Summary

Integration of rare large-effect expression variants improves polygenic risk prediction

• Integration: a new score that measures genes with outlier rare variants in each individual: IOGC

• Rare variants: MAF < 1%

• Expression: GTEx outliers

• Large effect: z-score filtering

• Improves:

Expression: GTEx outliers

1. individual A is an outlier in RNA-seq data of gene a
2. identify all rare variants in gene a of A
3. remove variants that are also present in other individuals B/C/D...

Study variants' effect to PRS-predicted phenotype

Why look at outliers

• control set (non-outliers): rare variants in non-outliers with CADD +/- 5
• Outliers & non-outliers are near each other
• But outliers have larger effect size

Why look at outliers - larger effect size

• PRS variants have larger allele count but smaller effect size

Why look at outliers - larger odds (ratio?)

• 10,000 permutations
• randomly choose one outlier sample and one non-outlier sample (next to PRS site) to compare absolute effect size

Why look at outliers - larger odds (ratio?)

Table 1

Table 2

• odds = 2 -> repeat and get a distribution of odds between outlier vs non-outlier
• then repeat and get a distribution of odds among non-outliers

Why permutation test?

• Not enough variants (8272) so cannot plot raw distribution

Why look at outliers - larger odds (ratio?)

Caveat:

• The authors said they built a contingency table and calculated odds ratio but Qinqin and I could not think of a way the contingency table can be built
• There was also no use of tests associated with contingency table: Chi-Square, Fisher's Exact, or Wald Test
• The p-value was obtained by Wilcoxon test (to test if the two distributions are the same)
• The author explained in later correspondence that the figure showed odds and not odds ratio - will revise the manuscript

Why look at outliers - larger variance

• Dispersion of mean effect sizes per gene for outlier (blue) and non-outlier variants (gray) across genes
• stratified by GTEx outlier Z-score.
• P-values were obtained using an Ansari Test (ansari.test)
  • non-parametric test for the differences in spread, assuming the centres of the two populations are identical

Outline

• Rare variants
• Expression
• Large effect: z-score stratification
• Integration
• Improves

Large effect: z-score stratification - larger dispersion

• larger z-scores -> larger dispersion

Large effect: z-score stratification - larger odds ratio

Outline

• Rare variants
• Expression
• Large effect: z-score stratification
• Integration: a new score that measures genes with outlier rare variants in each individual: IOGC
• Improves

IOGC - independent outlier gene count

• an individual has two genes
• gene 1 has three variants (a1, a2, a3)
• effect size: a1 -5, a2 +10, and a3 +3
• 𝑠𝑔𝑛(a1, a2, a3) = (-1, 1, 1)
• s1 = [-1, 1]
• similarly, s2 = [0, 1]
• IOGC = 2 - 1 = 1 (i.e. sum of protective genes - sum of risk genes)

Communication with the author

Q

• why collapse by genes instead of variants?

A

• some variants are very close together and probably in LD (i.e. double counting)
• (although this is difficult to estimate with rare variants at current sample sizes)
• genes can be linked to GTEx outlier expression

Communication with the author 2.0

Q

• why not include effect size?

A

• effect size estimates are very noisy at current samples sizes in UK Biobank
• (can look at the p-values and standard error of the estimates to appreciate this point)
• however, perhaps these estimate are sufficiently powered for effect direction estimate (risk/protective)
• as sample sizes increase, it would be a good idea to incorporate these estimates

Outline

• Rare variants
• Expression: outlier
• Large effect: Z-score
• Integration: IOGC

Mean change in BMI per unit change in IOGC score increases with increase in

• number of GTEx tissues where the variants are identified(g)
• outlier Z-score (h)

Outline

• Rare variants
• Expression: outlier
• Large effect: Z-score
• Integration: IOGC
• Improve

Improve: stand-alone predictor: across percentiles

• BMI increases with IOGC percentile
• Dashed line indicates cohort mean

Improve: stand-alone predictor: two percentile tails

• Rate of obesity, severe obesity (b) and age of obesity and HT can be stratified by IOGC

Improve: stratify PRS

Improve: more evidence for predicting diabetes

Improve: other traits (~2400)

• Shows that outlier can influence odds ratio but did not apply IOGC
• IOGC can potentially be helpful

Communication with the author 3.0

Q

• rare variants without GTEx filtering?
• would have done it as my first step if I were to study the effect of rare variants on improving PRS.

A

• The goal of the current paper is to link rare GTEx outlier-associated variants to effects on traits/disease
• Several other papers look at all rare variants using (for example) SKAT-O

Conclusion

Acknowledgements

• Hilary for letting me present and recommending this paper
• Qinqin for the late-night discussions
• Craig Smail (first author) for the very helpful and prompt correspondance