Qiuying Sha

Qiuying Sha


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  • Professor, Mathematical Sciences
  • Portage Health Foundation Endowed Professor of Population Health
  • PhD, Statistics, Michigan Technological University
  • MS, Mathematics, Heilongjiang University, China
  • BS, Mathematics, Heilongjiang University, China


Qiuying Sha obtained her BS and MS in Mathematics from Heilongjiang University in 1985 and 1988, respectively. In 1988, she accepted a faculty position in the Department of Mathematics at Heilongjiang University and had been working there for 11 years. Sha obtained her PhD in Statistics from Michigan Technological University in 2005. In the same year, she jointed the faculty of the Department of Mathematical Sciences at Michigan Tech. Sha’s research interests include developing statistical methods and computational tools for the analysis and interpretation of genomic data; various methods for genetic linkage and association studies to identify genetic variants underlying complex traits; analysis of genome-wide association data and sequence data under both family-based and population-based designs; microarray data analysis.

Areas of Expertise

  • Statistical Genetics
  • Applied Statistics

Representative Publications

  • Qin H, Feng T, Zhang S, Sha Q (2010) A data-driven weighting scheme for family-based genome-wide association studies. Eur J Hum Genet, 18:596-603.
  • Sha Q, Zhang S (2011) A test of Hardy Weinberg equilibrium in structured populations. Genetic Epidemiol 35:671-678
  • Niu A, Zhang S, Sha Q* (2011) A novel method to detect gene-gene interactions in structured populations: MDR-SP. Ann Hum Genet 75:742-754
  • Sha Q, Wang S, Zhang S (2012) Adaptive clustering and adaptive weighting methods to detect disease associated rare variants. Eur J Hum Genet doi:10.1038
  • Sha Q, Wang X, Wang X, Zhang S (2012) Detecting association of both rare and common variants by testing an optimally weighted combination of variants. Genetic Epidemiol 36:561-571
  • Fang S, Zhang S, Sha Q* (2013) Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families. Ann Hum Genet, 77(6): 524-534
  • Sha Q, Zhang S (2014) A rare variant association test based on combinations of single-variant tests. Genetic Epidemiol, 38: 494-501
  • Sha Q, Zhang S (2015) Test of rare variant association based on affected sib-pairs. Eur J Hum Genet, 23: 229-237
  • Wang Z, Wang X, Sha Q, Zhang S (2016) Joint analysis of multiple traits in rare variant association studies. Ann Hum Genet, 80: 162-171.