. A multi-center randomized trial investigating the strength of muscles, fatigue, and functional limitations is needed.     

Extent of height variability explained by known height-associated genetic variants in an isolated population of the Adriatic coast of Croatia

Background

Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.

Methodology/Principal Findings

In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20∼50 SNPs reported by the remaining individual GWA studies explained 3∼5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.

Conclusions/Significance

We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.     

Biased morph ratios and skewed mating success contribute to loss of genetic diversity in the distylous Pulmonaria officinalis

Background and Aims

In heterostylous plant species, skewed morph ratios are not uncommon and may arise from a range of factors. Despite the recognized importance of skewed morph ratios on overall reproductive success within populations, little is known about the impact of skewed morph ratios on population genetic diversity and differentiation in heterostylous species. This study specifically aimed to clarify the effect of population size and morph bias on population genetic diversity and differentiation in the temperate forest herb Pulmonaria officinalis. This species is characterized by a distylous breeding system and shows morph-specific differences in reproductive success.

Methods

Genetic diversity was determined for 27 P. officinalis populations in northern Belgium by using eight recently developed microsatellite markers. Multiple regressions were used to assess the relationship between genetic diversity, morph bias and population size, and F_ST-values were calculated for short- and long-styled morphs separately to study genetic differentiation as a function of morph type.

Key Results

For all genetic measures used, morph bias was more important in explaining patterns of genetic diversity than population size, and in all cases patterns of population genetic diversity followed a quadratic function, which showed a symmetrical decrease in genetic diversity with increasing morph bias. However, probably due to the reproductive advantage of L-morphs relative to S-morphs, maximum genetic diversity was found in populations showing an excess of L-morphs (60·7 % L-morph). On the other hand, no significant difference in pairwise genetic distances between populations was observed between L- (0·107) and S-morphs (0·106).

Conclusions

Our results indicate that significant deviations from equal morph ratios not only affect plant reproductive success but also population genetic diversity of heterostylous plant species. Hence, when defining conservation measures for populations of heterostylous plant species, morph ratios should be considered as an important trait affecting their long-term population viability.     

Quality Assessment of TPB-Based Questionnaires: A Systematic Review

Objective

This review is aimed at assessing the quality of questionnaires and their development process based on the theory of planned behavior (TPB) change model.

Methods

A systematic literature search for studies with the primary aim of TPB-based questionnaire development was conducted in relevant databases between 2002 and 2012 using selected search terms. Ten of 1,034 screened abstracts met the inclusion criteria and were assessed for methodological quality using two different appraisal tools: one for the overall methodological quality of each study and the other developed for the appraisal of the questionnaire content and development process. Both appraisal tools consisted of items regarding the likelihood of bias in each study and were eventually combined to give the overall quality score for each included study.

Results

8 of the 10 included studies showed low risk of bias in the overall quality assessment of each study, while 9 of the studies were of high quality based on the quality appraisal of questionnaire content and development process.

Conclusion

Quality appraisal of the questionnaires in the 10 reviewed studies was successfully conducted, highlighting the top problem areas (including: sample size estimation; inclusion of direct and indirect measures; and inclusion of questions on demographics) in the development of TPB-based questionnaires and the need for researchers to provide a more detailed account of their development process.     

Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies

Background

The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms.

Methodology

We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants.

Conclusion

We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20–99%.