Genetic correlation & pleiotropy

A related measure to heritability is the genetic correlation (r_g). Genetic correlation denotes the proportion of shared variance between two traits that can be attributed to genetics¹. The earliest use of genetic correlation dates back to the 70s and 80s, but has taken on a new role in the age of GWAS. Modern genetic correlation studies are able to use both datasets containing individual genomes and GWAS summary statistics. The former uses the genomic REML approach, the latter deploys the LDSC approach^2–4. Apart from a few practical advantages, a major conceptual advantage of the LDSC approach is that since it does not use individual-level genomes but uses GWAS summary statistics instead, the likelihood for confounding is reduced since summary statistics can come from different datasets^2,4. In addition, since LDSC uses GWAS summary statistics, it is methodologically indifferent to sample overlap or non-congruence². This feature allows for large-scale genetic correlation analyses using summary statistics from various sources, making it a powerful and efficient tool to analyse shared genetic variance between a large number of phenotypes and traits³.

Genetic correlation studies have identified a number of relevant features of shared genetic architecture^3,5. For example, psychiatric disorders show a high degree of genetic correlation^3,5,6, as well as strong correlations with a number of other phenotypes and traits⁷. Identifying the shared genetic architecture can also help leverage the known biology between one of the two traits to make inferences about the other². In addition, downstream analyses of genetic markers of traits in the analysis can help identify possible avenues for investigations into the traits that display a high degree of correlation with that trait⁸.

Cheverud’s Conjecture states that genetic correlation usually reflects phenotypic correlation^9,10. This is exemplified by the genetic correlation between a number of psychiatric symptoms in several twin studies^11–13. Cheverud’s conjecture has also been validated using the LDSC method, providing further evidence that phenotypic correlation may suggest a shared genetic component as well¹⁶. This finding is highly relevant in for instance psychiatric disorders, where clinical demarcation may be low and different psychiatric conditions show a large degree of overlapping symptoms¹⁸.

Genetic correlation is sensitive to the effect direction of different SNPs². This means that even though two traits involve the same genetic loci, if the direction of effect for the genetic markers in these loci are opposite varies widely the genetic correlation may be low (see Figure 6.1)¹⁹. This overlap in genetic loci despite divergent effect directions is still relevant for identification of biological processes associated with different traits. A single gene may be involved in multiple traits, a phenomenon referred to as pleiotropy²⁰. This pleiotropy between traits can be leveraged in order to provide a measure of shared polygenic architecture and can aid the discovery of new genetic markers in traits with known pleiotropy^19,21. Through approaches such as the conjunctional conjFDR and condFDR one is able to boost understanding of the underlying shared genetics and the role of the shared genetic markers in the traits of interest^19,21. This has led to insights into the shared genetic loci between psychiatric disorders^21–23, and between psychiatric disorders and various other relevant phenotypes such as cognitive scores²³, substance use²⁴, and cardiometabolic traits²⁵. These approaches allow researchers to make inferences about pleiotropy and shared genetic determinants even when genetic correlation is low²¹.

Heritability, genetic correlation, and genetic overlap are different but related measures of assessing the contribution of genetics to a single or multiple traits. Each measures a different concept and provides insights into the genetics of a trait in a different way. As mentioned, heritability measures the proportion of variance in a trait that can be attributed to genetics²⁶, agnostic to effect directions and calculated for a single trait. Genetic overlap and genetic correlation provide insights into the shared genetics between two traits. Genetic overlap quantifies the number of shared genetic determinants between two traits, regardless of effect direction²¹. Genetic correlation calculates the shared genetic variance between two traits as a product of the genetic heritability of each trait combined, where the effect direction is determined by the combined effects for each genetic variant⁴. As such, it is possible for two traits to have a high degree of genetic overlap despite showing no genetic correlation²⁷. For an illustration of this concept see Figure 6.1, adapted from Smeland et al. (2020)¹⁹.

Figure 6.1: Illustration showing the relationship betweeen genetic overlap and genetic correlation. Adapted from Smeland et al. (2020)¹⁹.

1.

van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. Genetic correlations of polygenic disease traits: From theory to practice. Nature Reviews Genetics [Internet]. 2019 Oct 1;20(10):567–81. Available from: https://doi.org/10.1038/s41576-019-0137-z

2.

Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics [Internet]. 2015 Mar 1;47(3):291–5. Available from: https://doi.org/10.1038/ng.3211

3.

Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nature Genetics [Internet]. 2015 Nov 1;47(11):1236–41. Available from: https://doi.org/10.1038/ng.3406

4.

Ni G, Moser G, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Wray NR, Lee SH. Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. Am J Hum Genet [Internet]. 2018/05/10 ed. 2018 Jun 7;102(6):1185–94. Available from: https://pubmed.ncbi.nlm.nih.gov/29754766

5.

Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Duncan L, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018 Jun 22;360(6395).

6.

Grotzinger AD. Shared genetic architecture across psychiatric disorders. Psychol Med. 2021 Oct;51(13):2210–6.

7.

Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. Nature Genetics [Internet]. 2018 Nov 1;50(11):1593–9. Available from: https://doi.org/10.1038/s41588-018-0248-z

8.

Rao S, Yin L, Xiang Y, So HC. Analysis of genetic differences between psychiatric disorders: Exploring pathways and cell types/tissues involved and ability to differentiate the disorders by polygenic scores. Translational Psychiatry [Internet]. 2021 Aug 13;11(1):426. Available from: https://doi.org/10.1038/s41398-021-01545-x

9.

Cheverud JM. A Comparison of Genetic and Phenotypic Correlations. Evolution [Internet]. 1988 [cited 2022 Apr 11];42(5):958–68. Available from: http://www.jstor.org/stable/2408911

10.

Sodini SM, Kemper KE, Wray NR, Trzaskowski M. Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans. Genetics [Internet]. 2018 Jul 1 [cited 2022 Apr 11];209(3):941–8. Available from: https://doi.org/10.1534/genetics.117.300630

11.

Baselmans BML, Willems YE, van Beijsterveldt CEM, Ligthart L, Willemsen G, Dolan CV, et al. Unraveling the Genetic and Environmental Relationship Between Well-Being and Depressive Symptoms Throughout the Lifespan. Frontiers in Psychiatry [Internet]. 2018;9. Available from: https://www.frontiersin.org/article/10.3389/fpsyt.2018.00261

12.

Gjerde LC, Røysamb E, Czajkowski N, Reichborn-Kjennerud T, Ørstavik RE, Kendler KS, et al. Strong Genetic Correlation Between Interview-Assessed Internalizing Disorders and a Brief Self-Report Symptom Scale. Twin Research and Human Genetics [Internet]. 2012/02/21 ed. 2011;14(1):64–72. Available from: https://www.cambridge.org/core/article/strong-genetic-correlation-between-interviewassessed-internalizing-disorders-and-a-brief-selfreport-symptom-scale/B3B1AAFF7F03747929C2978691D1C3BA

13.

Routledge KM, Williams LM, Harris AWF, Schofield PR, Clark CR, Gatt JM. Genetic correlations between wellbeing, depression and anxiety symptoms and behavioral responses to the emotional faces task in healthy twins. Psychiatry Research [Internet]. 2018 Jun 1;264:385–93. Available from: https://www.sciencedirect.com/science/article/pii/S0165178117320814

14.

Jamshidi J, Schofield PR, Gatt JM, Fullerton JM. Phenotypic and genetic analysis of a wellbeing factor score in the UK Biobank and the impact of childhood maltreatment and psychiatric illness. Translational Psychiatry [Internet]. 2022 Mar 19;12(1):113. Available from: https://doi.org/10.1038/s41398-022-01874-5

15.

Legge SE, Jones HJ, Kendall KM, Pardiñas AF, Menzies G, Bracher-Smith M, et al. Association of Genetic Liability to Psychotic Experiences With Neuropsychotic Disorders and Traits. JAMA Psychiatry [Internet]. 2019 Dec 1 [cited 2022 Jun 2];76(12):1256–65. Available from: https://doi.org/10.1001/jamapsychiatry.2019.2508

16.

Sodini SM, Kemper KE, Wray NR, Trzaskowski M. Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans. Genetics. 2018 Jul;209(3):941–8.

17.

Andreasen NC. A Unitary Model of Schizophrenia: Bleuler’s "Fragmented Phrene" as Schizencephaly. Archives of General Psychiatry [Internet]. 1999 Sep 1 [cited 2022 Jun 2];56(9):781–7. Available from: https://doi.org/10.1001/archpsyc.56.9.781

18.

Craddock N, Owen MJ. The Kraepelinian dichotomy – going, going … but still not gone. British Journal of Psychiatry [Internet]. 2018/01/02 ed. 2010;196(2):92–5. Available from: https://www.cambridge.org/core/article/kraepelinian-dichotomy-going-going-but-still-not-gone/9BD6BEDF610DD5C7E7F376518CB515A0

19.

Smeland OB, Frei O, Dale AM, Andreassen OA. The polygenic architecture of schizophrenia — rethinking pathogenesis and nosology. Nature Reviews Neurology [Internet]. 2020 Jul 1;16(7):366–79. Available from: https://doi.org/10.1038/s41582-020-0364-0

20.

Lee PH, Feng YCA, Smoller JW. Pleiotropy and Cross-Disorder Genetics Among Psychiatric Disorders. Biol Psychiatry. 2021 Jan 1;89(1):20–31.

21.

Andreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR, et al. Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate. PLOS Genetics [Internet]. 2013 Apr 25;9(4):e1003455. Available from: https://doi.org/10.1371/journal.pgen.1003455

22.

O’Connell KS, Shadrin A, Bahrami S, Smeland OB, Bettella F, Frei O, et al. Identification of genetic overlap and novel risk loci for attention-deficit/hyperactivity disorder and bipolar disorder. Mol Psychiatry. 2021 Aug;26(8):4055–65.

23.

Smeland OB, Bahrami S, Frei O, Shadrin A, O’Connell K, Savage J, et al. Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence. Mol Psychiatry. 2020 Apr;25(4):844–53.

24.

Wiström ED, O’Connell KS, Karadag N, Bahrami S, Hindley GFL, Lin A, et al. Genome-wide analysis reveals genetic overlap between alcohol use behaviours, schizophrenia and bipolar disorder and identifies novel shared risk loci. Addiction. 2022 Mar;117(3):600–10.

25.

Torgersen K, Rahman Z, Bahrami S, Hindley GFL, Parker N, Frei O, et al. Shared genetic loci between depression and cardiometabolic traits. PLoS Genet. 2022 May 13;18(5):e1010161.

26.

Speed D, Cai N, Johnson MR, Nejentsev S, Balding DJ, the UCLEB Consortium. Reevaluation of SNP heritability in complex human traits. Nature Genetics [Internet]. 2017 Jul 1;49(7):986–92. Available from: https://doi.org/10.1038/ng.3865

27.

Frei O, Holland D, Smeland OB, Shadrin AA, Fan CC, Maeland S, et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nature Communications [Internet]. 2019 Jun 3;10(1):2417. Available from: https://doi.org/10.1038/s41467-019-10310-0