Abstract

Data on familial recurrence rates of complex diseases such as multiple sclerosis give important hints to aetiological factors such as the importance of genes and environment. By linking national registries, we sought to avoid common limitations of clinic-based studies such as low numbers, poor representation of the population and selection bias. Through the Swedish Multiple Sclerosis Registry and a nationwide hospital registry, a total of 28 396 patients with multiple sclerosis were identified. We used the national Multi-Generation Registry to identify first and second degree relatives as well as cousins, and the Swedish Twin Registry to identify twins of patients with multiple sclerosis. Crude and age corrected familial risks were estimated for cases and found to be in the same range as previously published figures. Matched population-based controls were used to calculate relative risks, revealing lower estimates of familial multiple sclerosis risks than previously reported, with a sibling recurrence risk (λs = 7.1; 95% confidence interval: 6.42–7.86). Surprisingly, despite a well-established lower prevalence of multiple sclerosis amongst males, the relative risks were equal among maternal and paternal relations. A previously reported increased risk in maternal relations could thus not be replicated. An observed higher transmission rate from fathers to sons compared with mothers to sons suggested a higher transmission to offspring from the less prevalent sex; therefore, presence of the so-called ‘Carter effect’ could not be excluded. We estimated the heritability of multiple sclerosis using 74 757 twin pairs with known zygosity, of which 315 were affected with multiple sclerosis, and added information from 2.5 million sibling pairs to increase power. The heritability was estimated to be 0.64 (0.36–0.76), whereas the shared environmental component was estimated to be 0.01 (0.00–0.18). In summary, whereas multiple sclerosis is to a great extent an inherited trait, the familial relative risks may be lower than usually reported.

Introduction

Multiple sclerosis is a complex disease with over 100 confirmed associated genes or genetic loci [International Multiple Sclerosis Genetics Consortium (IMSGC) et al., 2013] and a low but steadily increasing number of confirmed environmental risk factors (Haahr et al., 1995; Munger et al., 2004; Hedström et al., 2009). By studying familial recurrence risks of a disease, several important questions can be answered such as the relative contribution of genes and environment in its aetiology, the number of genes contributing to disease susceptibility and if a parent-of-origin effect exists.

Familial recurrence risks of multiple sclerosis have been well studied and considerable efforts have been made to gather data on familial aggregation. A recent meta-analysis found >500 studies on familial risks in multiple sclerosis (O’Gorman et al., 2013). The results, however, have varied widely, with the highest risk estimates in the northern countries, often attributed to prevalence increasing with latitude (Ebers, 2005; Islam et al., 2006; O’Gorman et al., 2011, 2013). Differences in data collection have questioned the validity of meta-analyses (Hawkes and Macgregor, 2009). A commonality for the previously published studies on familial risk and twins is that most contain patients that are recruited either in a clinical setting or from public appeals, with a few exceptions using national or regional case registries (Prokopenko et al., 2003; Nielsen et al., 2005). These methods increase the risk that the sample will be skewed with a higher concordance rate than the population (Hawkes, 1997). It is not trivial to ascertain whether a population representative sample is obtained, as recruited groups often tend to have a higher proportion of females and concordant pairs (Lykken et al., 1987). Additional difficulties include recall bias as well as validating the diagnosis in the patients’ relatives (Ramagopalan et al., 2007).

By linking the medical registries to the population registries in Sweden, it is possible to match controls, and thereby control for different prevalence throughout time and between genders.

This method of using matched controls has previously been used in a number of studies to successfully determine familial recurrence risk in different disorders, including obsessive–compulsive disorder (Mataix-Cols et al., 2013), autism (Sullivan et al., 2012) and criminal conduct (Frisell et al., 2011), but so far has not be applied to multiple sclerosis.

Here, we present a comprehensive study of familial multiple sclerosis recurrence risks based on ∼15 million individuals residing in Sweden.

Materials and methods

Registries

In Sweden, a unique personal identifying number (PIN) is assigned to everyone at birth or at immigration. This number was used to link several nationwide registries and obtain information on multiple sclerosis diagnosis and relatives.

The Multi Generation Registry contains parents, and adoptive parents, for all persons born in Sweden in 1932 or later and residing in Sweden since 1961 (Statistics Sweden, 2005). The total Population Registry has information on sex, year of birth and country of birth for all people with a Swedish personal identity number (n = 14 912 098). The Cause of Death Registry holds the date and cause of a person’s death.

The Swedish Twin Registry is one of the most complete twin registries in the world, with birthdate and zygosity for 191 911 twins born in Sweden since 1876 (Lichtenstein et al., 2002, 2006). Zygosity is determined through a questionnaire and/or DNA testing. This is further described in Magnusson et al. (2013).

The National Inpatient Register, also referred to as the hospital discharge register, is held by the National Board of Health and Welfare. It contains information on admission date, discharge date, primary diagnoses and up to eight secondary diagnoses, classified according to International Classification of Disease standards, for all public health service inpatient admissions since 1964. The registry became nationwide in 1968 and since 2001 it also includes information from outpatient visits to specialist care. The study presented here had access to the data from 1968. An external validation of the register made by Ludvigsson et al. (2011) showed that the National Inpatient Register has had full coverage since 1989. The National Inpatient Register was complemented with the local Primary Care Registry for the Stockholm region. Established in 2002, the Primary Care Registry for Stockholm carries diagnosis codes in the International Classification of Disease (ICD) standard with dates for visits to primary healthcare providers in the region. Although in reality the National Inpatient Register and the Primary Care Registry in Stockholm are two different registries, throughout this article they are jointly assessed and addressed as the National Inpatient Register.

The Swedish Multiple Sclerosis Registry is a nationwide quality registry for patients diagnosed with multiple sclerosis. Most multiple sclerosis specialists in Sweden use the Swedish Multiple Sclerosis Registry to enter information on age at onset and sex as well as clinical parameters, such as disease course, bouts and treatment. The registry dates back to 2002 and contains mostly prevalent cases, although some clinics have entered data retrospectively. The validation study of the National Inpatient Register by Ludvigsson et al. (2011) found the overlap between the National Inpatient Register and Swedish Multiple Sclerosis Registry to be 52.9%, and 76.4% of all the cases in Swedish Multiple Sclerosis Registry were in the National Inpatient Register (Ludvigsson et al., 2011).

Classification of patients

To be classified as a multiple sclerosis patient, the individual had to be either in the Swedish Multiple Sclerosis Registry or in the National Inpatient Register with a diagnosis code for multiple sclerosis according to ICD-10 (G35), ICD-9 (340) or ICD-8 (340), or in the Primary Care Registry for Stockholm with ICD-10 code G35. If the person was not in the Swedish Multiple Sclerosis Registry, the first admission with a multiple sclerosis diagnosis was chosen as the date of onset. For simplicity, this date will be referred to as age at onset, despite the fact that it reflects the first recorded admission to hospital, which often is much later than the first symptom.

Statistical methods

The cumulative age at onset distribution was estimated for the Swedish Multiple Sclerosis Registry and the full data set. Crude and age-adjusted risks were calculated using Strömgrens unmodified method (Risch, 1983) for the latter, with the age at onset distribution from the Swedish Multiple Sclerosis Registry used to obtain the previous distribution.

For the relative risks analyses, we constructed a data set with up to 10 randomly selected control pairs per case. Multiple sclerosis pairs for whom no suitable matched controls were available were excluded from the risk ratio analyses. The controls were matched on year of birth and sex, and their relatives were matched on the multiple sclerosis patient’s relative’s year of birth, sex and, where applicable, maternal/paternal relation to the index patient. Any control that had died before reaching the age of the multiple sclerosis index patient’s age of onset were excluded from the analysis, as were offspring adopted away. Index patients were included once for every relation investigated, and could thus occur more than once in the analyses. A Cox proportional hazards model [‘coxph’ function from the ‘survival’ package (Therneau and Grambsch, 2000; Therneau, 2013) in R (R Core Team, 2013)] with a robust sandwich estimator was used to estimate risk ratios and 95% confidence intervals (CI). Included in this model were sex and year of birth for the control(s), and age at onset, sex, year of birth, and if matched on maternal/paternal relation for the patient with multiple sclerosis. For confidence intervals, the robust standard error was used. To correct for multiple testing, the Bonferroni method was applied using a factor of 76. The PROC FREQ statement in the SAS software version 9.2 was used to estimate tetrachoric correlations and confidence intervals were calculated using the estimated asymptotic standard error.

Twins and their zygosity were identified through the Swedish Twin Registry. An analysis of the heritability was made using OpenMx (Boker et al., 2011) in R. In OpenMx, twin pairs are used to estimate the variation within a trait, which is then explained by three parameters. ‘A’, more commonly referred to as h2, denotes the genetic part of the contribution to disease. ‘C’ is the shared environmental component within a family, and ‘E’ is the non-shared environmental component (Neale and Cardon, 1992). Sex was included in the model as a covariate. To increase power, from every family in the Multi Generation Registry the two oldest siblings and half-siblings with no more than 5 years of age difference were included in the analysis. All siblings adopted or adopted away were excluded.

Testing for a possible increase in transmission from the lower prevalent sex to offspring, also known as the Carter effect, was conducted with Pearson’s chi-squared test by assessing the differences in transmission rates between maternal and paternal parent to children using the stats package in R. Confidence intervals for the odds ratios (OR) were calculated with Fisher’s conditional maximum likelihood estimation in R using the ‘oddsratio’ function from the ‘epitools’ package (Aragon 2012).

Results

Demographics

The Swedish Multiple Sclerosis Registry contained data on 11 949 patients with recorded dates of onset. In the National Inpatient Register and Stockholm registries, 27 078 additional individuals with multiple sclerosis were found, comprising a data set of 28 396 unique multiple sclerosis patients. Out of these, 235 lacked onset data and were only included in the calculation of the crude risks. For a flow chart of the selection process, see Supplementary Fig. 1. In total, 38.6% of the cases appeared in both the Swedish Multiple Sclerosis Registry and the National Inpatient Register, and 189 patients were only in the Stockholm registry. For characteristics and differences between the Swedish Multiple Sclerosis Registry and the National Inpatient Register individuals, see Table 1. At the time of the study, 67% of the patients were alive.

Table 1

Characteristics of the multiple sclerosis patients

Group n Individuals unique for registry, n Mean age at onset, years Mean year of birth Mean year at onset % Female Alive at time of study (%) 
Swedish Multiple Sclerosis Registry 11 949 1083 33.7 1959 1994 70.8 11 248 (94.1) 
National Inpatient Register 27 078 16 212 47.3 1946 1994 66.1 17 801 (65.7) 
Total 28 161 10 866* 43.8 1947 1991 66.20 18 872 (67.0) 
Group n Individuals unique for registry, n Mean age at onset, years Mean year of birth Mean year at onset % Female Alive at time of study (%) 
Swedish Multiple Sclerosis Registry 11 949 1083 33.7 1959 1994 70.8 11 248 (94.1) 
National Inpatient Register 27 078 16 212 47.3 1946 1994 66.1 17 801 (65.7) 
Total 28 161 10 866* 43.8 1947 1991 66.20 18 872 (67.0) 

Note that the data in the Swedish Multiple Sclerosis Registry reflect the actual age at onset determined by a neurologist, whereas the National Inpatient Register reflects the first recorded contact for multiple sclerosis to a hospital if before 2001, and first visit to a hospital or first visit to a specialist if after 2001.

*Assessed from both registries.

Age at onset

A mean age at onset of 33.7 years of age was observed in the Swedish Multiple Sclerosis Registry. See Table 2 for details and comparison against the total cohort. As expected, the age at onset from the Swedish Multiple Sclerosis Registry deviated significantly from the age at first admission to hospital in the National Inpatient Register cohort. By age 59, 99.9% of the patients in the Swedish Multiple Sclerosis Registry had reached the onset of disease, whereas in the National Inpatient Register cohort some were not in the records for hospitalization as a result of multiple sclerosis until >90 years of age.

Table 2

Age at onset and age at first hospitalization for patients with multiple sclerosis

 Swedish Multiple Sclerosis Registry
 
Combined
 
Age range n Proportion (%) Cumulative proportion (%) n Proportion (%) Cumulative proportion (%) 
0–9 26 0.2 0.2 52 0.2 0.2 
10–19 971 8.1 8.3 1167 4.1 4.3 
20–29 3950 33.1 44.4 5234 18.6 22.9 
30–39 3717 31.1 72.5 6297 22.4 45.3 
40–49 2286 19.1 91.6 5848 20.8 66.1 
50–59 834 7.0 99.9 4660 16.5 82.6 
60–69 150 1.3 100 2817 10.0 92.6 
70–79 15 0.1 100 1519 5.4 98 
80–89 100 514 1.8 99.8 
90+ 100 53 0.2 100 
 Swedish Multiple Sclerosis Registry
 
Combined
 
Age range n Proportion (%) Cumulative proportion (%) n Proportion (%) Cumulative proportion (%) 
0–9 26 0.2 0.2 52 0.2 0.2 
10–19 971 8.1 8.3 1167 4.1 4.3 
20–29 3950 33.1 44.4 5234 18.6 22.9 
30–39 3717 31.1 72.5 6297 22.4 45.3 
40–49 2286 19.1 91.6 5848 20.8 66.1 
50–59 834 7.0 99.9 4660 16.5 82.6 
60–69 150 1.3 100 2817 10.0 92.6 
70–79 15 0.1 100 1519 5.4 98 
80–89 100 514 1.8 99.8 
90+ 100 53 0.2 100 

In the Swedish Multiple Sclerosis Registry cohort the age of onset is estimated by a neurologist. For the patients identified through the National Inpatient Register, the date is their first recorded inpatient hospital visit for multiple sclerosis or, if after 2001, first recorded hospitalization or visit out-patient visit to a neurologist.

Crude risks

Crude risks are shown in Tables 3 and 4. For parent–child pairs, the highest crude risk was for daughters, and for siblings the highest risk was found for sisters of patients with multiple sclerosis. The overall highest age-adjusted risk was for sisters of an affected brother. For half-siblings, maternal half-sisters had the highest risk, and among the remaining second degree relatives and cousins, the risks were <1%, except for maternal aunts. Figure 1 shows the age-adjusted risks for the different relations.

Figure 1

Age-adjusted risks for the different groups of relatives. DZ = dizygotic; MZ = monozygotic.

Figure 1

Age-adjusted risks for the different groups of relatives. DZ = dizygotic; MZ = monozygotic.

Table 3

Crude and age-adjusted risks for first degree, half-siblings and adopted relatives

Proband Female
 
Male
 
Total
 
Relative n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) 
Monozygotic       78 (12) 15.38 17.26 (8.38–26.14) 
Dizygotic       237 (4) 1.69 1.92 (0.00–0.38) 
Child       43 078 (526) 1.22 2.03 (1.86–2.20) 
Daughter 14 206 (251) 1.77 2.96 (2.60–3.32) 6737 (107) 1.59 2.57 (2.09–3.05) 20 943 (358) 1.71 2.83 (2.54–3.12) 
Son 15 003 (99) 0.66 1.12 (0.90–1.34) 7132 (69) 0.97 1.55 (1.12–1.91) 22 135 (168) 0.76 1.26 (1.07–1.45) 
Sibling       28 531 (652) 2.29 2.55 (2.09–3.01) 
Sister 9537 (288) 3.02 3.36 (2.98–3.74) 4379 (136) 3.11 3.43 (2.86–4.00) 13 916 (424) 3.05 3.38 (3.16–3.60) 
Brother 10 038 (136) 1.35 1.52 (1.13–1.78) 4577 (92) 2.01 2.23 (1.77–2.69) 14 615 (228) 1.56 1.74 (1.51–1.97) 
Maternal half-sibling       4359 (62) 1.42 1.68 (1.26–2.10) 
Sister 1382 (29) 2.10 2.40 (1.26–2.94) 681 (13) 1.91 2.14 (0.96–3.32) 2063 (42) 2.04 2.46 (1.72–3.20) 
Brother 1569 (12) 0.76 0.95 (0.52–1.49) 727 (8) 1.10 1.31 (0.41–2.21) 2296 (20) 0.87 1.51 (0.96–2.06) 
Paternal half-sibling       4117 (44) 1.07 1.40 (0.99–1.81) 
Sister 1400 (16) 1.14 1.54 (0.79–2.29) 647 (10) 1.55 2.01 (0.78–3.24) 2047 (26) 1.27 1.69 (0.99–1.81) 
Brother 1468 (10) 0.68 0.92 (0.35–1.49) 662 (8) 1.21 1.55 (0.05–2.62) 2130 (18) 0.85 1.12 (0.60–1.64) 
Adopted child       497 (2) 0.4 0.67 (0.00–1.58) 
Adopted sibling       65 (1) 1.54 1.76 (0.00–5.18) 
Adoption       562 (3) 0.53 0.84 (0.00–1.79) 
Proband Female
 
Male
 
Total
 
Relative n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) 
Monozygotic       78 (12) 15.38 17.26 (8.38–26.14) 
Dizygotic       237 (4) 1.69 1.92 (0.00–0.38) 
Child       43 078 (526) 1.22 2.03 (1.86–2.20) 
Daughter 14 206 (251) 1.77 2.96 (2.60–3.32) 6737 (107) 1.59 2.57 (2.09–3.05) 20 943 (358) 1.71 2.83 (2.54–3.12) 
Son 15 003 (99) 0.66 1.12 (0.90–1.34) 7132 (69) 0.97 1.55 (1.12–1.91) 22 135 (168) 0.76 1.26 (1.07–1.45) 
Sibling       28 531 (652) 2.29 2.55 (2.09–3.01) 
Sister 9537 (288) 3.02 3.36 (2.98–3.74) 4379 (136) 3.11 3.43 (2.86–4.00) 13 916 (424) 3.05 3.38 (3.16–3.60) 
Brother 10 038 (136) 1.35 1.52 (1.13–1.78) 4577 (92) 2.01 2.23 (1.77–2.69) 14 615 (228) 1.56 1.74 (1.51–1.97) 
Maternal half-sibling       4359 (62) 1.42 1.68 (1.26–2.10) 
Sister 1382 (29) 2.10 2.40 (1.26–2.94) 681 (13) 1.91 2.14 (0.96–3.32) 2063 (42) 2.04 2.46 (1.72–3.20) 
Brother 1569 (12) 0.76 0.95 (0.52–1.49) 727 (8) 1.10 1.31 (0.41–2.21) 2296 (20) 0.87 1.51 (0.96–2.06) 
Paternal half-sibling       4117 (44) 1.07 1.40 (0.99–1.81) 
Sister 1400 (16) 1.14 1.54 (0.79–2.29) 647 (10) 1.55 2.01 (0.78–3.24) 2047 (26) 1.27 1.69 (0.99–1.81) 
Brother 1468 (10) 0.68 0.92 (0.35–1.49) 662 (8) 1.21 1.55 (0.05–2.62) 2130 (18) 0.85 1.12 (0.60–1.64) 
Adopted child       497 (2) 0.4 0.67 (0.00–1.58) 
Adopted sibling       65 (1) 1.54 1.76 (0.00–5.18) 
Adoption       562 (3) 0.53 0.84 (0.00–1.79) 

The age adjusted risks were calculated using Strömgren’s unmodified method. The confidence intervals were estimated using the binomial distribution with the sum of the weights as the total sample size.

Table 4

Crude and age-corrected risks for second degree relatives and cousins

Proband Female
 
Male
 
Total
 
Relative n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) 
Grandparent       23 073 (66) 0.29 0.28 (0.18-0.38) 
Maternal grandmother 4632 (19) 0.41 0.35 (0.18–0.52) 1858 (0) – – 6490 (19) 0.29 0.25 (0.13–0.37) 
Maternal grandfather 4433 (15) 0.34 0.37 (0.06–0.19) 1756 (3) 0.17 0.17 (0.00–0.36) 6189 (18) 0.29 0.30 (0–16–0.44) 
Paternal grandmother 3766 (12) 0.32 0.33 (0.14–0.52) 1564 (4) 0.26 0.26 (0.00–0.51) 5330 (16) 0.30 0.31 (0.16–0.46) 
Paternal grandfather 3598 (9) 0.25 0.26 (0.09–0.43) 1466 (4) 0.27 0.28 (0.00–0.55) 5064 (13) 0.26 0.26 (0.12–0.40) 
Aunt/uncle       20 024 (202) 1.01 1.00 (0.86–1.14) 
Maternal aunt 3841 (61) 1.59 1.63 (1.22–2.04) 1543 (20) 1.30 1.29 (0.71–1.87) 5384 (81) 1.50 1.53 (1–20–1.86) 
Maternal uncle 4009 (32) 0.80 0.81 (0.53–1.09) 1645 (13) 0.79 0.83 (0.38–1.28) 5654 (45) 0.80 0.81 (0.57–1.05) 
Paternal aunt 3057 (33) 1.08 1.44 (1.04–1.87) 1368 (21) 1.54 1.51 (0.85–2.17) 4425 (54) 1.22 1.12 (0.80–1.44) 
Paternal uncle 3259 (16) 0.49 0.51 (0.26–0.76) 1302 (6) 0.46 0.48 (0.10–0.86) 4561 (22) 0.48 0.50 (0.29–0.71) 
Cousin       34 424 (127) 0.37 0.57 (0.47–0.67) 
Female maternal cousin 6287 (26) 0.41 0.66 (0.41–0.91) 2550 (11) 0.43 0.70 (0.29–1.11) 8837 (37) 0.42 0.67 (0.45–0.89) 
Male maternal cousin 6498 (15) 0.23 0.38 (0.19–0.57) 2739 (8) 0.29 0.47 (0.14–0.77) 9237 (23) 0.25 0.40 (0.24–0.56) 
Female paternal cousin 5497 (33) 0.60 0.91 (0.60–1.22) 2378 (14) 0.59 0.83 (0.38–1.28) 7875 (47) 0.60 0.89 (0.63–1.15) 
Male paternal cousin 6005 (14) 0.23 0.33 (0.15–0.51) 2470 (6) 0.24 0.38 (0.08–0.68) 8475 (20) 0.24 0.34 (0.19–0.49) 
Proband Female
 
Male
 
Total
 
Relative n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) n (affected) Crude risk (%) Age-adjusted risk (%) (95% CI) 
Grandparent       23 073 (66) 0.29 0.28 (0.18-0.38) 
Maternal grandmother 4632 (19) 0.41 0.35 (0.18–0.52) 1858 (0) – – 6490 (19) 0.29 0.25 (0.13–0.37) 
Maternal grandfather 4433 (15) 0.34 0.37 (0.06–0.19) 1756 (3) 0.17 0.17 (0.00–0.36) 6189 (18) 0.29 0.30 (0–16–0.44) 
Paternal grandmother 3766 (12) 0.32 0.33 (0.14–0.52) 1564 (4) 0.26 0.26 (0.00–0.51) 5330 (16) 0.30 0.31 (0.16–0.46) 
Paternal grandfather 3598 (9) 0.25 0.26 (0.09–0.43) 1466 (4) 0.27 0.28 (0.00–0.55) 5064 (13) 0.26 0.26 (0.12–0.40) 
Aunt/uncle       20 024 (202) 1.01 1.00 (0.86–1.14) 
Maternal aunt 3841 (61) 1.59 1.63 (1.22–2.04) 1543 (20) 1.30 1.29 (0.71–1.87) 5384 (81) 1.50 1.53 (1–20–1.86) 
Maternal uncle 4009 (32) 0.80 0.81 (0.53–1.09) 1645 (13) 0.79 0.83 (0.38–1.28) 5654 (45) 0.80 0.81 (0.57–1.05) 
Paternal aunt 3057 (33) 1.08 1.44 (1.04–1.87) 1368 (21) 1.54 1.51 (0.85–2.17) 4425 (54) 1.22 1.12 (0.80–1.44) 
Paternal uncle 3259 (16) 0.49 0.51 (0.26–0.76) 1302 (6) 0.46 0.48 (0.10–0.86) 4561 (22) 0.48 0.50 (0.29–0.71) 
Cousin       34 424 (127) 0.37 0.57 (0.47–0.67) 
Female maternal cousin 6287 (26) 0.41 0.66 (0.41–0.91) 2550 (11) 0.43 0.70 (0.29–1.11) 8837 (37) 0.42 0.67 (0.45–0.89) 
Male maternal cousin 6498 (15) 0.23 0.38 (0.19–0.57) 2739 (8) 0.29 0.47 (0.14–0.77) 9237 (23) 0.25 0.40 (0.24–0.56) 
Female paternal cousin 5497 (33) 0.60 0.91 (0.60–1.22) 2378 (14) 0.59 0.83 (0.38–1.28) 7875 (47) 0.60 0.89 (0.63–1.15) 
Male paternal cousin 6005 (14) 0.23 0.33 (0.15–0.51) 2470 (6) 0.24 0.38 (0.08–0.68) 8475 (20) 0.24 0.34 (0.19–0.49) 

The age adjusted risks were calculated using Strömgren’s unmodified method.

The confidence intervals were estimated using the binomial distribution with the sum of the weights as the total sample size.

Relative risks

Relative risks are presented in Tables 5 and 6. After matching with controls, the proportions of cases remaining in the analysis were all >98%. Compared with matched controls, which account for the lower prevalence in males, similar risk estimates between relatives to males and female probands were found. The confidence intervals were largely overlapping, showing a non-significant difference between the sexes. Among children of affected parents, daughters had a slightly lower point estimate of risk compared with that of sons, and for fathers, the risks for daughters and sons were significantly different, with a higher risk for the sons. The overall highest risk was for brother–brother pairs, with no overlap with either mother–daughter or father–daughter pairs, which both had significantly lower risk. Among cousins, paternal female–female cousins were the only significant relation (uncorrected P-value = 0.00024). Figure 2 shows the relative risks with confidence intervals for the different groups.

Figure 2

Relative risk ratios with confidence intervals for different groups of relatives. DZ = dizygotic; MZ = monozygotic.

Figure 2

Relative risk ratios with confidence intervals for different groups of relatives. DZ = dizygotic; MZ = monozygotic.

Table 5

Relative risks and tetrachoric correlations for first-degree, half-siblings and adopted relatives

Proband Female
 
Male
 
Total
 
 Cases Controls Relative risk Cases Controls Relative risk Cases Controls Relative risk 
Monozygotic 57 (8) 570 (5) 11.62 (6.47–20.85) 21 (4) 210 (1) 127.14 (0.57–28 572.27) 78 (12) 780 (6) 23.62 (8.71–64.02) 
Dizygotic 83* (2) 830* (7) 2.84* (0.69–11.62) 39* (2) 390* (4) 2.03* (1.00–4.13) 237 (4) 2,361 (19) 2.18 (0.71–6.68) 
Child       42 743 (515) 426 808 (899) 5.77 (5.17–6.45) 
Daughter 14 089 (245) 140 752 (413) 5.99 (5.82–6.16) 6695 (105) 66 776 (234) 4.54 (3.58–5.74) 20 784 (350) 207 528 (647) 5.46 (4.79–6.23) 
Son 14 859 (96) 148 421 (170) 5.6 (4.41–7.26) 7100 (69) 70 859 (82) 8.43 (6.07–11.69) 21 959 (165) 219 280 (252) 6.56 (5.38–8.00) 
Sibling       27 216 (639) 281 694 (905) 7.13 (6.42–7.93) 
Sister 9443 (284) 94 290 (421) 6.82 (5.86–7.95) 4338 (133) 43 338 (189) 7.15 (5.68–9.00) 13 781 (417) 137 631 (610) 6.92 (6.10–7.86) 
Brother 9903 (133) 98 828 (190) 7.02 (5.61–8.80) 4532 (89) 45 235 (105) 8.56 (6.38–11.55) 14 435 (9) 144 063 (295) 7.57 (6.32–9.06) 
Maternal half-sibling       4322 (60) 43 140 (135) 4.45 (3.25–6.09) 
Sister 1364 (27) 13 618 (60) 4.54 (2.90–7.12) 675 (13) 6737 (24) 5.41 (2.69–10.90) 2039 (40) 20 355 (84) 4.79 (3.28–6.99) 
Brother 1557 (12) 15 529 (35) 3.39 (1.75–6.58) 726 (8) 7256 (16) 5.03 (2.14–11.82) 2283 (20) 22 785 (51) 3.90 (2.31–6.57) 
Paternal half-sibling       4115 (44) 40 954 (134) 3.29 (2.29–4.72) 
Sister 1367 (16) 13 620 (57) 2.80 (1.62–4.86) 642 (10) 6,391 (33) 3.05 (1.40–6.62) 2009 (26) 20 011 (90) 2.89 (1.84–4.53) 
Brother 1451 (10) 14 425 (27) 3.71 (1.86–4.40) 655 (8) 6518 (17) 4.70 (1.96–1.29) 2106 (18) 20 943 (44) 4.10 (2.39–7.04) 
Adopted child       494 (2) 4,687 (11) 1.73 (0.37–8.04) 
Adopted sibling       56 (1) 275 (3) 1.87 (0.23–15.46) 
Adoption       550 (3) 4,692 (14) 1.78 (0.49–6.06) 
Proband Female
 
Male
 
Total
 
 Cases Controls Relative risk Cases Controls Relative risk Cases Controls Relative risk 
Monozygotic 57 (8) 570 (5) 11.62 (6.47–20.85) 21 (4) 210 (1) 127.14 (0.57–28 572.27) 78 (12) 780 (6) 23.62 (8.71–64.02) 
Dizygotic 83* (2) 830* (7) 2.84* (0.69–11.62) 39* (2) 390* (4) 2.03* (1.00–4.13) 237 (4) 2,361 (19) 2.18 (0.71–6.68) 
Child       42 743 (515) 426 808 (899) 5.77 (5.17–6.45) 
Daughter 14 089 (245) 140 752 (413) 5.99 (5.82–6.16) 6695 (105) 66 776 (234) 4.54 (3.58–5.74) 20 784 (350) 207 528 (647) 5.46 (4.79–6.23) 
Son 14 859 (96) 148 421 (170) 5.6 (4.41–7.26) 7100 (69) 70 859 (82) 8.43 (6.07–11.69) 21 959 (165) 219 280 (252) 6.56 (5.38–8.00) 
Sibling       27 216 (639) 281 694 (905) 7.13 (6.42–7.93) 
Sister 9443 (284) 94 290 (421) 6.82 (5.86–7.95) 4338 (133) 43 338 (189) 7.15 (5.68–9.00) 13 781 (417) 137 631 (610) 6.92 (6.10–7.86) 
Brother 9903 (133) 98 828 (190) 7.02 (5.61–8.80) 4532 (89) 45 235 (105) 8.56 (6.38–11.55) 14 435 (9) 144 063 (295) 7.57 (6.32–9.06) 
Maternal half-sibling       4322 (60) 43 140 (135) 4.45 (3.25–6.09) 
Sister 1364 (27) 13 618 (60) 4.54 (2.90–7.12) 675 (13) 6737 (24) 5.41 (2.69–10.90) 2039 (40) 20 355 (84) 4.79 (3.28–6.99) 
Brother 1557 (12) 15 529 (35) 3.39 (1.75–6.58) 726 (8) 7256 (16) 5.03 (2.14–11.82) 2283 (20) 22 785 (51) 3.90 (2.31–6.57) 
Paternal half-sibling       4115 (44) 40 954 (134) 3.29 (2.29–4.72) 
Sister 1367 (16) 13 620 (57) 2.80 (1.62–4.86) 642 (10) 6,391 (33) 3.05 (1.40–6.62) 2009 (26) 20 011 (90) 2.89 (1.84–4.53) 
Brother 1451 (10) 14 425 (27) 3.71 (1.86–4.40) 655 (8) 6518 (17) 4.70 (1.96–1.29) 2106 (18) 20 943 (44) 4.10 (2.39–7.04) 
Adopted child       494 (2) 4,687 (11) 1.73 (0.37–8.04) 
Adopted sibling       56 (1) 275 (3) 1.87 (0.23–15.46) 
Adoption       550 (3) 4,692 (14) 1.78 (0.49–6.06) 

Results with a significant risk ratio are marked in bold.

The number of affected relatives are given in the parenthesis. A 95% CI is shown for relative risk.

*Only done for same sex pairs.

Table 6

Relative risks and tetrachoric correlations for second degree relatives and cousins

Proband Female
 
Male
 
Total
 
Relative Cases Controls Relative risk Cases Controls Relative risk Cases Controls 
Grandparent       22 630 (63) 225 172 (322) 1.95 (1.49–2.55) 
Maternal grandmother 4543 (16) 45 261 (83) 1.92 (1.13–3.27) 1828 (0) 18 206 (35) – 6371 (16) 63 467 (118) 1.35 (0.80–2.27) 
Maternal grandfather 4344 (15) 43 193 (38) 3.96 (2.17–7.25) 1,723 (3) 17 118 (21) 1.41 (0.42–4.76) 6067 (18) 60 311 (59) 3.05 (1.79–5.19) 
Paternal grandmother 3693 (12) 36 786 (65) 1.85 (1.00–3.43) 1537 (4) 15 297 (30) 1.33 (0.47–0.77) 5230 (16) 52 083 (95) 1.68 (0.99–2.86) 
Paternal grandfather 3523 (9) 35 004 (37) 2.42 (1.18–4.94) 1439 (4) 14 307 (13) 3.08 (1.00–9.49) 4962 (13) 49 311 (50) 2.59 (1.42–4.72) 
Uncle/aunt       19 844 (195) 197 697 (758) 2.58 (2.19–3.02) 
Maternal aunt 3834 (61) 38 187 (193) 3.17 (2.35–4.28) 1527 (20) 15 223 (89) 2.26 (1.38–3.69) 5361 (81) 53 410 (282) 2.89 (2.23–3.73) 
Maternal uncle 4028 (31) 40 175 (94) 3.31 (2.21–4.97) 1638 (13) 16 344 (33) 3.94 (2.10–7.40) 5666 (44) 56 519 (127) 3.48 (2.47–4.89) 
Paternal aunt 2995 (28) 29 846 (158) 1.77 (1.18–2.65) 1349 (20) 13 372 (79) 2.52 (1.52–4.617 4344 (48) 43 218 (237) 2.02 (1.47–2.77) 
Paternal uncle 3201 (16) 31 904 (83) 1.93 (1.06–3.48) 1272 (6) 12 646 (29) 2.06 (0.88–4.86) 4473 (22) 44 550 (112) 1.96 (1.20–3.20) 
Cousin       33 754 (125) 335 910 (762) 1.63 (1.36–1.97) 
Maternal female cousin 6164 (26) 61 389 (214) 1.21 (0.81–1.81) 2490 (11) 24 807 (81) 1.35 (0.71–2.55) 8,654 (37) 86 196 (295) 1.25 (0.89–1.75) 
Maternal male cousin 6381 (15) 63 477 (72) 2.07 (1.18–3.62) 2682 (8) 26 733 (32) – 9063 (23) 90 210 (104) 2.49 (1.15–5.42) 
Paternal female cousin 5393 (33) 53 702 (10) 1.83 (1.26–2.65) 2336 (13) 23 226 (75) 1.72 (0.96–3.08) 7729 (46) 76 928 (255) 1.80 (1.32–2.44) 
Paternal male cousin 5891 (13) 58 592 (74) 1.75 (0.97–3.16) 2418 (6) 23 984 (34) 1.75 (0.73–4.22) 8308 (19) 82 573 (108) 1.75 (1.07–2.86) 
Proband Female
 
Male
 
Total
 
Relative Cases Controls Relative risk Cases Controls Relative risk Cases Controls 
Grandparent       22 630 (63) 225 172 (322) 1.95 (1.49–2.55) 
Maternal grandmother 4543 (16) 45 261 (83) 1.92 (1.13–3.27) 1828 (0) 18 206 (35) – 6371 (16) 63 467 (118) 1.35 (0.80–2.27) 
Maternal grandfather 4344 (15) 43 193 (38) 3.96 (2.17–7.25) 1,723 (3) 17 118 (21) 1.41 (0.42–4.76) 6067 (18) 60 311 (59) 3.05 (1.79–5.19) 
Paternal grandmother 3693 (12) 36 786 (65) 1.85 (1.00–3.43) 1537 (4) 15 297 (30) 1.33 (0.47–0.77) 5230 (16) 52 083 (95) 1.68 (0.99–2.86) 
Paternal grandfather 3523 (9) 35 004 (37) 2.42 (1.18–4.94) 1439 (4) 14 307 (13) 3.08 (1.00–9.49) 4962 (13) 49 311 (50) 2.59 (1.42–4.72) 
Uncle/aunt       19 844 (195) 197 697 (758) 2.58 (2.19–3.02) 
Maternal aunt 3834 (61) 38 187 (193) 3.17 (2.35–4.28) 1527 (20) 15 223 (89) 2.26 (1.38–3.69) 5361 (81) 53 410 (282) 2.89 (2.23–3.73) 
Maternal uncle 4028 (31) 40 175 (94) 3.31 (2.21–4.97) 1638 (13) 16 344 (33) 3.94 (2.10–7.40) 5666 (44) 56 519 (127) 3.48 (2.47–4.89) 
Paternal aunt 2995 (28) 29 846 (158) 1.77 (1.18–2.65) 1349 (20) 13 372 (79) 2.52 (1.52–4.617 4344 (48) 43 218 (237) 2.02 (1.47–2.77) 
Paternal uncle 3201 (16) 31 904 (83) 1.93 (1.06–3.48) 1272 (6) 12 646 (29) 2.06 (0.88–4.86) 4473 (22) 44 550 (112) 1.96 (1.20–3.20) 
Cousin       33 754 (125) 335 910 (762) 1.63 (1.36–1.97) 
Maternal female cousin 6164 (26) 61 389 (214) 1.21 (0.81–1.81) 2490 (11) 24 807 (81) 1.35 (0.71–2.55) 8,654 (37) 86 196 (295) 1.25 (0.89–1.75) 
Maternal male cousin 6381 (15) 63 477 (72) 2.07 (1.18–3.62) 2682 (8) 26 733 (32) – 9063 (23) 90 210 (104) 2.49 (1.15–5.42) 
Paternal female cousin 5393 (33) 53 702 (10) 1.83 (1.26–2.65) 2336 (13) 23 226 (75) 1.72 (0.96–3.08) 7729 (46) 76 928 (255) 1.80 (1.32–2.44) 
Paternal male cousin 5891 (13) 58 592 (74) 1.75 (0.97–3.16) 2418 (6) 23 984 (34) 1.75 (0.73–4.22) 8308 (19) 82 573 (108) 1.75 (1.07–2.86) 

Results with a significant risk ratio are marked in bold.

The number of affected relatives is given in the parenthesis. A 95% confidence interval is shown for relative risk and correlation.

Adoption

Four hundred and ninety-seven adopted children to parents with multiple sclerosis and 65 adoptive siblings were identified. Among these, two children and one sibling were affected (Table 3). The result was not significant when compared with controls, both in the respective groups and when combining the groups (Table 5). It should be noted that the numbers of adoptees are small, resulting in low power and correspondingly wide confidence intervals. However, in spite of this, the study assesses a full population and as such is one of the largest adoption studies in multiple sclerosis to date.

Twins and heritability

A total of 348 proband twins with MS were identified in the Swedish Twin Registry within 340 distinct sets of pairs. Of the probands, 78 were monozygotic (MZ, 72 pairs), 237 were dizygotic (DZ, 235 pairs) and 33 pairs (10%) were excluded due to unknown zygosity. Of the monozygotic twins, six concordant pairs (12 probands) were identified and two concordant dizygotic pairs (four probands) were found, making the proband-wise concordance rate in monozygotic twins 15.38 and in dizygotic twins 1.69. No concordant dizygotic pairs of different sexes were found. For demographics, see Table 7.

Table 7

Demographics for twins with multiple sclerosis

Zygosity Twins (affected co-twins) Proband-wise concordance (%) Mean age at onset Mean year of birth Females (%) Nationwide rate of zygosity (%) 
Monozygotic 78 (12) 15.38 41.37 1953 73.1 19.97 
Dizygotic 237 (4) 1.69 43.39 1948 67.9 58.05 
Dizygotic same sex 122 (4) 3.28 45.52 1946 68.0 26.81 
Dizygotic different sex 115 (0) 41.14 1951 67.8 31.24 
Unknown 33 (0) 37.29 1961 57.6 21.97 
Zygosity Twins (affected co-twins) Proband-wise concordance (%) Mean age at onset Mean year of birth Females (%) Nationwide rate of zygosity (%) 
Monozygotic 78 (12) 15.38 41.37 1953 73.1 19.97 
Dizygotic 237 (4) 1.69 43.39 1948 67.9 58.05 
Dizygotic same sex 122 (4) 3.28 45.52 1946 68.0 26.81 
Dizygotic different sex 115 (0) 41.14 1951 67.8 31.24 
Unknown 33 (0) 37.29 1961 57.6 21.97 

The nationwide proportion of the sample is included for comparison.

Using the 189/100 000 prevalence for Sweden reported by Ahlgren et al. (2011), we expect 363 twins out of the 191 911 in the registry to be diagnosed with multiple sclerosis. This suggests the study has coverage of 96%.

Heritability

Based on twin data, the h2 estimate, adjusted for sex, was estimated to be 0.64, CI: 0.28–0.77, the shared environmental component, C, was 0 (0–0), and the individual variance, E, was 0.36 (0.23–0.52).

After adding siblings and half-siblings to increase the power of the heritability analysis, a total sample size of n = 2 534 465 individuals (monozygotic = 19 122; dizygotic = 55 635; siblings = 2 262 902; maternal half-siblings = 96 577; paternal half-siblings = 100 499) was obtained. The resulting estimates were h2, A: 0.64 (0.36–0.76); environmental component, C: 0.01 (0–0.18); and individual variance, E: 0.35 (0.24–0.51).

Parental transmission/the Carter effect

A total of 13 923 father–child and 29 265 mother–child pairs were identified. In contrast with previous studies (Kantarci et al., 2006; Herrera et al., 2007), a significant difference in transmission from father to son compared with mother to son was found (P-value = 0.013). There was no significant difference in transmission to daughter between mother and father, a difference that was found in the Kantarci et al. (2006) study. Examining transmission from father to son/daughter and from mother to son/daughter, a significant difference existed for both genders, with a higher transmission rate to daughters (OR 1.65 and 2.72 for fathers and mothers, respectively, see Table 8 for full details). Evaluating tetrachoric correlations (Supplementary Tables 1 and 2), sibling pairs and the parent–child pairs had equal point estimates of the correlation, and brother–brother full sibling pairs had the highest point estimate of the correlation (0.42, CI: 0.36–0.47), together with brother–sister pairs (0.42, CI: 0.36–0.45). The confidence intervals did not overlap for either mother–son pairs (0.31, CI: 0.26–0.36) or father–daughter pairs (0.29, CI: 0.25–0.34).

Table 8

Transmission from parent to child

 Transmitted Non-transmitted OR (95% CI) P-value 
Father All 176 13 747 1.07 (0.89–1.29) 0.48 
Mother 345 28 920 
Father Daughter 107 6654 0.91 (0.72–1.15) 0.44 
Mother 248 14,010 
Father Son 69 7,093 1.50 (1.08–2.06) 0.013 
Mother 97 14,910 
Father Daughter 107 6,654 1.65 (1.21–2.28) 0.0014 
Son 69 7,093 
Mother Daughter 248 14,010 2.72 (2.14–3.48) <2.2 × 10−16 
Son 97 14,910 
 Transmitted Non-transmitted OR (95% CI) P-value 
Father All 176 13 747 1.07 (0.89–1.29) 0.48 
Mother 345 28 920 
Father Daughter 107 6654 0.91 (0.72–1.15) 0.44 
Mother 248 14,010 
Father Son 69 7,093 1.50 (1.08–2.06) 0.013 
Mother 97 14,910 
Father Daughter 107 6,654 1.65 (1.21–2.28) 0.0014 
Son 69 7,093 
Mother Daughter 248 14,010 2.72 (2.14–3.48) <2.2 × 10−16 
Son 97 14,910 

Discussion

This paper presents strikingly lower risk ratios than most previous studies of familial recurrence in multiple sclerosis (Sadovnick and Baird, 1988; Ebers et al., 1995; Robertson et al., 1996; Sazdovitch et al., 2000; Marrosu et al., 2002; Prokopenko et al., 2003; O’Gorman et al., 2011, 2013). With 28 161 patients with multiple sclerosis and an estimated 96% coverage of the Swedish multiple sclerosis population, this is the most complete study of a single population to date. By using nationwide registries, problems that may be present in clinic-based studies such as recall bias and skewed sampling (Lykken et al., 1987; Hawkes, 1997) are minimized. The previously reported h2 estimates have ranged from 0.25 to 0.76 (Hawkes and Macgregor, 2009), placing the results of this study firmly within this range. This estimate once again confirms that multiple sclerosis is indeed a complex disease with a substantial genetic influence.

The twin study presented here is based on 96% coverage of the expected number of twins in Sweden. However, the number of concordant pairs is still low in absolute numbers, which makes sufficient power difficult to achieve. In a meta-analysis on familial recurrence data performed by O’Gorman et al. (2013), a combined proband-wise crude risk for monozygotic twins of 17.25% and an age-adjusted risk of 18.44% was reported, figures not far from the results presented here, based on a single population. In contrast, after adding controls and calculating recurrence risks, the relative risk estimates contrast sharply, with 23.6 in the current study, compared with 116.7 in the meta-analysis. Although this study is outnumbered by O’Gorman et al. (2013) the extensive coverage of our registries within a single population and the use of matched controls may offer an advantage. Previous studies, based on assumed prevalence figures rather than randomized controls, may have underestimated the population background risk of multiple sclerosis.

A decreased prevalence among dizygotic twins, as suggested by Hansen et al. (2005), was not confirmed, although the relative risk in this study was non-significant and the dizygotic concordance rate was in fact lower than that of other siblings. This may suggest that a possibly lower prevalence among dizygotic twins may exist and supports Hansen et al.’s (2005) suggestion of a beneficial effect for the immune system of placental exposure of a genetically different individual.

The relative risks presented in this paper are to our knowledge the first for multiple sclerosis using randomly selected controls from the general population. As the lifetime risk and prevalence in females are higher (Ahlgren et al., 2011), calculating relative risks by comparing with a single background risk estimate for both sexes would erroneously increase the risk for female relations and underestimate the risk for males. Also, by matching on sex and year of birth, the possible differences in prevalence during different time periods and in different sexes (Ahlgren et al., 2011), and different coverage of the registries during different years (Ludvigsson et al., 2011), have been taken into account and the variance reduced. The age-adjusted risks are similar to the meta-analysis (Table 9), but when comparing the relative risks, the risks presented here are maximally half of those in the meta-analysis. Although there could be differences among populations, it has previously been thought that the heritability in multiple sclerosis increases with latitude, a theory our results do not support. As the results from the registry-based study performed in Denmark by Nielsen et al. (2005) and a previous report from Sweden by Hemminki et al. (2009) also using registries, are similar to the ones presented here, we believe that the difference is best attributed to the use of matched controls, large sample sizes and the use of registries to identify patients with multiple sclerosis and their relatives. The distribution of relatives to probands, with ∼2–3 times as big group of relatives to a female proband compared to the males, is the same as the general gender ratio of about 2–3:1 in multiple sclerosis, thereby demonstrating the rigidity of using nationwide registries for identification of both patients and relatives and speaking for the validity of the findings. Compared with the meta-analysis by O’Gorman et al. (2013), this study has the largest sample size for all relations except twins, for example containing 43 188 children to multiple sclerosis patients.

Table 9

Comparison with O’Gorman et al. (2013) meta-analysis

Estimate Age-corrected risks
 
Relative risks
 
 O’Gorman et al. (2013) This study O’Gorman et al. (2013) This study 
Monozygotic 18.44 17.26 116.69 (83.32–163.41) 23.62 (8.71–64.20) 
Dizygotic 4.61 1.92 29.84 (15.39–57.87) 2.18 (0.71–6.68) 
Sibling 2.68 2.55 16.77 (13.89–20.26) 7.13 (6.42–7.93) 
Child 2.07 2.55 14.12 (9.91–20.13) 5.77 (5.17–6.45) 
Aunt/uncle 0.75 1.00 4.57 (2.70–7.73) 2.58 (2.19–3.02) 
Cousin 0.73 0.57 4.79 (2.98–7.69) 1.63 (1.36–1.97) 
Estimate Age-corrected risks
 
Relative risks
 
 O’Gorman et al. (2013) This study O’Gorman et al. (2013) This study 
Monozygotic 18.44 17.26 116.69 (83.32–163.41) 23.62 (8.71–64.20) 
Dizygotic 4.61 1.92 29.84 (15.39–57.87) 2.18 (0.71–6.68) 
Sibling 2.68 2.55 16.77 (13.89–20.26) 7.13 (6.42–7.93) 
Child 2.07 2.55 14.12 (9.91–20.13) 5.77 (5.17–6.45) 
Aunt/uncle 0.75 1.00 4.57 (2.70–7.73) 2.58 (2.19–3.02) 
Cousin 0.73 0.57 4.79 (2.98–7.69) 1.63 (1.36–1.97) 

The Carter effect has long been debated in multiple sclerosis. Carter (1961) presented a liability threshold model with gender differences as an explanation for the lower prevalence of pyloric stenosis in females. Simply put, the lower prevalent sex requires a higher number of risk genes to develop disease. Chakraborty (1986) extended on this, discussing the importance of using correlation as a measure when investigating a Carter effect. In multiple sclerosis, a possible Carter effect would show up as a higher correlation between male–male and male–female pairs, and a lower correlation for female–male pairs. Previously published data have emphasized a maternal parent-of-origin effect (Ebers et al., 2004; Herrera et al., 2008). By using population-based matched controls, we were able to control for potential confounding introduced by a possible change in the proportion of females among patients with multiple sclerosis throughout the 1900s and sampling and/or recall bias that might be present in a clinic-based study. In this study, no maternal effect was observed. The similar distributions and overlapping confidence intervals in the sex-stratified analysis for both relative risks and correlations between parent-child, full siblings and half-siblings, speak for a modest difference in transmission. However, the significant difference in transmission rate from father to son compared to mother–son, and the lower difference in risk between the sons and daughters of a father, compared to those of a mother (Table 8), is what would be expected if the Carter effect was present. The presence of a Carter effect in multiple sclerosis can thus not be excluded, although if present it is most likely not of great effect.

As seen in the result from the heritability analysis, a rather high non-shared environmental component is estimated. Inclusion of full and half siblings introduces more assumptions in the model, one of them being that paternal half siblings are assumed to share a household less frequently than maternal half siblings. This assumption is supported by a report from the Board of Social Welfare (Socialstyrelsen) (Statistics Sweden, 1994) showing that the majority of the children in Sweden live with their mother’s home as their primary address. The heritability analysis performed on the subset using only twins, can be considered a sensitivity analysis, and revealed almost identical results as the twin-only analysis. The risk for adoptees to a multiple sclerosis parent was not significantly different from that of controls, as previously reported by Ebers et al. (1995), and in line with the finding of the heritability study, as well as other observations, as the lack of increased risk for spouses of patients with multiple sclerosis (Nielsen et al., 2005). Many of the environmental factors associated with multiple sclerosis, such as smoking, night shift work and body mass index, are considered as risks that would contribute to the non-shared environmental component.

In conclusion, we present data that adjust familial recurrence risks for multiple sclerosis downwards, using an estimated 96% of the total Swedish multiple sclerosis patient population and matched controls. Lambda s, the standardized measure of familial aggregation (Rybicki and Elston, 2000), was found to be as low as 7.1. In the context of gene mapping efforts, our findings suggest a theoretically smaller number of multiple sclerosis risk genes, indicating that a greater proportion of the genes contributing to multiple sclerosis susceptibility have been identified than previously thought.

Acknowledgements

The authors wish to thank Leszek Stawiarz for sharing his expertise regarding Swedish multiple sclerosis registries.

Funding

This study was supported by grants from The Swedish research council, Karolinska Institutet, Stockholm County Council, the Jensen foundation and Swedish Research Council for Health, Working Life and Welfare.

Supplementary material

Supplementary material is available at Brain online.

References

Ahlgren
C
Odén
A
Lycke
J
High nationwide prevalence of multiple sclerosis in Sweden
Mult Scler
 , 
2011
, vol. 
17
 (pg. 
901
-
8
)
Aragon
TJ
epitools: epidemiology tools
2012
 
Available from: http://medepi.com/epitools (16 January 2014, date last accessed)
Boker
S
Neale
M
Maes
H
Wilde
M
Spiegel
M
Brick
T
, et al.  . 
OpenMx: an open source extended structural equation modeling framework
Psychometrika
 , 
2011
, vol. 
76
 (pg. 
306
-
17
)
Carter
CO
The inheritance of congenital Pyloric Stenosis
Br Med Bull
 , 
1961
, vol. 
17
 (pg. 
251
-
4
)
Chakraborty
R
The inheritance of pyloric stenosis explained by a multipfactorial threshold model with sex dimorphism for liability
Genet Epidemiol
 , 
1986
, vol. 
3
 (pg. 
1
-
15
)
Ebers
GC
A twin consensus in MS
Mult Scler
 , 
2005
, vol. 
11
 (pg. 
497
-
9
)
Ebers
GC
Sadovnick
AD
Dyment
DA
Yee
IM
Willer
CJ
Risch
N
Parent-of-origin effect in multiple sclerosis: observations in half-siblings
Lancet
 , 
2004
, vol. 
363
 (pg. 
1773
-
4
)
Ebers
GC
Sadovnick
AD
Risch
NJ
A genetic basis for familial aggregation in multiple sclerosis. Canadian Collaborative Study Group
Nature
 , 
1995
, vol. 
377
 (pg. 
50
-
1
)
Frisell
T
Lichtenstein
P
Långström
N
Violent crime runs in families: a total population study of 12.5 million individuals
Psychol Med
 , 
2011
, vol. 
41
 (pg. 
97
-
105
)
Haahr
S
Koch-Henriksen
N
Møller-Larsen
A
Eriksen
LS
Andersen
HM
Increased risk of multiple sclerosis after late Epstein-Barr virus infection: a historical prospective study
Mult Scler
 , 
1995
, vol. 
1
 (pg. 
73
-
7
)
Hansen
T
Skytthe
A
Stenager
E
Petersen
HC
Kyvik
KO
Brønnum-Hansen
H
Risk for multiple sclerosis in dizygotic and monozygotic twins
Mult Scler
 , 
2005
, vol. 
11
 (pg. 
500
-
3
)
Hawkes
CH
Twin studies in medicine—what do they tell us?
QJM
 , 
1997
, vol. 
90
 (pg. 
311
-
21
)
Hawkes
CH
Macgregor
AJ
Twin studies and the heritability of MS: a conclusion
Mult Scler
 , 
2009
, vol. 
15
 (pg. 
661
-
7
)
Hedström
AK
Bäärnhielm
M
Olsson
T
Alfredsson
L
Tobacco smoking, but not Swedish snuff use, increases the risk of multiple sclerosis
Neurology
 , 
2009
, vol. 
73
 (pg. 
696
-
701
)
Hemminki
K
Li
X
Sundquist
J
Hillert
J
Sundquist
K
Risk for multiple sclerosis in relatives and spouses of patients diagnosed with autoimmune and related conditions
Neurogenetics
 , 
2009
, vol. 
10
 (pg. 
5
-
11
)
Herrera
BM
Ramagopalan
SV
Orton
S
Chao
MJ
Yee
IM
Sadovnick
AD
, et al.  . 
Parental transmission of MS in a population-based Canadian cohort
Neurology
 , 
2007
, vol. 
69
 (pg. 
1208
-
12
)
Herrera
BM
Ramagopalan
SV
Lincoln
MR
Orton
SM
Chao
MJ
Sadovnick
AD
, et al.  . 
Parent-of-origin effects in MS: observations from avuncular pairs
Neurology
 , 
2008
, vol. 
71
 (pg. 
799
-
803
)
 
International Multiple Sclerosis Genetics Consortium (IMSGC), Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 2013; 45: 1353–60
Islam
T
Gauderman
WJ
Cozen
W
Hamilton
AS
Burnett
ME
Mack
TM
Differential twin concordance for multiple sclerosis by latitude of birthplace
Ann Neurol
 , 
2006
, vol. 
60
 (pg. 
56
-
64
)
Kantarci
OH
Barcellos
LF
Atkinson
EJ
Ramsay
PP
Lincoln
R
Achenbach
SJ
, et al.  . 
Men transmit multiple sclerosis more often to their children vs women: the Carter effect
Neurology
 , 
2006
, vol. 
67
 (pg. 
305
-
10
)
Lichtenstein
P
Sullivan
PF
Cnattingius
S
Gatz
M
Johansson
S
Carlström
E
, et al.  . 
The Swedish Twin Registry in the third millenium: an update
Twin Res Hum Genet
 , 
2006
, vol. 
9
 (pg. 
875
-
82
)
Lichtenstein
P
De Faire
U
Floderus
B
Svartengren
M
Svedberg
P
Pedersen
NL
The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies
J Intern Med
 , 
2002
, vol. 
252
 (pg. 
184
-
205
)
Ludvigsson
JF
Andersson
E
Ekbom
A
Feychting
M
Kim
JL
Reuterwall
C
, et al.  . 
External review and validation of the Swedish National Inpatient Register
BMC Public Health
 , 
2011
, vol. 
11
 pg. 
450
 
Lykken
DT
McGue
M
Tellegen
A
Recruitment bias in twin research: the rule of two-thirds reconsidered
Behav Genet
 , 
1987
, vol. 
17
 (pg. 
343
-
62
)
Magnusson
PKE
Almqvist
C
Rahman
I
Ganna
A
Viktorin
A
Walum
H
, et al.  . 
The Swedish twin registry: establishment of a biobank and other recent developments
Twin Res Hum Genet
 , 
2013
, vol. 
16
 (pg. 
317
-
29
)
Marrosu
MG
Lai
M
Cocco
E
Loi
V
Spinicci
G
Pischedda
MP
, et al.  . 
Genetic factors and the founder effect explain familial multiple sclerosis in Sardinia
Neurology
 , 
2002
, vol. 
58
 (pg. 
283
-
8
)
Mataix-Cols
D
Boman
M
Monzani
B
Rück
C
Serlachius
E
Långström
N
, et al.  . 
Population-based, multigenerational family clustering study of obsessive-compulsive disorder
JAMA Psychiatry
 , 
2013
, vol. 
70
 (pg. 
709
-
17
)
Munger
KL
Zhang
SM
O'Reilly
E
Hernán
MA
Olek
MJ
Willett
WC
, et al.  . 
Vitamin D intake and incidence of multiple sclerosis
Neurology
 , 
2004
, vol. 
62
 (pg. 
60
-
5
)
Neale
MC
Cardon
LR
Methodology for genetic studies of twins and families
 , 
1992
Dordrecht, The Netherlands
Kluwer Academic Publishers
Nielsen
NM
Westergaard
T
Rostgaard
K
Frisch
M
Hjalgrim
H
Wohlfahrt
J
, et al.  . 
Familial risk of multiple sclerosis: a nationwide cohort study
Am J Epidemiol
 , 
2005
, vol. 
162
 (pg. 
774
-
8
)
O’Gorman
C
Freeman
S
Taylor
BV
Butzkueven
H
Australian and New Zealand MS Genetics Consortium (ANZgene)
Broadley
SA
Familial recurrence risks for multiple sclerosis in Australia
J Neurol Neurosurg Psychiatry
 , 
2011
, vol. 
82
 (pg. 
1351
-
4
)
O’Gorman
C
Lin
R
Stankovich
J
Broadley
SA
Modelling genetic susceptibility to multiple sclerosis with family data
Neuroepidemiology
 , 
2013
, vol. 
40
 (pg. 
1
-
12
)
Prokopenko
I
Montomoli
C
Ferrai
R
Musu
L
Piras
ML
Ticca
A
, et al.  . 
Risk for relatives of patients with multiple sclerosis in central Sardinia, Italy
Neuroepidemiology
 , 
2003
, vol. 
22
 (pg. 
290
-
6
)
R Core Team
R: a language and environment for statistical computing
 , 
2013
Vienna, Austria
R Foundation for Statistical Computing
Ramagopalan
SV
Dyment
DA
Valdar
W
Herrera
BM
Criscuoli
M
Yee
IM
, et al.  . 
Autoimmune disease in families with multiple sclerosis: a population-based study
Lancet Neurol
 , 
2007
, vol. 
6
 (pg. 
604
-
10
)
Risch
N
Estimating morbidity risks with variable age of onset: review of methods and a maximum likelihood approach
Biometrics
 , 
1983
, vol. 
39
 (pg. 
929
-
39
)
Robertson
NP
Fraser
M
Deans
J
Clayton
D
Walker
N
Compston
DA
Age-adjusted recurrence risks for relatives of patients with multiple sclerosis
Brain
 , 
1996
, vol. 
119
 (pg. 
449
-
55
)
Rybicki
BA
Elston
RC
The relationship between the sibling recurrence-risk ratio and genotype relative risk
Am J Hum Genet
 , 
2000
, vol. 
66
 (pg. 
593
-
604
)
Sadovnick
AD
Baird
PA
The familial nature of multiple sclerosis: age-corrected empiric recurrence risks for children and siblings of patients
Neurology
 , 
1988
, vol. 
38
 (pg. 
990
-
1
)
Sazdovitch
V
Verdier-Taillefer
MH
Heinzlef
O
Alamowitch
S
Roullet
E
[Familial multiple sclerosis: study of 357 consecutive patients]
Rev Neurol (Paris)
 , 
2000
, vol. 
156
 (pg. 
638
-
40
)
Statistics Sweden
Fakta om den svenska familjen
Demografiska rapporter
 , 
1994
Stockholm
SCB
Statistics Sweden
Multi-generation register 2005-A description of contents and quality
 , 
2005
Sullivan
PF
Magnusson
C
Reichenberg
A
Boman
M
Dalman
C
Davidson
M
, et al.  . 
Family history of schizophrenia and bipolar disorder as risk factors for autism
Arch Gen Psychiatry
 , 
2012
, vol. 
69
 (pg. 
1099
-
103
)
Therneau
TM
Grambsch
PM
Modeling survival data: extending the {C}ox model
 , 
2000
New York
Springer
Therneau
TM
A package for survival analysis in S R-package version 2
 , 
2013
 
Available from: http://CRAN.R-project.org/package=survival (16 January 2014, date last accessed)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com