Publications by authors named "Xueqiu Jian"

30 Publications

  • Page 1 of 1

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores.

Nat Commun 2021 06 7;12(1):3417. Epub 2021 Jun 7.

Servei de Neurologia, Hospital Universitari i Politècnic La Fe, Valencia, Spain.

Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease.
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http://dx.doi.org/10.1038/s41467-021-22491-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184987PMC
June 2021

Multiomics integrative analysis identifies allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis.

Aging (Albany NY) 2021 Apr 12;13(7):9277-9329. Epub 2021 Apr 12.

Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Alzheimer's disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by haplotype ( and ). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in and AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies.
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http://dx.doi.org/10.18632/aging.202950DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064208PMC
April 2021

Cerebral small vessel disease genomics and its implications across the lifespan.

Nat Commun 2020 12 8;11(1):6285. Epub 2020 Dec 8.

University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA.

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
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http://dx.doi.org/10.1038/s41467-020-19111-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722866PMC
December 2020

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Nat Commun 2020 09 22;11(1):4796. Epub 2020 Sep 22.

Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
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http://dx.doi.org/10.1038/s41467-020-18367-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508833PMC
September 2020

Genome-wide association study of cognitive function in diverse Hispanics/Latinos: results from the Hispanic Community Health Study/Study of Latinos.

Transl Psychiatry 2020 07 22;10(1):245. Epub 2020 Jul 22.

Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Cognitive function such as reasoning, attention, memory, and language is strongly correlated with brain aging. Compared to non-Hispanic whites, Hispanics/Latinos have a higher risk of cognitive impairment and dementia. The genetic determinants of cognitive function have not been widely explored in this diverse and admixed population. We conducted a genome-wide association analysis of cognitive function in up to 7600 middle aged and older Hispanics/Latinos (mean = 55 years) from the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Four cognitive measures were examined: the Brief Spanish English Verbal Learning Test (B-SEVLT), the Word Fluency Test (WFT), the Digit Symbol Substitution Test (DSST), the Six-Item Screener (SIS). Four novel loci were identified: one for B-SEVLT at 4p14, two for WFT at 3p14.1 and 6p21.32, and one for DSST at 10p13. These loci implicate genes highly expressed in brain and previously connected to neurological diseases (UBE2K, FRMD4B, the HLA gene complex). By applying tissue-specific gene expression prediction models to our genotype data, additional genes highly expressed in brain showed suggestive associations with cognitive measures possibly indicating novel biological mechanisms, including IFT122 in the hippocampus for SIS, SNX31 in the basal ganglia for B-SEVLT, RPS6KB2 in the frontal cortex for WFT, and CSPG5 in the hypothalamus for DSST. These findings provide new information about the genetic determinants of cognitive function in this unique population. In addition, we derived a measure of general cognitive function based on these cognitive tests and generated genome-wide association summary results, providing a resource to the research community for comparison, replication, and meta-analysis in future genetic studies in Hispanics/Latinos.
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http://dx.doi.org/10.1038/s41398-020-00930-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376098PMC
July 2020

Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes.

Cereb Cortex 2020 06;30(7):4121-4139

Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04109 Leipzig, Germany.

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.
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http://dx.doi.org/10.1093/cercor/bhaa035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947185PMC
June 2020

Genetic architecture of subcortical brain structures in 38,851 individuals.

Nat Genet 2019 11 21;51(11):1624-1636. Epub 2019 Oct 21.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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http://dx.doi.org/10.1038/s41588-019-0511-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055269PMC
November 2019

A prospective study of serum metabolites and risk of ischemic stroke.

Neurology 2019 04 13;92(16):e1890-e1898. Epub 2019 Mar 13.

From the Brown Foundation Institute of Molecular Medicine, McGovern Medical School (D.S., X.J., M.F.), and School of Public Health (B.Y., E.B., M.F.), The University of Texas Health Science Center at Houston; Institute for Stroke and Dementia Research (S.T., M.D.), Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany; Johns Hopkins University School of Medicine (R.F.G.), Baltimore, MD; The University of Mississippi Medical Center (T.H.M.), Jackson; German Center for Neurodegenerative Diseases (DZNE, Munich) (M.D.); and Munich Cluster for Systems Neurology (SyNergy) (S.T., M.D.), Germany.

Objective: To identify promising blood-based biomarkers and novel etiologic pathways of disease risk, we applied an untargeted serum metabolomics profiling in a community-based prospective study of ischemic stroke (IS).

Methods: In 3,904 men and women from the Atherosclerosis Risk In Communities study, Cox proportional hazard models were used to estimate the association of incident IS with the standardized level of 245 fasting serum metabolites individually, adjusting for age, sex, race, field center, batch, diabetes, hypertension, current smoking status, body mass index, and estimated glomerular filtration rate. Validation of results was carried out in an independent sample of 114 IS cases and 112 healthy controls.

Results: Serum levels of 2 long-chain dicarboxylic acids, tetradecanedioate and hexadecanedioate, were strongly correlated ( = 0.88) and were associated with incident IS after adjusting for covariates (hazard ratio [95% confidence interval (CI)] 1.11 [1.06-1.16] and 1.12 [1.07-1.17], respectively; < 0.0001). Analyses by IS subtypes suggested that these associations were specific to cardioembolic stroke (CES). Associations of tetradecanedioate and hexadecanedioate with IS were independently confirmed (odds ratio [95% CI] 1.76 [1.21; 2.56] and 1.60 [1.11; 2.32], respectively).

Conclusion: Two serum long-chain dicarboxylic acids, metabolic products of ω-oxidation of fatty acids, were associated with IS and CES independently of known risk factors. Pathways related to intracellular hexadecanedioate synthesis or those involved in its clearance from the circulation may mediate IS risk. These results highlight the potential of metabolomics to discover novel circulating biomarkers for stroke and to unravel novel pathways for IS and its subtypes.
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http://dx.doi.org/10.1212/WNL.0000000000007279DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550501PMC
April 2019

Association of variants in HTRA1 and NOTCH3 with MRI-defined extremes of cerebral small vessel disease in older subjects.

Brain 2019 04;142(4):1009-1023

The University of Texas Health Science Center at Houston, Houston, TX, USA.

We report a composite extreme phenotype design using distribution of white matter hyperintensities and brain infarcts in a population-based cohort of older persons for gene-mapping of cerebral small vessel disease. We demonstrate its application in the 3C-Dijon whole exome sequencing (WES) study (n = 1924, nWESextremes = 512), with both single variant and gene-based association tests. We used other population-based cohort studies participating in the CHARGE consortium for replication, using whole exome sequencing (nWES = 2,868, nWESextremes = 956) and genome-wide genotypes (nGW = 9924, nGWextremes = 3308). We restricted our study to candidate genes known to harbour mutations for Mendelian small vessel disease: NOTCH3, HTRA1, COL4A1, COL4A2 and TREX1. We identified significant associations of a common intronic variant in HTRA1, rs2293871 using single variant association testing (Pdiscovery = 8.21 × 10-5, Preplication = 5.25 × 10-3, Pcombined = 4.72 × 10-5) and of NOTCH3 using gene-based tests (Pdiscovery = 1.61 × 10-2, Preplication = 3.99 × 10-2, Pcombined = 5.31 × 10-3). Follow-up analysis identified significant association of rs2293871 with small vessel ischaemic stroke, and two blood expression quantitative trait loci of HTRA1 in linkage disequilibrium. Additionally, we identified two participants in the 3C-Dijon cohort (0.4%) carrying heterozygote genotypes at known pathogenic variants for familial small vessel disease within NOTCH3 and HTRA1. In conclusion, our proof-of-concept study provides strong evidence that using a novel composite MRI-derived phenotype for extremes of small vessel disease can facilitate the identification of genetic variants underlying small vessel disease, both common variants and those with rare and low frequency. The findings demonstrate shared mechanisms and a continuum between genes underlying Mendelian small vessel disease and those contributing to the common, multifactorial form of the disease.
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http://dx.doi.org/10.1093/brain/awz024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439324PMC
April 2019

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.

Nat Genet 2019 03 28;51(3):414-430. Epub 2019 Feb 28.

Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades-Universitat Internacional de Catalunya, Barcelona, Spain.

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.
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http://dx.doi.org/10.1038/s41588-019-0358-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6463297PMC
March 2019

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

Neurology 2019 Jan 16. Epub 2019 Jan 16.

Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.

Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.

Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, = 1.77 × 10; and LINC00539/ZDHHC20, = 5.82 × 10. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits ( value for BI, = 9.38 × 10; = 5.23 × 10 for hypertension), smoking ( = 4.4 × 10; = 1.2 × 10), diabetes ( = 1.7 × 10; = 2.8 × 10), previous cardiovascular disease ( = 1.0 × 10; = 2.3 × 10), stroke ( = 3.9 × 10; = 3.2 × 10), and MRI-defined white matter hyperintensity burden ( = 1.43 × 10; = 3.16 × 10), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI ( ≤ 0.0022), without indication of directional pleiotropy.

Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.
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http://dx.doi.org/10.1212/WNL.0000000000006851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369905PMC
January 2019

Apolipoprotein E genotypes among diverse middle-aged and older Latinos: Study of Latinos-Investigation of Neurocognitive Aging results (HCHS/SOL).

Sci Rep 2018 12 13;8(1):17578. Epub 2018 Dec 13.

Institute of Molecular Medicine, McGovern Medical School University of Texas Health Science Center, Houston, TX, USA.

The apoE4 isoform is associated with increased cholesterol, cardiovascular risk, and Alzheimer's Disease risk, however, its distribution is not well-understood among US Latinos. Latinos living in the US are highly Amerindian, European and African admixed, which varies by region and country of origin. However, Latino genetic diversity is understudied and consequently poorly understood, which has significant implications for understanding disease risk in nearly one-fifth of the US population. In this report we describe apoE distributions in a large and representative sample of diverse, genetically determined US Latinos.
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http://dx.doi.org/10.1038/s41598-018-35573-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6292877PMC
December 2018

Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume.

Nat Commun 2018 09 26;9(1):3945. Epub 2018 Sep 26.

Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald 17475, Germany.

The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρ = -0.59, p-value = 3.14 × 10), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology.
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http://dx.doi.org/10.1038/s41467-018-06234-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158214PMC
September 2018

Whole exome sequencing study identifies novel rare and common Alzheimer's-Associated variants involved in immune response and transcriptional regulation.

Mol Psychiatry 2020 08 14;25(8):1859-1875. Epub 2018 Aug 14.

McDonnell Genome Institute, Washington University, St. Louis, MO, USA.

The Alzheimer's Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.
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http://dx.doi.org/10.1038/s41380-018-0112-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375806PMC
August 2020

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.

Stroke 2018 08;49(8):1812-1819

Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC.

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P=4.5×10) partially independent of known common signal ( P=1.4×10). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; P=1.9×10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P=2.8×10). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
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http://dx.doi.org/10.1161/STROKEAHA.118.020689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202149PMC
August 2018

Imaging Endophenotypes of Stroke as a Target for Genetic Studies.

Stroke 2018 06 14;49(6):1557-1562. Epub 2018 May 14.

From the Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston.

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http://dx.doi.org/10.1161/STROKEAHA.117.017073DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970992PMC
June 2018

Functional annotation of genomic variants in studies of late-onset Alzheimer's disease.

Bioinformatics 2018 08;34(16):2724-2731

Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.

Motivation: Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied.

Results: In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%).

Availability And Implementation: Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty177DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084586PMC
August 2018

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.

Nat Genet 2018 04 12;50(4):524-537. Epub 2018 Mar 12.

Institute of Cardiovascular Research, Royal Holloway University of London, London, UK, and Ashford and St Peters Hospital, Surrey, UK.

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.
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http://dx.doi.org/10.1038/s41588-018-0058-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968830PMC
April 2018

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease.

Nat Genet 2017 09 17;49(9):1373-1384. Epub 2017 Jul 17.

Boston University School of Medicine, Boston, Massachusetts, USA.

We identified rare coding variants associated with Alzheimer's disease in a three-stage case-control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 × 10) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 × 10) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 × 10, odds ratio (OR) = 0.68, minor allele frequency (MAF) = 0.0059, MAF = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 × 10, OR = 1.43, MAF = 0.011, MAF = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 × 10, OR = 1.67, MAF = 0.0143, MAF = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.
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http://dx.doi.org/10.1038/ng.3916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5669039PMC
September 2017

In Silico Prediction of Deleteriousness for Nonsynonymous and Splice-Altering Single Nucleotide Variants in the Human Genome.

Methods Mol Biol 2017 ;1498:191-197

Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Pressler St, E529, Houston, TX, 77030, USA.

In silico prediction methods have increasingly been valuable and popular in molecular biology, especially in human genetics, for deleteriousness prediction to filter and prioritize huge amounts of DNA variation identified by sequencing human genomes. There is a rich collection of available methods developed upon different levels/aspects of knowledge about how DNA variations affect gene expression. Given the fact that their predictions are not always consistent or even opposite of what was expected, using consensus prediction or majority vote among these methods is preferred to trusting any single one. Because querying different databases for different methods is both tedious and time-consuming for such big data sets, one database integrating predictions from multiple databases can facilitate the process. In this chapter, we describe the general steps of obtaining comprehensive predictions and annotations for large numbers of variants from dbNSFP, the first and probably the most widely used database of its kind.
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http://dx.doi.org/10.1007/978-1-4939-6472-7_13DOI Listing
January 2018

Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies.

Hum Mol Genet 2015 Apr 30;24(8):2125-37. Epub 2014 Dec 30.

Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences and

Accurate deleteriousness prediction for nonsynonymous variants is crucial for distinguishing pathogenic mutations from background polymorphisms in whole exome sequencing (WES) studies. Although many deleteriousness prediction methods have been developed, their prediction results are sometimes inconsistent with each other and their relative merits are still unclear in practical applications. To address these issues, we comprehensively evaluated the predictive performance of 18 current deleteriousness-scoring methods, including 11 function prediction scores (PolyPhen-2, SIFT, MutationTaster, Mutation Assessor, FATHMM, LRT, PANTHER, PhD-SNP, SNAP, SNPs&GO and MutPred), 3 conservation scores (GERP++, SiPhy and PhyloP) and 4 ensemble scores (CADD, PON-P, KGGSeq and CONDEL). We found that FATHMM and KGGSeq had the highest discriminative power among independent scores and ensemble scores, respectively. Moreover, to ensure unbiased performance evaluation of these prediction scores, we manually collected three distinct testing datasets, on which no current prediction scores were tuned. In addition, we developed two new ensemble scores that integrate nine independent scores and allele frequency. Our scores achieved the highest discriminative power compared with all the deleteriousness prediction scores tested and showed low false-positive prediction rate for benign yet rare nonsynonymous variants, which demonstrated the value of combining information from multiple orthologous approaches. Finally, to facilitate variant prioritization in WES studies, we have pre-computed our ensemble scores for 87 347 044 possible variants in the whole-exome and made them publicly available through the ANNOVAR software and the dbNSFP database.
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http://dx.doi.org/10.1093/hmg/ddu733DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4375422PMC
April 2015

In silico prediction of splice-altering single nucleotide variants in the human genome.

Nucleic Acids Res 2014 Dec;42(22):13534-44

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
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http://dx.doi.org/10.1093/nar/gku1206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267638PMC
December 2014

In silico tools for splicing defect prediction: a survey from the viewpoint of end users.

Genet Med 2014 Jul 21;16(7):497-503. Epub 2013 Nov 21.

Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5' and 3' splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed.
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http://dx.doi.org/10.1038/gim.2013.176DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029872PMC
July 2014

dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations.

Hum Mutat 2013 Sep 10;34(9):E2393-402. Epub 2013 Jul 10.

Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.

dbNSFP is a database developed for functional prediction and annotation of all potential non-synonymous single-nucleotide variants (nsSNVs) in the human genome. This database significantly facilitates the process of querying predictions and annotations from different databases/web-servers for large amounts of nsSNVs discovered in exome-sequencing studies. Here we report a recent major update of the database to version 2.0. We have rebuilt the SNV collection based on GENCODE 9 and currently the database includes 87,347,043 nsSNVs and 2,270,742 essential splice site SNVs (an 18% increase compared to dbNSFP v1.0). For each nsSNV dbNSFP v2.0 has added two prediction scores (MutationAssessor and FATHMM) and two conservation scores (GERP++ and SiPhy). The original five prediction and conservation scores in v1.0 (SIFT, Polyphen2, LRT, MutationTaster and PhyloP) have been updated. Rich functional annotations for SNVs and genes have also been added into the new version, including allele frequencies observed in the 1000 Genomes Project phase 1 data and the NHLBI Exome Sequencing Project, various gene IDs from different databases, functional descriptions of genes, gene expression and gene interaction information, among others. dbNSFP v2.0 is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP.
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http://dx.doi.org/10.1002/humu.22376DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109890PMC
September 2013

Polymorphisms in ABLIM1 are associated with personality traits and alcohol dependence.

J Mol Neurosci 2012 Feb 6;46(2):265-71. Epub 2011 May 6.

Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, PO Box 70259, Lamb Hall, Johnson City, TN, 37614-1700, USA.

Personality traits like novelty seeking (NS), harm avoidance (HA), and reward dependence (RD) are known to be moderately heritable (30-60%). These personality traits and their comorbidities, such as alcohol dependence (AD), may share genetic components. We examined 11,120 single nucleotide polymorphisms (SNPs) genotyped in 292 nuclear families from the Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). A family-based association analysis was performed using the FBAT program. NS, HA, and RD were treated as quantitative traits and AD as a binary trait. Based on a multivariate association test of three quantitative traits in FBAT, we observed 20 SNPs with p < 10(-3). Interestingly, several genes (TESK2, TIPARP, THEMIS, ABLIM1, RFX4, STON2 and LILRA1) are associated with three personality traits with p < 10(-3) using single trait analysis and AD. Especially, SNP rs727532 within ABLIM1 gene at 10q25 showed the most significant association (p = 6.4 × 10(-5)) in the multivariate test and strong associations with NS, HA, RD, and AD (p = 4.48 × 10(-4), 1.2 × 10(-5), 5.6 × 10(-5), 3.12 × 10(-4), respectively) in the COGA sample. In addition, the association of rs727532 with AD was confirmed in a replication study. This study reports some newly recognized associations between several genetic loci and both AD and three personality traits.
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http://dx.doi.org/10.1007/s12031-011-9530-6DOI Listing
February 2012

dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions.

Hum Mutat 2011 Aug;32(8):894-9

Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.

With the advance of sequencing technologies, whole exome sequencing has increasingly been used to identify mutations that cause human diseases, especially rare Mendelian diseases. Among the analysis steps, functional prediction (of being deleterious) plays an important role in filtering or prioritizing nonsynonymous SNP (NS) for further analysis. Unfortunately, different prediction algorithms use different information and each has its own strength and weakness. It has been suggested that investigators should use predictions from multiple algorithms instead of relying on a single one. However, querying predictions from different databases/Web-servers for different algorithms is both tedious and time consuming, especially when dealing with a huge number of NSs identified by exome sequencing. To facilitate the process, we developed dbNSFP (database for nonsynonymous SNPs' functional predictions). It compiles prediction scores from four new and popular algorithms (SIFT, Polyphen2, LRT, and MutationTaster), along with a conservation score (PhyloP) and other related information, for every potential NS in the human genome (a total of 75,931,005). It is the first integrated database of functional predictions from multiple algorithms for the comprehensive collection of human NSs. dbNSFP is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP.
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http://dx.doi.org/10.1002/humu.21517DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3145015PMC
August 2011

Family-based association analysis of alcohol dependence in the COGA sample and replication in the Australian twin-family study.

J Neural Transm (Vienna) 2011 Sep 29;118(9):1293-9. Epub 2011 Mar 29.

Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, PO Box 70259, Lamb Hall, Johnson City, TN 37614-1700, USA.

Family, twin, and adoption studies have indicated that genetic and environmental factors contribute to the development of alcohol dependence (AD). We conducted a low-density genome-wide association analysis to identify genetic variants influencing AD. We used 11,120 SNPs from the Affymetrix 10K Genechips genotyped in 116 Caucasian pedigrees (272 nuclear families) from Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). Family-based association analyses for AD were performed by the PBAT program for autosomal SNPs and by the FBAT program for X-chromosome SNPs. We identified 37 SNPs associated with AD (P < 10(-3)), thirteen of which were located in known genes. The most significant association with AD was observed with SNP rs1986644 (P = 8.51 × 10(-6)) at 13q22 near EDNRB gene. The next best signal was at 1q41 in USH2A (rs532342, P = 1.07 × 10(-5)) and the third region was at 3q25.31 in TIPARP (rs1367311, P = 2.31 × 10(-5)). Furthermore, we found support for association of MAOA gene (P = 4.14 × 10(-4) for rs979606). Six of the 37 AD associated SNPs were confirmed to be associated with AD in Australian twin-family study sample (P < 0.05). Interestingly, four SNPs in DSCAML1 at 11q23 reached the genome-wide significance (the top SNP is rs10892169 with P = 5.31 × 10(-9)), while rs637547 in NKAIN2 at 6q21 showed strong association with AD (P = 5.11 × 10(-7)) in the replication sample. These findings offer the potential for new insights into the pathogenesis of AD and will serve as a resource for replication in other populations to elucidate the potential role of these genetic variants in AD.
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http://dx.doi.org/10.1007/s00702-011-0628-3DOI Listing
September 2011
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