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 .

Background

Expression quantitative trait locus (eQTL) mapping is used to find loci that are responsible for the transcriptional activity of a particular gene. In recent eQTL studies, expression profiles were derived from either homogenized whole brain or collections of large brain regions. However, the brain is a very heterogeneous organ, and expression profiles of different brain regions vary significantly. Because of the importance and potential power of eQTL studies in identifying regulatory networks, we analyzed gene expression patterns in different brain regions from multiple inbred mouse strains and investigated the implications for the design and analysis of eQTL studies.

Results

Gene expression profiles of five brain regions in six inbred mouse strains were studied. Few genes exhibited a significant strain-specific expression pattern, whereas a large number of genes exhibited brain region-specific patterns. We constructed phylogenetic trees based on the expression relationships between the strains and compared them with a DNA-level relationship tree. The trees based on the expression of strain-specific genes were constant across brain regions and mirrored DNA-level variation. However, the trees based on region-specific genes exhibited a different set of strain relationships, depending on the brain region. An eQTL analysis showed enrichment of cis-acting regulators among strain-specific genes, whereas brain region-specific genes appear to be mainly regulated by trans-acting elements.

Conclusion

Our results suggest that many regulatory networks are highly brain region specific and indicate the importance of conducting eQTL mapping studies using data from brain regions or tissues that are physiologically and phenotypically relevant to the trait of interest.

Abstract Background Accurate determination of genetic ancestry is of high interest for many areas such as biomedical research, personal genomics and forensics. It remains an important topic in genetic association studies, as it has been shown that population stratification, if not appropriately considered, can lead to false-positive and -negative results. While large association studies typically extract ancestry information from available genome-wide SNP genotypes, many important clinical data sets on rare phenotypes and historical collections assembled before the GWAS area are in need of a feasible method (i.e., ease of genotyping, small number of markers) to infer the geographic origin and potential admixture of the study subjects. Here we report on the development, application and limitations of a small, multiplexable ancestry informative marker (AIM) panel of SNPs (or AISNP) developed specifically for this purpose. Results Based on worldwide populations from the HGDP, a 41-AIM AISNP panel for multiplex application with the ABI SNPlex and a subset with 31 AIMs for the Sequenome iPLEX system were selected and found to be highly informative for inferring ancestry among the seven continental regions Africa, the Middle East, Europe, Central/South Asia, East Asia, the Americas and Oceania. The panel was found to be least informative for Eurasian populations, and additional AIMs for a higher resolution are suggested. A large reference set including over 4,000 subjects collected from 120 global populations was assembled to facilitate accurate ancestry determination. We show practical applications of this AIM panel, discuss its limitations for admixed individuals and suggest ways to incorporate ancestry information into genetic association studies. Conclusion We demonstrated the utility of a small AISNP panel specifically developed to discern global ancestry. We believe that it will find wide application because of its feasibility and potential for a wide range of applications.

Consumer uptake of direct-to-consumer (DTC) DNA ancestry testing is accelerating, yet few empirical studies have examined test impacts on recipients despite the DTC ancestry industry being two decades old. Participants in a longitudinal cohort study of response to health-related DTC genomic testing also received personal DNA ancestry testing at no additional cost. Baseline survey data from the primary study were analyzed together with responses to an additional follow-up survey focused on the response to ancestry results. Ancestry results were generated for 3466 individuals. Of those, 1317 accessed their results, and 322 individuals completed an ancestry response survey, in other words, approximately one in ten who received ancestry testing responded to the survey. Self-reported race/ethnicity was predictive of those most likely to view their results. While 46% of survey responders (N = 147) reported their ancestry results as surprising or unexpected, less than 1% (N = 3) were distressed by them. Importantly, however, 21% (N = 67) reported that their results reshaped their personal identity. Most (81%; N = 260) planned to share results with family, and 12% (N = 39) intended to share results with a healthcare provider. Many (61%; N = 196) reported test benefits (e.g., health insights), while 12% (N = 38) reported negative aspects (e.g., lack of utility). Over half (N = 162) reported being more likely to have other genetic tests in the future. DNA ancestry testing affected individuals with respect to personal identity, intentions to share genetic information with family and healthcare providers, and the likelihood to engage with other genetic tests in the future. These findings have implications for medical care and research, specifically, provider readiness to engage with genetic ancestry information.

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.

There is considerable debate about the most efficient way to interrogate rare coding variants in association studies. The options include direct genotyping of specific known coding variants in genes or, alternatively, sequencing across the entire exome to capture known as well as novel variants. Each strategy has advantages and disadvantages, but the availability of cost-efficient exome arrays has made the former appealing. Here we consider the utility of a direct genotyping chip, the Illumina HumanExome array (HE), by evaluating its content based on: 1. functionality; and 2. amenability to imputation. We explored these issues by genotyping a large, ethnically diverse cohort on the HumanOmniExpressExome array (HOEE) which combines the HE with content from the GWAS array (HOE). We find that the use of the HE is likely to be a cost-effective way of expanding GWAS, but does have some drawbacks that deserve consideration when planning studies.

OBJECTIVE: The NIH Toolbox Cognition Battery (NTCB) was designed to provide a brief, efficient computerized test of key neuropsychological functions appropriate for use in children as young as 3 years of age. This report describes the performance of a large group of typically developing children and adolescents and examines the impact of age and sociocultural variables on test performance. METHOD: The NTCB was administered to a sample of 1,020 typically developing males and females ranging in age from 3 to 20 years, diverse in terms of socioeconomic status (SES) and race/ethnicity, as part of the new publicly accessible Pediatric Imaging, Neurocognition, and Genetics (PING) data resource, at 9 sites across the United States. RESULTS: General additive models of nonlinear age-functions were estimated from age-differences in test performance on the 8 NTCB subtests while controlling for family SES and genetic ancestry factors (GAFs). Age accounted for the majority of the variance across all NTCB scores, with additional significant contributions of gender on some measures, and of SES and race/ethnicity (GAFs) on all. After adjusting for age and gender, SES and GAFs explained a substantial proportion of the remaining unexplained variance in Picture Vocabulary scores. CONCLUSIONS: The results highlight the sensitivity to developmental effects and efficiency of this new computerized assessment battery for neurodevelopmental research. Limitations are observed in the form of some ceiling effects in older children, some floor effects, particularly on executive function tests in the youngest participants, and evidence for variable measurement sensitivity to cultural/socioeconomic factors.

Objective

The NIH Toolbox Cognition Battery (NTCB) was designed to provide a brief, efficient computerized test of key neuropsychological functions appropriate for use in children as young as 3 years of age. This report describes the performance of a large group of typically developing children and adolescents and examines the impact of age and sociocultural variables on test performance.

Method

The NTCB was administered to a sample of 1,020 typically developing males and females ranging in age from 3 to 20 years, diverse in terms of socioeconomic status (SES) and race/ethnicity, as part of the new publicly accessible Pediatric Imaging, Neurocognition, and Genetics (PING) data resource, at 9 sites across the United States.

Results

General additive models of nonlinear age-functions were estimated from age-differences in test performance on the 8 NTCB subtests while controlling for family SES and genetic ancestry factors (GAFs). Age accounted for the majority of the variance across all NTCB scores, with additional significant contributions of gender on some measures, and of SES and race/ethnicity (GAFs) on all. After adjusting for age and gender, SES and GAFs explained a substantial proportion of the remaining unexplained variance in Picture Vocabulary scores.

Conclusions

The results highlight the sensitivity to developmental effects and efficiency of this new computerized assessment battery for neurodevelopmental research. Limitations are observed in the form of some ceiling effects in older children, some floor effects, particularly on executive function tests in the youngest participants, and evidence for variable measurement sensitivity to cultural/socioeconomic factors.

Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. We investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1,099 typically developing individuals between 3 and 20 years of age. Income was logarithmically associated with brain surface area. Among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data imply that income relates most strongly to brain structure among the most disadvantaged children.

Dyslexia and language impairment (LI) are complex traits with substantial genetic components. We recently completed an association scan of the DYX2 locus, where we observed associations of markers in DCDC2, KIAA0319, ACOT13, and FAM65B with reading-, language-, and IQ-related traits. Additionally, the effects of reading-associated DYX3 markers were recently characterized using structural neuroimaging techniques. Here, we assessed the neuroimaging implications of associated DYX2 and DYX3 markers, using cortical volume, cortical thickness, and fractional anisotropy. To accomplish this, we examined eight DYX2 and three DYX3 markers in 332 subjects in the Pediatrics Imaging Neurocognition Genetics study. Imaging-genetic associations were examined by multiple linear regression, testing for influence of genotype on neuroimaging. Markers in DYX2 genes KIAA0319 and FAM65B were associated with cortical thickness in the left orbitofrontal region and global fractional anisotropy, respectively. KIAA0319 and ACOT13 were suggestively associated with overall fractional anisotropy and left pars opercularis cortical thickness, respectively. DYX3 markers showed suggestive associations with cortical thickness and volume measures in temporal regions. Notably, we did not replicate association of DYX3 markers with hippocampal measures. In summary, we performed a neuroimaging follow-up of reading-, language-, and IQ-associated DYX2 and DYX3 markers. DYX2 associations with cortical thickness may reflect variations in their role in neuronal migration. Furthermore, our findings complement gene expression and imaging studies implicating DYX3 markers in temporal regions. These studies offer insight into where and how DYX2 and DYX3 risk variants may influence neuroimaging traits. Future studies should further connect the pathways to risk variants associated with neuroimaging/neurocognitive outcomes.