A Kong

A Kong

University of Oxford

H-index: 148

Europe-United Kingdom

About A Kong

A Kong, With an exceptional h-index of 148 and a recent h-index of 87 (since 2020), a distinguished researcher at University of Oxford, specializes in the field of Statistical Genetics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Studying the genetics of participation using footprints left on the ascertained genotypes

The genetics of participation: method and analysis

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Polygenic Prediction Within and Between Families from a 3-Million-Person GWAS of Educational Attainment

Family analysis with Mendelian imputations

Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects

A Kong Information

University

University of Oxford

Position

___

Citations(all)

114067

Citations(since 2020)

30462

Cited By

96305

hIndex(all)

148

hIndex(since 2020)

87

i10Index(all)

220

i10Index(since 2020)

189

Email

University Profile Page

University of Oxford

A Kong Skills & Research Interests

Statistical Genetics

Top articles of A Kong

Studying the genetics of participation using footprints left on the ascertained genotypes

Authors

Stefania Benonisdottir,Augustine Kong

Journal

Nature Genetics

Published Date

2023/8

The trait of participating in a genetic study probably has a genetic component. Identifying this component is difficult as we cannot compare genetic information of participants with nonparticipants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than nonparticipants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. In nonoverlapping samples, a polygenic score constructed from that analysis is significantly associated with educational attainment, body mass index and being invited to a dietary study. The estimated correlation between the genetic components underlying participation in UK Biobank and educational attainment is estimated to be 36.6%—substantial but far …

The genetics of participation: method and analysis

Authors

Stefania Benonisdottir,Augustine Kong

Journal

bioRxiv

Published Date

2022/2/14

Participation in a genetic study likely has a genetic component. Identifying such component is difficult as we cannot compare genetic information of participants with non-participants directly, the latter being unavailable. Here, we show that alleles that are more common in participants than non-participants would be further enriched in genetic segments shared by two related participants. Genome-wide analysis was performed by comparing allele frequencies in shared and not-shared genetic segments of first-degree relative pairs of the UK Biobank. A polygenic score constructed from that analysis, in non-overlapping samples, is associated with educational attainment (P = 2.1 × 10−52), body mass index (P = 1.5 × 10−19), and participation in a dietary study (P = 6.9 × 10−21). Further analysis shows that inclination to participate is a behavioural trait in its own right, and not simply a consequence of other established phenotypes. Understanding the basis of this trait is important for data analyses and the design of future surveys, genetic or otherwise.

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Authors

Alexander I Young,Seyed Moeen Nehzati,Stefania Benonisdottir,Aysu Okbay,Hariharan Jayashankar,Chanwook Lee,David Cesarini,Daniel J Benjamin,Patrick Turley,Augustine Kong

Journal

Nature Genetics

Published Date

2022/6

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine …

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Authors

Aysu Okbay,Yeda Wu,Nancy Wang,Hariharan Jayashankar,Michael Bennett,Seyed Moeen Nehzati,Julia Sidorenko,Hyeokmoon Kweon,Grant Goldman,Tamara Gjorgjieva,Yunxuan Jiang,Barry Hicks,Chao Tian,David A Hinds,Rafael Ahlskog,Patrik KE Magnusson,Sven Oskarsson,Caroline Hayward,Archie Campbell,David J Porteous,Jeremy Freese,Pamela Herd,Chelsea Watson,Jonathan Jala,Dalton Conley,Philipp D Koellinger,Magnus Johannesson,David Laibson,Michelle N Meyer,James J Lee,Augustine Kong,Loic Yengo,David Cesarini,Patrick Turley,Peter M Visscher,Jonathan P Beauchamp,Daniel J Benjamin,Alexander I Young

Journal

Nature genetics

Published Date

2022/4

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of~ 3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (ie, controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Polygenic Prediction Within and Between Families from a 3-Million-Person GWAS of Educational Attainment

Authors

Aysu Okbay,Yeda Wu,Nancy Wang,Hariharan Jayashankar,Michael Bennett,Seyed Moeen Nehzati,Julia Sidorenko,Hyeokmoon Kweon,Grant Goldman,Tamara Gjorgjieva,Yunxuan Jiang,Chao Tian,Rafael Ahlskog,Patrik KE Magnusson,Sven Oskarsson,Caroline Hayward,Archie Campbell,David J Porteous,Jeremy Freese,Pamela Herd,Chelsea Watson,Jonathan Jala,Dalton Conley,Philipp D Koellinger,Magnus Johannesson,David Laibson,Michelle N Meyer,James J Lee,Augustine Kong,Loic Yengo,David Cesarini,Patrick Turley,Peter M Visscher,Jonathan P Beauchamp,Daniel J Benjamin,Alexander Young

Journal

Behavior Genetics

Published Date

2021

Polygenic Prediction Within and Between Families from a 3-Million-Person GWAS of Educational Attainment

Family analysis with Mendelian imputations

Authors

Augustine Kong,Stefania Benonisdottir,Alexander I Young

Journal

BioRxiv

Published Date

2020/7/3

Genotype-phenotype associations can be results of direct effects, genetic nurturing effects and population stratification confounding. Genotypes from parents and siblings of the proband can be used to statistically disentangle these effects. To maximize power, a comprehensive framework for utilizing various combinations of parents’ and siblings’ genotypes is introduced. Central to the approach is mendelian imputation, a method that utilizes identity by descent (IBD) information to non-linearly impute genotypes into untyped relatives using genotypes of typed individuals. Applying the method to UK Biobank probands with at least one parent or sibling genotyped, for an educational attainment (EA) polygenic score that has an R2 of 5.7% with EA, its predictive power based on direct genetic effect alone is demonstrated to be only about 1.4%. For women, the EA polygenic score has a bigger estimated direct effect on age-at-first-birth than EA itself.

Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects

Authors

Alexander I Young,Seyed Moeen Nehzati,Chanwook Lee,Stefania Benonisdottir,David Cesarini,Daniel J Benjamin,Patrick Turley,Augustine Kong

Journal

BioRxiv

Published Date

2020/7/3

Associations between genotype and phenotype derive from four sources: direct genetic effects, indirect genetic effects from relatives, population stratification, and correlations with other variants affecting the phenotype through assortative mating. Genome-wide association studies (GWAS) of unrelated individuals have limited ability to distinguish the different sources of genotype-phenotype association, confusing interpretation of results and potentially leading to bias when those results are applied – in genetic prediction of traits, for example. With genetic data on families, the randomisation of genetic material during meiosis can be used to distinguish direct genetic effects from other sources of genotype-phenotype association. Genetic data on siblings is the most common form of genetic data on close relatives. We develop a method that takes advantage of identity-by-descent sharing between siblings to impute missing parental genotypes. Compared to no imputation, this increases the effective sample size for estimation of direct genetic effects and indirect parental effects by up to one third and one half respectively. We develop a related method for imputing missing parental genotypes when a parent-offspring pair is observed. We provide the imputation methods in a software package, SNIPar (single nucleotide imputation of parents), that also estimates genome-wide direct and indirect effects of SNPs. We apply this to a sample of 45,826 White British individuals in the UK Biobank who have at least one genotyped first degree relative. We estimate direct and indirect genetic effects for ∼5 million genome-wide SNPs for five traits. We estimate the …

See List of Professors in A Kong University(University of Oxford)

A Kong FAQs

What is A Kong's h-index at University of Oxford?

The h-index of A Kong has been 87 since 2020 and 148 in total.

What are A Kong's top articles?

The articles with the titles of

Studying the genetics of participation using footprints left on the ascertained genotypes

The genetics of participation: method and analysis

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Polygenic Prediction Within and Between Families from a 3-Million-Person GWAS of Educational Attainment

Family analysis with Mendelian imputations

Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects

are the top articles of A Kong at University of Oxford.

What are A Kong's research interests?

The research interests of A Kong are: Statistical Genetics

What is A Kong's total number of citations?

A Kong has 114,067 citations in total.

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