By Andrea S. Foulkes
The huge array of molecular point info now on hand offers fascinating possibilities to represent the genetic underpinnings of complicated illnesses whereas studying novel organic pathways to affliction development. during this introductory graduate point textual content, Dr. Foulkes elucidates middle techniques that undergird the wide variety of analytic innovations and software program instruments for the research of information derived from population-based genetic investigations. utilized Statistical Genetics with R bargains a transparent and cogent presentation of numerous primary statistical ways that researchers from a number of disciplines, together with medication, public wellbeing and fitness, epidemiology, data and machine technology, will locate invaluable in exploring this rising box. Couched within the language of biostatistics, this article may be simply followed for public future health and clinical university curricula.
The textual content covers key genetic information ideas and statistical rules to supply the reader with a powerful origin in tools for candidate gene and genome-wide organization experiences. those comprise equipment for unobservable haplotypic part, a number of trying out changes, and high-dimensional information research. Emphasis is on research of information coming up from reviews of unrelated participants and the aptitude interaction between genetic elements and extra conventional, epidemiological danger components for illness. whereas theoretically rigorous, the analytic concepts are provided at a degree that would attract researchers and scholars with restricted wisdom of statistical genetics. The textual content assumes the reader has accomplished a primary path in biostatistics, makes use of publicly on hand facts units for representation, and offers large examples utilizing the open resource, publicly on hand statistical software program atmosphere R.
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Extra resources for Applied Statistical Genetics with R: For Population-based Association Studies (Use R!)
Adjusted analyses take into account potential confounders in the relationship between exposure (genotype in our setting) and the outcome. Multivariable methods involve multiple predictors and provide a venue for assessing interaction and conditional association. In the remainder of this chapter and Chapter 3, we use the term p-value without explicitly defining it, assuming the reader is broadly familiar with hypothesis testing. We define this term as well as other relevant testing concepts, such as type-1 and type-2 error rates, in Chapter 4.
1 (Chi-squared test for association). Suppose we are interested in determining whether there is an association between any of the SNPs within the esr1 gene and an indicator of body mass index (BMI) > 25 at baseline of the FAMuSS study.
Pearson’s χ2 -test and Fisher’s exact test A formal test of association between a categorical exposure (genotype) and categorical disease variable (trait) is conducted using Pearson’s χ2 -test or Fisher’s exact test. In the context of a 2 × 2 table, a test of no association between the rows and columns is equivalent to a test of the single null hypothesis, H0 : OR = 1. Pearson’s χ2 -test involves first determining the expected cell counts of a corresponding contingency table under the assumption of independence between the genotype and trait.