Troubleshooting Model Fit Issues In Founder Event Analysis

Introduction

Encountering model fit issues in founder event analysis is a common challenge in population genetics. This article addresses a specific case where a user faced difficulties in fitting a model to their data, resulting in None values for key parameters. We will delve into the problem, analyze the provided information, and offer potential solutions to resolve the model fit issues. Understanding model fit is crucial for accurate founder event analysis, as it ensures the model appropriately captures the underlying genetic dynamics of the population. A poorly fitted model can lead to incorrect inferences about the timing and magnitude of founder events. Therefore, this guide aims to provide a comprehensive approach to troubleshooting these issues, ensuring your analyses are robust and reliable. We will explore the intricacies of the provided data, the parameters used, and the log file outputs to identify the root causes of the problem. By addressing these issues systematically, we aim to empower researchers to effectively conduct founder event analysis and derive meaningful insights from their genetic data.

Problem Description

A user reported encountering issues while running a model to detect founder events in their data. The model failed to fit, resulting in None values for the parameters A, t, and c, as well as the NRMSD (Normalized Root Mean Square Deviation). The user provided the parameter file and log file for analysis. This scenario is not uncommon in genetic studies, as model fitting can be sensitive to various factors such as data quality, parameter settings, and the underlying population structure. The absence of parameter estimates and the NRMSD indicates a fundamental problem with the model's ability to converge and provide meaningful results. It is essential to address these issues to ensure the validity of downstream analyses and interpretations. Our approach will be to dissect the provided information, starting with the parameter settings and progressing through the log file outputs, to identify the specific reasons for the model's failure to fit. Each parameter plays a vital role in the model's behavior, and their interplay can significantly impact the overall fit. We will also examine the data characteristics, such as the number of SNPs and sample sizes, to determine if they contribute to the observed issues. Ultimately, our goal is to provide a clear pathway for resolving the model fit problems and achieving robust results in founder event analysis.

Analysis of Parameter File

The parameter file contains crucial settings for the analysis. Let's examine each parameter and its potential impact on the model fit:

• genotypename: Test.eigenstratgeno - This specifies the name of the genotype file. Ensuring the file path is correct and the file is not corrupted is a fundamental step. Genotype data forms the backbone of the analysis, and any issues with the input file can cascade into fitting problems. It's essential to verify the file's integrity and format to ensure it aligns with the expectations of the analysis tool.
• snpname: Test.snp - This is the name of the SNP file, which should contain information about the SNPs used in the analysis. Similar to the genotype file, the SNP file's accuracy and format are paramount. SNP information, including genomic positions and allele frequencies, directly influences the distance calculations and subsequent model fitting. Any discrepancies or errors in this file can lead to significant inaccuracies in the results.
• indivname: Test.ind - This file lists the individuals included in the analysis. Sample information is critical for defining populations and outgroups, which are essential components of founder event analysis. Ensuring the individual file is correctly formatted and contains accurate population assignments is a prerequisite for successful model fitting.
• targetpop: NPG - This defines the target population for the analysis. The choice of target population is crucial as it determines the focus of the founder event analysis. The target population should be carefully selected based on the research question and the underlying population history. Misidentification or inaccurate definition of the target population can lead to misleading results.
• outpop: RANDOM - This specifies that the outgroup population is randomly selected. While random selection can be useful in some cases, it might not be optimal for founder event analysis. A well-defined outgroup is essential for providing a baseline against which to compare the genetic drift in the target population. A poorly chosen outgroup can introduce noise and hinder the model's ability to fit the data accurately.
• outpopsize: 5 - This sets the size of the randomly selected outgroup to 5 individuals. The size of the outgroup can impact the analysis, particularly if the outgroup is not representative of the ancestral population. Outgroup size should be carefully considered, as too small a size can lead to biased estimates, while too large a size can dilute the signal of founder events.
• outputprefix: results/Test - This defines the prefix for output files. Correctly specifying the output prefix ensures that results are saved in the desired location and with appropriate filenames. Proper output management is crucial for organizing and interpreting the results of the analysis.
• binsize: 0.001 - This parameter sets the size of the distance bins. The choice of bin size can influence the resolution of the analysis. Bin size determines the granularity of the distance intervals used to calculate allele sharing. An inappropriately chosen bin size can either smooth out important details or introduce noise into the analysis.
• mindis: 0.001 - This specifies the minimum genetic distance to consider. Setting a minimum distance helps avoid issues with very closely linked SNPs. Minimum genetic distance helps to filter out SNPs that are in strong linkage disequilibrium, which can violate the assumptions of the model. Properly setting this parameter can improve the accuracy and robustness of the analysis.
• maxdis: 0.3 - This is the maximum genetic distance to consider. Maximum genetic distance is an important parameter as it defines the range over which allele sharing is calculated. A value that is too low may miss important long-range correlations, while a value that is too high can introduce noise from distant regions of the genome.
• maxpropsharingmissing: 1 - This allows for up to 100% missing data in allele sharing calculations. While allowing for missing data is important, too much missing data can negatively impact the model fit. Missing data can introduce uncertainty into the allele sharing calculations, which can compromise the model's ability to fit the data. It's important to strike a balance between including SNPs with some missing data and excluding SNPs with excessive missingness.
• minmaf: 0 - This sets the minimum minor allele frequency (MAF) to 0. While this includes all SNPs, very low MAF SNPs can introduce noise. Minor allele frequency filtering is a common practice in genetic analyses to remove rare variants that may be prone to errors or have undue influence on the results. A minimum MAF of 0 means that no SNPs are filtered based on allele frequency, which could potentially include noisy or unreliable variants.
• haploid: NO - This indicates that the data is diploid. This setting is consistent with the log file output. Ploidy is a fundamental characteristic of the genetic data, and correctly specifying it is essential for the analysis to proceed appropriately. Incorrectly setting the ploidy can lead to errors in the calculations and invalidate the results.
• dopseudohaploid: NO - This setting is related to handling diploid data and should be consistent with the haploid setting. The dopseudohaploid parameter is relevant when dealing with diploid data and determines whether to treat the individuals as pseudo-haploid. This setting should be consistent with the haploid parameter to ensure correct interpretation of the genotypes.
• morgans: NO - This implies that distances are not in Morgans. This should be consistent with the distance measures used in the data. Genetic distance units must be correctly specified for the analysis to function properly. If the distances are in centimorgans (cM), setting morgans to NO is appropriate. Inconsistent distance units can lead to errors in the model fitting.
• onlyfit: NO - This indicates that the analysis should not only fit the model but also perform other steps. The onlyfit parameter controls whether the analysis should only focus on fitting the model or perform additional steps such as data preprocessing and post-processing. Setting it to NO ensures that the complete analysis pipeline is executed.
• usefft: YES - This enables the Fast Fourier Transform (FFT) algorithm, which is generally a good choice for computational efficiency. FFT algorithms can significantly speed up calculations involving allele sharing correlations, making them a practical choice for large datasets. However, it's essential to ensure that the FFT algorithm is correctly implemented and does not introduce any artifacts into the results.
• qbins: 100 - This sets the number of bins for quantile normalization, a common step in allele sharing analysis. Quantile normalization is a technique used to reduce the impact of technical variation in the data. The number of bins (qbins) determines the granularity of the normalization process. An appropriately chosen number of bins can help to improve the robustness of the analysis.
• seed: 31 - This sets the random seed for reproducibility. Random seed is crucial for ensuring that the results of the analysis are reproducible, particularly when random sampling or other stochastic processes are involved. Setting a seed allows the analysis to be rerun with the same random choices, facilitating verification and comparison of results.
• blocksizename: None - This indicates that no block size file is provided, so the number of SNPs per chromosome will be used as weights. Block size refers to genomic regions that are treated as independent units for analysis. If no block size file is provided, the analysis may use chromosome lengths or other default methods to define blocks. This can influence the weighting of different genomic regions in the model fitting.

Log File Analysis

The log file provides a detailed record of the analysis process. Here's a breakdown of key sections and potential issues:

• Version Information: The log starts with the software version (Version 10.0). This is useful for ensuring compatibility and reproducibility.
• Input Files: The log confirms the input files used (Test.eigenstratgeno, Test.snp, Test.ind). Verifying input files is a critical first step in troubleshooting, as it ensures that the analysis is using the correct data. Errors in file paths or file formats can lead to immediate failures or subtle biases in the results.
• Parameters: The log echoes the parameter settings, allowing for a quick check. This includes the target population (NPG), outgroup definition (randomly picking 5 individuals), minMAF (0.0), and more. Double-checking the parameters in the log file against the parameter file is essential to confirm that the settings were correctly parsed by the analysis tool. Any discrepancies between the intended settings and the actual settings can lead to unexpected behavior.
• MAF Filtering: The log shows the number of SNPs before and after MAF filtering for each chromosome. This is helpful for understanding the data filtering process. MAF filtering statistics provide insights into the distribution of allele frequencies in the dataset. Large differences between the raw and filtered SNP counts may indicate issues with the data quality or the chosen MAF threshold. Monitoring these statistics can help identify potential problems with the data preprocessing steps.
• Chromosome Analysis: The log details the number of raw and MAF-filtered SNPs for each chromosome. This can reveal potential issues with specific chromosomes. Analyzing the SNP counts per chromosome can reveal potential issues with data coverage or quality. Substantial variations in the number of SNPs across chromosomes might indicate regions with low sequencing depth or other technical artifacts. These variations can impact the model fitting and should be investigated.
• Time Taken: The log indicates the time taken for the initial processing steps (20.15 min). This provides a baseline for performance and can help identify bottlenecks. Timing information can be useful for performance optimization and identifying potential bottlenecks in the analysis pipeline. Unexpectedly long processing times may indicate issues with computational resources or algorithmic efficiency.
• Substracting by cross-population allele sharing correlation: This step is a key part of the analysis. Errors in this step could lead to model fit issues. Cross-population allele sharing correlation is a crucial step in the analysis, as it aims to remove background noise and isolate the signal of founder events. Errors in this step can significantly affect the model fitting and lead to inaccurate parameter estimates. Careful examination of the log messages related to this step is essential for identifying potential problems.
• Exponential Fitting: The log mentions running the exponential fitting with weighted jackknife. The absence of parameter estimates in the .fit file suggests a problem in this step. Exponential fitting is the core of the founder event analysis, where the model parameters are estimated based on the observed allele sharing patterns. Failures in this step, indicated by the absence of parameter estimates in the output file, suggest that the model was unable to converge or that the data does not fit the model's assumptions. Investigating this step is crucial for resolving the model fit issues.
• End: The log concludes with