Featured Researchers

Ani Manichaikul, PhD

Ani Manichaikul, PhD

Please describe the research questions of your lab.

My research is focused on gene mapping for complex diseases with a particular focus on gene mapping in multi-ethnic populations and interpreting genetic association signals in the context of race/ethnic heterogeneity. Most of the genetic analysis efforts in my lab have initiated in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) in which we work very actively. As a result, our projects focus primarily on the phenotypes represented in MESA including pulmonary function and lung CT traits, as well cardiovascular measures, including atherosclerosis and related biomarkers of disease. Building on many of the genome-wide association study (GWAS) efforts we have been involved with over the past decade, my group is very interested in using statistical and computational approaches to shed light on the mechanisms underlying genetic loci identified by GWAS. We are also interested in gaining a better understanding of the heterogeneity underlying GWAS signals. For example, we are trying to dissect genetic loci that were identified in European ancestry populations and show little evidence of association in African Americans, and vice versa.

What genetics/genomics techniques do you utilize in your lab?

There are three major classes of genetics/genomic approaches that I currently apply in my lab. First, I try to gain access to the best quality data currently available for the study of our research questions of interest. In order to access the most interesting data sets, we generally work closely with consortia such as the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, as well as the NHLBI Trans-omics for Precision Medicine (TOPMed) program. Through these collaborations, we have been involved in numerous GWAS efforts and we are currently working on some of the earliest efforts for large-scale whole genome sequence analysis of complex traits, including pulmonary function and lung CT traits, in TOPMed. Second, we are actively applying integrative approaches that allow us to combine GWAS data sets with other 'omics data, including RNA-seq, proteomics, metabolomics and methylation data. Third, for detailed follow-up of GWAS regions of interest, we often perform more customized analyses within the MESA cohort for which we have a rich set of phenotypes and other covariates, as well as biomarker data, which provide us with the opportunity to examine the role of risk factors, global and local ancestry as mediators or modifiers of genetic associations that we have identified in large-scale consortium efforts.

Describe a key technique/assay/instrument utilized in your lab, and what novel insights does it bring to your research question?

The integrative approach that I use most is called PrediXcan, and I find it very useful since it provides a systematic framework to combine GWAS data sets with RNA-seq and other high-throughput 'omics data sets. There are a couple of different ways to conceptualize the PrediXcan approach. One way to think about it is that PrediXcan provides a framework for genome-wide imputation of gene expression levels using SNP genotypes, subsequently allowing us to examine the association of imputed expression with phenotypes of interests. Another way to interpret PrediXcan is as a gene-based test where the SNP weights for the test are based on each SNP's evidence as an expression quantitative trait locus (eQTL). In any case, applying the PrediXcan approach is a very useful way for us to identify 'omics parameters that are the likely downstream functional targets of the SNPs in genetic loci identified by GWAS.

At what point in your life did you decide you wanted to be a scientist/physician?

I don't think there was a particular formative moment for me. I have been obsessed with numbers since about as far back as I can remember. As I kid, I was interested in weather forecasts and interest rates. When I was in high school, I did an internship on the genetics of pitch perception and taste at the NIDCD. I realized that I really enjoyed looking for patterns in biological data, and I have continued to work in this field (statistical analysis for biomedical applications) ever since.

In your opinion, what is one of the most important discoveries in the field of respiratory illness/disease/function that was dependent on genomics or similar techniques?

There is the whole body of knowledge surrounding alpha-1 antitrypsin deficiency which is now one of the most well-characterized genetic phenomena and certainly owes some of that to early genomic techniques. More recently, it has been exciting to see the success of genetic risk prediction, in particular the prediction of COPD that has been spearheaded by excellent work from the UK Biobank investigators.

Briefly describe your favorite publication involving genomics/omics that you were involved with in general-audience terms.

Two of my favorite publications from my early career were (1) population structure of Hispanics in the United States, published in 2012 (PMID: 22511882 ), and (2) our first GWAS of emphysema on CT scan in the Multi-Ethnic Study of Atherosclerosis, published in 2014 (PMID: 24383474). These two papers laid the foundation for my current research efforts. In particular, I would trace my current focus on elucidating the genetics of lung function and emphysema in non-European ancestry populations back to these two papers.

What is your favorite aspect of ATS?

I've found ATS to include a very collaborative, interdisciplinary group of researchers. I also found the assemblies to be an excellent way to navigate this very large organization.

How could your research assist scientists and clinicians in other assemblies at ATS?

Through our GWAS studies and integration with downstream 'omics data sets, we have generated a lot of hypotheses about the genes and pathways that may contribute to increased risk of impaired lung function. I believe this information will eventually be useful in clinical settings, allowing physicians to screen more efficiently for individuals at increased genetic risk of disease. In addition, it would be great to strike of collaborations with investigators interested in collaborating on studies focused on elucidating biological mechanisms underlying some of the genes and pathways that we have identified.

Would you be open to collaborations with GG and/or non-GG scientists and clinicians? Do you have any potential lab openings currently or in the near future?

Yes, we are currently (and almost always) looking for talented staff biostatisticians, postdocs or PhD students who are interested in working on statistical analyses in genetics and genomics.