The partnership between statisticians and the Institutional Animal Care and Use Committee (IACUC).

In this tutorial we explore the valuable partnership between statisticians and Institutional Animal Care and Use Committees (IACUCs) in the context of animal research, shedding light on the critical role statisticians play in ensuring the ethical and scientifically rigorous use of animals in research. Pharmaceutical statisticians have increasingly become vital members of these committees, contributing expertise in study design, data analysis, and interpretation, and working more generally to facilitate the integration of good statistical practices into experimental procedures. We review the "3Rs" principles (Replacement, Reduction, and Refinement) which are the foundation for the humane use of animals in scientific research, and how statisticians can partner with IACUC to help ensure robust and reproducible research while adhering to the 3Rs principles. We also highlight emerging areas of interest, such as the use of virtual control groups.

Comprehensive engineering of the tarantula venom peptide huwentoxin-IV to inhibit the human voltage-gated sodium channel hNav1.7.

Pain is a significant public health burden in the United States, and current treatment approaches rely heavily on opioids, which often have limited efficacy and can lead to addiction. In humans, functional loss of the voltage-gated sodium channel Nav1.7 leads to pain insensitivity without deficits in the central nervous system. Accordingly, discovery of a selective Nav1.7 antagonist should provide an analgesic without abuse liability and an improved side-effect profile. Huwentoxin-IV, a component of tarantula venom, potently blocks sodium channels and is an attractive scaffold for engineering a Nav1.7-selective molecule. To define the functional impact of alterations in huwentoxin-IV sequence, we produced a library of 373 point mutants and tested them for Nav1.7 and Nav1.2 activity. We then combined favorable individual changes to produce combinatorial mutants that showed further improvements in Nav1.7 potency (E1N, E4D, Y33W, Q34S-Nav1.7 pIC50 = 8.1 ± 0.08) and increased selectivity over other Nav isoforms (E1N, R26K, Q34S, G36I, Nav1.7 pIC50 = 7.2 ± 0.1, Nav1.2 pIC50 = 6.1 ± 0.18, Nav1.3 pIC50 = 6.4 ± 1.0), Nav1.4 is inactive at 3 µm, and Nav1.5 is inactive at 10 µm We also substituted noncoded amino acids at select positions in huwentoxin-IV. Based on these results, we identify key determinants of huwentoxin's Nav1.7 inhibition and propose a model for huwentoxin-IV's interaction with Nav1.7. These findings uncover fundamental features of huwentoxin involved in Nav1.7 blockade, provide a foundation for additional optimization of this molecule, and offer a basis for the development of a safe and effective analgesic.

Longitudinal CSF proteomics identifies NPTX2 as a prognostic biomarker of Alzheimer's disease.

INTRODUCTION: Biomarkers that reflect pathologic processes affecting neuronal function during preclinical and early stages of Alzheimer's disease (AD) are needed to aid drug development. METHODS: A targeted, stable isotope, quantitative mass spectrometry-based investigation of longitudinal changes in concentrations of previously identified candidate biomarkers was performed in cerebrospinal fluid (CSF) of Alzheimer's Disease Neuroimaging Initiative participants who were classified as cognitively normal (CN; n = 76) or with mild cognitive impairment (MCI; n = 111) at baseline. RESULTS: Of the candidate biomarkers, the CSF concentration of neuronal pentraxin 2 (NPTX2), a protein involved in synaptic function, exhibited rates of change that were significantly different between three comparison groups (i.e., CN vs. MCI participants; AD pathology positive vs. negative defined by phosphorylated tau181/amyloid beta1-42 ratio; and clinical progressors vs. non-progressors). The rate of change of NPTX2 also significantly correlated with declining cognition. DISCUSSION: CSF NPTX2 concentration is a strong prognostic biomarker candidate of accelerated cognitive decline with potential use as a therapeutic target.

Patterns of population epigenomic diversity.

Natural epigenetic variation provides a source for the generation of phenotypic diversity, but to understand its contribution to such diversity, its interaction with genetic variation requires further investigation. Here we report population-wide DNA sequencing of genomes, transcriptomes and methylomes of wild Arabidopsis thaliana accessions. Single cytosine methylation polymorphisms are not linked to genotype. However, the rate of linkage disequilibrium decay amongst differentially methylated regions targeted by RNA-directed DNA methylation is similar to the rate for single nucleotide polymorphisms. Association analyses of these RNA-directed DNA methylation regions with genetic variants identified thousands of methylation quantitative trait loci, which revealed the population estimate of genetically dependent methylation variation. Analysis of invariably methylated transposons and genes across this population indicates that loci targeted by RNA-directed DNA methylation are epigenetically activated in pollen and seeds, which facilitates proper development of these structures.

Genome-wide association study of paliperidone efficacy.

OBJECTIVE: Clinical response to the atypical antipsychotic paliperidone is known to vary among schizophrenic patients. We carried out a genome-wide association study to identify common genetic variants predictive of paliperidone efficacy. METHODS: We leveraged a collection of 1390 samples from individuals of European ancestry enrolled in 12 clinical studies investigating the efficacy of the extended-release tablet paliperidone ER (n1=490) and the once-monthly injection paliperidone palmitate (n2=550 and n3=350). We carried out a genome-wide association study using a general linear model (GLM) analysis on three separate cohorts, followed by meta-analysis and using a mixed linear model analysis on all samples. The variations in response explained by each single nucleotide polymorphism (hSNP) were estimated. RESULTS: No SNP passed genome-wide significance in the GLM-based analyses with suggestive signals from rs56240334 [P=7.97×10 for change in the Clinical Global Impression Scale-Severity (CGI-S); P=8.72×10 for change in the total Positive and Negative Syndrome Scale (PANSS)] in the intron of ADCK1. The mixed linear model-based association P-values for rs56240334 were consistent with the results from GLM-based analyses and the association with change in CGI-S (P=4.26×10) reached genome-wide significance (i.e. P<5×10). We also found suggestive evidence for a polygenic contribution toward paliperidone treatment response with estimates of heritability, hSNP, ranging from 0.31 to 0.43 for change in the total PANSS score, the PANSS positive Marder factor score, and CGI-S. CONCLUSION: Genetic variations in the ADCK1 gene may differentially predict paliperidone efficacy in schizophrenic patients. However, this finding should be replicated in additional samples.

The Pediatric Imaging, Neurocognition, and Genetics (PING) Data Repository.

The main objective of the multi-site Pediatric Imaging, Neurocognition, and Genetics (PING) study was to create a large repository of standardized measurements of behavioral and imaging phenotypes accompanied by whole genome genotyping acquired from typically-developing children varying widely in age (3 to 20 years). This cross-sectional study produced sharable data from 1493 children, and these data have been described in several publications focusing on brain and cognitive development. Researchers may gain access to these data by applying for an account on the PING portal and filing a data use agreement. Here we describe the recruiting and screening of the children and give a brief overview of the assessments performed, the imaging methods applied, the genetic data produced, and the numbers of cases for whom different data types are available. We also cite sources of more detailed information about the methods and data. Finally we describe the procedures for accessing the data and for using the PING data exploration portal.

Statistical analysis strategies for association studies involving rare variants.

The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.

Complex patterns of genomic admixture within southern Africa.

Within-population genetic diversity is greatest within Africa, while between-population genetic diversity is directly proportional to geographic distance. The most divergent contemporary human populations include the click-speaking forager peoples of southern Africa, broadly defined as Khoesan. Both intra- (Bantu expansion) and inter-continental migration (European-driven colonization) have resulted in complex patterns of admixture between ancient geographically isolated Khoesan and more recently diverged populations. Using gender-specific analysis and almost 1 million autosomal markers, we determine the significance of estimated ancestral contributions that have shaped five contemporary southern African populations in a cohort of 103 individuals. Limited by lack of available data for homogenous Khoesan representation, we identify the Ju/'hoan (n = 19) as a distinct early diverging human lineage with little to no significant non-Khoesan contribution. In contrast to the Ju/'hoan, we identify ancient signatures of Khoesan and Bantu unions resulting in significant Khoesan- and Bantu-derived contributions to the Southern Bantu amaXhosa (n = 15) and Khoesan !Xun (n = 14), respectively. Our data further suggests that contemporary !Xun represent distinct Khoesan prehistories. Khoesan assimilation with European settlement at the most southern tip of Africa resulted in significant ancestral Khoesan contributions to the Coloured (n = 25) and Baster (n = 30) populations. The latter populations were further impacted by 170 years of East Indian slave trade and intra-continental migrations resulting in a complex pattern of genetic variation (admixture). The populations of southern Africa provide a unique opportunity to investigate the genomic variability from some of the oldest human lineages to the implications of complex admixture patterns including ancient and recently diverged human lineages.

Fast individual ancestry inference from DNA sequence data leveraging allele frequencies for multiple populations.

BACKGROUND: Estimation of individual ancestry from genetic data is useful for the analysis of disease association studies, understanding human population history and interpreting personal genomic variation. New, computationally efficient methods are needed for ancestry inference that can effectively utilize existing information about allele frequencies associated with different human populations and can work directly with DNA sequence reads. RESULTS: We describe a fast method for estimating the relative contribution of known reference populations to an individual's genetic ancestry. Our method utilizes allele frequencies from the reference populations and individual genotype or sequence data to obtain a maximum likelihood estimate of the global admixture proportions using the BFGS optimization algorithm. It accounts for the uncertainty in genotypes present in sequence data by using genotype likelihoods and does not require individual genotype data from external reference panels. Simulation studies and application of the method to real datasets demonstrate that our method is significantly times faster than previous methods and has comparable accuracy. Using data from the 1000 Genomes project, we show that estimates of the genome-wide average ancestry for admixed individuals are consistent between exome sequence data and whole-genome low-coverage sequence data. Finally, we demonstrate that our method can be used to estimate admixture proportions using pooled sequence data making it a valuable tool for controlling for population stratification in sequencing based association studies that utilize DNA pooling. CONCLUSIONS: Our method is an efficient and versatile tool for estimating ancestry from DNA sequence data and is available from https://sites.google.com/site/vibansal/software/iAdmix .

Long-term influence of normal variation in neonatal characteristics on human brain development.

It is now recognized that a number of cognitive, behavioral, and mental health outcomes across the lifespan can be traced to fetal development. Although the direct mediation is unknown, the substantial variance in fetal growth, most commonly indexed by birth weight, may affect lifespan brain development. We investigated effects of normal variance in birth weight on MRI-derived measures of brain development in 628 healthy children, adolescents, and young adults in the large-scale multicenter Pediatric Imaging, Neurocognition, and Genetics study. This heterogeneous sample was recruited through geographically dispersed sites in the United States. The influence of birth weight on cortical thickness, surface area, and striatal and total brain volumes was investigated, controlling for variance in age, sex, household income, and genetic ancestry factors. Birth weight was found to exert robust positive effects on regional cortical surface area in multiple regions as well as total brain and caudate volumes. These effects were continuous across birth weight ranges and ages and were not confined to subsets of the sample. The findings show that (i) aspects of later child and adolescent brain development are influenced at birth and (ii) relatively small differences in birth weight across groups and conditions typically compared in neuropsychiatric research (e.g., Attention Deficit Hyperactivity Disorder, schizophrenia, and personality disorders) may influence group differences observed in brain parameters of interest at a later stage in life. These findings should serve to increase our attention to early influences.

Multimodal imaging of the self-regulating developing brain.

Self-regulation refers to the ability to control behavior, cognition, and emotions, and self-regulation failure is related to a range of neuropsychiatric problems. It is poorly understood how structural maturation of the brain brings about the gradual improvement in self-regulation during childhood. In a large-scale multicenter effort, 735 children (4-21 y) underwent structural MRI for quantification of cortical thickness and surface area and diffusion tensor imaging for quantification of the quality of major fiber connections. Brain development was related to a standardized measure of cognitive control (the flanker task from the National Institutes of Health Toolbox), a critical component of self-regulation. Ability to inhibit responses and impose cognitive control increased rapidly during preteen years. Surface area of the anterior cingulate cortex accounted for a significant proportion of the variance in cognitive performance. This finding is intriguing, because characteristics of the anterior cingulum are shown to be related to impulse, attention, and executive problems in neurodevelopmental disorders, indicating a neural foundation for self-regulation abilities along a continuum from normality to pathology. The relationship was strongest in the younger children. Properties of large-fiber connections added to the picture by explaining additional variance in cognitive control. Although cognitive control was related to surface area of the anterior cingulate independently of basic processes of mental speed, the relationship between white matter quality and cognitive control could be fully accounted for by speed. The results underscore the need for integration of different aspects of brain maturation to understand the foundations of cognitive development.

Admixture and clinical phenotypic variation.

All human populations exhibit some level of genetic differentiation. This differentiation, or population stratification, has many interacting sources, including historical migrations, population isolation over time, genetic drift, and selection and adaptation. If differentiated populations remained isolated from each other over a long period of time such that there is no mating of individuals between those populations, then some level of global consanguinity within those populations will lead to the formation of gene pools that will become more and more distinct over time. Global genetic differentiation of this sort can lead to overt phenotypic differences between populations if phenotypically relevant variants either arise uniquely within those populations or begin to exhibit frequency differences across the populations. This can occur at the single variant level for monogenic phenotypes or at the level of aggregate variant frequency differences across the many loci that contribute to a phenotype with a multifactorial or polygenic basis. However, if individuals begin to interbreed (or 'admix') from populations with different frequencies of phenotypically relevant genetic variants, then these admixed individuals will exhibit the phenotype to varying degrees. The level of phenotypic expression will depend on the degree to which the admixed individuals have inherited causative variants that have descended from the ancestral population in which those variants were present (or, more likely, simply more frequent). We review studies that consider the association between the degree of admixture (or ancestry) and phenotypes of clinical relevance. We find a great deal of literature-based evidence for associations between the degree of admixture and phenotypic variation for a number of admixed populations and phenotypes, although not all this evidence is confirmatory. We also consider the implications of such associations for gene-mapping initiatives as well as general clinical epidemiology studies and medical practice. We end with some thoughts on the future of studies exploring phenotypic differences among admixed individuals as well as individuals with different ancestral backgrounds.

Correlation analysis of genetic admixture and social identification with body mass index in a Native American community.

OBJECTIVES: Body mass index (BMI) is a well-known measure of obesity with a multitude of genetic and non-genetic determinants. Identifying the underlying factors associated with BMI is difficult because of its multifactorial etiology that varies as a function of geoethnic background and socioeconomic setting. Thus, we pursued a study exploring the influence of the degree of Native American admixture on BMI (as well as weight and height individually) in a community sample of Native Americans (n = 846) while accommodating a variety of socioeconomic and cultural factors. METHODS: Participants' degree of Native American (NA) ancestry was estimated using a genome-wide panel of markers. The participants also completed an extensive survey of cultural and social identity measures: the Indian Culture Scale (ICS) and the Orthogonal Cultural Identification Scale (OCIS). Multiple linear regression was used to examine the relation between these measures and BMI. RESULTS: Our results suggest that BMI is correlated positively with the proportion of NA ancestry. Age was also significantly associated with BMI, while gender and socioeconomic measures (education and income) were not. For the two cultural identity measures, the ICS showed a positive correlation with BMI, while OCIS was not associated with BMI. CONCLUSIONS: Taken together, these results suggest that genetic and cultural environmental factors, rather than socioeconomic factors, account for a substantial proportion of variation in BMI in this population. Further, significant correlations between degree of NA ancestry and BMI suggest that admixture mapping may be appropriate to identify loci associated with BMI in this population.

Global developmental gene expression and pathway analysis of normal brain development and mouse models of human neuronal migration defects.

Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε), and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can be used to define novel candidates for related human diseases.

Association and ancestry analysis of sequence variants in ADH and ALDH using alcohol-related phenotypes in a Native American community sample.

Higher rates of alcohol use and other drug-dependence have been observed in some Native American (NA) populations relative to other ethnic groups in the US. Previous studies have shown that alcohol dehydrogenase (ADH) genes and aldehyde dehydrogenase (ALDH) genes may affect the risk of development of alcohol dependence, and that polymorphisms within these genes may differentially affect risk for the disorder depending on the ethnic group evaluated. We evaluated variations in the ADH and ALDH genes in a large study investigating risk factors for substance use in a NA population. We assessed ancestry admixture and tested for associations between alcohol-related phenotypes in the genomic regions around the ADH1-7 and ALDH2 and ALDH1A1 genes. Seventy-two ADH variants showed significant evidence of association with a severity level of alcohol drinking-related dependence symptoms phenotype. These significant variants spanned across the entire 7 ADH gene cluster regions. Two significant associations, one in ADH and one in ALDH2, were observed with alcohol dependence diagnosis. Seventeen variants showed significant association with the largest number of alcohol drinks ingested during any 24-hour period. Variants in or near ADH7 were significantly negatively associated with alcohol-related phenotypes, suggesting a potential protective effect of this gene. In addition, our results suggested that a higher degree of NA ancestry is associated with higher frequencies of potential risk variants and lower frequencies of potential protective variants for alcohol dependence phenotypes.

A probabilistic method for the detection and genotyping of small indels from population-scale sequence data.

MOTIVATION: High-throughput sequencing technologies have made population-scale studies of human genetic variation possible. Accurate and comprehensive detection of DNA sequence variants is crucial for the success of these studies. Small insertions and deletions represent the second most frequent class of variation in the human genome after single nucleotide polymorphisms (SNPs). Although several alignment tools for the gapped alignment of sequence reads to a reference genome are available, computational methods for discriminating indels from sequencing errors and genotyping indels directly from sequence reads are needed. RESULTS: We describe a probabilistic method for the accurate detection and genotyping of short indels from population-scale sequence data. In this approach, aligned sequence reads from a population of individuals are used to automatically account for context-specific sequencing errors associated with indels. We applied this approach to population sequence datasets from the 1000 Genomes exon pilot project generated using the Roche 454 and Illumina sequencing platforms, and were able to detect a significantly greater number of indels than reported previously. Comparison to indels identified in the 1000 Genomes pilot project demonstrated the sensitivity of our method. The consistency in the number of indels and the fraction of indels whose length is a multiple of three across different human populations and two different sequencing platforms indicated that our method has a low false discovery rate. Finally, the method represents a general approach for the detection and genotyping of small-scale DNA sequence variants for population-scale sequencing projects. AVAILABILITY: A program implementing this method is available at http://polymorphism.scripps.edu/~vbansal/software/piCALL/

Model-specific tests on variance heterogeneity for detection of potentially interacting genetic loci.

BACKGROUND: Trait variances among genotype groups at a locus are expected to differ in the presence of an interaction between this locus and another locus or environment. A simple maximum test on variance heterogeneity can thus be used to identify potentially interacting single nucleotide polymorphisms (SNPs). RESULTS: We propose a multiple contrast test for variance heterogeneity that compares the mean of Levene residuals for each genotype group with their average as an alternative to a global Levene test. We applied this test to a Bogalusa Heart Study dataset to screen for potentially interacting SNPs across the whole genome that influence a number of quantitative traits. A user-friendly implementation of this method is available in the R statistical software package multcomp. CONCLUSIONS: We show that the proposed multiple contrast test of model-specific variance heterogeneity can be used to test for potential interactions between SNPs and unknown alleles, loci or covariates and provide valuable additional information compared with traditional tests. Although the test is statistically valid for severely unbalanced designs, care is needed in interpreting the results at loci with low allele frequencies.

Design, synthesis and preclinical evaluation of bio-conjugated amylinomimetic peptides as long-acting amylin receptor agonists.

Pramlintide is an equipotent amylin analogue that reduces food intake and body weight in obese subjects and has been clinically approved as an adjunctive therapy for the treatment of adult diabetic patients. However, due to its extremely short half-life in vivo, a regimen of multiple daily administrations is required for achieving clinical effectiveness. Herein is described the development of prototypical long-acting pramlintide bioconjugates, in which pramlintide's disulfide-linked macrocycle was replaced by a cyclic thioether motif. This modification enabled stable chemical conjugation to a half-life extending antibody. In contrast to pramlintide (t1/2 < 0.75 h), bioconjugates 35 and 38 have terminal half-lives of ∼2 days in mice and attain significant exposure levels that are maintained up to 7 days. Single dose subcutaneous administration of 35 in lean mice, given 18-20 h prior to oral acetaminophen (AAP) administration, significantly reduced gastric emptying (as determined by plasma AAP levels). In a separate study, similar administration of 35 in fasted lean mice effected a reduction in food intake for up to 48 h. These data are consistent with durable amylinomimetic responses and provide the basis for further development of such long-acting amylinomimetic conjugates for the potential treatment of obesity and associated pathologies.

Accommodating linkage disequilibrium in genetic-association analyses via ridge regression.

Large-scale genetic-association studies that take advantage of an extremely dense set of genetic markers have begun to produce very compelling statistical associations between multiple makers exhibiting strong linkage disequilibrium (LD) in a single genomic region and a phenotype of interest. However, the ultimate biological or "functional" significance of these multiple associations has been difficult to discern. In fact, the LD relationships between not only the markers found to be associated with the phenotype but also potential functionally or causally relevant genetic variations that reside near those markers have been exploited in such studies. Unfortunately, LD, especially strong LD, between variations at neighboring loci can make it difficult to distinguish the functionally relevant variations from nonfunctional variations. Although there are (rare) situations in which it is impossible to determine the independent phenotypic effects of variations in LD, there are strategies for accommodating LD between variations at different loci, and they can be used to tease out their independent effects on a phenotype. These strategies make it possible to differentiate potentially causative from noncausative variations. We describe one such approach involving ridge regression. We showcase the method by using both simulated and real data. Our results suggest that ridge regression and related techniques have the potential to distinguish causative from noncausative variations in association studies.