8 Powerful Statistical Methods in Genetics Research: Unraveling genetic mysteries with Statistics

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The powerful combination of genetics and statistics allows us to make groundbreaking discoveries, ranging from understanding complex diseases to tracing our ancestry. However, statistical analysis of genetics data involves quite a few challenges, including the complexity and high dimensionality of genetic data, the need for robust statistical models to handle population structure and admixture, and the presence of missing or incomplete data. Researchers also encounter difficulties in analyzing rare variants, integrating different data types, and interpreting statistical results in a biological context. In this blog post, we will take a look at 8 statistical methods that have been used in genetics research, particularly for big data.  

1. Lasso penalized regression and association mapping 

Imagine searching for a needle in a haystack. Well, that’s precisely what researchers often face when trying to identify genetic variants associated with complex traits or diseases. Here’s where LASSO (Least Absolute Shrinkage And Selection Operator) Penalized Regression and Association Mapping come to our rescue. These statistical methods help us identify the most relevant genetic markers among thousands or even millions of possibilities, greatly enhancing our ability to pinpoint genes linked to specific traits. Li and Sillanpää (2012) provide a comprehensive review of how LASSO-related penalized regression methods can be used, with examples from various public datasets.ⁱ  

2. Ethnic admixture estimation 

The human population is incredibly diverse, with rich ancestral backgrounds blending together. Ethnic admixture estimation allows us to disentangle this complex tapestry of genetic heritage. Statistical techniques like ancestry informative markers and admixture models provide us with valuable insights into an individual’s genetic makeup, allowing us to trace our ancestry back to different populations and understand the impact of genetic diversity on health and disease. To understand in more detail how principal component analysis and model-based estimation of ancestry can be applied in ethnic admixture estimation, you can refer to Thornton et al. (2014).ⁱⁱ 

3. Matrix completion for genotype and sequence data 

Genomics research generates vast amounts of data, including genotypes and sequence information. However, incomplete or missing data can hinder our analyses. This is where matrix completion techniques come in handy. By leveraging statistical algorithms, we can fill in missing genetic information, enabling us to utilize the full potential of large-scale genotype and sequence datasets for discoveries and insights. For an example of a genotype imputation method based on matrix completion, you can check out Chi et al. (2013).ⁱⁱⁱ 

4. Fused LASSO and copy number variation 

Copy Number Variations (CNVs), which involve the duplication or deletion of genetic material, can play a significant role in disease development. The Fused LASSO statistical approach helps identify CNVs by detecting breakpoints in the genome. Using this approach, researchers can pinpoint regions where the DNA structure has been altered, which helps them understand how CNVs contribute to various diseases. If you would like an example of how fused lasso is used, you can refer to Noghabi et al. (2016).^iv 

5. Haplotyping 

Haplotyping refers to the reconstruction of haplotypes, which are sets of genetic variants that tend to be inherited together. Statistical methods for haplotyping allow us to piece together the puzzle of how genetic variants interact with each other and influence traits or disease risk. These techniques enable us to identify specific combinations of genetic variants associated with a particular phenotype, helping us unravel the intricate relationship between genetics and complex traits. One such statistical technique has been described in detail by Kuleshov et al. (2014).^v 

6. Estimation of relatedness 

In genetics, understanding the relatedness between individuals is crucial for studying hereditary diseases, population genetics, and even forensic investigations. Statistical methods for estimating relatedness, such as the method of moments or maximum likelihood estimation, enable us to infer relationships by analyzing genetic similarity. These techniques help uncover familial connections, study population history, and improve our understanding of genetic inheritance patterns. Liu et al. (2013) provide a comprehensive review of both methods and software used for estimating genetic ancestry in human beings.^vi 

7. Variance components models 

Genetic traits are often influenced by a combination of genetic and environmental factors. Variance components models are statistical tools that help quantify the contribution of genetic variation to a particular trait. By decomposing the total variation into genetic and environmental components, we can estimate the heritability of traits, which makes it easier for us to identify genetic factors and understand the interplay between genes and the environment. For an example of the application of variance components models, you can see how Hunter et al. (2021) applied them to behavior genetics data.^vii 

8. Rare variant testing 

While common genetic variants have been extensively studied, rare variants can also play a crucial role in complex traits and diseases. Statistical methods for rare variant testing help us identify these less frequent genetic variations that may have a substantial impact on disease susceptibility. These techniques employ sophisticated statistical models to evaluate the association between rare variants and traits. Nicolae (2016) provides a comprehensive review of the various association tests available for rare variants.^viii 

Leverage sophisticated statistical techniques to take your genetics research to the next level! Consult a biostatistician through Editage’s Statistical Analysis & Review Services!