About

Anita L. DeStefano, PhD, is a Professor of Biostatistics and Neurology at Boston University School of Public Health. She served as Graduate Affair Faculty Fellow for Diversity and Inclusion from 2018 to 2024 and was an inaugural Associate Director of the BU Genome Science Institute, holding that position from 2008 through 2018. Dr. DeStefano developed a course in Statistical Genetics and has been involved in teaching and coordinating courses such as Introduction to Statistical Computing for over 18 years. She has played a significant role in developing research computing resources for Boston University Medical Campus and currently co-chairs the BU IS&T Research Computing Governance Committee. Her main research interest is statistical genetics, with investigations into the genetic contributions to Parkinson Disease, stroke, Alzheimer’s disease, and related neurocognitive phenotypes. She has been an investigator in multiple large-scale genomic studies, including the GenePD study, Framingham Heart Study, and the Alzheimer Disease Sequencing Project, among others. Dr. DeStefano holds a PhD in Animal Breeding/Biometry from Cornell University, an MS in Dairy Science from Virginia Polytechnic Institute and State University, and a BS in Veterinary Science from Cornell University.

Research topics

• Biology
• Genetics
• Medicine
• Bioinformatics
• Internal medicine
• Computer Science
• Machine Learning
• Demography
• Evolutionary biology

Selected publications

• Whole genome sequencing analysis of over 3500 individuals dementia-free over 85 years old

  Journal of Alzheimer s Disease · 2026-04-23

  articleOpen accessSenior authorCorresponding

  Background Identifying genetic variants conferring resilience to Alzheimer's disease and related dementia (ADRD) may hold promise for developing therapeutics. Objective To determine genetic associations with being dementia-free at age 85 (DF85). Methods We examined genetic associations, using whole genome sequencing data, with DF85 in three Trans-Omics for Precision Medicine cohorts and the Alzheimer's Disease Sequencing Project Phenotype Harmonization Consortium. We tested common variants individually and aggregation of rare (MAF ≤ 1%) coding and non-coding variants in DF85 participants (n = 3657) against individuals who were not DF85 (n = 20,010). We verified associations using a stricter control set who developed dementia before age 85 (n = 5552). Results We observed an association at APOE (rs429358, MAF = 0.21, odds ratio [OR] = 0.49, 95% confidence interval [CI] = 0.46–0.53, p = 1.0 × 10 −92 ) as well as for two common variants (rs16892237-A near MAL2 , MAF = 0.08, OR = 1.34, 95% CI = 1.21–1.48, p = 1.1 × 10 −8 and rs8004018-G near GCH1 , MAF = 0.16, OR = 1.24, 95% CI = 1.15–1.34, p = 1.7 × 10 −9 ) and an aggregate of rare loss of function and disruptive missense variants in FBXW10 on chr 17 (p = 1.4 × 10 −7 ) associated with DF85. Conclusions Through a genome-wide assessment of a resilience-focused outcome, we identified common and rare genetic variants contributing to DF85 status. Genes associated with DF85 may delay onset of ADRD and provide translational impact.

  PublisherOA PDFDOI

• Association of genetic risk of Alzheimer’s disease and cognitive function in two European populations

  Scientific Reports · 2025-02-21 · 8 citations

  articleOpen access

  Although there is some evidence of an association between Alzheimer's disease polygenic risk score (AD PRS) and cognitive function, limited validations have been performed in large populations. We investigated the relationship between AD PRS and cognitive function in the UK Biobank in over 276,000 participants and further validated the association in the Alzheimer's Disease Neuroimaging Initiative (ADNI) sample. We developed the AD PRS (excluded the APOE variants) in the middle age UK Biobank participants (age ranged 39-72, mean age 57 years) of European ancestries by LDpred2. To validate the association of AD PRS and cognitive function internally in the UK Biobank, we linearly regressed standardized cognitive function on continuous standardized AD PRS with age at cognitive test, sex, genotyping array, first 10 principal components of genotyping, smoking, education in years, body mass index, and apolipoprotein E gene ε4 (APOE4) risk allele dosages. To validate the associations externally, we ran the linear mixed effects model in the ADNI sample free of dementia (age ranged 55-91, mean age 73), including similar covariates as fixed effects and participants' IDs as the random effect. Stratification by age, APOE4 carrier status, and cognitive status (cognitively normal or mild cognitive impairment) was also investigated. Our study validated the associations of AD PRS and cognitive function in both midlife and late-life observational cohorts. Although not all of the cognitive measures were significantly associated with AD PRS, non-verbal fluid reasoning (matrix pattern completion, β = - 0.022, P = 0.003), processing speed (such as symbol digit substitution, β = - 0.017, P = 1.08E-05), short-term memory and attention (such as pairs matching, β = - 0.014, P = 1.66E-10), and reaction time (β = - 0.010, P = 1.19E-06) were inversely associated with increasing AD PRS in the UK Biobank. Higher likelihood of cognitive impairment was also associated with higher AD PRS in the ADNI cognitive normal individuals (AD assessment scale β = 0.079, P = 0.02). In summary, we confirmed that poorer cognitive function was associated with a higher polygenic AD risk, and suggested the potential utility of the AD PRS in identifying those who may be at risk for further cognitive decline.

  PublisherOA PDFDOI

• Frequency of variants in Mendelian Alzheimer's disease genes within the Alzheimer's Disease Sequencing Project

  Journal of Alzheimer s Disease · 2025-03-14 · 3 citations

  articleOpen access

  Background Prior studies examined variants within presenilin-2 ( PSEN2 ), presenilin-1 ( PSEN1 ), and amyloid precursor protein ( APP ) genes. However, previously-reported clinically-relevant variants and other predicted damaging missense (DM) variants have not been characterized in a newer release of the Alzheimer's Disease Sequencing Project (ADSP). Objective To characterize previously-reported clinically-relevant variants and DM variants in PSEN2, PSEN1, APP within the participants from the ADSP. Methods We identified rare variants (MAF < 1%) in PSEN2 , PSEN1, and APP in 14,641 individuals with whole genome sequencing and 16,849 individuals with whole exome sequencing available (N total = 31,490). We additionally curated variants from ClinVar, OMIM, and Alzforum and report carriers of variants in clinical databases as well as predicted DM variants in these genes. Results We detected 31 previously-reported clinically-relevant variants with alternate alleles observed within the ADSP: 4 variants in PSEN2 , 25 in PSEN1 , and 2 in APP . The overall variant carrier rate for the 31 clinically-relevant variants in the ADSP was 0.3%. We observed that 79.5% of the variant carriers were cases compared to 3.9% were controls. In those with AD, the mean age of onset of AD among carriers of these clinically-relevant variants was 19.6 ± 1.4 years earlier compared with noncarriers (p = 7.8 × 10 −57 ). Additionally, we identified 197 rare variants (MAF < 1%) within ADSP participants not reported in known clinical databases. Conclusions A small proportion of individuals in the ADSP are carriers of a previously-reported clinically-relevant variant allele for AD and these participants have significantly earlier age of AD onset compared to noncarriers.

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• Domain mapping of disease mutations reveals pathogenic SORL1 variants in Alzheimer’s disease

  Molecular Neurodegeneration · 2025-12-01 · 1 citations

  articleOpen access

  Protein truncating variants (PTVs) in SORL1 are observed almost exclusively in Alzheimer’s Disease (AD) cases, but the effect of rare SORL1 missense variants is unclear. To identify high-priority missense variants (HPVs), we applied ‘domain mapping of disease mutations’ for the 637 unique coding SORL1 variants detected in 18,959 AD-cases and 21,893 non-demented controls. In this sample, PTVs and HPVs associated with respectively a 35- and 10-fold increased risk of early onset AD and 17- and 6-fold increased risk of overall AD. The median age at onset (AAO) of PTV- and HPV-carriers was 62 and 64 years, and APOE-genotype contributed to AAO-variability. The median AAO of PTV- and HPV-carriers is ~8–10 years earlier than wild-type SORL1 carriers, matched for APOE-genotype. Specific HPVs are highly penetrant and lead to earlier AAOs than PTVs, suggesting possible dominant negative effects. Our results justify a debate on whether HPV carriers should be considered for clinical counseling.

  PublisherOA PDFDOI

• Multiple-testing corrections in case-control studies using identity-by-descent segments

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-07

  preprintOpen access

  Abstract Identity-by-descent (IBD) mapping provides complementary signals to genome-wide association studies (GWAS) when multiple causal haplotypes or variants are present, but not directly tested. However, failing to correct for multiple testing in case-control studies using IBD segments can lead to false discoveries. We propose the difference between case-case and control-control IBD rates as an IBD mapping statistic. For our hypothesis test, we use a computationally efficient approach from the stochastic processes literature to derive genome-wide significance levels that control the family-wise error rate (FWER). Whole genome simulations indicate that our method conservatively controls the FWER. Because positive selection can lead to false discoveries, we pair our IBD mapping approach with a selection scan so that one can contrast results for evidence of confounding due to recent sweeps or other mechanisms, like population structure, that increase IBD sharing. We developed automated and reproducible workflows to phase haplotypes, call local ancestry probabilities, and perform the IBD mapping scan, the former two tasks being important preprocessing steps for haplotype analyses. We applied our methods to search for Alzheimer’s disease (AD) risk loci in the Alzheimer’s Disease Sequencing Project (ADSP) genome data. We identified six genome-wide significant signals of AD risk among samples genetically similar to African and European reference populations and self-identified Amish samples. Variants in the six potential risk loci we detected have previously been associated with AD, dementia, and memory decline. Three genes at two potential risk loci have already been nominated as therapeutic targets. Overall, our scalable approach makes further use of large consortia resources, which are expensive to collect but provide insights into disease mechanisms. Highlights We propose a computationally efficient method to address multiple testing when scanning along the genome for differences in identity-by-descent rates of case-case and control-control pairs. Whole genome simulations indicate that our method conservatively controls the desired family-wise error rate. We performed three case-control scans from ancestry cohorts in the Alzheimer’s Disease Sequencing Project, detecting six genome-wide significant signals around potential risk loci. We show that positive selection can confound IBD mapping tests in samples genetically similar to Europeans.

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• Integrating genetic and transcriptomic data to identify genes underlying obesity risk loci

  International Journal of Obesity · 2025-09-26 · 1 citations

  articleOpen access

  Abstract Background Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. Methods We analyzed genotype and blood gene expression data from up to 5619 samples in the Framingham Heart Study (FHS). Using 3992 single-nucleotide polymorphisms (SNPs) at 97 BMI loci and 1408 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript ( P BMI and P SNP , respectively) and then a correlated meta-analysis between the full summary data sets ( P META ). Transcripts were prioritized if we identified transcripts that met Bonferroni-corrected significance within each omic, showed stronger associations in the correlated meta-analysis than each omic, and had corresponding SNPs in the SNP-transcript-BMI association that were at least nominally associated with BMI in FHS data. We tested for generalization of identified association in a Hispanic ancestry sample of blood gene expression data and other samples in hypothalamus, nucleus accumbens, liver, and visceral adipose tissue (VAT) with significant threshold: P META < 0.05 & P META < P SNP & P META < P BMI . Results Among 308 significant SNP-transcript-BMI associations, we identified seven genes ( NT5C2 , GSTM3 , SNAPC3 , SPNS1 , TMEM245 , YPEL3 , and ZNF646 ) in five association regions. We generalized results for SNAPC3 and YPEL3 in Hispanic ancestry sample, for YPEL3 in the nucleus accumbens, ZNF646 and GSTM3 in VAT, and NT5C2 , SNAPC3 , TMEM245 , YPEL3 , and ZNF646 in liver. Conclusion The identified genes help link the genetic variation at obesity-risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.

  PublisherOA PDFDOI

• Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole‐genome sequencing from the Alzheimer's Disease Sequencing Project

  Alzheimer s & Dementia · 2024-10-20 · 20 citations

  articleOpen access

  Abstract INTRODUCTION Alzheimer's disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. METHODS We investigated the association of AD with both common variants and aggregates of rare coding and non‐coding variants in 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data. RESULTS Pooled‐population analyses of all individuals identified genetic variants at apolipoprotein E ( APOE ) and BIN1 associated with AD ( p < 5 × 10 −8 ). Subgroup‐specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and non‐coding variants in the region. Common variants in LINC00320 were observed associated with AD in Black individuals ( p = 1.9 × 10 −9 ). Finally, we observed rare non‐coding variants in the promoter of TOMM40 distinct of APOE in pooled‐population analyses ( p = 7.2 × 10 −8 ). DISCUSSION We observed that complementary pooled‐population and subgroup‐specific analyses offered unique insights into the genetic architecture of AD. Highlights We determine the association of genetic variants with Alzheimer's disease (AD) using 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data. We identified genetic variants at apolipoprotein E ( APOE ), BIN1 , PSEN1 , and LINC00320 associated with AD. We observed rare non‐coding variants in the promoter of TOMM40 distinct of APOE .

  PublisherOA PDFDOI

• Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

  medRxiv · 2024-06-12 · 3 citations

  preprintOpen access

  ABSTRACT Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (P BMI and P SNP , respectively) and then a correlated meta-analysis between the full summary data sets (P META ). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes ( NT5C2 , GSTM3 , SNAPC3 , SPNS1 , TMEM245 , YPEL3 , and ZNF646 ) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3 . We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (P META <0.05 & P META <P SNP & P META <P BMI ) results for YPEL3 in nucleus accumbens and NT5C2 , SNAPC3 , TMEM245 , YPEL3 , and ZNF646 in liver. The identified genes help link the genetic variation at obesity risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.

  PublisherOA PDFDOI

• Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data

  Alzheimer s & Dementia · 2024-03-21 · 21 citations

  articleOpen accessCorresponding

  INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

  PublisherOA PDFDOI

• The plasma miRNAome in ADNI: Signatures to aid the detection of at‐risk individuals

  Alzheimer s & Dementia · 2024-09-18 · 21 citations

  articleOpen access

  INTRODUCTION: MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). METHODS: We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. RESULTS: We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. DISCUSSION: Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. HIGHLIGHTS: We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01DK099269

  NIH · $2.6M · 2019

• Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches

  NIH · $617k · 2021–2021

• Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches

  NIH · $11.1M · 2018–2026

Frequent coauthors

• Sudha Seshadri

  Framingham Heart Study

  1039 shared

• Cornelia M. van Duijn

  495 shared

• Eric Boerwinkle

  The University of Texas Health Science Center at Houston

  453 shared

• Lindsay A. Farrer

  Framingham Heart Study

  434 shared

• Claudia L. Satizábal

  Institute for Neurodegenerative Disorders

  422 shared

• Richard Mayeux

  Columbia University

  398 shared

• Joshua C. Bis

  University of Washington

  397 shared

• Margaret A. Pericak‐Vance

  Dr. John T. Macdonald Foundation

  389 shared

Labs

• Admissions TeamPI