DOI: 10.5061/dryad.gtht76hmz
title: Improving genome-wide association discovery and genomic prediction accuracy in biobank data

description: Genetically informed, deep-phenotyped biobanks are an important research
 resource and it is imperative that the most powerful, versatile, and
 efficient analysis approaches are used. Here, we apply our recently
 developed Bayesian grouped mixture of regressions model (GMRM) in the UK
 and Estonian Biobanks and obtain the highest genomic prediction accuracy
 reported to date across 21 heritable traits. When compared to other
 approaches, GMRM accuracy was greater than annotation prediction models
 run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%),
 respectively, and was 18% (SE 3%) greater than a baseline BayesR model
 without single-nucleotide polymorphism (SNP) markers grouped into minor
 allele frequency–linkage disequilibrium (MAF-LD) annotation categories.
 For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout
 sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM
 prediction model to provide mixed-linear model association (MLMA) SNP
 marker estimates for genome-wide association (GWAS) discovery, which
 increased the independent loci detected to 16,162 in unrelated UK Biobank
 individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62
 and 65% increase, respectively. The average χ2 value of the leading
 markers increased by 15.24 (SE 0.41) for every 1% increase in prediction
 accuracy gained over a baseline BayesR model across the traits. Thus, we
 show that modeling genetic associations accounting for MAF and LD
 differences among SNP markers, and incorporating prior knowledge of
 genomic function, is important for both genomic prediction and discovery
 in large-scale individual-level studies.
description: From the measurements, tests, and electronic health record data
 available in the UK Biobank data, we selected 12 blood based biomarkers, 3
 of the most common heritable complex diseases, and 6 quantitative
 measures. The full list of traits, the UK Biobank coding of the data used,
 and the covariates adjusted for are given in Table S1. For the
 quantitative measures and blood-based biomarkers we adjusted the values by
 the covariates, removed any individuals with a phenotype greater or less
 than 7 SD from the mean (assuming these are measurement errors), and
 standardized the values to have zero mean and variance 1. 
 For the common complex diseases, we determined disease status
 using a combination of information available. For high blood pressure
 (BP), we used self-report information of whether high blood pressure was
 diagnosed by a doctor (UK Biobank code 6150-0.0), the age high blood
 pressure was diagnosed (2966-0.0), and whether the individual reported
 taking blood pressure medication (6153-0.0, 6177-0.0). For type-2 diabetes
 (T2D), we used self-report information of whether diabetes was diagnosed
 by a doctor (2443-0.0), the age diabetes was diagnosed (2976-0.0), and
 whether the individual reported taking diabetes medication (6153-0.0,
 6177-0.0). For cardiovascular disease (CAD), we used self-report
 information of whether a heart attack was diagnosed by a doctor
 (3894-0.0), the age angina was diagnosed (3627-0.0), and whether the
 individual reported heart problem diagnosed by a doctor (6150-0.0) the
 date of myocardial infarction (42000-0.0). For each disease, we then
 combined this with primary death ICD10 codes (40001-0.0), causes of
 operative procedures (41201-0.0), and the main (41202-0.0), secondary
 (41204-0.0) and inpatient ICD10 codes (41270-0.0). For BP we selected
 ICD10 codes I10, for T2D we selected ICD10 codes E11 to E14 and excluded
 from the analysis individuals with E10 (type-1 diabetes), and for CAD we
 selected ICD10 code I20-I29. Thus, for the purposes of this analysis, we
 define these diseases broadly simply to maximise the number of cases
 available for analysis. For each disease, individuals with neither a
 self-report indication or a relevant ICD10 diagnosis, were then assigned a
 zero value as a control. We restricted our discovery
 analysis of the UK Biobank to a sample of European-ancestry individuals.
 To infer ancestry, we used both self-reported ethnic background (21000-0)
 selecting coding 1 and genetic ethnicity (22006-0) selecting coding 1. We
 also took the 488,377 genotyped participants and projected them onto the
 first two genotypic principal components (PC) calculated from 2,504
 individuals of the 1,000 Genomes project with known ancestries. Using the
 obtained PC loadings, we then assigned each participant to the closest
 population in the 1000 Genomes data: European, African, East-Asian,
 South-Asian or Admixed, selecting individuals with PC1 projection &lt;
 absolute value 4 and PC 2 projection &lt; absolute value 3. Samples
 were excluded if in the UK Biobank quality control procedures they (i)
 were identified as extreme heterozygosity or missing genotype outliers;
 (ii) had a genetically inferred gender that did not match the
 self-reported gender; (iii) were identified to have putative sex
 chromosome aneuploidy; (iv) were excluded from kinship inference; (v) had
 withdrawn their consent for their data to be used. We used the imputed
 autosomal genotype data of the UK Biobank provided as part of the data
 release. We used the genotype probabilities to hard-call the genotypes for
 variants with an imputation quality score above 0.3. The
 hard-call-threshold was 0.1, setting the genotypes with probability
 &lt;=0.9 as missing. From the good quality markers (with missingness
 less than 5% and p-value for Hardy-Weinberg test larger than 10-6, as
 determined in the set of unrelated Europeans) we selected those with minor
 allele frequency (MAF) &gt; 0.0002 and rs identifier, in the set of
 European-ancestry participants, providing a data set 9,144,511 SNPs. From
 this we took the overlap with the Estonian Genome centre data described
 below to give a final set of 8,430,446 markers. For computational
 convenience we then removed markers in very high LD selecting one marker
 from any set of markers with LD R<sup>2</sup> &gt;
 0.8 within a 1MB window. These filters resulted in a data set with 458,747
 individuals and 2,174,071 markers. We apply our GMRM model to each UK
 Biobank trait, running two short chains for 5000 iterations and combining
 the last 2000 posterior samples together. Here, we provide the posterior
 mean effect size estimates fo each SNP and the mixed-linear model
 association regression coefficient, SE, t-statistic, and association
 p-value.