Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations

Alzheimer disease (AD) is the most common form of senile dementia, with high incidence late in life in many populations including Caribbean Hispanic (CH) populations. Such admixed populations, descended from more than one ancestral population, can present challenges for genetic studies, including limited sample sizes and unique analytical constraints. Therefore, CH populations and other admixed populations have not been well represented in studies of AD, and much of the genetic variation contributing to AD risk in these populations remains unknown. Here, we conduct genome-wide analysis of AD in multiplex CH families from the Alzheimer Disease Sequencing Project (ADSP). We developed, validated, and applied an implementation of a logistic mixed model for admixture mapping with binary traits that leverages genetic ancestry to identify ancestry-of-origin loci contributing to AD. We identified three loci on chromosome 13q33.3 associated with reduced risk of AD, where associations were driven by Native American (NAM) ancestry. This AD admixture mapping signal spans the FAM155A, ABHD13, TNFSF13B, LIG4, and MYO16 genes and was supported by evidence for association in an independent sample from the Alzheimer's Genetics in Argentina—Alzheimer Argentina consortium (AGA-ALZAR) study with considerable NAM ancestry. We also provide evidence of NAM haplotypes and key variants within 13q33.3 that segregate with AD in the ADSP whole-genome sequencing data. Interestingly, the widely used genome-wide association study approach failed to identify associations in this region. Our findings underscore the potential of leveraging genetic ancestry diversity in recently admixed populations to improve genetic mapping, in this case for AD-relevant loci.Fil: Horimoto, Andrea R.V.R.. University of Washington; Estados UnidosFil: Boyken, Lisa A.. University of Washington; Estados UnidosFil: Blue, Elizabeth E.. University of Washington; Estados Unidos. Brotman Baty Institute for Precision Medicine; Estados UnidosFil: Grinde, Kelsey E.. University of Washington; Estados Unidos. Macalester College; Estados UnidosFil: Nafikov, Rafael A.. University of Washington; Estados UnidosFil: Sohi, Harkirat K.. University of Washington; Estados UnidosFil: Nato, Alejandro Q.. University of Washington; Estados Unidos. Marshall University; Estados UnidosFil: Bis, Joshua C.. University of Washington; Estados UnidosFil: Brusco, Luis Ignacio. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Morelli, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ramirez, Alfredo Jose. University Of Cologne; Alemania. Universitat Bonn; Alemania. German Center for Neurodegenerative Diseases; Alemania. University Of Texas Health Science Center At San Antonio (ut Health San Antonio) ; University Of Texas At San Antonio; . Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Dalmasso, Maria Carolina. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; Argentina. University Of Cologne; AlemaniaFil: Temple, Seth. University of Washington; Estados UnidosFil: Satizabal, Claudia. University Of Texas Health Science Center At San Antonio (ut Health San Antonio) ; University Of Texas At San Antonio; . University of Texas at San Antonio; Estados UnidosFil: Browning, Sharon R.. University of Washington; Estados UnidosFil: Seshadri, Sudha. University Of Texas Health Science Center At San Antonio (ut Health San Antonio) ; University Of Texas At San Antonio; . University of Texas at San Antonio; Estados UnidosFil: Wijsman, Ellen M.. University of Washington; Estados UnidosFil: Thornton, Timothy A.. University of Washington; Estados Unido

The Milky Way Tomography With SDSS. III. Stellar Kinematics

We study Milky Way kinematics using a sample of 18.8 million main-sequence stars with r 20 degrees). We find that in the region defined by 1 kpc < Z < 5 kpc and 3 kpc < R < 13 kpc, the rotational velocity for disk stars smoothly decreases, and all three components of the velocity dispersion increase, with distance from the Galactic plane. In contrast, the velocity ellipsoid for halo stars is aligned with a spherical coordinate system and appears to be spatially invariant within the probed volume. The velocity distribution of nearby (Z < 1 kpc) K/M stars is complex, and cannot be described by a standard Schwarzschild ellipsoid. For stars in a distance-limited subsample of stars (< 100 pc), we detect a multi-modal velocity distribution consistent with that seen by HIPPARCOS. This strong non-Gaussianity significantly affects the measurements of the velocity-ellipsoid tilt and vertex deviation when using the Schwarzschild approximation. We develop and test a simple descriptive model for the overall kinematic behavior that captures these features over most of the probed volume, and can be used to search for substructure in kinematic and metallicity space. We use this model to predict further improvements in kinematic mapping of the Galaxy expected from Gaia and the Large Synoptic Survey Telescope.NSF AST-615991, AST-0707901, AST-0551161, AST-02-38683, AST-06-07634, AST-0807444, PHY05-51164NASA NAG5-13057, NAG5-13147, NNXO-8AH83GPhysics Frontier Center/Joint Institute for Nuclear Astrophysics (JINA) PHY 08-22648U.S. National Science FoundationMarie Curie Research Training Network ELSA (European Leadership in Space Astrometry) MRTN-CT-2006-033481Fermi Research Alliance, LLC, United States Department of Energy DE-AC02-07CH11359Alfred P. Sloan FoundationParticipating InstitutionsJapanese MonbukagakushoMax Planck SocietyHigher Education Funding Council for EnglandMcDonald Observator

The Milky Way Tomography with SDSS: III. Stellar Kinematics

We study Milky Way kinematics using a sample of 18.8 million main-sequence stars with r<20 and proper-motion measurements derived from SDSS and POSS astrometry, including ~170,000 stars with radial-velocity measurements from the SDSS spectroscopic survey. Distances to stars are determined using a photometric parallax relation, covering a distance range from ~100 pc to 10 kpc over a quarter of the sky at high Galactic latitudes (|b|>20 degrees). We find that in the region defined by 1 kpc <Z< 5 kpc and 3 kpc <R< 13 kpc, the rotational velocity for disk stars smoothly decreases, and all three components of the velocity dispersion increase, with distance from the Galactic plane. In contrast, the velocity ellipsoid for halo stars is aligned with a spherical coordinate system and appears to be spatially invariant within the probed volume. The velocity distribution of nearby ($Z<1$ kpc) K/M stars is complex, and cannot be described by a standard Schwarzschild ellipsoid. For stars in a distance-limited subsample of stars (<100 pc), we detect a multimodal velocity distribution consistent with that seen by HIPPARCOS. This strong non-Gaussianity significantly affects the measurements of the velocity ellipsoid tilt and vertex deviation when using the Schwarzschild approximation. We develop and test a simple descriptive model for the overall kinematic behavior that captures these features over most of the probed volume, and can be used to search for substructure in kinematic and metallicity space. We use this model to predict further improvements in kinematic mapping of the Galaxy expected from Gaia and LSST.Comment: 90 pages, 26 figures, submitted to Ap

The Milky Way Tomography with SDSS: II. Stellar Metallicity

Using effective temperature and metallicity derived from SDSS spectra for ~60,000 F and G type main sequence stars (0.2<g-r<0.6), we develop polynomial models for estimating these parameters from the SDSS u-g and g-r colors. We apply this method to SDSS photometric data for about 2 million F/G stars and measure the unbiased metallicity distribution for a complete volume-limited sample of stars at distances between 500 pc and 8 kpc. The metallicity distribution can be exquisitely modeled using two components with a spatially varying number ratio, that correspond to disk and halo. The two components also possess the kinematics expected for disk and halo stars. The metallicity of the halo component is spatially invariant, while the median disk metallicity smoothly decreases with distance from the Galactic plane from -0.6 at 500 pc to -0.8 beyond several kpc. The absence of a correlation between metallicity and kinematics for disk stars is in a conflict with the traditional decomposition in terms of thin and thick disks. We detect coherent substructures in the kinematics--metallicity space, such as the Monoceros stream, which rotates faster than the LSR, and has a median metallicity of [Fe/H]=-0.96, with an rms scatter of only ~0.15 dex. We extrapolate our results to the performance expected from the Large Synoptic Survey Telescope (LSST) and estimate that the LSST will obtain metallicity measurements accurate to 0.2 dex or better, with proper motion measurements accurate to ~0.2 mas/yr, for about 200 million F/G dwarf stars within a distance limit of ~100 kpc (g<23.5). [abridged]Comment: 40 pages, 21 figures, emulateApJ style, accepted to ApJ, high resolution figures are available from http://www.astro.washington.edu/ivezic/sdss/mw/astroph0804.385

Understanding the differences in cognitively defined subgroups in Alzheimer's disease: A data science approach

Thesis (Ph.D.)--University of Washington, 2022My work connects two types of data in Alzheimer’s Disease (AD): structural MRI data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and cognition data in the form of AD subgroups. The subgroups (AD-Executive, AD-Language, AD-Memory and AD-Visuospatial), defined by Crane et al. (2017), are based on cognitive test scores from the time of AD diagnosis, and each subgroup is characterized by marked impairment in the specified cognitive domain relative to the other domains. My dissertation’s focus is on data science and mathematical methods to understand how volumes of 70 brain regions of interest (ROIs) might differ across pairs of AD subgroups in cross-sectional data in time, specifically data from the time of AD diagnosis (Aim 1) and in longitudinal data (Aim 2). My work demonstrates a careful assessment and implementation of methods to best utilize the data available that is currently small in sample size, with imbalanced AD subgroup sizes and noisy in nature. In both aims, the following pairs of AD subgroups were compared: a.) AD-Language vs. AD-Memory, b.) AD-Memory vs. AD-Visuospatial and c.) AD-Language vs. AD-Visuospatial. The AD-Executive group was excluded from the current analyses due to its small sample size. In Aim 1, I explored supervised machine learning classification methods that provide insight into variable importance for identifying the most important brain ROIs for distinguishing between pairs of AD subgroups. I determined random forest to be the most appropriate method for this task, given the characteristics of the data. Prior to building classification models, I addressed specific challenges in cross-sectional data: potential noise due to non-ROI variables and imbalanced AD subgroup sizes. A challenge in using classification models in the domain of AD subgroups is that there is no gold standard for knowing how separable the AD subgroups are based on ROI volumes. The work presented here may be the first to establish a starting benchmark for classification accuracies for distinguishing between pairs of AD subgroups based on ROI volumes, although these models are not intended to be used for prediction in a clinical setting but rather to understand which brain regions are most important to distinguish the AD subgroups. In Aim 2, I used linear mixed effects (LME) modeling on longitudinal data to determine which of the 70 ROIs’ volume trajectories differ the most across pairs of AD subgroups in terms of longitudinal volume and rate of change of volume with respect to time. First, I laid out criteria for using data from specific MRI scans in an effort to reduce noise in data, instead of using the default longitudinal dataset. Given the small sample size of the AD subgroups and irregular data, I implemented LME modeling for each ROI on the original dataset consisting of all time points and also on a series of subsets of data that were obtained by restricting each AD subgroup’s data to time points with a specific minimum number of subjects available. An important finding of my work is that there was some overlap in the top ROIs that were determined to be important based on cross-sectional and longitudinal data analyses, for distinguishing between pairs of AD subgroups. Results from my Ph.D. work have potential implications for decisions about which brain regions may be relevant for future neuropathological studies in studying AD subgroups

PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers

Motivation: Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility. Results: We developed a pedigree-based analysis pipeline (PBAP) suite of programs geared towards SNPs and sequence data. PBAP performs quality control, marker selection and file preparation. PBAP sets up files for MORGAN, which can handle analyses for small and large pedigrees, typically human, and results can be used with other programs and for downstream analyses. We evaluate and illustrate its features with two real datasets

Whole exome sequencing in extended families with autism spectrum disorder implicates four candidate genes

Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders, characterized by impairment in communication and social interactions, and by repetitive behaviors. ASDs are highly heritable, and estimates of the number of risk loci range from hundreds to \u3e1000. We considered 7 extended families (size 12–47 individuals), each with ≥3 individuals affected by ASD. All individuals were genotyped with dense SNP panels. A small subset of each family was typed with whole exome sequence (WES). We used a 3-step approach for variant identification. First, we used family-specific parametric linkage analysis of the SNP data to identify regions of interest. Second, we filtered variants in these regions based on frequency and function, obtaining exactly 200 candidates. Third, we compared two approaches to narrowing this list further. We used information from the SNP data to impute exome variant dosages into those without WES. We regressed affected status on variant allele dosage, using pedigree-based kinship matrices to account for relationships. The p value for the test of the null hypothesis that variant allele dosage is unrelated to phenotype was used to indicate strength of evidence supporting the variant. A cutoff of p = 0.05 gave 28 variants. As an alternative third filter, we required Mendelian inheritance in those with WES, resulting in 70 variants. The imputation- and association-based approach was effective. We identified four strong candidate genes for ASD (SEZ6L, HISPPD1, FEZF1, SAMD11), all of which have been previously implicated in other studies, or have a strong biological argument for their relevance

Analysis of individual families implicates noncoding DNA variation and multiple biological pathways in Alzheimer’s disease risk

Background Late‐onset Alzheimer’s disease (AD) is a complex disorder with multiple genetic risk factors. Linkage and association analysis have mapped dozens of loci in pooled analysis of many pedigrees or large numbers of unrelated cases and controls. Identification of the underlying DNA risk variants in the regions of interest (ROIs) has been complicated by both the genetic heterogeneity and the cost, until recently, of comprehensive DNA sequencing in ROIs. The known loci also leave much heritability unexplained. Method We used the families in the AD Sequencing Project (ADSP) discovery family sample to identify variants of interest from whole genome sequences (WGS), and through the variants, genes implicated in risk. We used SNP‐based multipoint linkage analysis to identify ROIs with rare VOIs, carrying out analysis without trimming pedigrees. We pursued all ROIs with family‐specific lodmax scores >1.9, reducing the variants of interest by several filters. We carried out pedigree‐based genotype imputation from the available WGS data, followed by family‐based association analysis, filtered for low population minor allele frequency. We prioritized genes with a low false‐discovery rate for association of single‐cell transcription in brain with AD disease state (PMID:31209304), and genes with high expression in bulk brain (PMID: 24309898). Result We obtained 46 distinct ROIs representing lodmax1.9‐3.5 per ROI in each of 26 of the 110 ADSP discovery families analyzed. 29 ROIs further investigated in 16 of the families yielded 59 prioritized genes, with 1‐11 genes/ROI. Only 4 out of 321 variants that passed all filters in these genes were in exons, with minimal overlap with genes identified in AD GWASs. Only one ROI occurred in two families, with evidence for a shared‐haplotype between these families, implicating FBXO2 and FBXO44. Both genes are implicated in ubiquitination, while FBXO2 interacts with BACE1. Multiple pathways, both known and new, are implicated, including the ubiquitin‐proteasome system, neural development and maintenance, and mitochondrial functions. Conclusion This analysis underscores the evidence for extensive genetic heterogeneity and rare variants underlying AD risk, along with multiple potential mechanisms. The preponderance of prioritize non‐coding variants suggests alterations in gene regulation and/or expression as an aspect of AD genetic risk