Weighted burden analysis of rare coding variants in 470,000 exome-sequenced UK Biobank subject characterises effects on hyperlipidaemia risk

Curtis, David

doi:10.1101/2023.10.19.23297272

Cited by 2 publications

(2 citation statements)

References 23 publications

(30 reference statements)

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“…The genes selected for this study consisted of those which had previously produced exome-wide significant results in weighted burden analyses using phenotypes of hypertension, hyperlipidaemia and type 2 diabetes (Curtis, 2023a(Curtis, , 2023b(Curtis, , 2023c. These genes and phenotypes are listed in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…2. List of genes used for these analyses along with the SLP obtained in the original analyses with the corresponding phenotype (Curtis, 2023a(Curtis, , 2023b(Curtis, , 2023c. Variants which impaired functioning of NPC1L1, PCSK9, ANGPTL3 and APOC3 were found to be protective against hyperlipidaemia so for convenience the phenotype of interest is stated to be "Not hyperlipidaemia".…”

Section: Competing Interestsmentioning

confidence: 99%

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Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

Curtis

2023

Preprint

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An important issue pertinent to the analysis of sequence data to detect association between rare variants in a gene and a given phenotype is the ability to annotate nonsynonymous variants in terms of their likely importance as affecting protein function. While a number of software tools attempt to do this, AlphaMissense was recently released and was shown to have good performance using benchmarks based on variants causing severe disease and on functional assays. Here, we assess the performance of AlphaMissense across 18 genes which had previously demonstrated association between rare coding variants and hyperlipidaemia, hypertension or type 2 diabetes. Ability to detect association, expressed as the signed log p value (SLP) was compared between AlphaMissense and 43 other annotation methods. The results demonstrated marked variability between genes regarding the extent to which nonsynonymous variants contributed to evidence for association and also between the performance of different methods of annotating the nonsynonymous variants. Although AlphaMissense produced the highest SLP on average across genes, it produced the maximum SLP for only 4 genes. For some genes, other methods produced a considerably higher SLP and there were examples of genes where AlphaMissense produced no evidence for association while another method performed well. The marked inconsistency across genes means that it is difficult to decide on an optimal method of analysis of sequence data. The fact that different methods perform well for different genes suggests that if one wished to use sequence data for individual risk prediction then gene-specific annotation methods should be used. It would be desirable to have the ability to recognise characteristics of a gene which could facilitate the selection of an annotation method which would best discriminate variants of interest within that gene.This research has been conducted using the UK Biobank Resource.

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Section: Methodsmentioning

confidence: 99%

Section: Competing Interestsmentioning

confidence: 99%

Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

Curtis

2023

Preprint

Self Cite

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Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

Curtis

2024

Eur J Hum Genet

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An important issue in the analysis of rare variant association studies is the ability to annotate nonsynonymous variants in terms of their likely importance as affecting protein function. To address this, AlphaMissense was recently released and was shown to have good performance using benchmarks based on variants causing severe disease and on functional assays. Here, we assess the performance of AlphaMissense across 18 genes which had previously demonstrated association between rare coding variants and hyperlipidaemia, hypertension or type 2 diabetes. The strength of evidence in favour of association, expressed as the signed log p value (SLP), was compared between AlphaMissense and 43 other annotation methods. The results demonstrated marked variability between genes regarding the extent to which nonsynonymous variants contributed to evidence for association and also between the performance of different methods of annotating the nonsynonymous variants. Although AlphaMissense produced the highest SLP on average across genes, it produced the maximum SLP for only 4 genes. For some genes, other methods produced a considerably higher SLP and there were examples of genes where AlphaMissense produced no evidence for association while another method performed well. The marked inconsistency across genes means that it is difficult to decide on an optimal method of analysis of sequence data. The fact that different methods perform well for different genes suggests that if one wished to use sequence data for individual risk prediction then gene-specific annotation methods should be used.

show abstract

Weighted burden analysis of rare coding variants in 470,000 exome-sequenced UK Biobank subject characterises effects on hyperlipidaemia risk

Cited by 2 publications

References 23 publications

Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

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