Biomedical subjects

Matthew C Keller

Publications and source records attributed to Matthew C Keller.

10 recordsLinked to original sources

PubMed2026

A novel method for across-chromosome phasing without relative data.

MOTIVATION: Across-chromosome phasing identifies which haplotypes of different chromosomes come from the same parent. This differs from within-chromosome phasing, which uses linkage disequilibrium patterns to determine which alleles were co-inherited within each chromosome but does not match haplotypes across different chromosomes. While across-chromosome phasing can be conducted using genotypes from parents or close relatives, current methods perform poorly for samples of unrelated individuals. Here, we introduce a novel approach for across-chromosome phasing that employs a window-based SNP-similarity metric, eliminating the need for data from close relatives or detection of identical-by-descent haplotypes. RESULTS: Using UK Biobank offspring with both parents genotyped as a gold standard, we evaluated the performance of our method by phasing the offspring without using parental data. In genomic data with no within-chromosome phase errors, our algorithm achieved a mean across-chromosome phasing accuracy of 95%, with 53% of individuals phased perfectly. When data was pre-phased computationally using a standard within-chromosome phasing algorithm, mean accuracy for across-chromosome phasing dropped to 83.1%. Thus, our method is limited primarily by the accuracy of within-chromosome phasing accuracy and can approach near-perfect across-chromosome phasing accuracy as within-chromosome phasing accuracy improves. AVAILABILITY AND IMPLEMENTATION: The implementation was executed within a multi-node computational environment of University of Colorado Boulder Research Computing (Blanca Cluster: https://www.colorado.edu/rc/resources/blanca), employing parallelization techniques in the C programming language. The source code has been made publicly accessible online at https://github.com/emmanuelsapin/AcrossChromosomesPhasing, thereby facilitating reproducibility of the results for researchers with authorized access to the UK Biobank dataset.

Algorithms↗

PubMed2024

The importance of family-based sampling for biobanks.

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges.

Humans↗

PubMed2006

Resolving the paradox of common, harmful, heritable mental disorders: which evolutionary genetic models work best?

Given that natural selection is so powerful at optimizing complex adaptations, why does it seem unable to eliminate genes (susceptibility alleles) that predispose to common, harmful, heritable mental disorders, such as schizophrenia or bipolar disorder? We assess three leading explanations for this apparent paradox from evolutionary genetic theory: (1) ancestral neutrality (susceptibility alleles were not harmful among ancestors), (2) balancing selection (susceptibility alleles sometimes increased fitness), and (3) polygenic mutation-selection balance (mental disorders reflect the inevitable mutational load on the thousands of genes underlying human behavior). The first two explanations are commonly assumed in psychiatric genetics and Darwinian psychiatry, while mutation-selection has often been discounted. All three models can explain persistent genetic variance in some traits under some conditions, but the first two have serious problems in explaining human mental disorders. Ancestral neutrality fails to explain low mental disorder frequencies and requires implausibly small selection coefficients against mental disorders given the data on the reproductive costs and impairment of mental disorders. Balancing selection (including spatio-temporal variation in selection, heterozygote advantage, antagonistic pleiotropy, and frequency-dependent selection) tends to favor environmentally contingent adaptations (which would show no heritability) or high-frequency alleles (which psychiatric genetics would have already found). Only polygenic mutation-selection balance seems consistent with the data on mental disorder prevalence rates, fitness costs, the likely rarity of susceptibility alleles, and the increased risks of mental disorders with brain trauma, inbreeding, and paternal age. This evolutionary genetic framework for mental disorders has wide-ranging implications for psychology, psychiatry, behavior genetics, molecular genetics, and evolutionary approaches to studying human behavior.

Adaptation, Physiological↗

PubMed2006

The evolutionary significance of depressive symptoms: different adverse situations lead to different depressive symptom patterns.

Although much depression may be dysfunctional, the capacity to experience normal depressive symptoms in response to certain adverse situations appears to have been shaped by natural selection. If this is true, then different kinds of situations may evoke different patterns of depressive symptoms that are well suited to solving the adaptive challenges specific to each situation. The authors called this the situation-symptom congruence hypothesis. They tested this hypothesis by asking 445 participants to identify depressive symptoms that followed a recent adverse situation. Guilt, rumination, fatigue, and pessimism were prominent following failed efforts; crying, sadness, and desire for social support were prominent following social losses. These significant differences were replicated in an experiment in which 113 students were randomly assigned to visualize a major failure or the death of a loved one.

Adolescent↗

PubMed2005

Increased sensitivity in neuroimaging analyses using robust regression.

Robust regression techniques are a class of estimators that are relatively insensitive to the presence of one or more outliers in the data. They are especially well suited to data that require large numbers of statistical tests and may contain outliers due to factors not of experimental interest. Both these issues apply particularly to neuroimaging data analysis. We use simulations to compare several robust techniques against ordinary least squares (OLS) regression, and we apply robust regression to second-level (group "random effects") analyses in three fMRI datasets. Our results show that robust iteratively reweighted least squares (IRLS) at the 2nd level is a computationally efficient technique that both increases statistical power and decreases false positive rates in the presence of outliers. The benefits of IRLS are apparent with small samples (n = 10) and increase with larger sample sizes (n = 40) in the typical range of group neuroimaging experiments. When no true effects are present, IRLS controls false positive rates at an appropriate level. We show that IRLS can have substantial benefits in analysis of group data and in estimating hemodynamic response shapes from time series data. We provide software to implement IRLS in group neuroimaging analyses.

Algorithms↗

PubMed2005

Widespread evidence for non-additive genetic variation in Cloninger's and Eysenck's personality dimensions using a twin plus sibling design.

Studies using the classical twin design often conclude that most genetic variation underlying personality is additive in nature. However, studies analyzing only twins are very limited in their ability to detect non-additive genetic variation and are unable to detect sources of variation unique to twins, which can mask non-additive genetic variation. The current study assessed 9672 MZ and DZ twin individuals and 3241 of their siblings to investigate the environmental and genetic architecture underlying eight dimensions of personality: four from Eysenck's Personality Questionnaire and four from Cloninger's Temperament and Character Inventory. Broad-sense heritability estimates from best-fitting models were two to three times greater than the narrow-sense heritability estimates for Harm Avoidance, Novelty Seeking, Reward Dependence, Persistence, Extraversion, and Neuroticism. This genetic non-additivity could be due to dominance, additive-by-additive epistasis, or to additive genetic effects combined with higher-order epistasis. Environmental effects unique to twins were detected for both Lie and Psychoticism but accounted for little overall variation. Our results illustrate the increased sensitivity afforded by extending the classical twin design to include siblings, and may provide clues to the evolutionary origins of genetic variation underlying personality.

Australia↗

PubMed2005

Is low mood an adaptation? Evidence for subtypes with symptoms that match precipitants.

BACKGROUND: Although severe depression is dysfunctional, the capacity to experience normal low mood may be useful in certain fitness-threatening situations. Moreover, if specific kinds of situations recurred often enough in the course of evolution, natural selection may have shaped partially differentiated subtypes of low mood that are parallel to the subtypes of anxiety that protect against different kinds of danger. To test this hypothesis, we examined how symptoms of low mood differ depending upon the precipitating situation, and whether these differences match expectations of symptoms useful in each kind of situation. METHOD: 337 subjects who experienced a period of low mood within the last year wrote accounts describing perceived causes of their low mood and they filled out the CES-D depression inventory. Seven symptom scales were derived from analysis of CES-D data. Independent judges blindly coded the accounts into one of six precipitant categories. RESULTS: Different untoward situations were associated with different symptoms that were predicted to be useful in those situations. Social losses (death of a loved one, romantic breakups, and social isolation) were associated with greater crying and arousal. Failure to reach a goal, stress, and winter seasons were associated with more fatigue and pessimism. DISCUSSION: These results suggest that natural selection shaped not only a generic state of low mood but also partially differentiated subtypes shaped to cope with specific situations that were associated with fitness losses among our ancestors.

Adaptation, Psychological↗

PubMed2005

A warm heart and a clear head. The contingent effects of weather on mood and cognition.

Prior studies on the association between weather and psychological changes have produced mixed results. In part, this inconsistency may be because weather's psychological effects are moderated by two important factors: the season and time spent outside. In two correlational studies and an experiment manipulating participants' time outdoors (total N = 605), pleasant weather (higher temperature or barometric pressure) was related to higher mood, better memory, and "broadened" cognitive style during the spring as time spent outside increased. The same relationships between mood and weather were not observed during other times of year, and indeed hotter weather was associated with lower mood in the summer. These results are consistent with findings on seasonal affective disorder, and suggest that pleasant weather improves mood and broadens cognition in the spring because people have been deprived of such weather during the winter.

Adolescent↗

PubMed2005

Quantifying and addressing parameter indeterminacy in the classical twin design.

The classical twin design (CTD) is the most common method used to infer genetic and environmental causes of phenotypic variation. As has long been acknowledged, different combinations of the common environment/assortative mating, and additive, dominant, and epistatic genetic effects can lead to the same observed covariation between twin pairs, meaning that there is an inherent indeterminacy in parameter estimates arising from the CTD. The CTD circumvents this indeterminacy by assuming that higher-order epistasis is negligible and that the effects of either dominant genetic variation or the common environment are nonexistent. These assumptions, however, lead to consistent biases in parameter estimation. The current paper quantifies these biases and discusses alternative strategies for dealing with parameter indeterminacy in twin designs. One strategy is to model the similarity among other relatives in addition to twins (extended twin-family designs), which reduces but does not eliminate indeterminacy in parameter estimates. A more general strategy, applicable to all twin designs, is to present the parameter indeterminacy explicitly, as in a graph. Presenting the space of mathematically equally likely parameter values is important, not only because it aids the proper interpretation of twin design findings, but also because it keeps behavioral geneticists themselves mindful of methodological assumptions that can easily go unexamined.

Humans↗

PubMed2005

Estimating the extent of parameter bias in the classical twin design: a comparison of parameter estimates from extended twin-family and classical twin designs.

The classical twin design (CTD) circumvents parameter indeterminacy by assuming (1) negligible higher-order epistasis; and (2) either nonadditive genetic or common environmental effects are nonexistent, creating two potential sources of bias (Eaves et al., 1978; Grayson, 1989). Because the extended twin-family design (ETFD) uses many more unique covariance observations to estimate parameters, common environmental and nonadditive genetic parameters can be simultaneously estimated. The ETFD thereby corrects for what is likely to be the largest of the two sources of bias in CTD parameter estimates (Keller & Coventry, 2005). In the current paper, we assess the extent of this and other potential sources of bias in the CTD by comparing all published ETFD parameter estimates to CTD parameter estimates derived from the same data. CTD estimates of the common environment were lower than ETFD estimates of the common environment for some phenotypes, but for other phenotypes (e.g., stature in females and certain social attitudes), what appeared as the common environment was resolved to be assortative mating in the ETFD. On average, CTD estimates of nonadditive genetic factors were 43% lower, and additive genetic factors 63% higher, than ETFD estimates. However, broad-sense heritability estimates from the CTD were only 18% higher than ETFD estimates, highlighting that the CTD is useful for estimating broad-sense but not narrow-sense heritability. These results suggest that CTD estimates can be misleading when interpreted literally, but useful, albeit coarse, when interpreted properly.

Genetic Variation↗