# Novel Statistical methods for DNA Sequencing Data, and applications to Autism.

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $446,831

## Abstract

Summary
One of the major problems in human genetics is understanding the genetic causes
underlying complex phenotypes, including neuropsychiatric traits such as autism
spectrum disorders and schizophrenia. Despite tremendous work over the past few
decades, the underlying biological mechanisms are poorly understood in most cases.
Recent advances in high-throughput, massively parallel genomic technologies have
revolutionized the field of human genetics and promise to lead to important scientific
advances. Despite this progress in data generation, it remains very challenging to analyze
and interpret these data. The main focus of this proposal is the development of powerful
statistical methods for the integration of whole-genome sequencing data with rich
functional genomics data with the goal to improve the discovery of genes involved in
autism spectrum disorders. We propose to integrate data from many different sources,
including epigenetic data from projects such as ENCODE, Roadmap, and
PsychENCODE, eQTL data from the GTEx, PsychENCODE and CommonMind
consortia, data from large scale databases of genetic variation such as ExAC and
gnomAD, in order to predict functional effects of genetic variants in non-coding genetic
regions in a tissue and cell type specific manner, and generate functional maps across
large number of tissues and cell types in the human body that we can then use to identify
novel associations with autism in whole-genome sequencing studies. The proposed
functional predictions and functional maps will be broadly available in the popular
ANNOVAR database. We further propose to use these functional predictions in the
analysis of almost 20,000 whole genomes from three large whole genome sequencing
studies for autism. We believe that the proposed research is very timely and has the
potential to substantially improve the analysis of non-coding genetic variation, and hence
provide new insights into the biological mechanisms underlying risk to autism, and more
broadly to other neuropsychiatric diseases.

## Key facts

- **NIH application ID:** 10136719
- **Project number:** 5R01MH095797-08
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Iuliana Ionita
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $446,831
- **Award type:** 5
- **Project period:** 2012-04-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10136719

## Citation

> US National Institutes of Health, RePORTER application 10136719, Novel Statistical methods for DNA Sequencing Data, and applications to Autism. (5R01MH095797-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10136719. Licensed CC0.

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