Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

Turner, Paul; Corander, Jukka

doi:10.1186/s12859-021-04079-7

Cited by 12 publications

(22 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The LMM and wg-enet ĥ 2 agree closely across the two MIC phenotypes. The wg-enet values are about 5% lower (Table 2), but they are higher than previously reported for binary AMR [4]. The LDSC ĥ 2 are lower again, and this was the only method to report a difference ĥ 2 between the two MIC traits, consistent with lower LOSO prediction accuracy, and also lower numbers and significance of associations, for penicillin MIC compared with ceftriaxone MIC.…”

Section: Resultssupporting

confidence: 59%

“…Penicillin AMR ĥ2 in the Maela data set was recently reported in the range 0.67-0.83 [4]. We find even higher values for the quantitative penicillin MIC phenotype using LMM and wg-enet methods: 0.94-0.98 ( where the largest reduction in h 2 (measured using GEMMA [52]) was only 27% [9],…”

Section: Discussionmentioning

confidence: 51%

“…Penicillin AMR ĥ 2 in the Maela data set was recently reported in the range 0.67–0.83 [4]. We find even higher values for the quantitative penicillin MIC phenotype using LMM and wg-enet methods: 0.94–0.98 (Table 2), however, the LDSC ĥ 2 = 0.72 (S6 Fig) is within the range of the AMR estimates and in better agreement with the LOSO prediction results (Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…where v is the first principal component (PC) of the sequence distances (explaining > 90% of genetic variation) and a is the corresponding effect size. For the CD analyses, we also included the previous carriage covariate in (4). We note again that v does not remove all population structure effects and the S j tend to be inflated, but this is not important for LDSC estimation of h 2 g which uses the slope of the relationship of l j with S j .…”

Section: Estimation Of Heritabilitymentioning

confidence: 99%

“…In human genetics, the study of heritability across the genome has received considerable attention and the main statistical challenges related to robust estimation of SNP heritability are being overcome [1, 2]. Similar studies in bacteria are emerging [3, 4], but the pros and cons of the many available methods have not yet been extensively studied. We adopted popular methods from human genetics, using linear mixed models (LMMs) and linkage disequilibrium score regression (LDSC) to investigate genome-wide association and heritability, in combination with elastic-net regression for prediction of three traits (two not previously studied) in Streptococcus pneumoniae .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Genome-wide association, prediction and heritability in bacteria

Mallawaarachchi

Tonkin‐Hill

Croucher

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Advances in whole-genome genotyping and sequencing have allowed genome-wide analyses of association, prediction and heritability in many organisms. However, the application of such analyses to bacteria is still in its infancy, being limited by difficulties including the plasticity of bacterial genomes and their strong population structure. Here we propose a suite of genome-wide analyses for bacteria that combines methods from human genetics and previous bacterial studies, including linear mixed models, elastic net and LD-score regression. We introduce innovations such as frequency-based allele coding, testing for both insertion/deletion and nucleotide effects and partitioning heritability by genome region. Using a previously-published large cohort study, we analyse three phenotypes of a major human pathogen Streptococcus pneumoniae , including the first analyses of minimum inhibitory concentrations (MIC) for each of two antibiotics, penicillin and ceftriaxone. We show that these are very highly heritable leading to high prediction accuracy, which is explained by many genetic associations identified under good control of population structure effects. In the case of ceftriaxone MIC, these results are surprising because none of the isolates was resistant according to the inhibition zone diameter threshold. We estimate that just over half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes around a quarter of the heritability of ceftriaxone MIC. For the within-host survival phenotype carriage duration, no reliable associations were found but we observed moderate heritability and prediction accuracy, indicating a polygenic trait. While generating important new results for S. pneumoniae , we have critically assessed existing methods and introduced innovations that will be useful for future large-scale population genomics studies to help decipher the genetic architecture of bacterial traits.

show abstract

Section: Resultssupporting

confidence: 59%

Section: Discussionmentioning

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Section: Estimation Of Heritabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genome-wide association, prediction and heritability in bacteria

Mallawaarachchi

Tonkin‐Hill

Croucher

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Inferring the heritability of bacterial traits in the era of machine learning

Mai

Lees

Gladstone

et al. 2023

Bioinformatics Advances

Self Cite

View full text Add to dashboard Cite

Quantification of heritability is a fundamental desideratum in genetics, which allows an assessment of the contribution of additive genetic variation to the variability of a trait of interest. The traditional computational approaches for assessing the heritability of a trait have been developed in the field of quantitative genetics. However, the rise of modern population genomics with large sample sizes has led to the development of several new machine learning based approaches to inferring heritability. In this paper, we systematically summarize recent advances in machine learning which can be used to infer heritability. We focus on an application of these methods to bacterial genomes, where heritability plays a key role in understanding phenotypes such as antibiotic resistance and virulence, which are particularly important due to the rising frequency of antimicrobial resistance. By designing a heritability model incorporating realistic patterns of genome-wide linkage disequilibrium for a frequently recombining bacterial pathogen, we test the performance of a wide spectrum of different inference methods, including also GCTA. In addition to the synthetic data benchmark, we present a comparison of the methods for antibiotic resistance traits for multiple bacterial pathogens. Insights from the benchmarking and real data analyses indicate a highly variable performance of the different methods and suggest that heritability inference would likely benefit from tailoring of the methods to the specific genetic architecture of the target organism.

show abstract

Genome-wide association, prediction and heritability in bacteria with application toStreptococcus pneumoniae

Mallawaarachchi

Tonkin‐Hill

Croucher

et al. 2022

NAR Genomics and Bioinformatics

Self Cite

View full text Add to dashboard Cite

Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.

show abstract

Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

Cited by 12 publications

References 42 publications

Genome-wide association, prediction and heritability in bacteria

Genome-wide association, prediction and heritability in bacteria

Inferring the heritability of bacterial traits in the era of machine learning

Genome-wide association, prediction and heritability in bacteria with application toStreptococcus pneumoniae

Contact Info

Product

Resources

About