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**Statistical genetics** is the applied field of science, statistics and probability theory which is concerned with the quantitative analysis of genetic data. In broad terms, it involves developing and applying various statistical methods for analyzing and drawing scientific inferences from data on the genetics of living organisms even though it is mostly used to study human beings. The field of statistical genetics extends over to a number of other fields of knowledge including biology, genetics, bioinformatics, epidemiology, and biomathematics. It has witnessed a significant paradigm shift in recent year; from a subject that was mostly theory-based with little application of empirical evidence to a discipline which is heavily data-oriented where large genetic data repositories allow researchers to explore and create new scientific hypotheses. It has also gained much importance and momentum due to the several breakthroughs in genetic sciences. Rapid evolution of laboratory techniques has resulted in an overwhelming amount of new data such as the 3rd generation of genetic markers (SNPs), gene-status information, transcriptional profiling, knowledge about the functions of genes, and the DNA sequence of organisms. The advent of relatively low-cost and high–throughput technology for genotyping makes it possible for researchers to investigate the evolution of human populations, the etiology of several complex diseases, and the biological processes of genetic inheritance.

**Genetic statisticians** play a crucial role in the design and analysis of genetic studies using an array of complex data. They usually work on the methods and application of linkage analysis, gene statement array data analysis, comparative genomics, sequence analysis, allelic association tests, and genetic tree reconstruction among others. Further research in the field is generally concerned with the development of theory and methodology for research in three major areas namely; population genetics, genetic epidemiology, and quantitative genetics. From the perspective of medical science, developments in the design and analysis of studies that correlate drug response and genetic variability (pharmacogenetic studies) have the potential to deliver a significant breakthrough in the form of a personalized medicine approach to healthcare.

Statistics, in general, is classified into a number of categories as shown below.

Population genetics is an area of evolutionary biology which involves the study of genetic composition of various populations. It studies the genetic differences between and within populations, genetic drift, gene flow, mutation, and the changes and distributions in the frequency of phenotypes and genotypes in response to the evolutionary process of natural selection. Research in **population genetics** is focused on population structure, speciation and adaptation. It depends on the application of statistical and mathematical methods for studies that combine laboratory and field work with genetic theory. Models of population genetics are used to draw statistical inferences from the data on DNA sequences and to establish the validity of hypotheses and concepts. Studies in population genetics are distinguished from other, highly phenotypic methods of modeling evolution such as adaptive dynamics and the evolutionary game theory by its focus on genetic phenomena such as epistasis, dominance and mutation and genetic drift which are essentially random phenomena.

Most known human genes have been associated with uncommon health disorders. However, genes also play very crucial roles in the pathogenesis and etiology of most diseases in humans. As a relatively new area of genetic studies, genetic epidemiology seeks to explain how genetic factors and the interaction between genetic and environmental factors such as physical, biological, chemical, infectious and social agents cause diseases in populations through statistical studies. The term came into prominence around 1984-85 with the publication of research-based literature, increasing refinement of statistical methods, advancements in molecular techniques, and developments in molecular epidemiology. The case-control method is widely used in **genetic epidemiology** studies even though other traditional epidemiological methods such as cross-sectional and cohort studies can also be used to assess the genetic factors associated with diseases. Methodological issues which the researcher must consider include selecting the appropriate group for comparison, avoiding misclassification of genotypes, and clear and explicit statement of environmental interaction. Not including these issues in the design and analysis of studies can result in false and roundly unauthentic findings. Many non-traditional approaches such as case-only studies, affected relative-pair studies, and parental control studies have also emerged recently. All these approaches use internal control groups in place of the traditional external control groups to study the genetic factors of human disease.

Based on statistical methods and models and a number of strong assumptions, quantitative genetics which is also called the genetics of complex traits involves the study of character traits such as longevity, size and obesity which are not affected by the actions of a small number of major genes. While the assumptions are generally unrealistic, statistical methods and models usually explain how the traits are influenced by genetic and non-genetic or external factors. Statistical frameworks are also used in the analysis of certain traits, for example, litter size which takes only a few discrete values, and that of polygenic-based binary characters such as the rate of survival to adulthood. Fundamental questions that research in **quantitative genetics** tries to answer include the functions of genes, the way in which they interact with each other and environmental factors, reasons behind genetic variations, and the traits on which natural selection acts. There are several applications of quantitative genetics. It is essential to understanding how variations and co-variations may occur among related individuals in natural as well as managed populations; understanding the dynamics of evolution; and developing methods for disease alleviation and improvement of plants and animals. Statistical tools and methods such as analysis of variance (ANOVA) and path coefficients that were developed initially for quantitative genetics to describe how relatives resemble each other and to partition variations are now extensively used in across disciplines.

Association mapping which is also referred to as linkage distribution in genetic studies is the method by which **quantitative trait loci** (QTLs) are mapped using natural genome-wide gene distributions and other identifiable loci or markers to predict the association between markers and traits. It uses several statistical methods, encompasses fixed-model and mixed-model analyses, and takes into consideration population characteristics such as kinship, population structure and adaptive parameters to identify highly accurate traits and establish marker-trait association. Association mapping is widely used in the study of human disease and perennial crops that have complex systems of breeding. While the associations are often accurate and highly precise, there are some limitations such as multiple causal sites, linkage between causal and non-causal sites, and epistasis that can result in false findings of positive linkage. These limitations can be dealt with by using mixed-models and must be accounted for in the design of the study.

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