Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Smith, Samuel Pattillo; Shahamatdar, Sahar; Cheng, Wei; Zhang, Selena; Paik, Joseph; Graff, Misa; Haiman, Christopher A.; Matise, Tara C.; North, Kari E.; Peters, Ulrike; Gignoux, Chris; Wojcik, Genevieve L.; Crawford, Lorin; Ramachandran, Sohini

doi:10.1016/j.ajhg.2022.03.005

Cited by 9 publications

(5 citation statements)

References 95 publications

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“…This European by default mode creates a snowballing effect where secondary analysis increases further the genetic knowledge gap between European and non-European ancestries. Furthermore, the analysis of non-European do not only address a public health gap, but also offer opportunities and several recent publications illustrate the benefit of methodological development for the analysis of diverse populations in various applications such as: polygenic risk scores [13], association testing [14], and TWAS [15].…”

Section: Introductionmentioning

confidence: 99%

Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap

Troubat,

Fettahoglu,

Henches

et al. 2023

Preprint

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Background: Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. Methods: Here, we extend and assess the potential limitations and gain of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits accross five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), South-East Asian (SAS)). Results: We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 7%, 25% and 21% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome the p.vivax induced malaria. Conclusions: Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

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Section: Introductionmentioning

confidence: 99%

Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap

Troubat,

Fettahoglu,

Henches

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Individuals were first divided on the basis of ancestry, as in [ 61 ], resulting in 349,411 individuals of self-identified European descent, and 4,967 individuals of African descent. The latter group was identified both by self-identification and by an ADMIXTURE analysis as described in [ 61 ]. Applying the HBA1C filter described above resulted in 8,631 individuals in the European/elevated cohort, 268 individuals in the African/elevated cohort, 243,283 individuals in the European/normal cohort and 2,532 individuals in the African/normal cohort.…”

Section: Methodsmentioning

confidence: 99%

“…See S5 Ancestry mismatch experiment. Individuals were first divided on the basis of ancestry, as in [61], resulting in 349,411 individuals of self-identified European descent, and 4,967 individuals of African descent. The latter group was identified both by self-identification and by an ADMIX-TURE analysis as described in [61].…”

Section: Systemic Mismatch Between Training and Testing Set: Uk Bioba...mentioning

confidence: 99%

Enabling interpretable machine learning for biological data with reliability scores

2023

Self Cite

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Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. A natural question arises: how do we develop machine learning models that are inherently interpretable or explainable? In this manuscript, we describe the SWIF(r) reliability score (SRS), a method building on the SWIF(r) generative framework that reflects the trustworthiness of the classification of a specific instance. The concept of the reliability score has the potential to generalize to other machine learning methods. We demonstrate the utility of the SRS when faced with common challenges in machine learning including: 1) an unknown class present in testing data that was not present in training data, 2) systemic mismatch between training and testing data, and 3) instances of testing data that have missing values for some attributes. We explore these applications of the SRS using a range of biological datasets, from agricultural data on seed morphology, to 22 quantitative traits in the UK Biobank, and population genetic simulations and 1000 Genomes Project data. With each of these examples, we demonstrate how the SRS can allow researchers to interrogate their data and training approach thoroughly, and to pair their domain-specific knowledge with powerful machine-learning frameworks. We also compare the SRS to related tools for outlier and novelty detection, and find that it has comparable performance, with the advantage of being able to operate when some data are missing. The SRS, and the broader discussion of interpretable scientific machine learning, will aid researchers in the biological machine learning space as they seek to harness the power of machine learning without sacrificing rigor and biological insight.

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“…This European by default mode creates a snowball effect where secondary analysis further increases the genetic knowledge gap between European and non-European ancestries. Furthermore, the analysis of non-European do not only address a public health gap, but also offer opportunities and several recent publications illustrate the benefit of methodological development for the analysis of diverse populations in various applications such as: polygenic risk scores [ 13 ], association testing [ 14 ], and TWAS [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

Troubat,

Fettahoglu,

Henches

et al. 2024

BMC Genomics

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Background Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. Methods Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)). Results We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria. Conclusions Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

show abstract

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Cited by 9 publications

References 95 publications

Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap

Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap

Enabling interpretable machine learning for biological data with reliability scores

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

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