The Mutationathon highlights the importance of reaching standardization in estimates of pedigree-based germline mutation rates

Abstract

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various nonhuman species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and appropriately accounting for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a 'Mutationathon,' a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a twofold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria, and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

Keywords: computational pipeline; evolutionary biology; genetics; genomics; mutation rate; ngs analysis; pedigree-based estimation; rhesus macaque.

Figure 1.. Detection of a de novo mutation (DNM) in a trio sample (mother, father, and offspring).

Potential candidates for DNMs are sites where approximately half of the reads (indicated as gray bars) from the offspring have a variant (indicated in green) that is absent from the parental reads.

Figure 2.. Flow of the main steps to call de novo mutations (DNMs) from pedigree samples.

Each step lists the various choices in study design and methodology that might impact mutation rate estimates.

Figure 3.. Candidate de novo mutations (DNMs) from the Mutationathon.

(a) The pedigree of three generations of rhesus macaques was sequenced and shared with five groups of researchers. Sequencing coverage is indicated for each individual. (b) Upset plot of the 43 candidate DNMs found in Heineken by each research group (LB: Lucie Bergeron; SB: Søren Besenbacher; CV: Cyril Versoza; TT: Tychele Turner; RW: Richard Wang) detected a total of 43 candidate DNMs in Heineken. The first six vertical bars are the candidates shared by at least four different groups. The PCR amplification and Sanger sequencing validation showed that 33 candidates were true-positive DNMs, 6 were false-positive calls (red bars), and 4 did not successfully amplify (gray bars). See Materials and methods for details on the experiment and Figure 3—source data 2 for the results of the PCR experiment.

Figure 3—figure supplement 1.. Mutation spectrum of the trio of rhesus macaques.

’All TPs’ correspond to all true-positive de novo mutations (DNMs) validated by the PCR experiment. The different colors correspond to the true-positive DNMs found by each pipeline (LB: Lucie Bergeron; SB: Søren Besenbacher; CV: Cyril Versoza; TT: Tychele Turner; RW: Richard Wang).

Figure 4.. Estimated germline mutation rates from the Mutationathon.

(a) Number of candidate de novo mutations (DNMs) found by each group (Tychele Turner found two candidates on a sex chromosome). (b) Estimation of the denominator (i.e., the callable genome corrected by the false-negative rate [FNR]) by each group. (c) Estimated mutation rate per site per generation, the error bars correspond to the confidence intervals for binomial probabilities (calculated using the R package 'binconf').

Figure 5.. The impact of individual filters on the estimated rate of a trio of rhesus macaques.

The default filters used by Lucie Bergeron (LB) pipeline were DP < 0.5 × depth _individual; DP > 2 × depth_individual; GQ < 60; AB < 0.3; AB > 0.7, no AD filter.