Suzanne M Leal, PhD


My primary interest lies in understanding the genetic etiology of complex and Mendelian traits, with an emphasis on developing and applying statistical and epidemiological methods to tackle these complex problems. I have worked extensively on method and software development to aid in gene identification and elucidating the genetics of disease etiology. One of my interests is developing methods to analyze rare variants obtained from sequence data, including methods to analyze family data, detect epistasis, and pleiotropy. My publication on the combined multivariate collapsing (CMC) method was the first article to describe a rare-variant aggregate test to detect complex disease associations. I have also developed additional methods to identify associations with rare variants with application to population- and family-based sequence data. Rare-variant aggregate methods were also developed to perform parametric and non-parametric linkage analysis. Additionally, methods were developed to estimate the effect sizes for rare variants; simulate rare variants to compare methods and estimate power and type I error; and estimate sample sizes for complex and Mendelian traits. To facilitate the analysis of genetic data I have led the development of software that includes SEQSpark to perform annotation, quality control, and analysis of large-scale genetic data set. My work on method development has been guided by applied research, that provides me with insights on which methods and analysis tools will be most useful to researchers in the field. This applied research includes the study of a wide variety of complex and Mendelian traits. The complex diseases that I study include adiposity, age-related hearing impairment, Alzheimer’s disease (AD), asthma, tinnitus, amongst others. Analysis of complex disease includes analysis of data obtained from biobanks, e.g., UK Biobank as well as data collected to study a specific disease, e.g., Alzheimer’s Disease Sequencing Project. To understand the genetic etiology of complex traits association testing is performed using sequence data and other omics data. Analysis not only includes testing to detect main effect associations but also interactions e.g., gene x gene and gene x environment and pleiotropy as well as the estimation of genetic risk. The Mendelian traits that I am studying include nonsyndromic hearing impairment (NSHI), intellectual disability (ID), and skeletal disorders. For NSHI >1,500 families from Cameroon, Ghana, Hungary (Roma), Iran, Jordan, Mali, Pakistan, Poland, Senegal, South Africa, Switzerland, Turkey, and USA have been ascertained and lead to the identification and publication of 24 novel NSHI genes. For ID >270 families from Colombia, Finland, Hungary, Mali, and Pakistan have been ascertained and several new ID genes identified. We have also identified two novel postaxial polydactyl genes FAM92A and KIAA0825 through the study of consanguineous families from Pakistan. I also organize and teach courses nationally and internationally on gene mapping and statistical genetics as well as mentoring pre- and postdoctoral trainees.

Academic Appointments

  • Sergievsky Family Professor of Neurological Sciences (in Neurology and in the Gertrude H. Sergievsky Center)


  • Female

Credentials & Experience

Education & Training

  • MS, 1989 Biostatistics, Columbia University
  • PhD, 1994 Epidemiology, Columbia University
  • Fellowship: 1996 University of Tübingen


Research Interests:

  • Linkage analysis of human diseases
  • Whole-genome and candidate gene association studies
  • Development of new methods and software in gene mapping
  • Analysis of rare variant sequence data
  • Analysis of copy number variations