Research

 

 

Research Areas


 

The Keinan lab aims to improve the search for complex disease genes and genes underlying other complex traits, with the key driving hypothesis being that characterizing human population genomics can inform the design and analysis of medical genetic studies. This hypothesis received fresh support from the lab's work on the effect that recent human explosive population growth has had on the accumulation of rare genetic variants and the extensive implications of that discovery for gene-disease association studies (Keinan & Clark, Science 2012). Hence, the lab studies how demographic history and natural selection have shaped patterns of human genetic variation, and translates that knowledge to the study of the genetic basis of complex human diseases. Members of the lab come from varied backgrounds, including in computer science, statistics, genetics, genomics, physics, anthropology, and biology, which enables the collaborative development of computational and statistical methods, their efficient application to large-scale, genomic data sets, and the interpretation of discoveries in light of gene function and anthropological evidence.

 

 

Recent Projects


 

The X-Factor of Complex Disease

Methods, software, and extensive application for studying the X chromosome in association studies

 

  

 
The X chromosome plays an important role in human disease, especially those with sexually dimorphic characteristics. Analysis of X requires special attention due to its unique inheritance pattern leading to analytical complications that have resulted in the majority of GWAS either not considering or mishandling it with tools designed for non-sex chromosomes. We overcame many of the analytical complications by developing an array of X-specific methods that span all stages of GWAS, from genotype calling, through imputation and extensive QC, and to statistical association testing. Specifically, we develop new types of association tests, some of which applicable uniquely to the X chromosome. We implement the analysis pipeline and all methods as part of a publicly available software, XWAS (chromosome X-Wide Analysis toolSet). We apply these to conduct X-wide association studies in dozens of GWAS, with focus on autoimmune diseases, risk factors of coronary artery disease, and psychiatric disorders, all of which are very different between males and females. We discovered and replicated many novel significant X-linked associations, e.g. (i) variants in CENPI as contributing, with different effect sizes in males and females, to the risk of three different autoimmune diseases. Other, autosomal genes in the same family as CENPI have previously been associated to other autoimmune diseases; (ii) ARHGEF6 to Crohn's disease, and replicated in ulcerative colitis, another inflammatory bowel disorder. ARHGEF6 has been shown to interact with a gastric bacterium that has been associated to IBD. (iii) Significantly increased variance of systolic blood pressure in females that are heterozygous for a variant that might regulate ATRX, a gene that has been previously associated with alpha-thalassemia. With the availablility of the XWAS software package, we hope to bring the X chromosome to the GWAS era by enabling other researchers to include it in their studies.
 
 
Some related publications:

 

 


 

 

Recent human population growth

 

  

 
Human populations have experienced explosive growth since the Neolithic revolution. The Keinan lab studies the effect that this unique demographic scenario has had on patterns of genetic variation, as well as use genetic data to gain new insights into the way different human populations have grown and spread. They characterize how recent growth increases the abundance of rare genetic variants and how it affects the workings of natural selection. Based on these population genetics insights, they study how recent growth has shaped the genetic architecture of complex disease and, as a consequence, what methods for gene-disease association testing would be most powerful for associating rare variants with disease risk.
 
 
Some related publications:

 

 

 


 

 

Contrasting patterns of genetic variation between chromosome X and autosomes

 

 

The genetic diversity of chromosome X is expected, under equilibrium conditions, to be three-quarters of that of the autosomes in a population with equal numbers of males and females. However, deviations from this ratio can result from at least four factors known to have been prevalent in human ­history: (i) sex-biased demographic events leading to different ­ effective population sizes of males and females; (ii) changes in population size over time; (iii) natural selection, which also affects chromosome X differently; and (iv) ­differences in mutation rates between sexes or between chromosome X and the autosomes.  We are interested in understanding how these factors have shaped the varying patterns of variation of X and A in different human populations.

 

Some related publications:

 

 

 


 

 

The genetic basis of complex human disease and other complex traits

 

 

Genome-wide association studies (GWAS) have provided important insights into the genetic basis of complex human diseases and traits.  At the same time, the current generation of GWAS has left us with the challenge of "missing heritability," whereby for most complex diseases and traits only a relatively small fraction of estimated heritability has been explained to day.  We develop and apply statistical and computational methods for detecting the contribution of gene-gene interactions (epistasis), rare genetic variants, and the sex chromosomes to complex disease risk, thereby elucidating the role they play in explaining missing heritability.  This work is in collaboration with several GWAS consortia.  Further, we make the software that implements our methods publicly available, which allows application in additional studies, thereby accelerating the understanding of complex disease etiology.  Our main foci are autoimmune diseases and lipid levels as risk factors of coronary artery disease.

 

Some related publications: